EP2201489A1 - Modélisation, conception, analyse et compte rendu de système de puissance électrique via une plateforme d'application client-serveur - Google Patents

Modélisation, conception, analyse et compte rendu de système de puissance électrique via une plateforme d'application client-serveur

Info

Publication number
EP2201489A1
EP2201489A1 EP08837888A EP08837888A EP2201489A1 EP 2201489 A1 EP2201489 A1 EP 2201489A1 EP 08837888 A EP08837888 A EP 08837888A EP 08837888 A EP08837888 A EP 08837888A EP 2201489 A1 EP2201489 A1 EP 2201489A1
Authority
EP
European Patent Office
Prior art keywords
engine
electrical
data
real
virtual
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP08837888A
Other languages
German (de)
English (en)
Other versions
EP2201489A4 (fr
Inventor
Adib Nasle
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Power Analytics Corp
Original Assignee
Edsa Micro Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US12/121,552 external-priority patent/US9557723B2/en
Application filed by Edsa Micro Corp filed Critical Edsa Micro Corp
Publication of EP2201489A1 publication Critical patent/EP2201489A1/fr
Publication of EP2201489A4 publication Critical patent/EP2201489A4/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00—Systems involving the use of models or simulators of said systems
    • G05B17/02—Systems involving the use of models or simulators of said systems electric
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply

Definitions

  • the present invention relates generally to computer modeling and management of systems and, more particularly, to a Client-Server Application Framework for energy management systems for monitoring and managing the cost, quality, reliability of an electrical power system.
  • system models can be created on computers and then "operated" in a virtual environment to assist in the determination of system design parameters. All manner
  • simulation techniques and such techniques are relatively well developed, but they have not been applied in real-time, e.g., for real-time operational monitoring and management.
  • predictive failure analysis techniques do not generally use real-time data that reflect actual system operation. Greater efforts at real-time operational monitoring and management would provide more accurate and timely suggestions for operational decisions, and such techniques applied to failure analysis would provide improved predictions of system problems before they occur.
  • an electrical network model that can age and synchronize itself in realtime with the actual facility's operating conditions is critical to obtaining predictions that are reflective of the system's reliability, availability, health and performance in relation to the life cycle of the system.
  • Static systems simply cannot adjust to the many daily changes to the electrical system that occur at a facility (e.g., motors and pumps switching on or off, changes to on-site generation status, changes to utility electrical feed...etc.) nor can they age with the facility to accurately predict the required indices.
  • reliability indices and predictions are of little value as they are not reflective of the actual operational status of the facility and may lead to false conclusions. With such improved techniques, operational costs and risks can be greatly reduced.
  • mission critical electrical systems e.g., for data centers or nuclear power facilities
  • the systems must be as failure proof as possible, and many layers of redundancy must be designed in to ensure that there is always a backup in case of a failure.
  • Computer design and modeling programs allow for the design of such systems by allowing a designer to model the system and simulate its operation. Thus, the designer can ensure that the system will operate as intended before the facility is constructed.
  • the data and results are communicated in a fashion that is simple to understand while also painting a comprehensive and accurate picture for the user.
  • graphical displays e.g., two-dimensional and three-dimensional views
  • the unremitting rise in the costs of generating and/or procuring energy necessitates pro-actively and intelligently managing energy through technological solutions that allow energy to be treated as a business critical commodity that can be managed in realtime while also extending the tools that electrical system/network owners and operators need to observe, comprehend and manage the cost, quality and reliability of energy.
  • a system for intelligent web-based management of an electrical system can comprise a data acquisition component communicatively connected to a sensor configured to acquire real-time data output from the electrical system; a web application server communicatively connected to the data acquisition component, the web application server configured to transmit a user interface to a client terminal, the web application server comprising: a virtual system model database communicatively connected to the data acquisition component, the virtual system model database configured to store a virtual system model of the electrical system, a power analytic simulation engine comprising a virtual system modeling engine communicatively connected to the virtual system model database, the virtual system modeling engine configured to generate a predicted data output for the electrical system utilizing the virtual system model of the electrical system, and an analytics engine communicatively connected to the virtual system model database, the analytics engine configured to monitor the real-time data output and the predicted data output of the electrical system, and to initiate a
  • a method for interacting with an electrical system management application to perform power analytic analysis simulations on a virtual system model of the electrical power system can comprise: a web application server, receiving a user request to access the electrical management system from a client terminal, and sending a user interface for interaction with the electrical management system to a client terminal; the client terminal, receiving the user interface for interaction with the electrical management system, and sending user interaction data to the electrical system management application; and, the electrical system management application, performing analytic analysis on a virtual system model of the electrical power system.
  • Figure 1 is an illustration of a system for utilizing real-time data for predictive analysis of the performance of a monitored system, in accordance with one embodiment.
  • Figure 2 is a diagram illustrating a detailed view of an analytics server included in the system of figure 1 , in accordance with one embodiment.
  • Figure 3 is a diagram illustrating how the system of figure 1 operates to synchronize the operating parameters between a physical facility and a virtual system model of the facility, in accordance with one embodiment.
  • Figure 4 is an illustration of the scalability of a system for utilizing real-time data for predictive analysis of the performance of a monitored system, in accordance with one embodiment.
  • Figure 5 is a block diagram that shows the configuration details of the system illustrated in Figure 1 , in accordance with one embodiment.
  • Figure 6 is an illustration of a flowchart describing a method for real-time monitoring and predictive analysis of a monitored system, in accordance with one embodiment.
  • Figure 7 is an illustration of a flowchart describing a method for managing realtime updates to a virtual system model of a monitored system, in accordance with one embodiment.
  • Figure 8 is an illustration of a flowchart describing a method for synchronizing real-time system data with a virtual system model of a monitored system, in accordance with one embodiment.
  • Figure 9 is a flow chart illustrating an example method for updating the virtual model, in accordance with one embodiment.
  • Figure 10 is a diagram illustrating an example process for monitoring the status of protective devices in a monitored system and updating a virtual model based on monitored data, in accordance with one embodiment.
  • Figure 11 is a flowchart illustrating an example process for determining the protective capabilities of the protective devices being monitored, in accordance with one embodiment.
  • Figure 12 is a diagram illustrating an example process for determining the protective capabilities of a High Voltage Circuit Breaker (HVCB), in accordance with one embodiment.
  • HVCB High Voltage Circuit Breaker
  • Figure 13 is a flowchart illustrating an example process for determining the protective capabilities of the protective devices being monitored, in accordance with another embodiment.
  • Figure 14 is a diagram illustrating a process for evaluating the withstand capabilities of a MVCB, in accordance with one embodiment
  • Figure 15 is a flow chart illustrating an example process for analyzing the reliability of an electrical power distribution and transmission system, in accordance with one embodiment.
  • Figure 16 is a flow chart illustrating an example process for analyzing the reliability of an electrical power distribution and transmission system that takes weather information into account, in accordance with one embodiment.
  • Figure 17 is a diagram illustrating an example process for predicting in real-time various parameters associated with an alternating current (AC) arc flash incident, in accordance with one embodiment.
  • AC alternating current
  • Figure 18 is a flow chart illustrating an example process for real-time analysis of the operational stability of an electrical power distribution and transmission system, in accordance with one embodiment.
  • Figure 19 is a flow chart illustrating an example process for conducting a realtime power capacity assessment of an electrical power distribution and transmission system, in accordance with one embodiment.
  • Figure 20 is a flow chart illustrating an example process for performing real-time harmonics analysis of an electrical power distribution and transmission system, in accordance with one embodiment.
  • Figure 21 is a diagram illustrating how the HTM Pattern Recognition and
  • Machine Learning Engine works in conjunction with the other elements of the analytics system to make predictions about the operational aspects of a monitored system, in accordance with one embodiment.
  • Figure 22 is an illustration of the various cognitive layers that comprise the neocortical catalyst process used by the HTM Pattern Recognition and Machine Learning
  • Figure 23 is an example process for real-time three-dimensional (3D) visualization of the health, reliability, and performance of an electrical system, in accordance with one embodiment.
  • Figure 24 is a diagram illustrating how the 3D Visualization Engine works in conjunction with the other elements of the analytics system to provide 3D visualization of the health, reliability, and performance of an electrical system, in accordance with one embodiment.
  • Figure 25 provides a client terminal screenshot of some 2D and 3D model views that are generated by the power analytics server, in accordance with one embodiment.
  • Figure 26 is a diagram illustrating how the Schematic Interface Creator Engine works in conjunction with the other elements of the analytics system to automatically generate a schematic user interface for visualizing the health, reliability, and performance of an electrical system, in accordance with one embodiment.
  • Figure 27 is an example process for automatically generating a schematic user interface for visualizing the health, reliability, and performance of an electrical system, in accordance with one embodiment.
  • Figure 28 is a diagram illustrating how the Energy Management System Engine works in conjunction with the other elements of the analytics system to intelligently monitor and manage the cost, quality and reliability of energy generated and/or utilized by an electrical system, in accordance with one embodiment.
  • Figure 29 is a logic flow diagram depicting how the various elements of the
  • Energy Management System can interact to provide intelligent energy monitoring and management of an electrical system, in accordance with one embodiment.
  • Figure 30 is a diagram illustrating how the various elements of the Client/Server system can interact to provide intelligent energy monitoring and management of an electrical system, in accordance with one embodiment.
  • Figure 31 is a diagram illustrating various elements of the web application server illustrated in Figure 30, in accordance with one embodiment.
  • Figure 32 is a logic flow diagram depicting how the various elements of the
  • Client/Server system can interact to provide intelligent energy monitoring and management of an electrical system, in accordance with one embodiment.
  • a system denotes a set of components, real or abstract, comprising a whole where each component interacts with or is related to at least one other component within the whole.
  • systems include machinery, factories, electrical systems, processing plants, devices, chemical processes, biological systems, data centers, aircraft carriers, and the like.
  • An electrical system can designate a power generation and/or distribution system that is widely dispersed (i.e., power generation, transformers, and/or electrical distribution components distributed geographically throughout a large region) or bounded within a particular location (e.g., a power plant within a production facility, a bounded geographic area, on board a ship, a factory, a data center, etc.).
  • a network application is any application that is stored on an application server connected to a network (e.g., local area network, wide area network, etc.) in accordance with any contemporary client/server architecture model and can be accessed via the network.
  • the network application programming interface resides on the application server separate from the client machine.
  • the client interface would typically be a web browser (e.g. INTERNET EXPLORERTM, FIREFOXTM, NETSCAPETM, etc) that is in communication with the network application server via a network connection (e.g., HTTP, HTTPS, RSS, etc.).
  • FIG. 1 is an illustration of a system for utilizing real-time data for predictive analysis of the performance of a monitored system, in accordance with one embodiment.
  • the system 100 includes a series of sensors (i.e., Sensor A 104, Sensor B 106, Sensor C 108) interfaced with the various components of a monitored system 102, a data acquisition hub 112, an analytics server 116, and a thin-client device 128.
  • the monitored system 102 is an electrical power generation plant.
  • the monitored system 102 is an electrical power transmission infrastructure.
  • the monitored system 102 is an electrical power distribution system.
  • the monitored system 102 includes a combination of one or more electrical power generation plant(s), power transmission infrastructure(s), and/or an electrical power distribution system. It should be understood that the monitored system 102 can be any combination of components whose operations can be monitored with conventional sensors and where each component interacts with or is related to at least one other component within the combination. For a monitored system 102 that is an electrical power generation, transmission, or distribution system, the sensors can provide data such as voltage, frequency, current, power, power factor, and the like.
  • the sensors are configured to provide output values for system parameters that indicate the operational status and/or "health" of the monitored system 102.
  • the current output or voltage readings for the various components that comprise the power generation system is indicative of the overall health and/or operational condition of the system.
  • the sensors are configured to also measure additional data that can affect system operation.
  • the sensor output can include environmental information, e.g., temperature, humidity, etc., which can impact electrical power demand and can also affect the operation and efficiency of the power distribution system itself.
  • the sensors are configured to output data in an analog format.
  • electrical power sensor measurements are sometimes conveyed in an analog format as the measurements may be continuous in both time and amplitude.
  • the sensors are configured to output data in a digital format.
  • the same electrical power sensor measurements may be taken in discrete time increments that are not continuous in time or amplitude.
  • the sensors are configured to output data in either an analog or digital format depending on the sampling requirements of the monitored system 102.
  • the sensors can be configured to capture output data at split-second intervals to effectuate "real time" data capture.
  • the sensors can be configured to generate hundreds of thousands of data readings per second. It should be appreciated, however, that the number of data output readings taken by a sensor may be set to any value as long as the operational limits of the sensor and the data processing capabilities of the data acquisition hub 112 are not exceeded.
  • each sensor is communicatively connected to the data acquisition hub 112 via an analog or digital data connection 110.
  • the data acquisition hub 112 may be a standalone unit or integrated within the analytics server 116 and can be embodied as a piece of hardware, software, or some combination thereof.
  • the data connection 110 is a "hard wired" physical data connection (e.g., serial, network, etc.).
  • the data connection 110 is a wireless data connection.
  • RF radio frequency
  • BLUETOOTHTM infrared or equivalent connection between the sensor and the hub 112.
  • the data acquisition hub 112 is configured to communicate "real-time" data from the monitored system 102 to the analytics server 116 using a network connection 114.
  • the network connection 114 is a "hardwired" physical connection.
  • the data acquisition hub 112 may be communicatively connected (via Category 5 (CAT5), fiber optic or equivalent cabling) to a data server (not shown) that is communicatively connected (via CAT5, fiber optic or equivalent cabling) through the Internet and to the analytics server 116 server.
  • the analytics server 116 being also communicatively connected with the Internet (via CAT5, fiber optic, or equivalent cabling).
  • the network connection 114 is a wireless network connection (e.g., Wi-Fi, WLAN, etc.). For example, utilizing an 802.1 lb/g or equivalent transmission format. In practice, the network connection utilized is dependent upon the particular requirements of the monitored system 102.
  • Data acquisition hub 112 can also be configured to supply warning and alarms signals as well as control signals to monitored system 102 and/or sensors 104, 106, and 108 as described in more detail below.
  • the analytics server 116 hosts an analytics engine 118, virtual system modeling engine 124 and several databases 126, 130, and 132.
  • the virtual system modeling engine can, e.g., be a computer modeling system, such as described above. In this context, however, the modeling engine can be used to precisely model and mirror the actual electrical system.
  • Analytics engine 118 can be configured to generate predicted data for the monitored system and analyze difference between the predicted data and the real-time data received from hub 112.
  • Figure 2 is a diagram illustrating a more detailed view of analytic server 116.
  • analytic server 116 is interfaced with a monitored facility 102 via sensors 202, e.g., sensors 104, 106, and 108. Sensors 202 are configured to supply real-time data from within monitored facility 102. The real-time data is communicated to analytic server 116 via a hub 204. Hub 204 can be configure to provide real-time data to server 116 as well as alarming, sensing and control featured for facility 102.
  • the real-time data from hub 204 can be passed to a comparison engine 210, which can form part of analytics engine 118.
  • Comparison engine 210 can be configured to continuously compare the real-time data with predicted values generated by simulation engine 208. Based on the comparison, comparison engine 210 can be further configured to determine whether deviations between the real-time and the expected values exists, and if so to classify the deviation, e.g., high, marginal, low, etc. The deviation level can then be communicated to decision engine 212, which can also comprise part of analytics engine 118.
  • Decision engine 212 can be configured to look for significant deviations between the predicted values and real-time values as received from the comparison engine 210.
  • decision engine 212 can also be configured to determine whether an alarm condition exists, activate the alarm and communicate the alarm to Human-Machine Interface (HMI) 214 for display in real-time via, e.g., thin client 128.
  • HMI Human-Machine Interface
  • Decision engine 212 can also be configured to perform root cause analysis for significant deviations in order to determine the interdependencies and identify the parent-child failure relationships that may be occurring. In this manner, parent alarm conditions are not drowned out by multiple children alarm conditions, allowing the user/operator to focus on the main problem, at least at first.
  • HMI 214 can be displayed via HMI 214 along with an indication that processes and equipment dependent on the parent process or equipment are also in alarm condition.
  • server 116 can maintain a parent-child logical relationship between processes and equipment comprising facility 102. Further, the processes can be classified as critical, essential, non-essential, etc.
  • Decision engine 212 can also be configured to determine health and performance levels and indicate these levels for the various processes and equipment via HMI 214. All of which, when combined with the analytic capabilities of analytics engine 118 allows the operator to minimize the risk of catastrophic equipment failure by predicting future failures and providing prompt, informative information concerning potential/predicted failures before they occur. Avoiding catastrophic failures reduces risk and cost, and maximizes facility performance and up time.
  • Simulation engine 208 operates on complex logical models 206 of facility
  • models 102 are continuously and automatically synchronized with the actual facility status based on the real-time data provided by hub 204.
  • the models are updated based on current switch status, breaker status, e.g., open-closed, equipment on/off status, etc.
  • the models are automatically updated based on such status, which allows simulation engine to produce predicted data based on the current facility status. This in turn, allows accurate and meaningful comparisons of the real-time data to the predicted data.
  • Example models 206 that can be maintained and used by server 116 include power flow models used to calculate expected kW, kVAR, power factor values, etc., short circuit models used to calculate maximum and minimum available fault currents, protection models used to determine proper protection schemes and ensure selective coordination of protective devices, power quality models used to determine voltage and current distortions at any point in the network, to name just a few. It will be understood that different models can be used depending on the system being modeled.
  • hub 204 is configured to supply equipment identification associated with the real-time data. This identification can be cross referenced with identifications provided in the models.
  • comparison engine 210 determines whether the differential between the real-time sensor output value and the expected value exceeds a Defined Difference Tolerance (DDT) value (i.e., the "real-time" output values of the sensor output do not indicate an alarm condition) but below an alarm condition (i.e., alarm threshold value)
  • DDT Defined Difference Tolerance
  • a calibration request is generated by the analytics engine 118. If the differential exceeds, the alarm condition, an alarm or notification message is generated by the analytics engine 118. If the differential is below the DTT value, the analytics engine does nothing and continues to monitor the real-time data and expected data.
  • the alarm or notification message is sent directly to the client (i.e., user) 128, e.g., via HMI 214, for display in real-time on a web browser, pop-up message box, e-mail, or equivalent on the client 128 display panel.
  • the alarm or notification message is sent to a wireless mobile device (e.g., BLACKBERRYTM, laptop, pager, etc.) to be displayed for the user by way of a wireless router or equivalent device interfaced with the analytics server 116.
  • the alarm or notification message is sent to both the client 128 display and the wireless mobile device.
  • the alarm can be indicative of a need for a repair event or maintenance to be done on the monitored system. It should be noted, however, that calibration requests should not be allowed if an alarm condition exists to prevent the models form being calibrated to an abnormal state.
  • model(s) 206 can be updated or adjusted to reflect the actual facility configuration. This can include, but is not limited to, modifying the predicted data output from the simulation engine 208, adjusting the logic/processing parameters utilized by the model(s) 206, adding/subtracting functional elements from model(s) 206, etc. It should be understood, that any operational parameter of models 206 can be modified as long as the resulting modifications can be processed and registered by simulation engine 208. [0070] Referring back to figure 1, models 206 can be stored in the virtual system model database 126.
  • the virtual system model can include components for modeling reliability, voltage stability, and power flow.
  • models 206 can include dynamic control logic that permits a user to configure the models 206 by specifying control algorithms and logic blocks in addition to combinations and interconnections of generators, governors, relays, breakers, transmission line, and the like.
  • the voltage stability parameters can indicate capacity in terms of size, supply, and distribution, and can indicate availability in terms of remaining capacity of the presently configured system.
  • the power flow model can specify voltage, frequency, and power factor, thus representing the "health" of the system.
  • All of models 206 can be referred to as a virtual system model.
  • virtual system model database can be configured to store the virtual system model.
  • a duplicate, but synchronized copy of the virtual system model can be stored in a virtual simulation model database 130.
  • This duplicate model can be used for what-if simulations.
  • this model can be used to allow a system designer to make hypothetical changes to the facility and test the resulting effect, without taking down the facility or costly and time consuming analysis. Such hypothetical can be used to learn failure patterns and signatures as well as to test proposed modifications, upgrades, additions, etc., for the facility.
  • the real-time data, as well as trending produced by analytics engine 118 can be stored in a real-time data acquisition database 132.
  • the virtual system model is periodically calibrated and synchronized with "real-time" sensor data outputs so that the virtual system model provides data output values that are consistent with the actual "real-time” values received from the sensor output signals.
  • the virtual system models described herein are updated and calibrated with the real-time system operational data to provide better predictive output values. A divergence between the real-time sensor output values and the predicted output values generate either an alarm condition for the values in question and/or a calibration request that is sent to the calibration engine 134.
  • the analytics engine 118 can be configured to implement pattern/sequence recognition into a real-time decision loop that, e.g., is enabled by a new type of machine learning called associative memory, or hierarchical temporal memory (HTM), which is a biological approach to learning and pattern recognition.
  • Associative memory allows storage, discovery, and retrieval of learned associations between extremely large numbers of attributes in real time.
  • an associative memory stores information about how attributes and their respective features occur together. The predictive power of the associative memory technology comes from its ability to interpret and analyze these co-occurrences and to produce various metrics.
  • Associative memory is built through "experiential" learning in which each newly observed state is accumulated in the associative memory as a basis for interpreting future events.
  • the associative memory is able to learn normal patterns as a basis for identifying non-normal behavior and appropriate responses, and to associate patterns with particular outcomes, contexts or responses.
  • the analytics engine 118 is also better able to understand component mean time to failure rates through observation and system availability characteristics. This technology in combination with the virtual system model can be characterized as a "neocortical" model of the system under management.
  • This approach also presents a novel way to digest and comprehend alarms in a manageable and coherent way.
  • the neocortical model could assist in uncovering the patterns and sequencing of alarms to help pinpoint the location of the (impending) failure, its context, and even the cause.
  • responding to the alarms is done manually by experts who have gained familiarity with the system through years of experience. However, at times, the amount of information is so great that an individual cannot respond fast enough or does not have the necessary expertise.
  • An "intelligent" system like the neocortical system that observes and recommends possible responses could improve the alarm management process by either supporting the existing operator, or even managing the system autonomously.
  • the virtual system model database 126 can be configured to store one or more virtual system models, virtual simulation models, and real-time data values, each customized to a particular system being monitored by the analytics server 118.
  • the analytics server 118 can be utilized to monitor more than one system at a time.
  • the databases 126, 130, and 132 can be hosted on the analytics server 116 and communicatively interfaced with the analytics engine 118.
  • databases 126, 130, and 132 can be hosted on a separate database server (not shown) that is communicatively connected to the analytics server 116 in a manner that allows the virtual system modeling engine 124 and analytics engine 118 to access the databases as needed.
  • the client 128 can modify the virtual system model stored on the virtual system model database 126 by using a virtual system model development interface using well-known modeling tools that are separate from the other network interfaces. For example, dedicated software applications that run in conjunction with the network interface to allow a client 128 to create or modify the virtual system models.
  • the client 128 may utilize a variety of network interfaces (e.g., web browser,
  • sensors e.g., configuration files, etc.
  • analytics engine 118 e.g., configuration files, analytics logic, etc.
  • calibration parameters e.g., configuration files, calibration parameters, etc.
  • virtual system modeling engine 124 e.g., configuration files, simulation parameters, etc.
  • virtual system model of the system under management e.g., virtual system model operating parameters and configuration files.
  • data from those various components of the monitored system 102 can be displayed on a client 128 display panel for viewing by a system administrator or equivalent.
  • server 116 is configured to synchronize the physical world with the virtual and report, e.g., via visual, real-time display, deviations between the two as well as system health, alarm conditions, predicted failures, etc. This is illustrated with the aid of figure 3, in which the synchronization of the physical world (left side) and virtual world (right side) is illustrated.
  • sensors 202 produce real-time data 302 for the processes 312 and equipment 314 that make up facility 102.
  • simulations 304 of the virtual system model 206 provide predicted values 306, which are correlated and synchronized with the real-time data 302. The real-time data can then be compared to the predicted values so that differences 308 can be detected.
  • FIG. 4 is an illustration of the scalability of a system for utilizing real-time data for predictive analysis of the performance of a monitored system, in accordance with one embodiment.
  • an analytics central server 422 is communicatively connected with analytics server A 414, analytics server B 416, and analytics server n 418 (i.e., one or more other analytics servers) by way of one or more network connections 114.
  • Each of the analytics servers is communicatively connected with a respective data acquisition hub (i.e., Hub A 408, Hub B 410, Hub n 412) that communicates with one or more sensors that are interfaced with a system (i.e., Monitored System A 402, Monitored System B 404, Monitored System n 406) that the respective analytical server monitors.
  • a respective data acquisition hub i.e., Hub A 408, Hub B 410, Hub n 412
  • analytics server A 414 is communicative connected with data acquisition hub A 408, which communicates with one or more sensors interfaced with monitored system A 402.
  • Each analytics server i.e., analytics server A 414, analytics server B 416, analytics server n 418) is configured to monitor the sensor output data of its corresponding monitored system and feed that data to the central analytics server 422.
  • each of the analytics servers can function as a proxy agent of the central analytics server 422 during the modifying and/or adjusting of the operating parameters of the system sensors they monitor.
  • analytics server B 416 is configured to be utilized as a proxy to modify the operating parameters of the sensors interfaced with monitored system B 404.
  • the central analytics server 422, which is communicatively connected to one or more analytics server(s) can be used to enhance the scalability.
  • a central analytics server 422 can be used to monitor multiple electrical power generation facilities (i.e., monitored system A 402 can be a power generation facility located in city A while monitored system B 404 is a power generation facility located in city B) on an electrical power grid.
  • each of the analytics servers are configured to serve as proxies for the central analytics server 422 to enable a client 128 to modify and/or adjust the operating parameters of the sensors interfaced with the systems that they respectively monitor.
  • the client 128 can use the central analytics server 422, and vice versa, to modify and/or adjust the operating parameters of analytics server A 414 and utilize the same to modify and/or adjust the operating parameters of the sensors interfaced with monitored system A 402.
  • each of the analytics servers can be configured to allow a client 128 to modify the virtual system model through a virtual system model development interface using well-known modeling tools.
  • the central analytics server 422 can function to monitor and control a monitored system when its corresponding analytics server is out of operation.
  • central analytics server 422 can take over the functionality of analytics server B 416 when the server 416 is out of operation. That is, the central analytics server 422 can monitor the data output from monitored system B 404 and modify and/or adjust the operating parameters of the sensors that are interfaced with the system 404.
  • the network connection 114 is established through a wide area network (WAN) such as the Internet.
  • the network connection is established through a local area network (LAN) such as the company intranet.
  • the network connection 114 is a "hardwired" physical connection.
  • the data acquisition hub 112 may be communicatively connected (via Category 5 (CAT5), fiber optic or equivalent cabling) to a data server that is communicatively connected (via CAT5, fiber optic or equivalent cabling) through the Internet and to the analytics server 116 server hosting the analytics engine 118.
  • the network connection 114 is a wireless network connection (e.g., Wi-Fi, WLAN, etc.). For example, utilizing an 802.1 lb/g or equivalent transmission format.
  • FIG. 5 is a block diagram that shows the configuration details of analytics server 116 illustrated in Figure 1 in more detail. It should be understood that the configuration details in Figure 5 are merely one embodiment of the items described for Figure 1, and it should be understood that alternate configurations and arrangements of components could also provide the functionality described herein.
  • the analytics server 116 includes a variety of components.
  • the analytics server 116 is implemented in a Web-based configuration, so that the analytics server 116 includes (or communicates with) a secure web server 530 for communication with the sensor systems 519 (e.g., data acquisition units, metering devices, sensors, etc.) and external communication entities 534 (e.g., web browser, "thin client” applications, etc.).
  • a variety of user views and functions 532 are available to the client 128 such as: alarm reports, Active X controls, equipment views, view editor tool, custom user interface page, and XML parser.
  • the analytics server 116 also includes an alarm engine 506 and messaging engine 504, for the aforementioned external communications.
  • the alarm engine 506 is configured to work in conjunction with the messaging engine 504 to generate alarm or notification messages 502 (in the form of text messages, e-mails, paging, etc.) in response to the alarm conditions previously described.
  • the analytics server 116 determines alarm conditions based on output data it receives from the various sensor systems 519 through a communications connection (e.g., wireless 516, TCP/IP 518, Serial 520, etc) and simulated output data from a virtual system model 512, of the monitored system, processed by the analytics engines 118.
  • a communications connection e.g., wireless 516, TCP/IP 518, Serial 520, etc
  • the virtual system model 512 is created by a user through interacting with an external communication entity 534 by specifying the components that comprise the monitored system and by specifying relationships between the components of the monitored system.
  • the virtual system model 512 is automatically generated by the analytics engines 118 as components of the monitored system are brought online and interfaced with the analytics server 508.
  • a virtual system model database 526 is communicatively connected with the analytics server 116 and is configured to store one or more virtual system models 512, each of which represents a particular monitored system.
  • the analytics server 116 can conceivably monitor multiple electrical power generation systems (e.g., system A, system B, system C, etc.) spread across a wide geographic area (e.g., City A, City B, City C, etc.). Therefore, the analytics server 116 will utilize a different virtual system model 512 for each of the electrical power generation systems that it monitors.
  • Virtual simulation model database 538 can be configured to store a synchronized, duplicate copy of the virtual system model 512, and real-time data acquisition database 540 can store the real-time and trending data for the system(s) being monitored.
  • analytics server 116 can receive real-time data for various sensors, i.e., components, through data acquisition system 202.
  • analytics server 116 can comprise various drivers configured to interface with the various types of sensors, etc., comprising data acquisition system 202.
  • This data represents the real-time operational data for the various components. For example, the data may indicate that a certain component is operating at a certain voltage level and drawing certain amount of current. This information can then be fed to a modeling engine to generate a virtual system model 612 that is based on the actual real-time operational data.
  • Analytics engine 118 can be configured to compare predicted data based on the virtual system model 512 with real-time data received from data acquisition system 202 and to identify any differences. In some instances, analytics engine can be configured to identify these differences and then update, i.e., calibrate, the virtual system model 512 for use in future comparisons. In this manner, more accurate comparisons and warnings can be generated.
  • the differences will indicate a failure, or the potential for a failure.
  • the operating parameters will begin to change. This change may be sudden or it may be a progressive change over time.
  • Analytics engine 118 can detect such changes and issue warnings that can allow the changes to be detected before a failure occurs.
  • the analytic engine 118 can be configured to generate warnings that can be communicated via interface 532.
  • a user can access information from server 116 using thin client
  • reports can be generate and served to thin client 534 via server 540. These reports can, for example, comprise schematic or symbolic illustrations of the system being monitored. Status information for each component can be illustrated or communicated for each component. This information can be numerical, i.e., the voltage or current level. Or it can be symbolic, i.e., green for normal, red for failure or warning. In certain embodiments, intermediate levels of failure can also be communicated, i.e., yellow can be used to indicate operational conditions that project the potential for future failure. It should be noted that this information can be accessed in real-time. Moreover, via thin client 534, the information can be accessed form anywhere and anytime.
  • the Analytics Engine 118 is communicatively interfaced with a HTM Pattern Recognition and Machine Learning Engine 551.
  • the HTM Engine 551 is configured to work in conjunction with the Analytics Engine 118 and a virtual system model of the monitored system to make real-time predictions (i.e., forecasts) about various operational aspects of the monitored system.
  • the HTM Engine 551 works by processing and storing patterns observed during the normal operation of the monitored system over time. These observations are provided in the form of real-time data captured using a multitude of sensors that are imbedded within the monitored system.
  • the virtual system model is also updated with the real-time data such that the virtual system model "ages" along with the monitored system.
  • FIG. 6 is an illustration of a flowchart describing a method for real-time monitoring and predictive analysis of a monitored system, in accordance with one embodiment.
  • Method 600 begins with operation 602 where real-time data indicative of the monitored system status is processed to enable a virtual model of the monitored system under management to be calibrated and synchronized with the real-time data.
  • the monitored system 102 is a mission critical electrical power system.
  • the monitored system 102 can include an electrical power transmission infrastructure. In still another embodiment, the monitored system 102 includes a combination of thereof. It should be understood that the monitored system 102 can be any combination of components whose operations can be monitored with conventional sensors and where each component interacts with or is related to at least one other component within the combination.
  • Method 600 moves on to operation 604 where the virtual system model of the monitored system under management is updated in response to the real-time data. This may include, but is not limited to, modifying the simulated data output from the virtual system model, adjusting the logic/processing parameters utilized by the virtual system modeling engine to simulate the operation of the monitored system, adding/subtracting functional elements of the virtual system model, etc. It should be understood, that any operational parameter of the virtual system modeling engine and/or the virtual system model may be modified by the calibration engine as long as the resulting modifications can be processed and registered by the virtual system modeling engine.
  • Method 600 proceeds on to operation 606 where the simulated real-time data indicative of the monitored system status is compared with a corresponding virtual system model created at the design stage.
  • the design stage models which may be calibrated and updated based on real-time monitored data, are used as a basis for the predicted performance of the system.
  • the real-time monitored data can then provide the actual performance over time.
  • difference can be identified a tracked by, e.g., the analytics engine 118.
  • Analytics engines 118 can then track trends, determine alarm states, etc., and generate a real-time report of the system status in response to the comparison.
  • the analytics can be used to analyze the comparison and realtime data and determine if there is a problem that should be reported and what level the problem may be, e.g., low priority, high priority, critical, etc.
  • the analytics can also be used to predict future failures and time to failure, etc.
  • reports can be displayed on a conventional web browser (e.g. INTERNET EXPLORERTM, FIREFOXTM, NETSCAPETM, etc) that is rendered on a standard personal computing (PC) device.
  • the "real-time" report can be rendered on a "thin-client” computing device (e.g., CITRIXTM, WINDOWS TERMINAL SERVICESTM, telnet, or other equivalent thin-client terminal application).
  • the report can be displayed on a wireless mobile device (e.g., BLACKBERRYTM, laptop, pager, etc.).
  • the "real-time" report can include such information as the differential in a particular power parameter (i.e., current, voltage, etc.) between the real-time measurements and the virtual output data.
  • FIG. 7 is an illustration of a flowchart describing a method for managing real-time updates to a virtual system model of a monitored system, in accordance with one embodiment.
  • Method 700 begins with operation 702 where real-time data output from a sensor interfaced with the monitored system is received.
  • the sensor is configured to capture output data at split-second intervals to effectuate "real time" data capture.
  • the sensor is configured to generate hundreds of thousands of data readings per second. It should be appreciated, however, that the number of data output readings taken by the sensor may be set to any value as long as the operational limits of the sensor and the data processing capabilities of the data acquisition hub are not exceeded.
  • Method 700 moves to operation 704 where the real-time data is processed into a defined format.
  • the data is converted from an analog signal to a digital signal.
  • the data is converted from a digital signal to an analog signal. It should be understood, however, that the real-time data may be processed into any defined format as long as the analytics engine can utilize the resulting data in a comparison with simulated output data from a virtual system model of the monitored system.
  • Method 700 continues on to operation 706 where the predicted (i.e., simulated) data for the monitored system is generated using a virtual system model of the monitored system.
  • a virtual system modeling engine utilizes dynamic control logic stored in the virtual system model to generate the predicted output data.
  • the predicted data is supposed to be representative of data that should actually be generated and output from the monitored system.
  • Method 700 proceeds to operation 708 where a determination is made as to whether the difference between the real-time data output and the predicted system data falls between a set value and an alarm condition value, where if the difference falls between the set value and the alarm condition value a virtual system model calibration and a response can be generated. That is, if the comparison indicates that the differential between the "realtime" sensor output value and the corresponding "virtual" model data output value exceeds a Defined Difference Tolerance (DDT) value (i.e., the "real-time" output values of the sensor output do not indicate an alarm condition) but below an alarm condition (i.e., alarm threshold value), a response can be generated by the analytics engine.
  • DDT Defined Difference Tolerance
  • FIG. 8 is an illustration of a flowchart describing a method for synchronizing real-time system data with a virtual system model of a monitored system, in accordance with one embodiment.
  • Method 800 begins with operation 802 where a virtual system model calibration request is received.
  • a virtual model calibration request can be generated by an analytics engine whenever the difference between the real-time data output and the predicted system data falls between a set value and an alarm condition value.
  • Method 800 proceeds to operation 804 where the predicted system output value for the virtual system model is updated with a real-time output value for the monitored system. For example, if sensors interfaced with the monitored system outputs a real-time current value of A, then the predicted system output value for the virtual system model is adjusted to reflect a predicted current value of A.
  • Method 800 moves on to operation 806 where a difference between the realtime sensor value measurement from a sensor integrated with the monitored system and a predicted sensor value for the sensor is determined.
  • the analytics engine is configured to receive "real-time" data from sensors interfaced with the monitored system via the data acquisition hub (or, alternatively directly from the sensors) and "virtual" data from the virtual system modeling engine simulating the data output from a virtual system model of the monitored system.
  • the values are in units of electrical power output (i.e., current or voltage) from an electrical power generation or transmission system. It should be appreciated, however, that the values can essentially be any unit type as long as the sensors can be configured to output data in those units or the analytics engine can convert the output data received from the sensors into the desired unit type before performing the comparison.
  • Method 800 continues on to operation 808 where the operating parameters of the virtual system model are adjusted to minimize the difference.
  • the logic parameters of the virtual system model that a virtual system modeling engine uses to simulate the data output from actual sensors interfaced with the monitored system are adjusted so that the difference between the real-time data output and the simulated data output is minimized.
  • this operation will update and adjust any virtual system model output parameters that are functions of the virtual system model sensor values. For example, in a power distribution environment, output parameters of power load or demand factor might be a function of multiple sensor data values.
  • the operating parameters of the virtual system model that mimic the operation of the sensor will be adjusted to reflect the real-time data received from those sensors.
  • authorization from a system administrator is requested prior to the operating parameters of the virtual system model being adjusted. This is to ensure that the system administrator is aware of the changes that are being made to the virtual system model.
  • a report is generated to provide a summary of all the adjustments that have been made to the virtual system model.
  • virtual system modeling engine 124 can be configured to model various aspects of the system to produce predicted values for the operation of various components within monitored system 102. These predicted values can be compared to actual values being received via data acquisition hub 112. If the differences are greater than a certain threshold, e.g., the DTT, but not in an alarm condition, then a calibration instruction can be generated. The calibration instruction can cause a calibration engine 134 to update the virtual model being used by system modeling engine 124 to reflect the new operating information.
  • a certain threshold e.g., the DTT
  • step 902 data is collected from, e.g., sensors 104, 106, and 108.
  • the sensors can be configured to monitor protective devices within an electrical distribution system to determine and monitor the ability of the protective devices to withstand faults, which is describe in more detail below.
  • step 904 the data from the various sensors can be processed by analytics engine 118 in order to evaluate various parameters related to monitored system 102.
  • simulation engine 124 can be configured to generate predicted values for monitored system 102 using a virtual model of the system that can be compared to the parameters generated by analytics engine 118 in step 904. If there are differences between the actual values and the predicted values, then the virtual model can be updated to ensure that the virtual model ages with the actual system 102.
  • a logical model of a facilities electrical system, a data acquisition system (data acquisition hub 112), and power system simulation engines (modeling engine 124) can be integrated with a logic and methods based approach to the adjustment of key database parameters within a virtual model of the electrical system to evaluate the ability of protective devices within the electrical distribution system to withstand faults and also effectively "age" the virtual system with the actual system.
  • modeling engine 124 Only through such a process can predictions on the withstand abilities of protective devices, and the status, security and health of an electrical system be accurately calculated. Accuracy is important as the predictions can be used to arrive at actionable, mission critical or business critical conclusions that may lead to the re-alignment of the electrical distribution system for optimized performance or security.
  • Figures 10-12 are flow charts presenting logical flows for determining the ability of protective devices within an electrical distribution system to withstand faults and also effectively "age” the virtual system with the actual system in accordance with one embodiment.
  • Figure 10 is a diagram illustrating an example process for monitoring the status of protective devices in a monitored system 102 and updating a virtual model based on monitored data.
  • the status of the protective devices can be monitored in real time.
  • protective devices can include fuses, switches, relays, and circuit breakers. Accordingly, the status of the fuses/switches, relays, and/or circuit breakers, e.g., the open/close status, source and load status, and on or off status, can be monitored in step 1002.
  • step 1004 It can be determined, in step 1004, if there is any change in the status of the monitored devices. If there is a change, then in step 1006, the virtual model can be updated to reflect the status change, i.e., the corresponding virtual components data can be updated to reflect the actual status of the various protective devices.
  • step 1008 predicted values for the various components of monitored system 102 can be generated. But it should be noted that these values are based on the current, realtime status of the monitored system.
  • Real time sensor data can be received in step 1012. This real time data can be used to monitor the status in step 1002 and it can also be compared with the predicted values in step 1014. As noted above, the difference between the predicted values and the real time data can also be determined in step 1014.
  • meaningful predicted values based on the actual condition of monitored system 102 can be generated in steps 1004 to 1010. These predicted values can then be used to determine if further action should be taken based on the comparison of step 1014. For example, if it is determined in step 1016 that the difference between the predicted values and the real time sensor data is less than or equal to a certain threshold, e.g., DTT, then no action can be taken e.g., an instruction not to perform calibration can be issued in step 1018. Alternatively, if it is determined in step 1020 that the real time data is actually indicative of an alarm situation, e.g., is above an alarm threshold, then a do not calibrate instruction can be generated in step 1018 and an alarm can be generated as described above.
  • a certain threshold e.g., DTT
  • step 1024 If the real time sensor data is not indicative of an alarm condition, and the difference between the real time sensor data and the predicted values is greater than the threshold, as determined in step 1022, then an initiate calibration command can be generated in step 1024. [00117] If an initiate calibration command is issued in step 1024, then a function call to calibration engine 134 can be generated in step 1026. The function call will cause calibration engine 134 to update the virtual model in step 1028 based on the real time sensor data. A comparison between the real time data and predicted data can then be generated in step 1030 and the differences between the two computed. In step 1032, a user can be prompted as to whether or not the virtual model should in fact be updated. In other embodiments, the update can be automatic, and step 1032 can be skipped.
  • step 1034 the virtual model could be updated.
  • the virtual model loads, buses, demand factor, and/or percent running information can be updated based on the information obtained in step 1030.
  • An initiate simulation instruction can then be generated in step 1036, which can cause new predicted values to be generated based on the update of virtual model.
  • the predicted values generated in step 1008 are not only updated to reflect the actual operational status of monitored system 102, but they are also updated to reflect natural changes in monitored system 102 such as aging. Accordingly, realistic predicted values can be generated in step 1008.
  • Figure 11 is a flowchart illustrating an example process for determining the protective capabilities of the protective devices being monitored in step 1002.
  • the protective devices can be evaluated in terms of the International Electrotechnical Commission (IEC) standards or in accordance with the United States or American National Standards Institute (ANSI) standards. It will be understood, that the process described in relation to Figure 11 is not dependent on a particular standard being used.
  • IEC International Electrotechnical Commission
  • ANSI American National Standards Institute
  • a short circuit analysis can be performed for the protective device.
  • the protective device can be any one of a variety of protective device types.
  • the protective device can be a fuse or a switch, or some type of circuit breaker.
  • circuit breakers including Low Voltage Circuit Breakers (LVCBs), High Voltage Circuit Breakers (HVCBs), Mid Voltage Circuit Breakers (MVCBs), Miniature Circuit Breakers (MCBs), Molded Case Circuit Breakers (MCCBs), Vacuum Circuit Breakers, and Air Circuit Breakers, to name just a few. Any one of these various types of protective devices can be monitored and evaluated using the processes illustrated with respect to Figures 10-12.
  • the short circuit current, symmetric (I sym ) or asymmetric (I aS ym), and/or the peak current (I pea k) can be determined in step 1102.
  • the short circuit current at a delayed time Isymdeiay
  • a first cycle short circuit current (I sym ) and/or I peak can be determined in step 1102.
  • the short circuit current, symmetric or asymmetric can be determined in step 1102.
  • the short circuit current interrupting time can be calculated.
  • the fuse rating can first be determined in step 1104.
  • the fuse rating can be the current rating for the fuse.
  • the X/R can be calculated in step 1105 and the asymmetric short circuit current (I asym ) for the fuse can be determined in step 1106 using equation 1.
  • the inductants/reactants (X/R) ratio can be calculated instep 1108 and compared to a fuse test X/R to determine if the calculated X/R is greater than the fuse test X/R.
  • the calculated X/R can be determined using the predicted values provided in step 1008.
  • Various standard tests X/R values can be used for the fuse test X/R values in step 1108.
  • standard test X/R values for a LVCB can be as follows:
  • equation 12 can be used to calculate an adjusted symmetrical short circuit current (Iad j sym).
  • Iadjsym can be set equal to I sym in step 1110.
  • step 1114 If it is determined in step 1114 that the device rating is not greater than or equal to lad j sym, then it can be determined that the device as failed in step 1116. The percent rating can still be calculating in step 1120.
  • step 1122 it can first be determined whether they are fused in step 1122. If it is determined that the LVCB is not fused, then in step 1124 can be determined if the LVCB is an instantaneous trip LVCB. If it is determined that the LVCB is an instantaneous trip LVCB, then in step 1130 the first cycle fault X/R can be calculated and compared to a circuit breaker test X/R (see example values above) to determine if the fault X/R is greater than the circuit breaker test X/R. If the fault X/R is not greater than the circuit breaker test X/R, then in step 1132 it can be determined if the LVCB is peak rated.
  • step 1146 I peak Can be used in step 1146 below. If it is determined that the LVCB is not peak rated in step 1132, then lad j sym can be set equal to I sym in step 1140. In step 1146, it can be determined if the device rating is greater or equal to Iad j sym, or to I pea k as appropriate, for the LVCB. [00128] If it is determined that the device rating is greater than or equal to lad j sym, then it can be determined that the LVCB has passed in step 1148. The percent rating can then be determined using the equations for I a d JS ym defined above (step 1120) in step 1152.
  • step 1150 If it is determined that the device rating is not greater than or equal to Iad j sym, then it can be determined that the device has failed in step 1150. The percent rating can still be calculated in step 1152. [00129] If the calculated fault X/R is greater than the circuit breaker test X/R as
  • step 1130 it can be determined if the LVCB is peak rated in step 1134. If the LVCB is not peak rated, then the Iad j sym can be determined using equation 12. If the LVCB is peak rated, then I pea k can be determined using equation 11.
  • I PEAK 4 ⁇ I SYM ⁇ l .02 + 0.98e 3/(x/R) ⁇
  • a time delay calculation can be performed at step 1128 followed by calculation of the fault X/R and a determination of whether the fault X/R is greater than the circuit breaker test X/R. If it is not, then Iadjsym can be set equal to Isym in step 1136. If the calculated fault at X/R is greater than the circuit breaker test X/R, then Iadjsymdelay can be calculated in step 1138 using the following equation with, e.g., a 0.5 second maximum delay: ⁇ 60p/(CALCX/R)
  • the fault X/R can be calculated in step 1126 and compared to the circuit breaker test X/R in order to determine if the calculated fault X/R is greater than the circuit breaker test X/R. If it is greater, then I a d JS ym can be calculated in step 1154 using the following equation:
  • Iad j sym can be set equal to I sym in step 1156. It can then be determined if the device rating is greater than or equal to Iad j sym in step 1146. The pass/fail determinations can then be carried out in steps 1148 and 1150 respectively, and the percent rating can be determined in step 1152.
  • FIG. 12 is a diagram illustrating an example process for determining the protective capabilities of a HVCB.
  • X/R can be calculated in step 1157 and a peak current (I peak ) can be determined using equation 11 in step 1158.
  • I peak peak current
  • step 1162 it can be determined whether the HVCB's rating is greater than or equal to I peak as determined in step 1158. If the device rating is greater than or equal to I pea k, then the device has passed in step 1164. Otherwise, the device fails in step 1166. In either case, the percent rating can be determined in step 1168 using the following:
  • an interrupting time calculation can be made in step 1170.
  • a fault X/R can be calculated and then can be determined if the fault X/R is greater than or equal to a circuit breaker test X/R in step 1172.
  • the following circuit breaker test X/R can be used;
  • I a d ⁇ ntsym can be set equal to I sym in step 1174. If the calculated fault X/R is greater than the circuit breaker test X/R, then contact parting time for the circuit breaker can be determined in step 1176 and
  • equation 15 can then be used to determine Iad ⁇ ntsym in step 1178.
  • step 1180 it can be determined whether the device rating is greater than or equal to Iad j mtsym- The pass/fail determinations can then be made in steps 1182 and 1184 respectively and the percent rating can be calculated in step 1186 using the following:
  • Figure 13 is a flowchart illustrating an example process for determining the protective capabilities of the protective devices being monitored in step 1002 in accordance with another embodiment.
  • the process can start with a short circuit analysis in step 1302.
  • the protective device X/R can be modified as follows:
  • a selection can be made, as appropriate, between use of the symmetrical rating or asymmetrical rating for the device.
  • the Multiplying Factor (MF) for the device can then be calculated in step 1304.
  • the MF can then be used to determine Iad j asym or Iad j sym-
  • the LVF can be calculated for the device in step 1320. It should be noted that the LVF can vary depending on whether the LVCB is an instantaneous
  • step 1317 it can be determined in step 1317, e.g., using the following:
  • step 1316 it can again be determined whether the X/R is known. If it is known, then the LVF can be calculated in step 1319. If it is not known, then the X/R can be set equal to, e.g., 4.9.
  • step 1321 it can be determined if the LVF is less than 1 and if it is, then the LVF can be set equal to 1.
  • I mta d j can be determined using the following:
  • step 1323 it can be determined whether the device's symmetrical rating is
  • % rating I mtadj * 100/device rating.
  • Figure 14 is a diagram illustrating a process for evaluating the withstand capabilities of a MVCB in accordance with one embodiment.
  • a determination can be made as to whether the following calculations will be based on all remote inputs, all local inputs or on a No AC Decay (NACD) ratio.
  • NACD No AC Decay
  • a calculation can then be made of the total remote contribution, total local contribution, total contribution (Iintrmssym), and NACD. If the calculated NACD is equal to zero, then it can be determined that all contributions are local. If NACD is equal to 1, then it can be determined that all contributions are remote.
  • step 1332 the remote MF (MFr) can be calculated and I int can be calculated using the following:
  • MFl can be calculated and I int can be calculated using the following:
  • T , MF1*I mtrmssym [00148] If the contributions are from NACD, then the NACD, MFr, MFl, and AMFl can be calculated. If AMFl is less than 1 , then AMFl can be set equal to 1. I int can then be calculated using the following:
  • step 1338 the 3 -phase device duty cycle can be calculated and then it can be determined in step 1340, whether the device rating is greater than or equal to I int . Whether the device passed or failed can then be determined in steps 1342 and 1344, respectively.
  • the percent rating can be determined in step 1346 using the following:
  • % rating I mt * 100/3p device rating.
  • step 1348 it can be determined, in step 1348, whether the user has selected a fixed MF. If so, then in certain embodiments the peak duty (crest) can be determined in step 1349 and MFp can be set equal to 2.7 in step 1354. If a fixed MF has not been selected, then the peak duty (crest) can be calculated in step 1350 and MFp can be calculated in step 1358. In step 1362, the MFp can be used to calculate the following:
  • step 1366 it can be determined if the device peak rating (crest) is greater than or equal to Imompeak. It can then be determined whether the device passed or failed in steps 1368 and 1370 respectively, and the percent rating can be calculated as follows:
  • % rating I mompeak * 100/device peak (crest) rating.
  • MFm can be set equal to, e.g., 1.6. If a fixed MF has not been selected, then in step 1352 MFm can be calculated. MFm can then be used to determine the following:
  • step 1374 It can then be determined in step 1374 whether the device C&L, rms rating is greater than or equal to Imomsym- Whether the device passed or failed can then be determined in steps 1376 and 1378 respectively, and the percent rating can be calculated as follows:
  • % rating I momasym * 100/device C& L, rms rating.
  • the above methods provide a mean to determine the withstand capability of various protective devices, under various conditions and using various standards, using an aged, up to date virtual model of the system being monitored.
  • the influx of massive sensory data e.g., provided via sensors 104, 106, and 108, intelligent filtration of this dense stream of data into manageable and easily understandable knowledge.
  • Conventional systems do not make use of an on-line, real-time system snap shot captured by a real-time data acquisition platform to perform real time system availability evaluation.
  • FIG. 15 is a flow chart illustrating an example process for analyzing the reliability of an electrical power distribution and transmission system in accordance with one embodiment.
  • the inputs used in step 1502 can comprise power flow data, e.g., network connectivity, loads, generations, cables/transformer impedances, etc., which can be obtained from the predicted values generated in step 1008, reliability data associated with each power system component, lists of contingencies to be considered, which can vary by implementation including by region, site, etc., customer damage (load interruptions) costs, which can also vary by implementation, and load duration curve information.
  • Other inputs can include failure rates, repair rates, and required availability of the system and of the various components.
  • step 1504 a list of possible outage conditions and contingencies can be evaluated including loss of utility power supply, generators, UPS, and/or distribution lines and infrastructure.
  • step 1506 a power flow analysis for monitored system 102 under the various contingencies can be performed. This analysis can include the resulting failure rates, repair rates, cost of interruption or downtime versus the required system availability, etc.
  • step 1510 it can be determined if the system is operating in a deficient state when confronted with a specific contingency. If it is, then is step 1512, the impact on the system, load interruptions, costs, failure duration, system unavailability, etc. can all be evaluated.
  • step 1512 After the evaluation of step 1512, or if it is determined that the system is not in a deficient state in step 1510, then it can be determined if further contingencies need to be evaluated. If so, then the process can revert to step 1506 and further contingencies can be evaluated. If no more contingencies are to be evaluated, then a report can be generated in step 1514.
  • the report can include a system summary, total and detailed reliability indices, system availability, etc. The report can also identify system bottlenecks are potential problem areas.
  • the reliability indices can be based on the results of credible system contingencies involving both generation and transmission outages.
  • the reliability indices can include load point reliability indices, branch reliability indices, and system reliability indices. For example, various load/bus reliability indices can be determined such as probability and frequency of failure, expected load curtailed, expected energy not supplied, frequency of voltage violations, reactive power required, and expected customer outage cost.
  • the load point indices can be evaluated for the major load buses in the system and can be used in system design for comparing alternate system configurations and modifications.
  • Overall system reliability indices can include power interruption index, power supply average MW curtailment, power supply disturbance index, power energy curtailment index, severity index, and system availability.
  • the individual load point indices can be aggregated to produce a set of system indices. These indices are indicators of the overall adequacy of the composite system to meet the total system load demand and energy requirements and can be extremely useful for the system planner and management, allowing more informed decisions to be made both in planning and in managing the system.
  • the various analysis and techniques can be broadly classified as being either Monte Carlo simulation or Contingency Enumeration.
  • the process can also use AC, DC and fast linear network power flow solutions techniques and can support multiple contingency modeling, multiple load levels, automatic or user-selected contingency enumeration, use a variety of remedial actions, and provides sophisticated report generation.
  • step 1506 can include adequacy analysis of the power system being monitored based on a prescribed set of criteria by which the system must be judged as being in the success or failed state.
  • the system is considered to be in the failed state if the service at load buses is interrupted or its quality becomes unacceptable, i.e., if there are capacity deficiency, overloads, and/or under/over voltages
  • Various load models can be used in the process of figure 15 including multi-step load duration curve, curtailable and Firm, and Customer Outage Cost models. Additionally, various remedial actions can be proscribed or even initiated including MW and MVAR generation control, generator bus voltage control, phase shifter adjustment, MW generation rescheduling, and load curtailment (interruptible and firm).
  • figure 16 is a flow chart illustrating an example process for analyzing the reliability of an electrical power distribution and transmission system that takes weather information into account in accordance with one embodiment.
  • real-time weather data can be received, e.g., via a data feed such as an XML feed from National Oceanic and Atmosphere Administration (NOAA).
  • NOAA National Oceanic and Atmosphere Administration
  • this data can be converted into reliability data that can be used in step 1502.
  • a logical model of a facility electrical system can be integrated into a real-time environment, with a robust AC Arc Flash simulation engine (system modeling engine 124), a data acquisition system (data acquisition hub 112), and an automatic feedback system (calibration engine 134) that continuously synchronizes and calibrates the logical model to the actual operational conditions of the electrical system.
  • system modeling engine 124 system modeling engine 124
  • data acquisition system data acquisition hub 112
  • calibration engine 134 an automatic feedback system
  • FIG. 17 is a diagram illustrating an example process for predicting in real-time various parameters associated with an alternating current (AC) arc flash incident. These parameters can include for example, the arc flash incident energy, arc flash protection boundary, and required Personal Protective Equipment (PPE) levels, e.g., in order to comply with NFPA-70E and IEEE-1584.
  • updated virtual model data can be obtained for the system being model, e.g., the updated data of step 1006, and the operating modes for the system can be determined.
  • an AC 3-phase short circuit analysis can be performed in order to obtain bolted fault current values for the system.
  • step 1706 e.g., IEEE 1584 equations can be applied to the bolted fault values and any corresponding arcing currents can be calculated in step 1708.
  • the ratio of arc current to bolted current can then be used, in step 1710, to determine the arcing current in a specific protective device, such as a circuit breaker or fuse.
  • a coordinated time-current curve analysis can be performed for the protective device in step 1712.
  • the arcing current in the protective device and the time current analysis can be used to determine an associated fault clearing time, and in step 1716 a corresponding arc energy can be determined based on, e.g., IEEE 1584 equations applied to the fault clearing time and arcing current.
  • step 1718 the 100% arcing current can be calculated and for systems operating at less than IkV the 85% arcing current can also be calculated.
  • step 1720 the fault clearing time in the protective device can be determined at the 85% arcing current level.
  • step 1722 e.g., IEEE 1584 equations can be applied to the fault clearing time (determined in step 1720) and the arcing current to determine the 85% arc energy level, and in step 1724 the 100% arcing current can be compared with the 85% arcing current, with the higher of the two being selected.
  • IEEE 1584 equations for example, can then be applied to the selected arcing current in step 1726 and the PPE level and boundary distance can be determined in step 1728.
  • these values can be output, e.g., in the form of a display or report.
  • FIG. 18 is a flow chart illustrating an example process for real-time analysis of the operational stability of an electrical power distribution and transmission system in accordance with one embodiment.
  • the ability to predict, in real-time, the capability of a power system to maintain stability and/or recover from various contingency events and disturbances without violating system operational constraints is important.
  • This analysis determines the real-time ability of the power system to: 1. sustain power demand and maintain sufficient active and reactive power reserve to cope with ongoing changes in demand and system disturbances due to contingencies, 2. operate safely with minimum operating cost while maintaining an adequate level of reliability, and 3. provide an acceptably high level of power quality (maintaining voltage and frequency within tolerable limits) when operating under contingency conditions.
  • the dynamic time domain model data can be updated to re-align the virtual system model in real-time so that it mirrors the real operating conditions of the facility.
  • the updates to the domain model data coupled with the ability to calibrate and age the virtual system model of the facility as it ages (i.e., real-time condition of the facility), as describe above, provides a desirable approach to predicting the operational stability of the electrical power system operating under contingency situations. That is, these updates account for the natural aging effects of hardware that comprise the total electrical power system by continuously synchronizing and calibrating both the control logic used in the simulation and the actual operating conditions of the electrical system
  • the domain model data includes data that is reflective of both the static and non- static (rotating) components of the system.
  • Static components are those components that are assumed to display no changes during the time in which the transient contingency event takes place. Typical time frames for disturbance in these types of elements range from a few cycles of the operating frequency of the system up to a few seconds.
  • static components in an electrical system include but are not limited to transformers, cables, overhead lines, reactors, static capacitors, etc.
  • Non-static (rotating) components encompass synchronous machines including their associated controls (exciters, governors, etc), induction machines, compensators, motor operated valves (MOV), turbines, static var compensators, fault isolation units (FIU), static automatic bus transfer (SABT) units, etc.
  • Non-static components can be simulated using various techniques.
  • thermal (round rotor) and hydraulic (salient pole) units can be both simulated either by using a simple model or by the most complete two-axis including damper winding representation.
  • Induction Machines a complete two-axis model can be used. Also it is possible to model them by just providing the testing curves (current, power factor, and torque as a function of speed).
  • MOVs Motor Operated Valves
  • Each mode of operation consists of five distinct stages, a) start, b) full speed, c) unseating, d) travel, and e) stall.
  • the system supports user-defined model types for each of the stages. That is, "start” may be modeled as a constant current while “full speed” may be modeled by constant power. This same flexibility exists for all five distinct stages of the closing mode.
  • start may be modeled as a constant current while "full speed” may be modeled by constant power. This same flexibility exists for all five distinct stages of the closing mode.
  • AVR and Excitation Systems There are a number of models ranging form rotating (DC and AC) and analogue to static and digital controls. Additionally, the system offers a user-defined modeling capability, which can be used to define a new excitation model. e.
  • SVCs Static Var Compensators
  • FIUs Fault Isolation Units
  • SABT Static Automatic Bus Transfers
  • the time domain model data includes "built-in" dynamic model data for exciters, governors, transformers, relays, breakers, motors, and power system stabilizers (PSS) offered by a variety of manufactures.
  • dynamic model data for the electrical power system may be OEM manufacturer supplied control logic for electrical equipment such as automatic voltage regulators (AVR), governors, under load tap changing transformers, relays, breakers motors, etc.
  • AVR automatic voltage regulators
  • the time domain model data in order to cope with recent advances in power electronic and digital controllers, includes "user-defined" dynamic modeling data that is created by an authorized system administrator in accordance with user-defined control logic models.
  • the user-defined models interacts with the virtual system model of the electrical power system through "Interface Variables" 1816 that are created out of the user-defined control logic models.
  • the controls For example, to build a user-defined excitation model, the controls requires that generator terminal voltage to be measured and compared with a reference quantity (voltage set point). Based on the specific control logic of the excitation and AVR, the model would then compute the predicted generator field voltage and return that value back to the application.
  • the user-defined modeling supports a large number of pre-defined control blocks (functions) that are used to assemble the required control systems and put them into action in a real-time environment for assessing the strength and security of the power system.
  • the time domain model data includes both built-in dynamic model data and user-defined model data.
  • a contingency event can be chosen out of a diverse list of contingency events to be evaluated. That is, the operational stability of the electrical power system can be assessed under a number of different contingency event scenarios including but not limited to a singular event contingency or multiple event contingencies (that are simultaneous or sequenced in time).
  • the contingency events assessed are manually chosen by a system administrator in accordance with user requirements.
  • the contingency events assessed are automatically chosen in accordance with control logic that is dynamically adaptive to past observations of the electrical power system. That is the control logic "learns" which contingency events to simulate based on past observations of the electrical power system operating under various conditions.
  • Some examples of contingency events include but are not limited to:
  • a transient stability analysis of the electrical power system operating under the various chosen contingencies can be performed.
  • This analysis can include identification of system weaknesses and insecure contingency conditions. That is, the analysis can predict (forecast) the system's ability to sustain power demand, maintain sufficient active and reactive power reserve, operate safely with minimum operating cost while maintaining an adequate level of reliability, and provide an acceptably high level of power quality while being subjected to various contingency events.
  • the results of the analysis can be stored by an associative memory engine 1818 during step 1814 to support incremental learning about the operational characteristics of the system.
  • step 1810 it can be determined if the system is operating in a deficient state when confronted with a specific contingency. If it is, then in step 1812, a report is generated providing a summary of the operational stability of the system. The summary may include general predictions about the total security and stability of the system and/or detailed predictions about each component that makes up the system.
  • step 1808 can determine if further contingencies needs to be evaluated. If so, then the process can revert to step 1806 and further contingencies can be evaluated.
  • the results of real-time simulations performed in accordance with figure 18 can be communicated in step 1812 via a report, such as a print out or display of the status.
  • the information can be reported via a graphical user interface (thick or thin client) that illustrated the various components of the system in graphical format.
  • the report can simply comprise a graphical indication of the security or insecurity of a component, subsystem, or system, including the whole facility.
  • the results can also be forwarded to associative memory engine 1818, where they can be stored and made available for predictions, pattern/sequence recognition and ability to imagine, e.g., via memory agents or other techniques, some of which are describe below, in step 1820.
  • the process of figure 18 can be applied to a number of needs including but not limited to predicting system stability due to: Motor starting and motor sequencing, an example is the assessment of adequacy of a power system in emergency start up of auxiliaries; evaluation of the protections such as under frequency and under-voltage load shedding schemes, example of this is allocation of required load shedding for a potential loss of a power generation source; determination of critical clearing time of circuit breakers to maintain stability; and determination of the sequence of protective device operations and interactions.
  • FIG 19 is a flow chart illustrating an example process for conducting a realtime power capacity assessment of an electrical power distribution and transmission system, in accordance with one embodiment.
  • the stability of an electrical power system can be classified into two broad categories: transient (angular) stability and voltage stability (i.e., power capacity).
  • Voltage stability refers to the electrical system's ability to maintain acceptable voltage profiles under different system topologies and load changes (i.e., contingency events). That is, voltage stability analyses determine bus voltage profiles and power flows in the electrical system before, during, and immediately after a major disturbance.
  • voltage instability stems from the attempt of load dynamics to restore power consumption beyond the capability of the combined transmission and generation system.
  • One factor that comes into play is that unlike active power, reactive power cannot be transported over long distances. As such, a power system rich in reactive power resources is less likely to experience voltage stability problems. Overall, the voltage stability of a power system is of paramount importance in the planning and daily operation of an electrical system.
  • the voltage stability modeling data for the components comprising the electrical system can be updated to re-align the virtual system model in "real-time” so that it mirrors the real operating conditions of the facility.
  • the voltage stability modeling data includes system data that has direct influence on the electrical system's ability to maintain acceptable voltage profiles when the system is subjected to various contingencies, such as when system topology changes or when the system encounters power load changes.
  • Some examples of voltage stability modeling data are load scaling data, generation scaling data, load growth factor data, load growth increment data, etc.
  • the voltage stability modeling data includes "built-in” data supplied by an OEM manufacturer of the components that comprise the electrical equipment.
  • the voltage stability data in order to cope with recent advances power system controls, includes "user-defined” data that is created by an authorized system administrator in accordance with user-defined control logic models. The user-defined models interact with the virtual system model of the electrical power system through "Interface Variables" 1916 that are created out of the user-defined control logic models.
  • the voltage stability modeling data includes a combination of both built-in model data and user-defined model data
  • a contingency event can be chosen out of a diverse list of contingency events to be evaluated. That is, the voltage stability of the electrical power system can be assessed under a number of different contingency event scenarios including but not limited to a singular event contingency or multiple event contingencies (that are simultaneous or sequenced in time).
  • the contingency events assessed are manually chosen by a system administrator in accordance with user requirements.
  • the contingency events assessed are automatically chosen in accordance with control logic that is dynamically adaptive to past observations of the electrical power system. That is the control logic "learns" which contingency events to simulate based on past observations of the electrical power system operating under various conditions.
  • Some examples of contingency events include but are not limited to: loss of utility supply to the electrical system, loss of available power generation sources, system load changes/fluctuations, loss of distribution infrastructure associated with the electrical system, etc.
  • a voltage stability analysis of the electrical power system operating under the various chosen contingencies can be performed.
  • This analysis can include a prediction (forecast) of the total system power capacity, available system power capacity and utilized system power capacity of the electrical system of the electrical system under various contingencies. That is, the analysis can predict (forecast) the electrical system's ability to maintain acceptable voltage profiles during load changes and when the overall system topology undergoes changes.
  • the results of the analysis can be stored by an associative memory engine 1918 during step 1914 to support incremental learning about the power capacity characteristics of the system.
  • the results of the predictions, analysis, and real-time data may be fed, as needed, into the associative memory engine 1918 for pattern and sequence recognition in order to learn about the voltage stability of the electrical system in step 1920.
  • concurrent inputs of various electrical, environmental, mechanical, and other sensory data can be used to learn about and determine normality and abnormality of business and plant operations to provide a means of understanding failure modes and give recommendations.
  • step 1910 it can be determined if there is voltage instability in the system when confronted with a specific contingency. If it is, then in step 1912, a report is generated providing a summary of the specifics and source of the voltage instability.
  • the summary may include general predictions about the voltage stability of the overall system and/or detailed predictions about each component that makes up the system.
  • step 1908 can determine if further contingencies needs to be evaluated. If so, then the process can revert to step 1906 and further contingencies can be evaluated.
  • the results of real-time simulations performed in accordance with figure 19 can be communicated in step 1912 via a report, such as a print out or display of the status.
  • the information can be reported via a graphical user interface (thick or thin client) that illustrated the various components of the system in graphical format.
  • the report can simply comprise a graphical indication of the capacity of a subsystem or system, including the whole facility.
  • the results can also be forwarded to associative memory engine 1918, where they can be stored and made available for predictions, pattern/sequence recognition and ability to imagine, e.g., via memory agents or other techniques, some of which are describe below, in step 1920
  • the systems and methods described above can also be used to provide reports (step 1912) on, e.g., total system electrical capacity, total system capacity remaining, total capacity at all busbars and/or processes, total capacity remaining at all busbars and/or processes, total system loading, loading at each busbar and/or process, etc.
  • the process of figure 19 can receive input data related to power flow, e.g., network connectivity, loads, generations, cables/transformers, impedances, etc., power security, contingencies, and capacity assessment model data and can produce as outputs data related to the predicted and designed total system capacity, available capacity, and present capacity. This information can be used to make more informed decisions with respect to management of the facility.
  • FIG 20 is a flow chart illustrating an example process for performing real-time harmonics analysis of an electrical power distribution and transmission system, in accordance with one embodiment.
  • AC alternating current
  • DC direct current
  • harmonic distortion analysis As part of traditional power analysis.
  • Metering and sensor packages are currently available to monitor harmonic distortions within an electrical system.
  • the process provides a harmonics analysis solution that uses a real-time snap shot captured by a data acquisition system to perform a real-time system power quality evaluation at all locations regardless of power quality metering density.
  • This process integrates, in real-time, a logical simulation model (i.e., virtual system model) of the electrical system, a data acquisition system, and power system simulation engines with a logic based approach to synchronize the logical simulation model with conditions at the real electrical system to effectively "age" the simulation model along with the actual electrical system.
  • a logical simulation model i.e., virtual system model
  • step 2002 the harmonic frequency modeling data for the components comprising the electrical system can be updated to re-align the virtual system model in "realtime" so that it mirrors the real operating conditions of the facility.
  • These updates to the harmonic frequency modeling data coupled with the ability to calibrate and age the virtual system model of the facility as it ages (i.e., real-time condition of the facility), as describe above, provides a desirable approach to predicting occurrences of harmonic distortions within the electrical power system when operating under contingency situations.
  • Harmonic frequency modeling data has direct influence over how harmonic distortions are simulated during a harmonics analysis. Examples of data that is included with the harmonic frequency modeling data include: IEEE 519 and/or Mil 1399 compliant system simulation data, generator/cable/motor skin effect data, transformer phase shifting data, generator impedance data, induction motor impedance data, etc.
  • a contingency event can be chosen out of a diverse list of contingency events to be evaluated. That is, the electrical system can be assessed for harmonic distortions under a number of different contingency event scenarios including but not limited to a singular event contingency or multiple event contingencies (that are simultaneous or sequenced in time).
  • the contingency events assessed are manually chosen by a system administrator in accordance with user requirements.
  • the contingency events assessed are automatically chosen in accordance with control logic that is dynamically adaptive to past observations of the electrical power system. That is the control logic "learns" which contingency events to simulate based on past observations of the electrical power system operating under various conditions.
  • contingency events include but are not limited to additions (bringing online) and changes of equipment that effectuate a non-linear load on an electrical power system (e.g., as rectifiers, arc furnaces, AC/DC drives, variable frequency drives, diode-capacitor input power supplies, uninterruptible power supplies, etc.) or other equipment that draws power in short intermittent pulses from the electrical power system.
  • an electrical power system e.g., as rectifiers, arc furnaces, AC/DC drives, variable frequency drives, diode-capacitor input power supplies, uninterruptible power supplies, etc.
  • a harmonic distortion analysis of the electrical power system operating under the various chosen contingencies can be performed.
  • This analysis can include predictions (forecasts) of different types of harmonic distortion data at various points within the system.
  • Harmonic distortion data may include but are not limited to: Wave-shape Distortions/Oscillations data
  • the harmonics analysis can predict (forecast) various indicators (harmonics data) of harmonic distortions occurring within the electrical system as it is being subjected to various contingency situations.
  • the results of the analysis can be stored by an associative memory engine 2016 during step 2014 to support incremental learning about the harmonic distortion characteristics of the system. That is, the results of the predictions, analysis, and real-time data may be fed, as needed, into the associative memory engine 2016 for pattern and sequence recognition in order to learn about the harmonic distortion profile of the electrical system in step 2018.
  • concurrent inputs of various electrical, environmental, mechanical, and other sensory data can be used to learn about and determine normality and abnormality of business and plant operations to provide a means of understanding failure modes and give recommendations.
  • step 2010 it can be determined if there are harmonic distortions within the system when confronted with a specific contingency. If it is, then in step 2012, a report is generated providing a summary of specifics regarding the characteristics and sources of the harmonic distortions.
  • the summary may include forecasts about the different types of harmonic distortion data (e.g., Wave-shape Distortions/Oscillations data, Parallel and Series Resonant Condition data, etc.) generated at various points throughout the system.
  • the associative memory engine 2016 can make predictions about the natural oscillation response(s) of the facility and compare those predictions with the harmonic components of the non-linear loads that are fed or will be fed from the system as indicated form the data acquisition system and power quality meters.
  • step 2008 can determine if further contingencies needs to be evaluated. If so, then the process can revert to step 2006 and further contingencies can be evaluated.
  • the results of real-time simulations performed in accordance with figure 20 can be communicated in step 2012 via a report, such as a print out or display of the status.
  • the information can be reported via a graphical user interface (thick or thin client) that illustrated the various components of the system in graphical format.
  • the report can simply comprise a graphical indication of the harmonic status of subsystem or system, including the whole facility.
  • the results can also be forwarded to associative memory engine 2016, where they can be stored and made available for predictions, pattern/sequence recognition and ability to imagine, e.g., via memory agents or other techniques, some of which are describe below, in step 2018
  • the process of Figure 20 can receive input data related to power flow, e.g., network connectivity, loads, generations, cables/transformers, impedances, etc., power security, contingencies, and can produce as outputs data related to Point Specific Power Quality Indices, Branch Total Current Harmonic Distortion Indices, Bus and Node Total Voltage Harmonic Distortion Indices, Frequency Scan Indices for Positive Negative and Zero Sequences, Filter(s) Frequency Angle Response, Filter(s) Frequency Impedance Response, and Voltage and Current values over each filter elements (r, xl, xc).
  • input data related to power flow e.g., network connectivity, loads, generations, cables/transformers, impedances, etc., power security, contingencies
  • input data related to power flow e.g., network connectivity, loads, generations, cables/transformers, impedances, etc., power security, contingencies
  • input data related to power flow e.g., network connectivity, loads, generations
  • FIG. 21 is a diagram illustrating how the HTM Pattern Recognition and Machine Learning Engine works in conjunction with the other elements of the analytics system to make predictions about the operational aspects of a monitored system, in accordance with one embodiment.
  • the HTM Pattern Recognition and Machine Learning Engine 551 is housed within an analytics server 116 and communicatively connected via a network connection 114 with a data acquisition hub 112, a client terminal 128 and a virtual system model database 526.
  • the virtual system model database 526 is configured to store the virtual system model of the monitored system.
  • the virtual system model is constantly updated with real-time data from the data acquisition hub 112 to effectively account for the natural aging effects of the hardware that comprise the total monitored system, thus, mirroring the real operating conditions of the system. This provides a desirable approach to predicting the operational aspects of the monitored power system operating under contingency situations.
  • the HTM Machine Learning Engine 551 is configured to store and process patterns observed from real-time data fed from the hub 112 and predicted data output from a real-time virtual system model of the monitored system. These patterns can later be used by the HTM Engine 551 to make real-time predictions (forecasts) about the various operational aspects of the system.
  • the data acquisition hub 112 is communicatively connected via data connections 110 to a plurality of sensors that are embedded throughout a monitored system 102.
  • the data acquisition hub 112 may be a standalone unit or integrated within the analytics server 116 and can be embodied as a piece of hardware, software, or some combination thereof.
  • the data connections 110 are "hard wired" physical data connections (e.g., serial, network, etc.). For example, a serial or parallel cable connection between the sensors and the hub 112.
  • the data connections 110 are wireless data connections. For example, a radio frequency (RF), BLUETOOTHTM, infrared or equivalent connection between the sensor and the hub 112.
  • RF radio frequency
  • Examples of a monitored system includes machinery, factories, electrical systems, processing plants, devices, chemical processes, biological systems, data centers, aircraft carriers, and the like. It should be understood that the monitored system can be any combination of components whose operations can be monitored with conventional sensors and where each component interacts with or is related to at least one other component within the combination.
  • the client 128 is typically a conventional "thin-client” or “thick client” computing device that may utilize a variety of network interfaces (e.g., web browser, CITRIXTM, WINDOWS TERMINAL SERVICESTM, telnet, or other equivalent thin-client terminal applications, etc.) to access, configure, and modify the sensors (e.g., configuration files, etc.), power analytics engine (e.g., configuration files, analytics logic, etc.), calibration parameters (e.g., configuration files, calibration parameters, etc.), virtual system modeling engine (e.g., configuration files, simulation parameters, etc.) and virtual system model of the system under management (e.g., virtual system model operating parameters and configuration files).
  • network interfaces e.g., web browser, CITRIXTM, WINDOWS TERMINAL SERVICESTM, telnet, or other equivalent thin-client terminal applications, etc.
  • the sensors e.g., configuration files, etc.
  • power analytics engine e.g., configuration files, analytics logic, etc.
  • the data from the various components of the monitored system and the real-time predictions (forecasts) about the various operational aspects of the system can be displayed on a client 128 display panel for viewing by a system administrator or equivalent.
  • the data may be summarized in a hard copy report 2102.
  • the HTM Machine Learning Engine 551 is configured to work in conjunction with a real-time updated virtual system model of the monitored system to make predictions (forecasts) about certain operational aspects of the monitored system when it is subjected to a contingency event.
  • the HTM Machine Learning Engine 551 can be used to make predictions about the operational reliability of an electrical power system in response to contingency events such as a loss of power to the system, loss of distribution lines, damage to system infrastructure, changes in weather conditions, etc.
  • indicators of operational reliability include but are not limited to failure rates, repair rates, and required availability of the power system and of the various components that make up the system.
  • the operational aspects relate to an arc flash discharge contingency event that occurs during the operation of the power system.
  • arc flash related operational aspects include but are not limited to quantity of energy released by the arc flash event, required personal protective equipment (PPE) for personnel operating within the confines of the system during the arc flash event, and measurements of the arc flash safety boundary area around components comprising the power system.
  • PPE personal protective equipment
  • the operational aspect relates to the operational stability of the system during a contingency event. That is, the system's ability to sustain power demand, maintain sufficient active and reactive power reserve, operate safely with minimum operating cost while maintaining an adequate level of reliability, and provide an acceptably high level of power quality while being subjected to a contingency event.
  • the operational aspect relates to the voltage stability of the electrical system immediately after being subjected to a major disturbance (i.e., contingency event).
  • a major disturbance i.e., contingency event
  • voltage instability stems from the attempt of load dynamics to restore power consumption, after the disturbance, in a manner that is beyond the capability of the combined transmission and generation system.
  • Examples of predicted operational aspects that are indicative of the voltage stability of an electrical system subjected to a disturbance include the total system power capacity, available system power capacity and utilized system power capacity of the electrical system under being subjected to various contingencies.
  • voltage stability is the ability of the system to maintain acceptable voltage profiles while under the influence of the disturbances.
  • the operational aspect relates to harmonic distortions in the electrical system subjected to a major disturbance.
  • Harmonic distortions are characterized by non-sinusoidal (non-linear) voltage and current waveforms. Most harmonic distortions result from the generation of harmonic currents caused by nonlinear load signatures.
  • a nonlinear load is characteristic in products such as computers, printers, lighting and motor controllers, and much of today's solid-state equipment. With the advent of power semiconductors and the use of switching power supplies, the harmonics distortion problem has become more severe.
  • FIG. 22 is an illustration of the various cognitive layers that comprise the neocortical catalyst process used by the HTM Pattern Recognition and Machine Learning Engine to analyze and make predictions about the operational aspects of a monitored system, in accordance with one embodiment.
  • the neocortical catalyst process is executed by a neocortical model 2202 that is encapsulated by a real-time sensory system layer 2204, which is itself encapsulated by an associative memory model layer 2206.
  • a neocortical model 2202 represents the "ideal" state and performance of the monitored system and it is continually updated in real-time by the sensor layer 2204.
  • the sensory layer 2204 is essentially a data acquisition system comprised of a plurality of sensors imbedded within the electrical system and configured to provide realtime data feedback to the neocortical model 2202.
  • the associative memory layer observes the interactions between the neocortical model 2202 and the real-time sensory inputs from the sensory layer 2204 to learn and understand complex relationships inherent within the monitored system. As the neocortical model 2202 matures over time, the neocortical catalyst process becomes increasingly accurate in making predictions about the operational aspects of the monitored system. This combination of the neocortical model 2202, sensory layer 2204 and associative memory model layer 2206 works together to learn, refine, suggest and predict similarly to how the human neocortex operates.
  • Figure 23 is an example process for real-time three-dimensional (3D) visualization of the health, reliability, and performance of an electrical system, in accordance with one embodiment.
  • 3D three- dimensional
  • the power analytics server can determine which operating mode(s) that the electrical system is being simulated under. That is, the virtual system model of the electrical system can be modified by the user to simulate the system operating under multiple different operating scenarios (conditions) and system contingencies.
  • the power analytics server is configured to utilize the operating mode settings while simulating the operation of the electrical system to make predictions about the system's health, performance, availability and reliability.
  • the operating mode(s) relate to the multiple contingency events that the electrical system may be subjected to during regular operations.
  • the contingency events can be chosen out of a diverse list of contingency events to be evaluated.
  • the operational health, performance, availability and reliability of the electrical power system can be assessed under a number of different contingency event scenarios including but not limited to a singular event contingency or multiple event contingencies (that are simultaneous or sequenced in time). That is, in one embodiment, the contingency events assessed are manually chosen by a user in accordance with the his/her requirements. In another embodiment, the contingency events assessed are automatically chosen in accordance with control logic that is dynamically adaptive to past observations of the electrical power system. That is the control logic "learns" which contingency events to simulate based on past observations of the electrical power system operating under various conditions. [00224] Some examples of contingency events include but are not limited to:
  • the operating mode(s) can relate to the multiple load levels that the electrical system operates under. That is, the virtual system model of the electrical system can be simulated under various power system load configurations or capacity conditions.
  • the system is simulated as operating under a base load power configuration. That is, the electrical system can be simulated as operating continuously at its maximum rated power output. Under this configuration, power systems only shut down to perform maintenance or if something breaks. Accordingly, the ability to test under such conditions cannot be achieved in conventional systems.
  • the electrical system can be simulated as operating under a load following power configuration. That is, the electrical system is simulated as operating in a fluctuating manner by adjusting its power output as demand for electricity fluctuates throughout the day.
  • the electrical system is simulated as operating at various different power load capacity levels.
  • the electrical system may be simulated as operating at 10%, 25%, 50%, 75%, or 100% of its rated power generation capacity.
  • the operating mode(s) can relate to different system and load point reliability indices assigned to the components that make up the electrical system. In one embodiment, for example, changes can be made to the reliability indices of individual components. In another embodiment, changes can be made to all the components that make up the system.
  • the operating mode(s) relate to the different remedial measures or actions that are implemented on the electrical system to respond to the various contingency situations that the system may be subjected to.
  • remedial measures can relate to: the various types of uninterruptible power supply (UPS) systems operating on the electrical system, various protective devices that are integrated to the system, various operating limits and conditions that are placed on the system, etc.
  • the power analytics server is configured to utilize the operating mode settings, determined in step 2302, and the updated virtual system model of the electrical system to simulate and predict aspects relating to the real-time health, performance, reliability and availability of the electrical system.
  • the power analytics server can simulate and predict aspects relating to:
  • the predictions may also relate to the real-time ability of the electrical system to: 1. sustain power demand and maintain sufficient active and reactive power reserve to cope with ongoing changes in demand and system disturbances due to contingencies, 2. operate safely with minimum operating cost while maintaining an adequate level of reliability, and 3. provide an acceptably high level of power quality (maintaining voltage and frequency within tolerable limits) when operating under contingency conditions.
  • the power analytics server is configured to output the predictions in the form of a print out or display of text, graphics, charts, labels, and model views that readily communicates the health and predicted performance of the electrical system in an elegant and efficient fashion.
  • the information can be reported via a graphical user interface ("thick” or "thin” client) that illustrates the various components of the system in graphical format.
  • the report can simply comprise a graphical indication of the capacity of a subsystem or system, including the whole facility.
  • the results can also be forwarded to associative memory engine 2308, where they can be stored and made available for predictions, pattern/sequence recognition and ability to imagine, e.g., via memory agents or other techniques, some of which are describe below, in step 2308.
  • the model views are 3D (i.e., 3D Plant Lifecycle Model) model views of the various components, equipment and sub-systems that comprise the electrical system.
  • 3D model views 2502 are depicted in a client interface screenshot in Figure 25.
  • the 3D model views 2502 can be generated by an integrated 3D visualization engine that is an integrated part of the power analytics server.
  • the model views are 2D model views of the various components, equipment and sub-systems making up the electrical system.
  • An example of a 2D model view 2504 is also depicted in Figure 25.
  • the results of the simulation and predictive analysis can be stored by an associative memory engine to support incremental learning about the power capacity characteristics of the system. That is, the results of the predictions, analysis, and real-time data may be fed, as needed, into an machine learning engine for pattern and sequence recognition in order to learn about the health, performance, reliability and availability of the electrical system. Additionally, concurrent inputs of various electrical, environmental, mechanical, and other sensory data can be used to learn about and determine normality and abnormality of business and plant operations to provide a means of understanding failure modes and generate recommendations.
  • FIG. 24 is a diagram illustrating how the 3D Visualization Engine works in conjunction with the other elements of the analytics system to provide 3D visualization of the health, reliability, and performance of an electrical system, in accordance with one embodiment.
  • the 3D Visualization Engine 2402 is integrated within a power analytics server 116 that is communicatively connected via a network connection 114 with a data acquisition hub 112, a client terminal 128 and a virtual system model database 526.
  • the virtual system model database 526 is configured to store the virtual system model of the electrical system.
  • the virtual system model is constantly updated with real-time data from the data acquisition hub 112 to effectively account for the natural aging effects of the hardware that comprise the total electrical power system, thus, mirroring the real operating conditions of the system. This provides a desirable approach to predicting the operational aspects of the monitored power system and for communicating the predicted aspects through 3D visualization models of the facility.
  • the 3D visualization engine 2402 is interfaced with the predictive elements of the power analytics server and communicatively connected to the data acquisition hub 112 and the client 128.
  • the data acquisition hub 112 is communicatively connected via data connections 110 to a plurality of sensors that are embedded throughout the electrical system 102.
  • the data acquisition hub 112 may be a standalone unit or integrated within the analytics server 116 and can be embodied as a piece of hardware, software, or some combination thereof.
  • the data connections 110 are "hard wired" physical data connections (e.g., serial, network, etc.). For example, a serial or parallel cable connection between the sensors and the hub 112.
  • the data connections 110 are wireless data connections.
  • RF radio frequency
  • BLUETOOTHTM infrared or equivalent connection between the sensor and the hub 112.
  • Real-time system data readings can be fed continuously to the data acquisition hub 112 from the various sensors that are embedded within the electrical system 102.
  • the client 128 is typically a conventional thin-client or thick-client computing device that may utilize a variety of network interfaces (e.g., web browser, CITRIXTM, WINDOWS TERMINAL SERVICESTM, telnet, or other equivalent thin-client terminal applications, etc.) to access, configure, and modify the sensors (e.g., configuration files, etc.), analytics engine (e.g., configuration files, analytics logic, etc.), calibration parameters (e.g., configuration files, calibration parameters, etc.), virtual system modeling engine (e.g., configuration files, simulation parameters, choice of contingency event to simulate, etc.), 3D visualization engine (e.g., configuration files, 3D visualization parameters, etc.) and virtual system model of the system under management (e.g., virtual system model operating parameters and configuration files).
  • network interfaces e.g., web browser, CITRIXTM, WINDOWS TERMINAL SERVICESTM, telnet, or other equivalent thin-client terminal applications, etc.
  • the data from the various components of the electrical system and the real-time predictions (forecasts) about the health, performance, reliability and availability of the electrical system can be displayed on a client 128 display panel for viewing by a system administrator or equivalent.
  • the data may be summarized in a hard copy report 2404.
  • Figure 26 is a diagram illustrating how the Schematic User Interface Creator Engine works in conjunction with the other elements of the analytics system to automatically generate a schematic user interface for visualizing the health, reliability, and performance of an electrical system, in accordance with one embodiment.
  • Conventional electrical power system monitoring technologies typically rely on custom graphical design and user-interface development efforts in order to create a system schematic user interface (displayable on a client terminal) that can be linked to real-time sensory data output by the various components that comprise an electrical power system.
  • custom development efforts tend to be cumbersome and often require an extraordinarily amount of time to implement.
  • the tools can be configured to automatically read electrical system configuration data from a database containing a virtual system representation (i.e., virtual system model) of the electrical system, generate a schematic user interface view of the electrical system, and intelligently link the various components included in the user interface to the predicted, monitored and/or derived output/values of those various components.
  • a virtual system representation i.e., virtual system model
  • the Schematic User Interface Creator Engine 2602 can be integrated within a power analytics server 116 that can be communicatively connected via a network connection 114 with a data acquisition hub 112, a client terminal 128 and a virtual system model database 526.
  • the virtual system model database 526 can be configured to store the virtual system model of the electrical system.
  • the virtual system model can be constantly updated with real-time data from the data acquisition hub 112 to effectively account for the natural aging effects of the hardware that comprise the total electrical power system, thus, mirroring the real operating conditions of the system.
  • the Schematic User Interface Creator Engine 2602 can be configured to automatically create a schematic user interface that is representative of the electrical system and link that interface to the sensors monitoring the components (i.e., electrical equipment) that comprise the electrical system to enable real-time monitoring of the derived output/values from those components.
  • the user interface can include a visual representation of each piece of electrical equipment (associated/tagged with a unique identifier) that comprises the electrical system.
  • the schematic user interface is based on a one-line diagram construct.
  • the schematic user interface is based on a technical system schematic diagram construct.
  • the user interface can be based on any engineering diagram construct as long as the resulting interface can be displayed on a client terminal 128 to allow viewing by an operator/administrator.
  • the Schematic User Interface Creator Engine 2602 can also be interfaced with the predictive elements of the power analytics server.
  • the predictive elements of the power analytics server may relate to the real-time health, performance, reliability and availability of the electrical system.
  • the predictions can be indicative of the real-time ability of the electrical system to: 1. sustain power demand and maintain sufficient active and reactive power reserve to cope with ongoing changes in demand and system disturbances due to contingencies, 2. operate safely with minimum operating cost while maintaining an adequate level of reliability, and 3. provide an acceptably high level of power quality (maintaining voltage and frequency within tolerable limits) when operating under contingency conditions.
  • the data acquisition hub 112 can be communicatively connected via data connections 110 to a plurality of sensors that can be embedded throughout the electrical system 102.
  • the data acquisition hub 112 can be a standalone unit or integrated within the analytics server 116 and can be embodied as a piece of hardware, software, or some combination thereof.
  • the data connections 110 are "hard wired" physical data connections (e.g., serial, network, etc.). For example, a serial or parallel cable connection between the sensors and the hub 112.
  • the data connections 110 are wireless data connections. For example, a radio frequency (RF), BLUETOOTHTM, infrared or equivalent connection between the sensor and the hub 112.
  • RF radio frequency
  • BLUETOOTHTM infrared or equivalent connection between the sensor and the hub 112.
  • the client 128 can be a conventional thin-client or thick-client computing device that can utilize a variety of network interfaces (e.g., web browser, CITRIXTM, WINDOWS TERMINAL SERVICESTM, telnet, or other equivalent thin-client terminal applications, etc.) to access, configure, and modify the sensors (e.g., configuration files, etc.), power analytics engine (e.g., configuration files, analytics logic, etc.), calibration parameters (e.g., configuration files, calibration parameters, etc.), virtual system modeling engine (e.g., configuration files, simulation parameters, choice of contingency event to simulate, etc.), Schematic User Interface Creator Engine 2602 (e.g., configuration files, schematic interface algorithms, etc.) and virtual system model of the electrical system under management (e.g., virtual system model operating parameters and configuration files).
  • network interfaces e.g., web browser, CITRIXTM, WINDOWS TERMINAL SERVICESTM, telnet, or other equivalent thin-client terminal applications, etc.
  • the sensors
  • the real-time data from the various monitored components of the electrical system and the real-time predictions (forecasts) about the health, performance, reliability and availability of the electrical system can be simultaneously visualized on the schematic user interface that is displayed on a client terminal 128 for viewing by a system administrator or equivalent.
  • This schematic user interface can provide a desirable approach to communicating the monitored and predicted operational aspects of an electrical system to an operator/administrator.
  • the schematic user interface is rendered in a 2-dimensional (2D) graphical image format.
  • the schematic user interface is rendered in a 3-dimensional (3D) graphical image format.
  • Figure 27 is an example process for automatically generating a schematic user interface for visualizing the health, reliability, and performance of an electrical system, in accordance with one embodiment.
  • the operational steps that comprise the process are implemented through a schematic user interface creator engine (application/software tool) that runs on the power analytics server.
  • the operational steps that comprise the process are implemented through a schematic interface creator engine that runs on a separate (network application) server that is communicatively connected to the power analytics server.
  • the operational steps that comprise the process are implemented through a plurality of discrete applications that are distributed amongst one or more (network application) servers that are communicatively connected with the power analytics server. It should be understood, however, that the application(s) can be distributed in any configuration as long as the application(s) can communicatively access the power analytics server to implement the process.
  • system configuration data can be extracted from a virtual system model of the electrical system.
  • the virtual system model can be stored in one or more virtual system model database(s) that are communicatively connected to the power analytics server.
  • the configuration data can be stored in the memory of the power analytics server (or a network application server) and can include the unique identifiers (i.e., node IDs) of each of the components (i.e., piece of electrical equipment) that comprise the virtual system model, connectivity information (i.e., the electrical connectivity between the various virtual system model components and/or the data connectivity with the sensors monitoring those components) and/or equipment specific information such as bus or branch specific equipment type (e.g., generator, circuit breaker, transformer, motor, fuse, static load, etc.).
  • unique identifiers i.e., node IDs
  • connectivity information i.e., the electrical connectivity between the various virtual system model components and/or the data connectivity with the sensors monitoring those components
  • equipment specific information such as bus or branch specific equipment type (e.g.,
  • a logical construct of the virtual system model can be constructed from the system configuration data.
  • the logical construct can be created in an Extensible Markup Language (XML) format.
  • the logical construct can be created in an Extensible HyperText Markup Language (XHTML) format. It should be appreciated that the logical construct can be created using any mark-up language as long as it can be utilized to convey system configuration information about the components (i.e., electrical equipment) that make up the virtual system model.
  • one or more graphical objects can be generated to represent one or more pieces of electrical equipment included in the logical construct. This can be accomplished through the schematic user interface creator engine or equivalent application(s) parsing the system configuration data stored in the logical construct and generating appropriate symbol block(s) and/or graphical object(s) for each piece of electrical equipment that comprise the electrical system. The symbol block(s) or graphical object(s) that are generated can then be individually organized as buses and/or branches. [00246] In step 2708, the one or more graphical objects can be organized to generate a schematic user interface layout of the electrical system.
  • a self-executing algorithm can be either an integrated component of the schematic user interface creator engine or a separate discrete application that is configured to work in conjunction with the schematic user interface creator engine.
  • the self- executing algorithm is a .NET based application.
  • the self-executing algorithm is an ACTIVE X based application.
  • the self- executing algorithm is a JAVA based application. It should be understood, however, that the self-executing algorithm can be created using any type of programming language as long as the resulting algorithm can function either as a component of the schematic user interface creator engine or in conjunction with the same.
  • the self-executing algorithm is in a force directed layout format.
  • the self-executing algorithm is in a tree layout format.
  • the self-executing algorithm is in a layered diagraph layout format. It should be appreciated that the self-executing algorithm can follow any format as long as each of the one ore more graphical objects/symbol blocks in the resulting schematic user interface layout can later be linked to a corresponding piece of electrical equipment that comprise the electrical system.
  • the schematic user interface layout of the electrical system After the schematic user interface layout of the electrical system is generated, it can be further optimized using the schematic interface creator engine (or equivalent application) to scan the schematic user interface layout and re-align the graphical object(s) based on one or more user selected optimization criteria.
  • each of the one or more graphical objects in the schematic user interface layout can be communicatively linked to sensors configured to monitor the realtime operational status of the one or more pieces of electrical equipment represented by the one or more graphical objects.
  • This can be accomplished by intelligently linking the unique identifiers (e.g., equipment IDs) associated with each of the graphical objects/symbol blocks to their corresponding database files and creating a tag or communication channel with the same unique identifier to allow the files to be populated with data from the electrical equipment associated with each unique identifier.
  • a Graphical Object A with a unique identifier of "001" can be linked to the database file "A" which is associated with the "001" identifier.
  • a communication channel "001" can then be opened to allow data, acquired from a piece of electrical equipment associated with the "001" identifier, to populate database file "A.”
  • FIG. 28 is a diagram illustrating how the Energy Management System Engine works in conjunction with the other elements of the analytics system to intelligently monitor and manage the cost, quality and reliability of energy generated and/or utilized by an electrical system, in accordance with one embodiment.
  • Conventional approaches to energy management typically rely on real-time data readings supplied directly from power quality meters and sensors that are interfaced with the components (that comprise an electrical system) to provide a simple live metrics (e.g., voltage, current, frequency, etc.) of how the electrical system is operating.
  • conventional energy management systems may also permit basic historical trending and rudimentary statistical methods to be performed to generate a historical energy profile of how the electrical system has previously operated.
  • a conventional energy management system only presents a user (i.e., electrical system owner, administrator, and/or operator) with a view of how the electrical system is currently operating and/or has operated in the past without having the ability to make predictions about how the electrical system will operate in the future or allow the running of simulations based on user programmed "what-if ' scenarios.
  • the ability to predict the active power demand, system losses, reactive power demand and other energy parameters via a model (i.e., virtual system model) of the electrical system that can age and synchronize itself in real-time with the electrical system's actual operating conditions is critical in obtaining accurate predictions of the system's energy efficiency (i.e., cost of losses that are inherent to the electrical system and those due to the inefficient operation of the electrical system), energy costs, reliability, availability, health and performance. Without operational conditions synchronization or an aging ability, these predictions are of little value as they are not reflective of the actual facility status and may lead the user to make false conclusions.
  • the Energy Management System Engine 2802 can be integrated within a power analytics server 116 that can be communicatively connected via a network connection 114 with a data acquisition hub 112, a client terminal 128 and a virtual system model database 526.
  • the virtual system model database 526 can be configured to store the virtual system model of the electrical system 102.
  • the virtual system model can be constantly updated with real-time data from the data acquisition hub 112 to effectively account for the natural aging effects of the hardware that comprise the total electrical power system, thus, mirroring the real operating conditions of the system.
  • the Energy Management System Engine 2802 can also be interfaced with the predictive elements of the power analytics server.
  • the predictive elements of the power analytics server 116 may relate to the real-time health, performance, efficiency, reliability and availability of the electrical system 102.
  • the predictions can be indicative of the real-time ability of the electrical system 102 to: 1. sustain power demand and maintain sufficient active and reactive power reserve to cope with ongoing changes in demand and system disturbances due to contingencies, 2. operate safely with minimum operating cost while maintaining an adequate level of reliability, and 3. provide an acceptably high level of power quality (maintaining voltage and frequency within tolerable limits) when operating under contingency conditions.
  • the Energy Management System Engine 2802 can be configured to process the real-time data output, the predicted data output, historical data output and forecasted aspects about the operation of the electrical system 102 to generate a user interface that can convey an operational state of the electrical system 102 to a user (i.e., electrical system 102 owner/system administrator/operator).
  • the operational state can be the real-time operational performance of the electrical system 102.
  • the operational state can be the predicted operational performance of the electrical system 102.
  • the operational state can be a historical data trending display of the historical operational performance of the electrical system 102.
  • the Energy Management System Engine 2802 can be configured to generate a text-based or a graphics-based user interface that conveys real-time, predicted and/or historical operational performance of the electrical system 102 that can include realtime, predicted or historical information regarding the electrical system's 102 energy efficiency (i.e., the cost of energy utilized by the electrical system 102, the cost of intrinsic power losses within the electrical system 102, the cost of power losses due to the electrical system 102 running at poor power factors), reliability (i.e., the predicted ability of the electrical system 102 to withstand a contingency event that results in stress to the electrical system 102), availability (e.g., the predicted ability of the electrical system 102 to maintain availability of total power capacity), health and performance.
  • energy efficiency i.e., the cost of energy utilized by the electrical system 102, the cost of intrinsic power losses within the electrical system 102, the cost of power losses due to the electrical system 102 running at poor power factors
  • reliability i.e., the predicted ability of the electrical system 102 to withstand a cont
  • the user interface can also include a visual representation of each piece of electrical equipment (associated/tagged with a unique identifier) that comprises the electrical system 102.
  • the user interface is based on a one-line diagram construct.
  • the user interface is based on a technical system schematic diagram construct.
  • the user interface can be based on any engineering diagram construct as long as the resulting interface can be displayed on a client terminal 128 to allow viewing by an operator/administrator.
  • the data acquisition hub 112 can be communicatively connected via data connections 110 to a plurality of sensors that can be embedded throughout the electrical system 102.
  • the data acquisition hub 112 can be a standalone unit or integrated within the analytics server 116 and can be embodied as a piece of hardware, software, or some combination thereof.
  • the data connections 110 are "hard wired" physical data connections (e.g., serial, network, etc.). For example, a serial or parallel cable connection between the sensors and the hub 112.
  • the data connections 110 are wireless data connections. For example, a radio frequency (RF), BLUETOOTHTM, infrared or equivalent connection between the sensor and the hub 112.
  • RF radio frequency
  • BLUETOOTHTM infrared or equivalent connection between the sensor and the hub 112.
  • the client 128 can be a conventional thin-client or thick-client computing device that can utilize a variety of network interfaces (e.g., web browser, CITRIXTM, WINDOWS TERMINAL SERVICESTM, telnet, or other equivalent thin-client terminal applications, etc.) to access, configure, and modify the sensors (e.g., configuration files, etc.), power analytics engine (e.g., configuration files, analytics logic, etc.), calibration parameters (e.g., configuration files, calibration parameters, etc.), virtual system modeling engine (e.g., configuration files, simulation parameters, choice of contingency event to simulate, etc.), Energy Management System Engine 2802 (e.g., configuration files, etc.) and virtual system model of the electrical system 102 under management (e.g., virtual system model operating parameters and configuration files).
  • network interfaces e.g., web browser, CITRIXTM, WINDOWS TERMINAL SERVICESTM, telnet, or other equivalent thin-client terminal applications, etc.
  • the sensors e.g.
  • the real-time data from the various monitored components of the electrical system 102 and the real-time predictions (forecasts) about the health, performance, reliability and availability of the electrical system 102 can be simultaneously visualized on the user interface that is displayed on a client 128 terminal for viewing by a system administrator or equivalent.
  • This user interface can provide a desirable approach to communicating the monitored and predicted operational aspects of an electrical system 102 to an operator/administrator.
  • the user interface is rendered in a 2-dimensional (2D) graphical image format.
  • the user interface is rendered in a 3-dimensional (3D) graphical image format.
  • Figure 29 is a logic flow diagram depicting how the various elements of the
  • the Energy Management System can interact to provide intelligent energy monitoring and management of an electrical system, in accordance with one embodiment.
  • the Energy Management System can include a power analytics server 116 that can be communicatively connected to a real-time historical data trending database 2902, a virtual system model database 526, a data acquisition component 112 and a conventional web browser 2904.
  • the data acquisition component 112 can be configured to communicate "realtime" sensor data readings 2906 from the various sensors interfaced throughout the electrical system to the analytics server 116.
  • the connection is a "hardwire" physical connection.
  • the power analytics server 116 can be communicatively connected (via Category 5 (CAT5), fiber optic or equivalent cabling) to the data acquisition component 112.
  • the connection is a wireless connection (e.g., Wi-Fi, BLUETOOTH, etc.).
  • a wireless connection utilizing an 802.1 lb/g or equivalent transmission format.
  • the connection can be a combination of "hardwire” and “wireless” connection elements that are linked together based on the particular requirements of the Energy Management System.
  • the power analytics server 116 can be configured to host one or more analytic engines that allow the Energy Management System to perform its various functions.
  • the power analytics server 116 can host a machine learning engine 2908, a virtual system modeling engine 2922 and/or a utility power pricing engine 2914.
  • the machine learning engine 2908 can be configured to work in conjunction with the virtual system modeling engine 2922 and a virtual system model of the electrical system to make real-time predictions (i.e., forecasts) about the various operational aspects of the electrical system.
  • the machine learning engine 2908 work by processing and storing patterns observed during the normal operation of the electrical system over time. These observations are provided in the form of real-time data captured using a multitude of sensors that are imbedded within the electrical system.
  • the utility pricing engine 2914 can be configured to access a utility power pricing data source 2916 (that includes energy cost tables and other power billing data) to generate real-time energy cost and usage data 2910 that is reflective of the operational efficiency and performance of the electrical system.
  • Examples of real-time energy cost and usage data 2910 can include, but are not limited to: 1. the real-time cost of energy utilized by the electrical system (energy cost), 2. the real-time cost of intrinsic power losses within the electrical system (cost of losses), and/or 3. the real-time cost of power losses due to the electrical system running at poor power factors (cost due to poor power factor).
  • the utility power pricing data source 2916 is populated with static utility power pricing data 2918.
  • utility power pricing data that is either directly supplied by a user of the Energy Management System or data that is extracted from utility data pricing data sheets/tables that are downloaded from the utility power provider supplying electrical power to the electrical system.
  • the utility power pricing data source 2916 is populated with real-time dynamic power pricing data 2920 directly from the utility power provider supplying the electrical power to the electrical system.
  • the real-time dynamic power pricing data 2920 can be power spot pricing that is set by a utility power provider based on a variety of different real-time power grid operational factors (e.g., power grid load, cost of power generation, etc.).
  • the virtual system model database 526 can be configured to store a virtual system model of the electrical system.
  • the virtual system model can be constantly updated with real-time data from the data acquisition hub 112 to effectively account for the natural aging effects of the hardware that comprise the total electrical power system, thus, mirroring the real operating conditions of the system.
  • the virtual system model stored in the virtual system model database 526 can be accessed by the power analytic engines (i.e., machine learning engine 2908 and virtual system modeling engine 2922) that are part of the Energy Management System to make various types of power analytics forecasts/predictions including, but not limited to: power system reliability predictions 2928, power system operations forecasts 2926, power system state estimations 2924 and/or power system operational cost predictions (i.e., cost of energy, cost of losses and cost associated with running the electrical system inefficiently).
  • the real-time historical data trending database 2902 can be configured to store the real-time data output, the predicted data output and the forecasted aspects output from the power analytics server 116 and apply a historical trending algorithm to generate a historical data trending display that is indicative of the historical performance of the electrical system.
  • the historical data trending display can present many different categories of data relating to the historical operation of the electrical system, including but not limited to:
  • the network connection 114 is a "hardwired" physical connection.
  • the data acquisition hub 112 may be communicatively connected (via Category 5 (CAT5), fiber optic or equivalent cabling) to a data server (not shown) that is communicatively connected (via CAT5, fiber optic or equivalent cabling) through the Internet and to the analytics server 116 server.
  • the analytics server 116 being also communicatively connected with the Internet (via CAT5, fiber optic, or equivalent cabling).
  • the network connection 114 is a wireless network connection (e.g., Wi-Fi, WLAN, etc.). For example, utilizing an 802.1 lb/g or equivalent transmission format. In practice, the network connection utilized is dependent upon the particular requirements of the monitored system 102.
  • the energy management system engine hosted by the power analytics server 116, can be configured to collect and process the real-time data output, the predicted data output and the forecasted aspects output from the various analytic engines (i.e., a machine learning engine 2908, a virtual system modeling engine 2922 and/or a utility power pricing engine 2914) that comprise the Energy Management System and generate a text or graphical user interface that conveys an operational state of the electrical system.
  • analytic engines i.e., a machine learning engine 2908, a virtual system modeling engine 2922 and/or a utility power pricing engine 2914
  • the embodiments described herein can be practiced with other computer system configurations including hand-held devices, microprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers and the like.
  • the embodiments can also be practiced in distributing computing environments where tasks are performed by remote processing devices that are linked through a network.
  • the embodiments described herein can employ various computer- implemented operations involving data stored in computer systems. These operations are those requiring physical manipulation of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. Further, the manipulations performed are often referred to in terms, such as producing, identifying, determining, or comparing.
  • any of the operations that form part of the embodiments described herein are useful machine operations.
  • the invention also relates to a device or an apparatus for performing these operations.
  • the systems and methods described herein can be specially constructed for the required purposes, such as the carrier network discussed above, or it may be a general purpose computer selectively activated or configured by a computer program stored in the computer.
  • various general purpose machines may be used with computer programs written in accordance with the teachings herein, or it may be more convenient to construct a more specialized apparatus to perform the required operations.
  • Certain embodiments can also be embodied as computer readable code on a computer readable medium.
  • the computer readable medium is any data storage device that can store data, which can thereafter be read by a computer system.
  • Examples of the computer readable medium include hard drives, network attached storage (NAS), read-only memory, random-access memory, CD-ROMs, CD-Rs, CD-RWs, magnetic tapes, and other optical and non-optical data storage devices.
  • the computer readable medium can also be distributed over a network coupled computer systems so that the computer readable code is stored and executed in a distributed fashion.
  • Figure 30 is a diagram illustrating how the various elements of the Client/Server system can interact to provide intelligent energy monitoring and management of an electrical system, in accordance with one embodiment.
  • a true client/server application framework can combine a rich user interface capability of desktop power system engineering client software with the universally accessible, no-download features of web-delivered applications. Accordingly, web-delivered applications can deliver a true thin client system for such tasks as e.g. power system design, analysis, sizing calculations, and reporting applications.
  • This method is an ideal solution for addressing the need for an outstanding user experience that empowers power system engineers and design specialists to access applications via an all-in-one seamless, personalized, and highly visual engineering design and information workplace based on a RIA technology such as, for instance, AJAXTM, FLASHTM, DHTML, and others.
  • a system may consist of both a server-side component and client-side components and communicate via a web application server.
  • the server side system can be implemented as a Java Servlet that can run in any Java servlet container (e.g. a J2EE servlet container or a similar JAVA VIRTUAL MACHINETM technology).
  • the web application server can communicate with the energy monitoring infrastructure and deliver derived content to the client terminal (e.g. via servlets) related to, e.g., the virtual system modeling, system analytics, schematics, reports, or user management.
  • the web application server 3002 can be communicatively connected via a network connection 114 with a data acquisition hub 112, and a client terminal 128.
  • the data acquisition hub 112 can be communicatively connected via data connections 110 to a plurality of sensors that can be embedded throughout the electrical system 102.
  • the data acquisition hub 112 can be a standalone unit or integrated within the web application server 3140 and can be embodied as a piece of hardware, software, or some combination thereof.
  • the data connections 110 can be "hard wired" physical data connections (e.g., serial, network, etc.). For example, a serial or parallel cable connection between the sensors and the hub 112.
  • the data connections 110 are wireless data connections.
  • RF radio frequency
  • BLUETOOTHTM infrared or equivalent connection between the sensor and the hub 112.
  • Real-time system data readings can be fed continuously to the data acquisition hub 112 from the various sensors that are embedded within the electrical system 102.
  • the client 128 can be a conventional thin-client or thick-client computing device that can utilize a variety of network interfaces (e.g., web browser, CITRIXTM, WINDOWS TERMINAL SERVICESTM, telnet, or other equivalent thin-client terminal applications, etc.) to access, configure, and modify the sensors (e.g., configuration files, etc.), power analytics engine (e.g., configuration files, analytics logic, etc.), calibration parameters (e.g., configuration files, calibration parameters, etc.), virtual system modeling engine (e.g., configuration files, simulation parameters, choice of contingency event to simulate, etc.), power analytics engine(s) (e.g., configuration files, etc.) and virtual system model of the electrical system 102 under management (e.g., virtual system model operating parameters and configuration files).
  • network interfaces e.g., web browser, CITRIXTM, WINDOWS TERMINAL SERVICESTM, telnet, or other equivalent thin-client terminal applications, etc.
  • the real-time data from the various monitored components of the electrical system 102 and the real-time predictions (forecasts) about the health, performance, reliability and availability of the electrical system 102 can be simultaneously visualized on the user interface that is displayed on a client 128 terminal for viewing by a system administrator or equivalent.
  • This user interface can provide a desirable approach to communicating the monitored and predicted operational aspects of an electrical system 102 to an operator/administrator.
  • the user interface is rendered in a 2-dimensional (2D) graphical image format.
  • the user interface is rendered in a 3-dimensional (3D) graphical image format.
  • the virtual system model can be constantly updated with real-time data from the data acquisition hub 112 to effectively account for the natural aging effects of the hardware that comprise the total electrical power system, thus, mirroring the real operating conditions of the system.
  • FIG 31 is a diagram illustrating various elements of the web application server illustrated in Figure 30, in accordance with one embodiment.
  • the web application server 3002 can comprise a virtual system model database 526, a power analytic simulation engine 3110, and an analytics services engine 3140.
  • the power analytic simulation engine 3110 can comprise a virtual system modeling engine 124 (as previously described).
  • the power analytic simulation engine 3110 can also comprise a number of other engines as described above, for instance, an energy management system engine 2802, a schematic user interface creator engine 2602, a HTM Pattern Recognition and Machine Learning Engine 551, a reporting engine 3114, or an equipment library editor engine 3112.
  • the power analytic simulation engine 3110 may also include other engines described above which are communicatively connected to the virtual system model database 526.
  • a virtual system model database 526 can be communicatively connected to the data acquisition component, such as a data acquisition hub 112, and can be configured, for instance, to store a virtual system model of the electrical system.
  • a virtual system modeling engine 526 can be configured to generate a predicted data output for the electrical system utilizing the virtual system model of the electrical system.
  • An analytics engine 3140 can be communicatively connected to the virtual system model database, and can be configured to monitor the real-time data output and the predicted data output of the electrical system 102, and to initiate a calibration and synchronization operation to update the virtual system model when a difference between the real-time data output and the predicted data output exceeds a threshold.
  • the virtual system modeling engine 124 may be provided via a graphical and/or text based data entry and layout interface.
  • This engine can support modeling of electrical power system networks such as AC (e.g., 3phase, 1 phase, and 3 phase/ 1 phase mixed networks) and DC systems or AC/DC mixed networks. Additionally, it can support control system modeling and analysis for time domain simulation models or it can support grounding, cable sizing, and other power system modeling activities as needed.
  • the model database solution and function set can capture and store not only the network topography but also connectivity, interdependencies, costs, part numbers, weight, physical dimensions along with the mechanical, electrical, and other physics based characteristics of the equipment within the electrical network.
  • the database shall also support features such as revision control, revision history, tracking, trending and roll-back features along with Save, Save -As, import and export of data, drawings and other input sources.
  • the database can also contain business logic to expedite the modeling process.
  • the equipment library editor engine 3112 can communicate with an equipment library database 3134 to provide a library of equipment, operating characteristics (e.g. electrical characteristics, mechanical characteristics, etc.).
  • the equipment library editor engine 3112 can be used to add, edit, or delete database records within the equipment library database 3134.
  • the equipment library editor engine 3112 can allow users to create and interact with their own custom library of equipment or to a global library which is used by all users.
  • the analytics services engine 3140 in various embodiments, can be configured to perform functions and extract data from the virtual system model database 526. In one embodiment, the analytics services engine 3140 can perform requested simulations, reporting, and analysis via an appropriate graphical/textual/audio format as appropriate.
  • the analytics services engine 3140 can be used to input data and check connectivity errors. It can also be used for short circuit and fault analysis analytics (IEEE/ANSI/IEC and similar) for AC and DC systems.
  • the analytics services engine 3140 can provide Power Flow Simulation and voltage control features including load analysis for AC and DC systems.
  • the analytics services engine can be configured to provide protective device coordination and automatic selection expert system type technologies for AC and DC systems.
  • the analytics services engine 3140 can also be configured to provide arc flash simulation and reporting for AC and DC systems per latest NFPA and IEEE standards such as NFPA 7OE and IEEE 1584 for example. Additionally, the analytics services engine 3140 can be configured to perform transient stability simulation and reporting.
  • the analytics services engine 3140 can be configured to perform power quality analysis and harmonic simulations and mitigation including automatic filter sizing for complex power systems, including phase dependant networks.
  • the analytics services engine 3140 can also be configured to provide protective device evaluation for electrical power systems.
  • the power analytics engine 3110 can also provide reporting functionality via a reporting engine 3114.
  • the reporting engine 3114 can communicate to the virtual system model database 526 and/or a reporting database in order to generate a display for reports, permit saving, printing, formatting, and exporting reports.
  • the reports can include input data reports, error checking reports, etc., and can be generated in a graphical format, a textual format, or a combination of graphical and textual format.
  • the server side may include customer interface engines 3102, such as a customer management engine 3106 or an e-commerce engine 3104.
  • customer interface engines 3102 such as a customer management engine 3106 or an e-commerce engine 3104.
  • the web application server may also comprise or be in communication with additional databases 3130 as needed.
  • database(s) for users rights management to store electrical on-line diagram models, schematics, reports or other features.
  • an e-commerce engine 3104 can be configured to communicate with a billing database 3132 to provide an e-commerce component which can be configured to manage use payments and transactions.
  • a customer management engine 3106 can be configured to communicate with a customer database 3136 to provide an customer management component which can be configured to manage customer accounts records, end user rights, and user access levels.
  • support components can be added to the web application server. These components can include an engine component or a database component as needed. For example, a support component can be added along with on-line reference libraries and tutorials. These can be stored in various mediums for example as rich multimedia files, interactive references, or standard print materials stored in an electronic format (such as a PDF).
  • the web application server is able to transform the way electrical power system design and simulation applications work together on the Web and enables delivery of power system engineering applications that combine the rich user interface capabilities of desktop client software with the universally accessible, no-down features of web-delivered applications.
  • FIG 32 is a logic flow diagram depicting how the various elements of the Client/Server system can interact to provide intelligent energy monitoring and management of an electrical system, in accordance with one embodiment.
  • a method for interacting with an electrical system management application to perform power analytic analysis simulations on a virtual system model of the electrical power system can comprise: in step 3202, a web application server, receiving a user request to access the electrical management system from a client terminal, and in step 3204, sending a user interface for interaction with the electrical management system to a client terminal; in step 3206, the client terminal, receiving the user interface for interaction with the electrical management system, and in step 3208, sending user interaction data to the electrical system management application; and in step 3210, the electrical system management application, performing analytic analysis on a virtual system model of the electrical power system.
  • a method for interacting with an electrical system management application to perform power analytic analysis simulations on a virtual system model of the electrical power system can further comprise: the web application server, communicating the user interaction data to a virtual system model engine; the virtual system model engine, accessing a virtual system model of the electrical system stored in a virtual system model database, generating a predicted data output for the electrical system utilizing the virtual system model of the electrical system, and communicating the generated predicted data to the web application server.
  • a method for interacting with an electrical system management application to perform power analytic analysis simulations on a virtual system model of the electrical power system can further comprise: the web application server, communicating the user interaction data to an analytics engine; and the analytics engine, monitoring the real-time data output and the predicted data output of the electrical system stored in a virtual system model database, and initiating a calibration and synchronization operation to update the virtual system model when a difference between the real-time data output and the predicted data output exceeds a threshold.
  • any of the operations that form part of the embodiments described herein are useful machine operations.
  • the invention also relates to a device or an apparatus for performing these operations.
  • the systems and methods described herein can be specially constructed for the required purposes, such as the carrier network discussed above, or it may be a general purpose computer selectively activated or configured by a computer program stored in the computer.
  • various general purpose machines may be used with computer programs written in accordance with the teachings herein, or it may be more convenient to construct a more specialized apparatus to perform the required operations.
  • the embodiments described herein can also be embodied as computer readable code on a computer readable medium.
  • the computer readable medium is any data storage device that can store data, which can thereafter be read by a computer system.
  • Examples of the computer readable medium include hard drives, network attached storage (NAS), read-only memory, random-access memory, CD-ROMs, CD-Rs, CD-RWs, magnetic tapes, and other optical and non-optical data storage devices.
  • the computer readable medium can also be distributed over a network coupled computer systems so that the computer readable code is stored and executed in a distributed fashion.

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Abstract

L'invention concerne un système pour une surveillance et une gestion intelligentes basées sur le Web d'un système électrique. Le système comprend un composant d'acquisition de données connecté de manière communicante à un capteur configuré pour acquérir des données en temps réel sorties du système électrique, un serveur d'applications Web connecté de manière communicante au composant d'acquisition de données, le serveur d'applications Web étant configuré pour transmettre une interface utilisateur à un terminal de client, le serveur d'applications Web comprenant une base de données de modèles de systèmes virtuels connectée de manière communicante au composant d'acquisition de données, la base de données de modèles de systèmes virtuels étant configurée pour mémoriser un modèle de système virtuel du système électrique, un moteur de simulation analytique de puissance comprenant un moteur de modélisation de système virtuel, configuré de manière communicante pour générer une sortie de données prédites pour le système électrique par l'utilisation du modèle de système virtuel du système électrique, est connecté à la base de données de modèles de systèmes virtuels, et un moteur analytique est connecté de manière communicante à la base de données de modèles de systèmes virtuels.
EP08837888.0A 2007-10-11 2008-10-08 Modélisation, conception, analyse et compte rendu de système de puissance électrique via une plateforme d'application client-serveur Withdrawn EP2201489A4 (fr)

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US97934707P 2007-10-11 2007-10-11
US12/121,552 US9557723B2 (en) 2006-07-19 2008-05-15 Real-time predictive systems for intelligent energy monitoring and management of electrical power networks
PCT/US2008/079218 WO2009048963A1 (fr) 2007-10-11 2008-10-08 Modélisation, conception, analyse et compte rendu de système de puissance électrique via une plateforme d'application client-serveur

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