WO2020171814A1

WO2020171814A1 - Event selection for power grid disturbance

Info

Publication number: WO2020171814A1
Application number: PCT/US2019/018913
Authority: WO
Inventors: Honggang Wang; Weizhong Yan
Original assignee: General Electric Company
Priority date: 2019-02-21
Filing date: 2019-02-21
Publication date: 2020-08-27

Abstract

Provided is an event selection system for use in determining which disturbance events should be used for model validation and calibration. The system can limit the model validation and calibration to events which provide valuable dynamic modality. In one example, the method may include receiving a disturbance which is detected by a sensor of a power grid, extracting features from a signal of the disturbance based on dynamic changes in a pattern of the signal between a start and an end of the disturbance, identifying a magnitude and a diversity of the disturbance based on non-linear relationships between the extracted features of the signal, determining whether to use the disturbance for model validation based on the identified magnitude and the diversity, and storing the identified magnitude and diversity of the detected disturbance in a storage device based on the determination.

Description

EVENT SELECTION FOR POWER GRID DISTURBANCE

BACKGROUND

[0001] Grid planning and operating decisions are often based on the results of power system simulations. These simulations rely on power system models to predict system performance during anticipated disturbance events. Both technical and commercial segments of the industry must be confident that the dynamic simulation models, including all of their data, are accurate and up-to-date. Optimistic models can result in grid under-investment or unsafe operating conditions and ultimately widespread power outages. On the other hand, pessimistic models and assumptions can result in overly conservative grid operation and under-utilization of network capacity. Pessimistic models can also lead to unnecessary capital investment, thereby increasing the cost of electric power. Therefore, realistic models are needed for ensuring reliable and economic power system operation.

[0002] Power system models are the foundation of virtually all power system studies. Calculation of operating limits, planning for assessment of new generation and load growth, performance assessments of system integrity protection schemes (SIPS), and the like, depend on an approximate mathematical representation of the transmission, generation, and load of an electrical grid. Disturbances in the electrical grid provide a non-invasive opportunity to validate a power system model because the system can be tested without taking generators offline. However, in comparison to traditional staged-tests ( e.g ., which include a series of pre-defmed tests while a generator is offline) disturbance-based testing suffers from limited events and limited effectiveness in an operating space.

SUMMARY

[0003] The example embodiments are directed to a system which improves upon the prior art by automatically determining which disturbance events on the power grid should be used for model validation and calibration and which events should not be used. The system may receive a raw signal of the disturbance and extract dynamic features therefrom such as peak value, rising time, damping ratio, rate of change of frequency (ROCOF), energy function, cumulative deviation in energy, and the like. [0004] The system may evaluate the extracted features (e.g. , via an auto-associated model) to identify a magnitude and diversity of the disturbance. In addition, the system may perform a similarity evaluation on the disturbance to identify a similar event type with respect to previously stored disturbances. Furthermore, the system may determine whether or not to use the disturbance event based on the magnitude and diversity of the signal and the event type of the signal. Criteria may be predefined which identifies certain amounts of diversity, magnitude, and/or event types that the system would like to use for model validation and calibration, and the criteria can be adjusted.

[0005] In an aspect of an example embodiment, a computing system may include a processor configured to receive a disturbance which is detected by a sensor of a power grid, extract features from a signal of the disturbance based on dynamic changes in a pattern of the signal between a start and an end of the disturbance, identify a magnitude and a diversity of the disturbance based on non-linear relationships between the extracted features of the signal of the disturbance, and determine whether to use the disturbance for model validation based on the magnitude and the diversity, and a storage configured to store the magnitude and diversity of the disturbance based on the determination.

[0006] In an aspect of another example embodiment, a method may include one or more of receiving a disturbance which is detected by a sensor of a power grid, extracting features from a signal of the disturbance based on dynamic changes in a pattern of the signal between a start and an end of the disturbance, identifying a magnitude and a diversity of the disturbance based on non-linear relationships between the extracted features of the signal, determining whether to use the disturbance for model validation based on the identified magnitude and the diversity, and storing the identified magnitude and diversity of the detected disturbance in a storage device based on the determination.

[0007] Other features and aspects may be apparent from the following detailed description taken in conjunction with the drawings and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Features and advantages of the example embodiments, and the manner in which the same are accomplished, will become more readily apparent with reference to the following detailed description taken in conjunction with the accompanying drawings. [0009] FIG. 1 is a diagram illustrating a system for delivering electricity to a customer in accordance with an example embodiment.

[0010] FIG. 2 is a diagram illustrating a network topology of an electrical grid in accordance with an example embodiment.

[0011] FIG. 3 is a diagram illustrating a system including an enhanced disturbance management (EDM) module in accordance with an example embodiment.

[0012] FIG. 4 is a diagram illustrating a framework for validating power system models in accordance with an example embodiment.

[0013] FIG. 5 is a diagram illustrating temporal patterns of different disturbance events in accordance with an example embodiment.

[0014] FIGS. 6A-6D are diagrams illustrating systems associated with event selection in accordance with example embodiments.

[0015] FIG. 7 is a diagram illustrating a power system including the event detection system in accordance with an example embodiment.

[0016] FIG. 8 is a diagram illustrating a method of determining whether to select an event for power system model validation in accordance with an example embodiment.

[0017] FIG. 9 is a diagram illustrating a computing system for use in the methods and processed described herein.

[0018] FIG.10 is a diagram illustrating a user interface for event selection in an overall model validation software module in accordance with an example embodiment.

[0019] Throughout the drawings and the detailed description, unless otherwise described, the same drawing reference numerals will be understood to refer to the same elements, features, and structures. The relative size and depiction of these elements may be exaggerated or adjusted for clarity, illustration, and/or convenience.

DETAILED DESCRIPTION

[0020] In the following description, specific details are set forth in order to provide a thorough understanding of the various example embodiments. It should be appreciated that various modifications to the embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the disclosure. Moreover, in the following description, numerous details are set forth for the purpose of explanation.

However, one of ordinary skill in the art should understand that embodiments may be practiced without the use of these specific details. In other instances, well-known structures and processes are not shown or described in order not to obscure the description with unnecessary detail. Thus, the present disclosure is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

[0021] The behavior of power plants and electrical grids may change over time and should be checked and updated to assure that they remain accurate. Engineers use the processes of validation and calibration to make sure that a power system model can accurately predict the behavior of the modeled object ( e.g ., transmission system, generating unit, load, etc.) Validation assures that the model accurately represents the operation of the real system including model structure, correct assumptions, and that the output matches actual events. Once the model is validated, a calibration process may be used to make minor adjustments to the model and its parameters so that the model continues to provide accurate outputs. High-speed, time-synchronized data, collected using PMUs may facilitate model validation of the dynamic response to grid events. Grid operators may use, for example,

PMU data recorded during normal plant operations and grid events to validate grid and power plant models quickly and at lower cost.

[0022] The transmission operators, Regional reliability coordinators, Independent System Operators, and the like such as MISO, ISO-New England, PG&E, etc., can use this calibrated generator or power system model for power system stability study based on N-k contingencies, in every 5 to 10 minutes. If there is a stability issue (transient stability) for some specific contingency, the power flow may be redirected to relieve the stress-limiting factors. For example, the output of some power generators will be adjusted to redirect the power flow. Alternatively, adding more capacity (more power lines) to the existing system can be used to increase the transmission capacity.

[0023] With a model that accurately reflects oscillations and their causes, the grid operator can also diagnose the causes of operating events, such as wind-driven oscillations, and identify appropriate corrective measures before those oscillations spread to harm other assets or cause a loss of load. [0024] As used herein, devices, including those associated with the system and any other device described herein, may exchange information via any communication network which may be one or more of a Local Area Network (“LAN”), a Metropolitan Area Network (“MAN”), a Wide Area Network (“WAN”), a proprietary network, a Public Switched Telephone Network (“PSTN”), a Wireless Application Protocol (“WAP”) network, a Bluetooth network, a wireless LAN network, and/or an Internet Protocol (“IP”) network such as the Internet, an intranet, or an extranet. Note that any devices described herein may communicate via one or more such communication networks.

[0025] Multiple disturbances {i.e., events) may be perceived by a sensor such as a phasor measurement unit (PMU). The events are usually perceived by a difference in the static state of the grid before and after the disturbance. Each time an event is detected, a model validation and calibration process can be performed for a power generation system near the sensor. However, not every event provides value to the validation and calibration process. For example, some events may happen frequently around a PMU. These events may carry similar dynamic modality information which lack diversity needed to improve the model performance. As another example, some events may happen infrequently or may not be strongly perceived by the PMU, yet these events may carry valuable dynamic modality information for the model validation. Accordingly, there is a need for a system which can decipher between valuable events for power system model validation and calibration, and events which may not be valuable for model validation and calibration.

[0026] Some grid events may happen frequently around a PMU. These events may carry similar dynamic modality information and using them to conduct MV A may not be able improve the model performance. While other events may happen not very often or not strongly perceived by the PMU, yet they may carry valuable dynamic modality information for the MVA purpose. There is a further need to ensure the event selected can increase the diversity of the dynamic modality.

[0027] Traditional model validation is performed when disturbances are detected on the grid. For example, a system may compare a defined disturbance impact factor (DIF) to a predefined threshold. In this case, if the DIF is greater than the threshold, then the related PMU and its associated device are selected as a candidate for model validation. However, the DIF does not fully reflect the dynamic response of the power system associated with the PMU. Rather, DIF is based on user defined weights and steady-state changes in the power system parameters during the disturbance. In other words, the DIF may only considers the disturbance parameters at a start and at an end of the disturbance. Given the same steady state changes, there may be different dynamic response modes caused by different dynamic parameter sets in the power system model.

[0028] The example embodiments provide an event selection system which filters events to be used for model calibration based on the features included within the disturbance signal and not based on a disturbance impact factor. The event selection system can use various models to analyze/evaluate a dynamic temporal pattern of the disturbance signal that occurs between the start and the end of the disturbance to determine whether the disturbance provides a valuable dynamic modality.

[0029] In one example, the event selection system may extract multiple features from the disturbance signal and identify a magnitude and a diversity of the disturbance based on the extracted features. Here, the event selection system may process the features using an auto-associative model. In some embodiments, the event selection system may further perform a similarity analysis of the signal to determine whether the signal is close in distance to other signals which have been previously recorded. The similarity analysis may identify a previous disturbance event type that is most close to the currently detected disturbance.

Furthermore, the event selection system may fuse together the results of the magnitude and diversity with the disturbance event type identified from the similarity analysis and determine whether the event is unique or beneficial enough to use for model validation and calibration.

[0030] FIG. 1 illustrates a power delivery process 100 showing components that can facilitate the generation of power and the process of delivering power (e.g., delivering energy, electricity) to customer premises. Electric power can be generated at a power generation facility, and then carried by transmission power lines to substations having transformers. A local distribution system of smaller, lower -voltage transmission lines and substations carry power to the customer premises. In the example of FIG. 1, a power generation facility 105 generates electricity to meet the power demands of customers. A variety of facilities can generate electricity. For example, power generation facilities 105 can include power plants that burn coal, oil, or natural gas. Power generation facilities 105 can also comprise nuclear power plants, renewable sources of energy such as hydroelectric dams, wind turbines, and solar panels, and the like. The location of these electricity generators, and their distance from end users, can vary widely. [0031] The electricity that is generated by the power generation facility 105 may be stepped up or stepped down by transformers (e.g., transformer 110) which may be located at power plant substations adjacent to (and connected via power lines to) the power plant. In this example, the transformer 110 is a step-up transformer that will "step up" the voltage of the electricity. When power travels through power lines (e.g., metallic wires that conduct electricity), some of that power is wasted in the form of heat. The power loss is proportional to the amount of current being carried. Power companies keep the current low and compensate by stepping up the voltage. After the voltage is stepped up, the electricity is typically carried over long distances by high voltage power transmission lines, typically supported and elevated by transmission towers (e.g., transmission towers 115) that can be of various dimensions, materials, and heights.

[0032] In some embodiments, the voltage may be gradually reduced by step-down transformers as the electricity approaches customer premises. Transmission substations contain step-down transformers (e.g., transmission substation step-down transformer 120) that reduce the voltage of the electricity. The electricity can then be distributed on lower- voltage power lines. A typical transmission substation can serve tens of thousands of customers. The electricity leaving transmission substations can travel through power lines to distribution substations. Distribution substations contain step-down transformers (e.g., distribution substation step-down transformer 125) that further reduce the voltage of electricity and distribute the power to cities and towns through main power lines, which can serve hundreds of customers. Distribution lines carry lower voltage power to clusters of homes and businesses, and are typically supported by wooden poles. Of note, power lines can also be buried under the ground. Of note, substations can contain a variety of other equipment, including switches, breakers, regulators, batteries, etc.

[0033] The voltage from a branch line can further be reduced by transformers that are mounted on poles (e.g., step-down transformer on pole 130) that connect customer premises (e.g., customer premises 135) through a service drop power line. Customer premises 135 can be of any type and variety. Customer premises can be a residential customer premises, such as residential houses. Customer premises can be an industrial customer premises, such as factories. Customer premises can be commercial customer premises, such as an office building. If a particular customer premises has a heavier load (e.g., has a higher demand for power), then a larger transformer, instead of a pole transformer, might service that particular customer premises. [0034] FIG. 2 depicts an illustration of a power grid system 200 (e.g., an electrical grid) comprising multitudes of nodes 201-210. In this example, a node may represent a power generation facility, transmission substation, a distribution substation, and the like, and is intended to convey that such facilities and substations can be interconnected. In the examples herein, a node may be referred to as a“power system node.” The power grid system 200 can follow a structural topology, influenced by factors such as budget, system reliability, load demand (demand for power), land, and geology. The structural topology in many cities and towns, for example many of those in North America, tends to follow a classic radial topology. This is a tree-shape network wherein power from larger voltage lines and substations radiates out into progressively lower voltage lines and substations until the customer premises are reached.

[0035] A substation receives its power from a power generation facility, and the power may be stepped down with a transformer and sent through lines that spread out in all directions across the countryside. These feeders carry three-phase power and tend to follow major streets near the substation. As the distance from the substation grows, the fanout continues as smaller laterals spread out to cover areas missed by the feeders. This tree-like structure grows outward from the substation, but a single power failure can render inoperable entire branches of the tree. For reliability reasons, there are often unused backup connections from one substation to a nearby substation. This backup connection can be enabled in case of an emergency, such that a part of a substation's service area can be fed by another substation in case of any power failure events. Redundancy allows line failures to occur and power to be rerouted while workmen restore to service damaged or deactivated components. Neighboring power utilities also typically link their grids, thereby assisting one another to maintain a balance between power generation supply and loads (e.g., customer demand). Other topologies can be mesh topologies, looped systems (mostly found in Europe) and ring networks.

[0036] The result can be an interconnected power grid system 200 that can form complex networks of power plants and transformers connected by hundreds of thousands of miles of high-voltage transmission lines. While these interconnections can be useful in situations, the danger or risk can comprise the possibility that a shutdown in one sector could rapidly spread to other sectors, leading to massive power failures in a wide area. [0037] In the example of FIG. 2, disposed within the power grid system 200 are measurement devices 220A-220E. Throughout a power network, a variety of sensors, monitoring devices and measurement devices (collectively referred to herein as

"measurement devices") can be located at one or more nodes (e.g., nodes 201-210), in between nodes on lines, and the like, and can be used to provide monitoring data related to power flow measurements, or monitor the condition of one or more aspects of a power grid system. The measurement devices 220A-220E may be deployed within, or adjacent to, power transmission components (e.g., generating units, transformers, circuit breakers), including at substations. In some examples, the measurement devices 220A-220E can also be deployed along distribution lines.

[0038] The measurement devices 220A-220E may include sensors that measure a range of parameters such as magnitude and phase angle of voltage, current, harmonic distortion, real and reactive power, power factor, and fault current. Examples of some sensors include, but are not limited to, voltage and current sensors, PMUs, transformer-Metal Insulated Semiconducting (MIS) gas in oil sensors, circuit breaker sulfur hexafluoride density sensors, conductor temperature and current sensors that record overhead transmission conductor temperatures and current magnitudes, overhead insulator leakage current sensors, Transmission Line Surge Arrester (TLSA) sensors, and the like.

[0039] In the example of FIG. 2, the power grid system 200 may include the measurement devices 220A-220E located in various parts (e.g., such as nodes) throughout the grid. The measurement devices 220A-220E can be coupled via a network of transmission lines, as well as through wireless and wired communications mediums (e.g., cellular, ethernet, etc.). For example, a measurement device 220E can be coupled via a transmission line 222 from a network of transmission lines associated with the nodes 201-210.

Furthermore, a subset of the measurement devices can be associated with a sector of the power grid system 200.

[0040] In example embodiments, the reliability of the power grid system 200 can be facilitated through the use and analysis of the data received from measurement devices 220 A- 220E and monitoring of system conditions that are then communicated to a central control center, where a combination of automated actions and human decision assist in striving to ensure that the power grid system 200 is stable and balanced. For example, a measurement device may include a phasor measurement unit (PMU) which can capture data of a disturbance event. PMUs typically have a naming convention based on PMU information which is defined by a regional transmission authority. Meanwhile, power system nodes 201 - 210 have a naming convention based on utility companies. As a result, the measurement devices 220A-220E may have names that are not identical to or correlated with the names of the power system nodes 201-210. As further described herein, the system can perform automated tag mapping to correlate the measurement devices 220A-220E with corresponding power system nodes 201-210.

[0041] Among other operations, the system described herein may include an

Enhanced Disturbance Management (EDM) component (e.g., module) that is operable to read (e.g., obtain) monitoring data, for example, Supervisory Control and Data Acquisition (SCAD A) system data, PMU-based data, topology data, and the like, based on power flow measurements associated with measurement devices (e.g., PMUs, current sensors, voltage sensors, etc.) connected to an electrical power system (e.g., electric power system, electrical energy system, electric energy system, power grid system, etc.), wherein the monitoring data can comprise alarm data indicative of an electrical disturbance within the electrical power system, and topology data indicative of a topology of the electrical power system. The EDM component can be operable to correlate the alarm data, which can relate to, for example, an angle disturbance alarm, or, for example, a frequency disturbance alarm, with a change in the topology data.

[0042] FIG. 3 illustrates a system 300 including an EDM module 316 in accordance with an example embodiment. In this example, the EDM module 316 can determine a characterization (e.g., classification, causation, etc.) of the electrical disturbance in the power grid system based on the correlating of the alarm data with the topology data, determining a coherency level representative of the degree of correlation between the alarm data and the topology data, determining a Disturbance Impact Factor (DIF) indicative of an impact of the electrical disturbance on a location in the power grid system, and identify one or more sensors (PMUs) that have captured data of the disturbance. The EDM module 316 can further auto-map PMUs to one or more power system nodes on the grid, retrieve power model information of the power system nodes, and validate the retrieved power model based on the PMU information of the disturbance. In some embodiments, the EDM module 316 can also store and display disturbance history, event history, and a variety of other statistical information related to disturbances and events, including on a graphical user interface, or in a generated report. [0043] Measurement device 220 in FIG. 3 can obtain, monitor or facilitate the determination of electrical characteristics associated with the power grid system (e.g., the electrical power system), which can comprise, for example, power flows, voltage, current, harmonic distortion, frequency, real and reactive power, power factor, fault current, and phase angles. Measurement device 220 can also be associated with a protection relay, a Global Positioning System (GPS), a Phasor Data Concentrator (PDC), communication capabilities, or other functionalities.

[0044] Measurement devices 220 can provide real-time measurements of electrical characteristics or electrical parameters associated with the power grid system (e.g., the electrical power system). The measurement device 220 can, for example, repeatedly obtain measurements from the power grid system (e.g., the electrical power system) that can be used by the EDM module 316. The data generated or obtained by the measurement device 220 can be coded data (e.g., encoded data) associated with the power grid system that can input (or be fed into) a traditional SCADA/EMS system. The measurement device 220 can also be a PMU that repeatedly obtains subs-second measurements (e.g., 30 times per second). Here, the PMU data can be fed into, or input into, applications (e.g., Wide Area Monitoring System (WAMS) and WAMS-related applications) that can utilize the more dynamic PMU data (explained further below).

[0045] In the example of FIG. 3, the measurement device 220 includes a voltage sensor 302 and a current sensor 304 that feed data typically via other components, to, for example, a Supervisory Control and Data Acquisition (SCAD A) system (e.g., SCADA component 310). Voltage and current magnitudes can be measured and reported to a system operator every few seconds by the SCADA component 310. The SCADA component 310 can provide functions such as data acquisition, control of power plants, and alarm display. The SCADA component can also allow operators at a central control center to perform or facilitate management of energy flow in the power grid system. For example, operators can use a SCADA component (for example using a computer such as a laptop or desktop) to facilitate performance of certain tasks such opening or closing circuit breakers, or other switching operations that might divert the flow of electricity.

[0046] In some examples, the SCADA component 310 can receive measurement data from Remote Terminal Units (RTUs) connected to sensors in the power grid system,

Programmable Logic Controllers (PLCs) connected to sensors in the power grid system, or a communication system (e.g., a telemetry system) associated with the power grid system.

PLCs and RTUs can be installed at power plants, substations, and the intersections of transmission and distribution lines, and can be connected to various sensors, including the voltage sensor 302 and the current sensor 304. The PLCs and RTUs receive its data from the voltage and current sensors to which they are connected. The PLCs and RTUs can convert the measured information to digital form for transmission of the data to the SCAD A component. In example embodiments, the SCAD A component 310 can also comprise central host server or servers called master terminal units (MTUs), sometimes also referred to as a SCADA center. The MTU can also send signals to PLCs and RTUs to control equipment through actuators and switchboxes. In addition, the MTU can perform controlling, alarming, and networking with other nodes, etc. Thus, the SCADA component 310 can monitor the PLCs and RTUs, and can send information or alarms back to operators over telecommunications channels.

[0047] The SCADA component 310 can also be associated with a system for monitoring or controlling devices in the power grid system, such as an Energy Management System (EMS). An EMS can comprise one or more systems of computer-aided tools used by operators of the electric power grid systems to monitor, control, and optimize the

performance of the generation or transmission system. Often, an EMS is also referred to as SCADA/EMS or EMS/SCADA. In these respects, the SCADA/EMS or EMS/SCADA can also perform the functions of a SCADA. Or, a SCADA can be operable to send data (e.g., SCADA data) to the EMS, which can in turn provide the data to the EDM module 316. Other systems with which the EDM module 316 can be associated can comprise a situational awareness system for the power grid system, a visualization system for the power grid system, a monitoring system for the power grid system or a stability assessment system for the power grid system.

[0048] The SCADA component 310 can generate or provide SCADA data (e.g., SCADA DATA shown in FIG. 3) comprising, for example, real-time information (e.g., real time information associated with the devices in the power grid system) or sensor information (e.g., sensor information associated with the devices in the power grid system) that can be used by the EDM module 316. The SCADA data can be stored, for example, in a repository 314 (described further below). In example embodiments, data determined or generated by the SCADA component 310 can be employed to facilitate generation of topology data (topology data is further described below) that can be employed by the EDM module 316 for enhanced disturbance management, which is further described below.

[0049] The employment of current sensor 304 and voltage sensor 302 allow for fast response. Traditionally, the SCADA component 310 monitors power flow through lines, transformers, and other components relies on the taking of measurements every two to six seconds, and cannot be used to observe the dynamic characteristics of the power system because of its slow sampling rate (e.g., cannot detect the details of transient phenomena that occur on timescales of milliseconds (one 60 Hz cycle is 16 milliseconds). Additionally, although SCADA technology enables some coordination of transmission among utilities, the process can be slow, especially during emergencies, with much of the response based on telephone calls between human operators at the utility control centers. Furthermore, most PLCs and RTUs were developed before industry-wide standards for interoperability were established, and as such, neighboring utilities often use incompatible control protocols.

[0050] The measurement device 220 also includes one or more PMUs 306. A PMU 306 can be a standalone device or may be integrated into another piece of equipment such as a protective relay. PMUs 306 can be employed at substations, and can provide input into one or more software tools (e.g., WAMS, SCADA, EMS, and other applications). A PMU 306 can use voltage and current sensors (e.g., voltage sensors 302, current sensors 304) that can measure voltages and currents at principal intersecting locations (e.g., substations) on a power grid using a common time source for synchronization, and can output accurately time- stamped voltage and current phasors. The resulting measurement is often referred to as a synchrophasor (although the term synchrophasor refers to the synchronized phasor measurements taken by the PMU 306, some have also used the term to describe the device itself). Because these phasors are truly synchronized, synchronized comparison of two quantities is possible in real time, and this time synchronization allows synchronized real time measurements of multiple remote measurement points on the grid.

[0051] In addition to synchronously measuring voltages and currents, phase voltages and currents, frequency, frequency rate-of-change, circuit breaker status, switch status, etc., the high sampling rates (e.g., 30 times a second) provides "sub-second" resolution in contrast with SCADA-based measurements. These comparisons can be used to assess system conditions-such as: frequency changes, power in megawatts (MW), reactive power in mega volt ampere reactive (MVARs), voltage in kilovolts (KV), etc. As such, PMU measurements can provide improved visibility into dynamic grid conditions and can allow for real-time wide area monitoring of power system dynamics. Further, synchrophasors account for the actual frequency of the power delivery system at the time of measurement. These

measurements are important in alternating current (AC) power systems, as power flows from a higher to a lower voltage phase angle, and the difference between the two relates to power flow. Large phase angle differences between two distant PMUs can indicate the relative stress across the grid, even if the PMUs are not directly connected to each other by a single transmission line. This phase angle difference can be used to identify power grid instability, and a PMU can be used to generate an angle disturbance alarm (e.g., angle difference alarm) when it detects a phase angle difference.

[0052] Examples of disturbances that might cause the generation of an angle disturbance alarm can comprise, for example, a line out or line in disturbance (e.g., a line out disturbance in which a line that was in service has now gone out of service, or in the case of a line in disturbance, in which case a line that was out of service has been brought back into service, etc.). PMUs 306 can also be used to measure and detect frequency differences, resulting in frequency alarms being generated. As an example, unit out and unit in

disturbances can result in the generation of a frequency alarm (e.g., a generating unit was in service, but might have gone out of service, or a unit that was out of service has come back in to service— both can cause frequency disturbances in the system that can result in the generation of a frequency alarm, etc.). Still yet, PMUs 306 can also be used to detect oscillation disturbances (e.g., oscillation in the voltage, frequency, real power— any kind of oscillation), which can result in the generation of an alarm (e.g., oscillation alarm). Several other types of alarms can be generated based on PMU data from PMU based measurements. Although the disturbances mentioned (e.g., line in/out, unit in/out, load in/out) can result in angle or frequency disturbance alarms, an angle or frequency disturbance alarm might not necessarily mean that a particular type of disturbance occurred, only that it is indicative of that type of disturbance. For example, if a frequency disturbance alarm is detected, it might not necessarily be a unit in or unit out disturbance, but may be a load in or load out disturbance. The measurement requirements and compliance tests for a PMU 306 have been standardized by the Institute of Electrical and Electronics Engineers (IEEE), namely IEEE Standard C37.118.

[0053] In the example of FIG. 3, one or more Phasor Data Concentrators (PDCs) 312 are shown, which can comprise local PDCs at a substation. Here, PDCs 312 can be used to receive and time-synchronized PMU data from multiple PMUs 306 to produce a real-time, time-aligned output data stream. A PDC can exchange phasor data with PDCs at other locations. Multiple PDCs can also feed phasor data to a central PDC, which can be located at a control center. Through the use of multiple PDCs, multiple layers of concentration can be implemented within an individual synchrophasor data system. The PMU data collected by the PDC 312 can feed into other systems, for example, a central PDC, corporate PDC, regional PDC, the SCAD A component 310 (optionally indicated by a dashed connector), energy management system (EMS), synchrophasor applications software systems, a WAMS, the EDM module 316, or some other control center software system. With the very high sampling rates (typically 10 to 60 times a seconds) and the large number of PMU installations at the substations that are streaming data in real time, most phasor acquisition systems comprising PDCs are handling large amounts of data.

[0054] In this example, the measurement device 220, the SCAD A component 310, and PDCs/Central PDCs 312, can provide data (e.g., real-time data associated with devices, meters, sensors or other equipment in the power grid system) (including SCADA data and topology data), that can be used by the EDM module 316 for enhanced disturbance management. Both SCADA data and PMU data can be stored in one or more repositories 3014. In some example embodiments, the SCADA data and PMU data can be stored into the repository 314 by the SCADA component 310, or by the PDC 312. In other embodiments, the EDM module 316 can have one or more components or modules that are operable to receive SCADA data and PMU data and store the data into the repository 314 (indicated by dashed lines). The repository can comprise a local repository, or a networked repository. The data on the repository 314 can be accessed by SCADA component 310, the PDCs 312, others systems (not shown), and optionally by example embodiments of the EDM module 316. In example embodiments, the EDM module 316 can be operable to send instructions to one or more other systems (e.g., SCADA component 310, PDCs 312) to retrieve data stored on the repository 314 and provide it to the EDM module 316. In other embodiments, the EDM module 316 can facilitate retrieval of the data stored in repository 314, directly.

[0055] In example embodiments, the data stored in the repository 314 can be associated SCADA data and PMU data. The data can be indicative of measurements by measurement device 220 that are repeatedly obtained from a power grid system. In example embodiments, the data in repository 314 can comprise PMU/SCADA-based equipment data, such as, for example, data associated with a particular unit, line, transformer, or load within a power grid system (e.g., power grid system 200). The data can comprise voltage measurements, current measurements, frequency measurements, phasor data (e.g., voltage and current phasors), etc. The data can be location-tagged. For example, it can comprise a station identification of a particular station in which a power delivery device being measured is located (e.g., "CANADA8"). The data can comprise a particular node number designated for a location. The data can comprise the identity of the measure equipment (e.g., the identification number of a circuit breaker associated with an equipment). The data can also be time-tagged, indicating the time at which the data was measured by a measurement device. The PMU/SCADA-based equipment data can also contain, for example, information regarding a particular measurement device (e.g., a PMU ID identifying the PMU from which measurements were taken).

[0056] In example embodiments, the data stored in repository 314 can comprise not only collected and measured data from various measurement devices 220, the data can also comprise data derived from that collected and measured data. The data derived can comprise topology data (e.g., PMU/SCADA-based topology data), event data, and event analysis data, and EDM data (data generated by EDM module 316).

[0057] In example embodiments, the repository 314 can contain topology data (e.g., PMU/SCADA-based topology data) indicative of a topology for the power grid system 200. The topology of a power grid system can relate to the interconnections among power system components, such as generators, transformers, busbars, transmission lines, and loads. This topology can be obtained by determining the status of the switching components responsible for maintaining the connectivity status within the network. The switching components can be circuit breakers that are used to connect (or disconnect) any power system component (e.g., unit, line, transformer, etc.) to or from the rest of the power system network. Typical ways of determining topology can be by monitoring of the circuit breaker status, which can be done using measurement devices and components associated with those devices (e.g., RTUs,

SCAD A, PMUs). It can be determined as to which equipment has gone out of service, and actually, which circuit breaker has been opened or closed because of that equipment going out of service.

[0058] The topology data can be indicative of an arrangement (e.g., structural topology, such as radial, tree, etc.) or a power status of devices in the power grid system. Connectivity information or switching operation information originating from one or more measurement devices 220 can be used to generate the topology data. The topology data can be based on a location of devices in the power grid system, a connection status of devices in the power grid system or a connectivity state of devices in the power grid system (e.g., devices that receive or process power distributed in throughout the power grid system, such as transformers and breakers). For example, the topology data can indicate where devices are located, and which devices in the power grid system are connected to other devices in the power grid system (e.g., where devices in the power grid system are connected, etc.) or which devices in the power grid system are associated with a powered grid connection. The topology data can further comprise the connection status of devices (e.g., a transformer, etc.) that facilitate power delivery in the power grid system, and the statuses for switching operations associated with devices in the power grid system (e.g., an operation to interrupt, energize or de-energize or connect or disconnect) a portion of the power grid system by connecting or disconnecting one or more devices in the power grid system (e.g., open or close one or more switches associated with a device in the power grid system, connect or disconnect one or more transmission lines associated with a device in the power grid system etc.). Furthermore, the topology data can provide connectivity states of the devices in the power grid system (e.g., based on connection points, based on busses, etc.).

[0059] In example embodiments, the repository 314 can contain a variety of event and event analysis data, which can be derived based on PMU data, and in some embodiments, other data as well (e.g., SCADA data, other measurement data, etc.). The data can comprise information regarding events related to the power grid system 200. An event can comprise, for example, one or more disturbances to the power grid system. A disturbance can comprise, for example, a line disturbance (e.g., line in, or line out), a unit disturbance (e.g., unit in or unit out), or load disturbance (load in or load out). For each event, relevant information such as the station where the event occurred, the voltage level associated with the station (e.g., 500 kV), the node number related to the event, the equipment related to the event, the change in real and reactive power, and change in voltage per unit for the event. The event and event analysis data can also comprise EDM data, which can be data related to events. The various data stored in the repository 314, including equipment data, topology data, event data, event analysis data, EDM data, and other data, can be inputs into the various functionalities and operations that can be performed by the EDM module 316.

[0060] FIG. 4 illustrates an Automatic Model Validation (AMV) framework 400 for validating power system models in accordance with an example embodiment. In this example, the framework 400 may be a framework that implements or otherwise includes an EDM component such as EDM module 316 shown in FIG. 3. In this example, the AMV framework includes a disturbance event detection module 410, a PMU selection module 411, a tag mapping module 412, a data file collection module 413, a subsystem definition module 414, a model validation module 415, and a reporting module 416. The event selection system described in the example embodiments may perform the disturbance event monitoring 410 and PMU selection 411.

[0061] In response to an alarm, for example, when a PMU -based alarm is detected (e.g., an angle difference alarm or frequency disturbance alarm) the disturbance event detection module 410 of the EDM can respond to the alarm by correlating the alarm with the change in topology (e.g., topology data such as equipment energization status, equipment connection status, etc.) of the system that potentially gave rise to that alarm. This correlation of PMU, equipment data, and topology data can provide context to an alarm, and can be used to associate information with the event that comprises the following: a disturbance epicenter; a disturbance magnitude; a disturbance impact; and an event spread. The disturbance epicenter comprises information about the detection time of an event along with the epicenter of the event, including the exact cause (e.g., characterization) and location (e.g., localization) of the event, wherein an event can be comprised of one or more disturbances. In an event, a disturbance at one location can lead to a disturbance at another location, and so on. The disturbance event detection module 410 can attempt to determine a cause and categorize the disturbance as a line in/out, unit in/out, or load in/out, and also provide a coherency indicator that indicates how likely the disturbance is of a particular characterization. To characterize a disturbance (or determine a cause for the disturbance, classify the disturbance, etc.) PMU data can be correlated with other data, such as SCADA data, which includes topology data. PMUs are monitoring the effect of something that happened on the grid (e.g., an angle disturbance alarm or a frequency disturbance alarm, etc.) SCADA data can provide information on the topology (e.g., topology data, e.g., where the unit, line, or a transformer, and what circuit breaker is connected or involved, etc.), and how the topology reacted to a disturbance (e.g., what are the topological changes that have taken place).

[0062] The disturbance event monitoring module 410 can also provide a coherency indicator that indicates how likely the disturbance is of a particular characterization. As mentioned above, PMU-based data can result in the generation an angle disturbance alarm, and can also generate a frequency disturbance alarm. As an example, if a frequency disturbance alarm is generated, and the disturbance event monitoring module 410 correlates the PMU alarm with a change in topology that indicated that a unit has come out of service, this correlation is high because generating unit trips can cause a frequency alarm. The coherency is thus indicated as high for frequency because the PMU alarm and the topology change both match well with a cause (unit out) and effect (frequency alarm). The disturbance event monitoring module 410 can account for multiple scenarios. As another example, if a line has tripped, this disturbance will probably increase the angle separation across the line, but would not necessarily cause a frequency alarm. If an angle disturbance alarm has been generated, and the topology change shows that a line's status is that it has come out of service, the PMU angle disturbance alarm correlates highly with the topology change that took place, and thus the coherency for the angle disturbance alarm can be indicated as high.

[0063] An event can also comprise more than one disturbances, in which all the disturbances are part of one event. PMU detections (e.g., measurements) can result in the generation of alarms, and the continued generation of alarms for the same event. The disturbance event monitoring module 410 of the EDM can utilize the topology data (e.g., the equipment status, the breaker status, the topology exchanged etc.) to determine if the disturbances are part of the same event. If in the subsequent cycles the topology does not change any more, despite still receiving new alarms, then that might be an indication that the same disturbances that are resulting the continuous generation of the alarms (e.g., as opposed to different disturbances associated with a different event impacting other locations).

Overlapping (e.g., correlating, overlaying) the topology changes on top of the PMU based alarms can thus be a way to validate the alarms that are coming from the PMUs; PMU based alarms would still be generated, when there really are no more disturbances after a period of time.

[0064] The information about the disturbance epicenter can be at a granularity level that comprises information regarding the epicenter of an event, for example, the disturbance type, the equipment, the circuit breaker, the name or designation of the station, the voltage level associated with the station (e.g., 500 kV), and the name or designation of the node. The information can be derived from using, for example, PMU data, SCADA Data, and topology data. As mentioned, differences in, for example, angle, frequency, etc. can inform as to whether there is a disturbance event, which might be a line in/out, unit in/out, or load in/out, each of which can lead to an impact on certain electrical parameters, comprising, for example, change in real power, reactive power, frequency, and voltage per unit. Time synchronized information reported by the measurement devices can be used to determine which equipment was impacted first in time. Topology information can be used to determine whether other equipment (including at other stations or nodes) connected with the equipment that was impacted first in time also experienced a disturbance. An event can comprise one or more disturbances. The disturbance event monitoring module 410 can use topology information and time synchronized information, to determine that several equipment all connected together experienced a disturbance, and that one equipment was impacted first, and then a second equipment connected to the first one experienced a disturbance next, then a third equipment connected to the second. By making these determinations, an epicenter— the source of the event— can be determined.

[0065] In addition to determining the characterization of a disturbance, location of a disturbance, and epicenter of a disturbance, the disturbance event monitoring module 410 can also be operable to provide a magnitude and a diversity for each disturbance, as well as a most similar historical disturbance previously recorded, as described further in the examples of FIGS. 6A-6C. At the point of each disturbance, during the transition from pre-disturbance state to post-disturbance state, disturbance magnitude information comprising information about the change in electrical parameters (e.g. change in real power, change in reactive power, and change in voltage) can be determined by the disturbance event monitoring module 410. The magnitude information can be for the disturbance epicenter, for example. The magnitude information can also be for the most impacted PMU location, for example.

[0066] The disturbance event monitoring module 410 can also be operable to determine event spread information. Event spread information can comprise information about the spread or scope of the event (e.g., whether the event is a local event or a wide area event). A local event can be an event wherein only one location (e.g., one substation) is impacted, whereas a wide-spread event can be an event in which multiple locations have been impacted. Event spread information can also include information as to whether the event involves different types of equipment (e.g., a line and a load simultaneously, etc.).

[0067] When a new event (comprising of one or more disturbances) takes place, each new disturbance may be appended to an event history archive in a chronological manner, which can enable a post-event analysis to be performed. The information archived for each disturbance can be as follows: event detection time; correlation of system topological disturbances in the event with PMU-based alarms; disturbance type, location, and magnitude; most impacted location (amongst the pre-specified list of key locations) due to the disturbance.

[0068] According to various embodiments, the PMU selection module 411 may select or otherwise identify PMUs on the electrical grid which are affected by a disturbance event. The event selection system described herein may perform or otherwise implement the PMU selection module 411. For example, the PMU selection module 411 may identify PMUs that see adequate disturbance information which can be used for performing model validation of a device (generator, transmission system, load, etc.) which is associated with the PMU. For each of the selected PMUs, the PMU selection module 411 may determine whether there is a need to proceed to model validation. For example, the PMU selection module 411 may implement the system shown in FIG. 6 A.

[0069] When the PMUs associated with the disturbance event have been identified, the tag mapping module 412 may correlate a tag of an identified PMUs with a tag of one or more power system nodes on the electrical grid. Normally, a unique generator may be uniquely defined by both the generator name and the name of the station it is directly tied to. The needs for a tag mapping function arises from the potentially different naming

conventions from three different entities. Typically, the names of power system nodes such as generators, substations, etc., are defined by a generator owner (GO), while their

corresponding names in the EMS are defined by the utilities. Meanwhile, the names of the PMU and related substation/generators are defined by a third party-regional transmission owner (RTO), which is more recently than the previous two. The name convention for power system node tags in EMS is typically limited in size ( e.g ., 8 bits, etc.), while the names in the RTO’s PMU database tends to have more flexibility allowing for larger-sized tag names. In this case, there may be three different names for one generator in the three different databases: utility’s EMS, Generator Owner’s asset database and RTO’s PMU database.

Because the names are different, the PMU tags must be mapped to power system node tags to associate the event disturbance data captured by the PMU with one or more elements on the power grid (generator, substation, etc.). The tag mapping module 412 of the example embodiments performs an automated tag-mapping process to automatically associated the selected PMUs with corresponding subsystems on the electrical grid.

[0070] After the identification of grid event and selection of PMU and the affected station from EMS (as described in step 410 and 411 in Fig. 4), Substation names may be retrieved for the EMS system, and PMU names may be retrieved from a RTO’s PMU database. Name variants of the generator and the station may be generated for pairs by the tag mapping module 412 based on a database or predefined location dictionary. The name variants are also called name augmentation. The augmented name may then be correlated to the names in the ROT’s PMU database based on the word, term and contextual association between word and terms. The similarity-based metrics such as cosine similarity between the word and terms can be used. A correlation vector may be used for each pair of PMU and EMS system, and the highest correlated matched pair is the mapping result. Other approaches like rule-based inference, fuzzy logic can also be used to facilitate the mapping decision. Results of the tag mapping may be output to the user interface 430 to enable a user to review and correct mapping results. Furthermore, mapped pairs may be added to a database ( e.g ., AMV database 420) for future use.

[0071] The data file collection module 413 may establish connections with existing databases and retrieve naming information, power system model information, network topology information, and the like, which can be used by the different components of the EDM shown in FIG. 4

[0072] The subsystem definition module 414 may carve out which subsystems (generators, substations, etc.) should have power system models validated. Here, the subsystem module 414 may carve out and identify the subsystems based on PMU selection and tag mapping that is performed by the PMU selection module 411 and the tag mapping module 412. Furthermore, the power system models collected by the file collection unit 413 may associated with the subsystems for validation and calibration.

[0073] In this example, the model validation module 415 may determine whether a power system model of a subsystem (power system node) is valid. For example, the model validation module 415 may receive disturbance data monitored by one or more PMUs coupled to an electrical power distribution grid may be received. The disturbance data can include voltage (“F”), frequency (“’), and/or active and nonactive reactive (“ ” and“Q”) power measurements from one or more points of interest (POI) on the electrical power grid. A playback simulation using default model parameters and existing transient simulation software can be performed. These default parameters can be the current parameters incorporated in the power system model. The current parameters can be stored in a model parameter record. The simulation can be done by a power system simulation engine, including GE PSLF, Power Tech TSAT and Siemens PIT PSS/E, to perform a real-time power system simulation scenario.

[0074] The model validation module 415 may also comprise a model calibration unit with three functionalities. The first functionality is an event screening tool to select characteristics of disturbance event from a library of recorded event data. This functionality clusters similar events and determines the set of most representative events from the available measurements. The second functionality is a preconditioning tool for the parameter identifiability study. When implementing this functionality, a global quantification of parameter sensitivity magnitude and dependency will be achieved. The third functionality is a tool for simultaneous tuning of models using an augmented event comprised of multiple events.

[0075] The results of the simulated default model performance can be compared by the model validation module 415 to actual disturbance data measured on the power system.

If the default model performance is within ( e.g . , equal to or less than) a predetermined threshold of accuracy (e.g., specified by, for example, power system operators, designers, etc.), the model validation module 415 can end parameter conditioning and wait for disturbance data from a subsequent event.

[0076] If the default model performance is outside of the predetermined threshold, a parameter identifiability algorithm may be performed. In accordance with some

embodiments, the parameter identifiability analysis can determine the differing effects that various parameters can have on power system model. In some implementations, each parameter can represent a factor/coefficient in a term of a polynomial expression representing the power system model. To decide which parameters of the power system model are the best choice to tune, a parameter sensitivity study may be performed. The sensitivity study can vary the value of the parameter, compare the power system model result to monitored data, and then determine the perturbation’ s magnitude caused by the variation in parameter value. To calculate the model’s sensitivity to each parameter, playback simulation is conducted with the value of that parameter perturbed upward and downward. The difference in the model’s performance (i.e., when compared to the measured disturbance data) between the up, and the down perturbation yields the trajectory sensitivity matrix.

[0077] In accordance with some embodiments, the parameter identifiability analysis addresses two aspects: (a) magnitude of sensitivity of output to parameter change; and (b) dependencies among different parameter sensitivities. For example, if the sensitivity magnitude of a particular parameter is low, the parameter would appear in a row being close to zero in the parameter estimation problem’s Jacobian matrix. Also, if some of the parameter sensitivities have dependencies, it reflects that there is a linear dependence among the corresponding rows of the Jacobian. Both these scenarios lead to singularity of the Jacobian matrix, making the estimation problem infeasible. Therefore, it may be important to select parameters which are highly sensitive as well as result in no dependencies among parameter sensitivities. Next, parameter values in the active power system model may be updated, and the system may generate a report and/or display of the estimated parameter values(s), confidence metrics, and the model error response as compared to the measured data.

[0078] The reporting module 416 ma notify a user (via user interface 430) of the validity of a power system model that is determined by the EDM system. For example, the reporting module 416 may notify that a power system model is valid, invalid, and the like.

As another example, the reporting module 416 can provide additional information about a power system model. For example, the reporting module 416 may provide an update (to one or more parameters) of the power system model to be accepted by the user via the user interface 430. In this case, the reporting module 416 can retrieve updated parameter information from the model validation module 415 and provide those to the user interface 430 to efficiently update a power system model on behalf of a user.

[0079] In some embodiments, the model validation module 415, the reporting module 416, or the like, from the system 400 may provide subscription services for other entities such as GOs, TSOs, RTOs, and the like. The subscription services may provide calibration information, tag-mapping information, PMU information, and the like, to the other systems that operate and have interest in the electrical power grid.

[0080] The system shown in FIG. 4 further includes an expert system 420 which includes an automated model validation (AMV) database 421 and an inference engine 422. The AMV database 421 may store information that is collected by the EDM such as disturbance information (disturbance pattern, PMU selection, tag dictionaries, etc.).

Furthermore, reports and results generated by the model validation module 415 may be stored there as well. The inference engine 422 may query a calibrated case and reuse a subsystem, which are stored in the AMV database 421. [0081] In some embodiments, the inference engine 422 may analyze a parameter change for a mapped power system node and adjust a threshold for the power system node to thereby update a power system model associated therewith. The update may be determined based on a DIF of a PMU associated with the power system node. The inference engine 422 may provide rules, a knowledge base, and a learning algorithm for tag mapping between selected PMU tags and power system node tags. The inference engine 422 ma provide an initial value and other information to accelerate the model validation and calibration. In some embodiments, the inference engine 422 may extract event (disturbance) features, cluster events using similarity with previously detected events, and update clusters based on newly received event information.

[0082] FIG. 5 illustrates temporal patterns 510, 520, and 530 of different disturbance events in accordance with an example embodiment. In this example, a first disturbance 510 includes a start point 511 and an end point 512. The result of the disturbance 510 is a change in the static (or steady) state of the grid from state B to state A. Likewise, a second disturbance 520 includes a start point 521 and an end point 522. Just as in the disturbance 510, the result of the disturbance 520 is a change in the static state of the grid from state B to state A. Furthermore, a third disturbance 530 includes a start point 531 and an end point 532. Like the first disturbance 510 and the second disturbance 520, the third disturbance also includes a change in the static state from state B to state A. The start points and the end points may include the beginning and the end of the disturbance as determined by the system. In some cases, the signal may be the raw signal.

[0083] In this example, each of the three disturbance signals 510, 520, and 530 result in a same disturbance impact. However, the dynamic changes in the signal are very different. For example, the first disturbance signal 510 illustrates an example of an overdamped signal, the second disturbance 520 illustrates an example of an inverse response system, and the third disturbance 530 illustrates a first order with delay system, or a high order underdamped system. Traditional DIF analysis would generate a common disturbance (state B to state A) in the steady state analysis. Therefore, the DIF determination does not fully reflect the dynamic response of the power system associated with the PMU. Rather, the DIF is based on user defined weights and steady-state changes in the power system parameters during the disturbance. [0084] However, as shown in FIG. 5, different disturbances can have the same static state changes with different dynamic response modes caused by different dynamic parameter sets in the power system model. That is, when the system identifies a steady state A and a steady state B, the DIF will be the same because the DIF is defined based on a steady state values. The DIF is concerned with the initial value (B) and the resulting final value (A) to determine the delta. However, the overdamping and the inverse response are not captured by the DIF. Therefore, the DIF is missing dynamic changes in the pattern that occur between the initial value A and the final value B.

[0085] Furthermore, without an event screening mechanism, the system will process all events that are detected which results in too many events flowing through the model validation framework and it’s a waste of effort. Therefore, it is necessary and useful to provide an approach to screen events so that they can be used for model validation. For example, multiple events may be detected by a PMU but they may have similar modality.

For example, an event may be repeated every month, etc. Therefore, the power system model will not need to be calibrated or re-calibrated each time the same event keeps recurring.

Rather, it is better to reserve system resources for more diverse event types, or specific kinds of event types.

[0086] FIG. 6A illustrates an example of an event selection system 600A for model validation and calibration, in accordance with an example embodiment. The system 600A may perform an automated event selection for model validation and calibration in which some events are selected for validation and calibration and some are not selected but rather are left out of the validation and calibration process. For example, the system 600A may be implemented by a computing system such as a web server, a cloud platform, a personal computer, a workstation, and the like. In this example, the system 600A includes signal receiving module 610 receiving a raw disturbance signal. The disturbance signal may be detected by a sensor on the power grid, such as a PMU, etc. Here, the disturbance signal may be received from a node on the grid, detected by the system itself, and the like.

[0087] According to various embodiments, a feature extraction module 620 of the system may perform a feature extraction. For example, the system may extract dynamic related features from a time series temporal pattern representing the disturbance event such as shown in the examples of the signals in FIG. 5. For example, the features may include, but are not limited to one or more of peak value, rising time, settling time, damping ratio, 2^nd largest deviation over the 1^st largest deviation of frequency, voltage, power and reactive power, rate of change of frequency (ROCOF), energy function, cumulative deviation in energy, and the like.

[0088] A dynamic signal evaluation module 630 may perform a dynamic signal evaluation to identify dynamic modes of the event associated with the disturbance. In this case, rather than use the DIF, the system may identify an overall magnitude and diversity of the disturbance based on the extracted features. For example, the system may perform residual analysis on the extracted features based on auto-associated models, such as Auto- encoder(AE), and the like. For example, the input and output of the AE may be used to generate the overall magnitude (MSE) and the diversity (variance of the residuals) of the disturbance.

[0089] A similarity based evaluation module 640 may perform a similarity-based determination with respect to historical disturbance events. For example, the similarity evaluation module 640 may search a database of previously stored disturbances (events) and identify which previously stored event type the newly received disturbance signal is closest to in pattern. For example, the system may determine a similarity index ( e.g ., cosine or distance based) to determine how similar the newly identified event is from previously identified disturbance events stored by the system.

[0090] A decision making module 650 may determine whether the received disturbance is to be used for model validation and/or calibration based on the dynamic signal evaluation and the similarity identification. For example, the result of the dynamic modes evaluation results and the similarity based evaluation results may be synthesized or fused to determine whether the new event will be used for model validation or calibration. As an example, the decision fusion process can use max, min or weighted sum on both outputs.

[0091] Although FIG. 6 A illustrates both the dynamic signal evaluation module 630 in parallel with the similarity identification module 640 being used to determine whether to perform model validations, it is not required that both dynamic signal evaluation module 630 and similarity identification module 640 be include. As another example, the dynamic signal evaluation module 630 could be used without the similarity identification module 640, to determine whether to use the disturbance signal for model validation and calibration. In this example, the decision making module 650 would only use the results of the dynamic signal evaluation to choose whether to perform model validation and calibration using the disturbance signal.

[0092] The decision making module 650 may evaluate the dynamic mode evaluation identification which may provide the diversity and magnitude of the disturbance, and the similarity which identifies a type of the event. Then, embedded within the decision maker module 650 may be a selection criteria. For example, rules could be used based on criteria for event types, diversity, magnitude, and the like. As another example, a predetermined criteria may be implemented for certain plants on what is more important (diversity over magnitude, etc.). The decision making module 650 could give weights to each of the different factors (magnitude, diversity, event type, etc.). Rule based may also be used to identify specific scenarios of interest.

[0093] The system 600 A is capable of selecting events (disturbances) for use in model validation and calibration, while excluding events that are not unique or otherwise beneficial for model validation and calibration such as events which are similar to historical events, or events which do not provide enough information. Dynamics features may be extracted from the raw signal including peak value, rising time, settling time, damping ratio, ROCOF, energy function, cumulative deviation in energy, etc. Dynamic modes of the event may be evaluated to get magnitude and diversity of the dynamic modes excited based on residual analysis of auto-associated models, such as Auto-encoder(AE). In addition, a similarity based evaluation on the identified feature against instance in the existing feature database, the similarity index (like cosine or distance based) may be used to determine an event type of the disturbance. Further, a decision making module may determine whether the new event will be used for model validation and calibration.

[0094] Some of the advantages of the event selection system 600A include that that system uses normal data instead of abnormal event data to address a class imbalance issue. The system also removes the need for human labor, is less prone to human error, and is ready for autonomous model validation and calibration (MVC). The system can reduce the model calibration parameter subset which make it faster. Furthermore, the system may not rely on topology and hence a low cost solution.

[0095] FIG. 6B illustrates a system 600B for training a predictive model for use in the dynamic signal evaluation module 630, in accordance with an example embodiment.

Referring to FIG. 6B, the system 600B includes a normal data collection module 631 which collects data for training. A dynamic feature extraction module 632 identifies and extract features dynamically from the collected data. The associative model building component 633 builds a predictive algorithm ( e.g ., associative model) for identifying magnitude and distribution of a disturbance event, and a model training component 634 is executed to train the model based on the features extracted from the normal data.

[0096] The dynamic features and auto-associative model may be trained using the normal data where there is no event. The training may create a model that can identify magnitude and distribution from a raw disturbance signal. To carry out the training, a database can be used to store the data. To train the auto-associated model, offline training can be performed. The system 600B of FIG. B shows an example of how the model may be trained. Training may include a neural network which identifies patterns within the data which indicate magnitude and disbursement. The result of the training is a model structure (nodes and connections) in the neural network.

[0097] The resulting model may be incorporated into the dynamic signal evaluation module 630 and may be used to extract features to build a model that captures how these features behave simultaneously, what’s their relationship, at any time. The model tries to capture a complex non-linear relationship between the extracted features (e.g., could be 10, 50, 100 features, etc.). As an example, the auto associative model can use an autoencoder (1 type of auto associative model). The training may include an input/output (similar to neural network) but in this case the inputs and the outputs would be the same (auto -associated) to force the neural network to capture relationships.

[0098] When model training is complete, the dynamic feature formulas and model structure/parameter can be saved and deployed for on-line application. During application, the dynamic feature is firstly extracted from raw signal, for example, an event time period. Next, the residual of the raw feature and output of auto -associative model may be evaluated by the dynamic signal evaluation module 630. The magnitude and distribution of the residual that is generated may be used to determine whether this event will be used for model validation and calibration.

[0099] Once determined for use with the model validation and calibration, the magnitude and distribution of the residual may also be used to pinpoint the model parameter to be calibrated. For example, a strong voltage or reactive power related feature may favor the excitation/P SS subsystem parameters, while a stronger frequency or active power related feature may favor the govemor/turbine subsystem parameters.

[00100] FIG. 6C illustrates a system 600C for performing the similarity evaluation by the similarity evaluation module 640 shown in FIG. 6A. In this example, a raw signal module 610 receives a distribution event, and a dynamic feature is extracted from the raw signal (for example event time period) via the feature extraction module 620. In this example, a similarity between the extracted feature and existing features in a database 642 may be evaluated by the similarity evaluation module 640. The energy similarity measure may be used as the similarity index. The similarity index may be used to identify an event type stored in the database 642 that is most similar to the disturbance in the raw signal. The identified event type and the other parameters may be stored and used for model validation and calibration. Further, the event and its corresponding calibrated parameter (deviation) may be automatically saved in the event database 642 for future use.

[00101] Traditional event analysis requires abundant abnormal event data and labels which rarely happen and is less available. In contrast, the example embodiments use normal data instead of using abnormal event data to address a class imbalance issue. The system may automatically determine whether a disturbance event perceived by PMU should be used for model validation and calibration, by exploring the dynamic modality in the event data. This work removes the human labor, is less prone to human error, and the like. The magnitude and distribution of a residual signal can be used to identify the dynamic parameter subset who is sensitive to this event which can reduce the model calibration workload. The event analysis approach is pure data driven without using any topology information, which enables a more reliable and low cost deployment.

[00102] In the examples herein, FIGS. 6B and 6C are independent approaches for event selection. In particular, FIG. 6B illustrates a residual -based method in which an auto-associative model component is used to build a predictive algorithm ( e.g ., associative model) for identifying magnitude and distribution of a disturbance event, and a model training component that can be executed to train the model based on the features extracted from the normal data. The trained model implemented by the dynamic signal evaluation module 630 can be used to identify magnitude and distribution of a residual that is generated from a new disturbance event. The magnitude and disturbance can then be used to determine whether this event will be used for model validation and calibration. [00103] Meanwhile, FIG. 6C illustrates a method in which a disturbance event pattern is compared to historical disturbance patterns to identify a type of the disturbance for the newly received disturbance event based on historical events stored in a database. The event type for the disturbance can also be used to determine whether the event will be used for model validation and calibration.

[00104] Meanwhile, FIGS. 6 A and 6D illustrate two alternative approaches for incorporating the dynamic signal evaluation module 630 (auto -associative model) determined in FIG. 6B, and the similarity identification module 640 in FIG. 6C into a single system. For example, in FIG. 6 A, the dynamic evaluation module 630 and the similarity identification module 640 operate in parallel with one another and the output of both modules are input to the decision making module (MVC decision module 650). Meanwhile, in FIG. 6D, the output of the dynamic evaluation module 630 is provided as an input to the similarity identification module 640 instead of the decision making module.

[00105] FIG. 6D illustrates another system configuration 600D which is an alternative configuration with respect to system 600A. In particular, in FIG. 6D, the dynamic signal evaluation module 630 is in series with the similarity identification module 640. In this alternative configuration, the extracted feature from raw signal extracted by the feature extraction module 620, together with the feature generated by the dynamic signal evaluation module 630, which is residual magnitude and diversity, are input to the similarity

identification module 640 for similarity evaluation. MVC Decision 650 may determine whether to use the event for model validation and calibration based on the result of the similarity identification performed by the similarity identification module 640 and some decision making related parameter. The selected event during 650 will be automatically added to the event database 641.

[00106] FIG. 7 illustrates a power system 700 including an event selector system 730 in accordance with an example embodiment. Referring to FIG. 7, a power grid 710 represents a plurality of components ( e.g ., power generators, transformers, etc.) on an electrical grid for bringing power supply to consumers. Measurement devices such as voltage and current sensors 720 may be used to capture data from the power grid 710. The sensors 720 may feed data to other components, for example, a Supervisory Control and Data Acquisition (SCAD A) system (not shown). The SCADA component can provide functions such as data acquisition, control of power plants, and alarm display. The SCADA component can also allow operators at a central control center to perform or facilitate management of energy flow in the power grid system. For example, operators can use a SCADA component (for example using a computer such as a laptop or desktop) to facilitate performance of certain tasks such opening or closing circuit breakers, or other switching operations that might divert the flow of electricity.

[00107] In some examples, the system 700 may include one or more PMUs 722. A PMU 722 can be a standalone device or may be integrated into another piece of equipment such as a protective relay. PMUs 722 can be employed at substations, and can provide input into one or more software tools (e.g., WAMS, SCADA, EMS, and other applications). A PMU 722 can use the voltage and current sensors 720 that can measure voltages and currents at principal intersecting locations (e.g., substations) on a power grid using a common time source for synchronization, and can output accurately time- stamped voltage and current phasors. The resulting measurement is often referred to as a

synchrophasor (although the term synchrophasor refers to the synchronized phasor measurements taken by the PMU 722, some have also used the term to describe the device itself). Because these phasors are truly synchronized, synchronized comparison of two quantities is possible in real time, and this time synchronization allows synchronized real time measurements of multiple remote measurement points on the grid.

[00108] In addition to synchronously measuring voltages and currents, phase voltages and currents, frequency, frequency rate-of-change, circuit breaker status, switch status, etc., the high sampling rates (e.g., 30 times a second) provides "sub-second" resolution in contrast with SCADA-based measurements. These comparisons can be used to assess system conditions-such as: frequency changes, power in megawatts (MW), reactive power in mega volt ampere reactive (MVARs), voltage in kilovolts (KV), etc. As such, PMU measurements can provide improved visibility into dynamic grid conditions and can allow for real-time wide area monitoring of power system dynamics. Further, synchrophasors account for the actual frequency of the power delivery system at the time of measurement. These measurements are important in alternating current (AC) power systems, as power flows from a higher to a lower voltage phase angle, and the difference between the two relates to power flow. Large phase angle differences between two distant PMUs can indicate the relative stress across the grid, even if the PMUs are not directly connected to each other by a single transmission line. This phase angle difference can be used to identify power grid instability, and a PMU can be used to generate an angle disturbance alarm (e.g., angle difference alarm) when it detects a phase angle difference.

[00109] Examples of disturbances that might cause the generation of an angle disturbance alarm can comprise, for example, a line out or line in disturbance (e.g., a line out disturbance in which a line that was in service has now gone out of service, or in the case of a line in disturbance, in which case a line that was out of service has been brought back into service). PMUs 722 can also be used to measure and detect frequency differences, resulting in frequency alarms being generated. As an example, unit out and unit in disturbances can result in the generation of a frequency alarm (e.g., a generating unit was in service, but might have gone out of service, or a unit that was out of service has come back in to service— both can cause frequency disturbances in the system that can result in the generation of a frequency alarm.). Still yet, PMUs 722 can also be used to detect oscillation disturbances (e.g., oscillation in the voltage, frequency, real power— any kind of oscillation), which can result in the generation of an alarm (e.g., oscillation alarm). Several other types of alarms can be generated based on PMU data from PMU based measurements. Although the disturbances mentioned (e.g., line in/out, unit in/out, load in/out) can result in angle or frequency disturbance alarms, an angle or frequency disturbance alarm might not necessarily mean that a particular type of disturbance occurred, only that it is indicative of that type of disturbance. For example, if a frequency disturbance alarm is detected, it might not necessarily be a unit in or unit out disturbance, but may be a load in or load out disturbance. The measurement requirements and compliance tests for a PMU 722 have been standardized by the Institute of Electrical and Electronics Engineers (IEEE), namely IEEE Standard C37.118.

[00110] In the example of FIG. 7, one or more Phasor Data Concentrators (PDCs) 724 are shown, which can comprise local PDCs at a substation. Here, PDCs 724 can be used to receive and time- synchronized PMU data from multiple PMUs 722 to produce a real-time, time-aligned output data stream. A PDC can exchange phasor data with PDCs at other locations. Multiple PDCs can also feed phasor data to a central PDC, which can be located at a control center. Through the use of multiple PDCs, multiple layers of

concentration can be implemented within an individual synchrophasor data system. The PMU data collected by the PDC can feed into other systems, for example, a central PDC, corporate PDC, regional PDC, a SCADA component (optionally indicated by a dashed connector), energy management system (EMS), synchrophasor applications software systems, a WAMS, a EDM module, or some other control center software system. With the very high sampling rates (typically 10 to 60 times a seconds) and the large number of PMU installations at the substations that are streaming data in real time, most phasor acquisition systems comprising PDCs are handling large amounts of data.

[00111] In this example, the event selector 730 corresponds to the event selector system 600 A shown in FIG. 6 A. The event selector 730 may retrieve synchrophasor data from a synchrophasor repository 726 to identify power system nodes where disturbances are observed and whose dynamic model can be validated. However, rather than process all disturbances for validation and calibration, the event selector 730 may screen disturbances and identify and use disturbances of value (magnitude and diversity) and/or (event type) and filter out disturbances of less value. Here, the event selector 730 may include a dynamic feature repository 731 for storing dynamic features extracted from a disturbance and a past event repository storing previously recorded disturbances. The event selector 730 may include a dynamic signal evaluation module 733 and a similarity evaluation module 734 corresponding to the dynamic signal evaluation module 630 and the similarity evaluation module 640 in FIG. 6A . The event selector 730 may also include a decision module 735 corresponding to the decision maker module 650 in FIG. 6A.

[00112] In addition, the event selector 730 may include a user interface 736 for displaying disturbance information, model validation information, calibration information, PMU selection information, and the like.

[00113] The resulting disturbance events selected by the event selector 730 may be provided to the model validation module 740 for model validation and calibration based on dynamic models provided from an external source. The model validation module 740 may validate power system models of substations (or other power generation components on the electrical grid associated with the detected disturbance event that has been selected by the event selector 730. Furthermore, a reporting module 750 may output the results via the user interface 736.

[00114] FIG. 8 illustrates a method 800 of determining whether to select an event for power system model validation in accordance with an example embodiment. For example, the method 800 may be performed by a server, a cloud platform, a workstation, user device, and the like. Referring to FIG. 8, in 810, the method may include receiving a disturbance which is detected by a sensor of a power grid. The disturbance may be a raw signal detected by a sensor such as a PMU. The disturbance may include a start point and an end point, and a dynamic pattern between the start and end points.

[00115] In 820, the method may include extracting features from a signal of the disturbance based on dynamic changes in a pattern of the signal between a start and an end of the disturbance. For example, the extracting may include extracting a rate of change of frequency (ROCOF) of the disturbance based on the dynamic changes in the pattern of the signal between the start and the end of the detected disturbance. In some embodiments, the extracting may include extracting a signature in the pattern of the signal based on changes in a waveform of the pattern between the start and the end of the disturbance. In some embodiments, the extracting may include extracting one or more of a peak value, a rising time, a settling time, a damping ratio, an energy function, and a cumulative deviation in energy, from the signal between the start and the end of the disturbance.

[00116] In 830, the method may include identifying a magnitude and a diversity of the disturbance based on non-linear relationships between the extracted features of the signal. For example, the identifying comprises identifying the magnitude and the diversity of the disturbance via execution of an auto -associative model which receives the signal as input. In 840, the method may include determining whether to use the disturbance for model validation based on the identified magnitude and the diversity, and in 850, the method may include storing the identified magnitude and diversity of the detected disturbance in a storage device based on the determination.

[00117] In some embodiments, the method may further include identifying a previously stored disturbance event type having a pattern that is most similar to the dynamic changes in the pattern of the signal of the disturbance. In this example, the method may further include determining whether to perform power system model validation and calibration based on the identified magnitude and diversity of the disturbance and the identified previously stored disturbance event type. For example, the identifying the difference comprises determining a similarity index based on a distance between the pattern of the disturbance and the pattern of the previously stored disturbance event.

[00118] FIG. 9 illustrates a computing system 900 for use in the methods and processed described herein. For example, the computing system 900 may be a web server, a database, a cloud platform, or the like. In some embodiments, the computing system 900 may be distributed across multiple computing devices such as multiple database nodes. Referring to FIG. 9, the computing system 900 includes a network interface 910, a processor 920, an input / output 930, and a storage device 940 such as an in-memory storage, and the like. Although not shown in FIG. 9, the computing system 900 may also include or be electronically connected to other components such as a display, an input unit(s), a receiver, a transmitter, a persistent disk, and the like. The processor 920 may control the other components of the computing system 900.

[00119] The network interface 910 may transmit and receive data over a network such as the Internet, a private network, a public network, an enterprise network, and the like. The network interface 910 may be a wireless interface, a wired interface, or a combination thereof. The processor 920 may include one or more processing devices each including one or more processing cores. In some examples, the processor 920 is a multicore processor or a plurality of multicore processors. Also, the processor 920 may be fixed or it may be reconfigurable. The input / output 930 may include an interface, a port, a cable, a bus, a board, a wire, and the like, for inputting and outputting data to and from the computing system 900. For example, data may be output to an embedded display of the computing system 900, an externally connected display, a display connected to the cloud, another device, and the like. The network interface 910, the input / output 930, the storage 940, or a combination thereof, may interact with applications executing on other devices.

[00120] The storage device 940 is not limited to a particular storage device and may include any known memory device such as RAM, ROM, hard disk, and the like, and may or may not be included within a database system, a cloud environment, a web server, or the like. The storage 940 may store software modules or other instructions which can be executed by the processor 920 to perform the method shown in FIG. 6. According to various embodiments, the storage 940 may include a data store having a plurality of tables, partitions and sub-partitions. The storage 940 may be used to store database records, items, entries, and the like.

[00121] According to various embodiments, the processor 920 may receive a disturbance which is detected by a sensor of a power grid, extract features from a signal of the disturbance based on dynamic changes in a pattern of the signal between a start and an end of the disturbance, identify a magnitude and a diversity of the disturbance based on non linear relationships between the extracted features of the signal of the disturbance, and determine whether to use the disturbance for model validation based on the magnitude and the diversity. Furthermore, the storage 940 may store the magnitude and diversity of the disturbance based on the determination.

[00122] In some embodiments, the processor 920 may identify a previously stored disturbance event type having a pattern that is most similar to the dynamic changes in the pattern of the signal of the disturbance. In this example, the processor 920 may determine whether to perform power system model validation and calibration based on the identified magnitude and diversity of the disturbance and the identified previously stored disturbance event type. In some embodiments, the processor 920 may identify a similarity index based on a distance between the pattern of the disturbance and the pattern of the previously stored disturbance event.

[00123] FIG.10 illustrates a user interface 1000 for event selection 1010 in an overall model validation software module in accordance with an example embodiment. Here, FIG. 10 shows an example corresponding to figure 6D where the dynamic signal evaluation module 630 is in series with the similarity identification module 640. In this example, a table 1020 shows raw event information (event name, date, duration, etc.), and also the extracted residual magnitude and diversity as two key features. Each row 1022 corresponds to an individual event. In this example, similarity scores ranges from 0~10 where 0 means duplicate event in the database and 10 means a totally different event compared to existing event database. Note that even though the event El and E4 have a same score with respect to magnitude and diversity, El is selected due to low similarity with existing event database. This approach can maintain overall event diversity during event selection.

[00124] As will be appreciated based on the foregoing specification, the above- described examples of the disclosure may be implemented using computer programming or engineering techniques including computer software, firmware, hardware or any combination or subset thereof. Any such resulting program, having computer-readable code, may be embodied or provided within one or more non-transitory computer-readable media, thereby making a computer program product, i.e., an article of manufacture, according to the discussed examples of the disclosure. For example, the non-transitory computer-readable media may be, but is not limited to, a fixed drive, diskette, optical disk, magnetic tape, flash memory, semiconductor memory such as read-only memory (ROM), and/or any

transmitting/receiving medium such as the Internet, cloud storage, the internet of things, or other communication network or link. The article of manufacture containing the computer code may be made and/or used by executing the code directly from one medium, by copying the code from one medium to another medium, or by transmitting the code over a network

[00125] The computer programs (also referred to as programs, software, software applications,“apps”, or code) may include machine instructions for a programmable processor, and may be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms “machine-readable medium” and“computer-readable medium” refer to any computer program product, apparatus, cloud storage, internet of things, and/or device ( e.g ., magnetic discs, optical disks, memory, programmable logic devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The“machine-readable medium” and“computer-readable medium,” however, do not include transitory signals. The term“machine-readable signal” refers to any signal that may be used to provide machine instructions and/or any other kind of data to a programmable processor.

[00126] The above descriptions and illustrations of processes herein should not be considered to imply a fixed order for performing the process steps. Rather, the process steps may be performed in any order that is practicable, including simultaneous performance of at least some steps. Although the disclosure has been described in connection with specific examples, it should be understood that various changes, substitutions, and alterations apparent to those skilled in the art can be made to the disclosed embodiments without departing from the spirit and scope of the disclosure as set forth in the appended claims.

Claims

IN THE CLAIMS:

1. A computing system comprising:

a processor configured to receive a disturbance which is detected by a sensor of a power grid, extract features from a signal of the disturbance based on dynamic changes in a pattern of the signal between a start and an end of the disturbance, identify a magnitude and a diversity of the disturbance based on non-linear relationships between the extracted features of the signal of the disturbance, and determine whether to use the disturbance for model validation based on the magnitude and the diversity; and

a storage configured to store the magnitude and diversity of the disturbance based on the determination.

2. The computing system of claim 1, wherein the processor is further configured to identify a previously stored disturbance event type having a pattern that is most similar to the dynamic changes in the pattern of the signal of the disturbance.

3. The computing system of claim 2, wherein the processor is further configured to determine whether to perform power system model validation and calibration based on the identified magnitude and diversity of the disturbance and the identified previously stored disturbance event type.

4. The computing system of claim 2, wherein the processor is configured to identify a similarity index based on a distance between the pattern of the disturbance and the pattern of the previously stored disturbance event.

5. The computing system of claim 1, wherein the processor is configured to extract a signature in the pattern of the signal based on changes in a waveform of the pattern between the start and the end of the disturbance.

6. The computing system of claim 1, wherein the processor is configured to extract one or more of a peak value, bottom value, overshoot quantity, a rising time, a settling time, a phase shift, a damping ratio, an energy function, and a cumulative deviation in energy, Fourier transformation spectrum information, principal component, and a rate of change of frequency (ROCOF) of the disturbance from the signal between the start and the end of the disturbance.

7. The computing system of claim 1, wherein the processor is configured to identify the magnitude and the diversity of the disturbance via execution of an auto- associative model which receives the signal as input.

8. The computing system of claim 7, wherein the magnitude and the diversity of the disturbance are part of the pattern of the disturbance, which is compared with a pattern of a previously stored disturbance event stored in a disturbance database for similarity analysis.

9. The computing system of claim 8, wherein the previously stored disturbance event is selected for model validation and also stored in the disturbance database in response to a generated similarity index for the event being below a threshold.

10. A method comprising:

receiving a disturbance which is detected by a sensor of a power grid;

extracting features from a signal of the disturbance based on dynamic changes in a pattern of the signal between a start and an end of the disturbance;

identifying a magnitude and a diversity of the disturbance based on non-linear relationships between the extracted features of the signal;

determining whether to use the disturbance for model validation based on the identified magnitude and the diversity; and

storing the identified magnitude and diversity of the detected disturbance in a storage device based on the determination.

11. The method of claim 10, further comprising identifying a previously stored disturbance event type having a pattern that is most similar to the dynamic changes in the pattern of the signal of the disturbance.

12. The method of claim 11, further comprising determining whether to perform power system model validation and calibration based on the identified magnitude and diversity of the disturbance and the identified previously stored disturbance event type.

13. The method of claim 11, wherein the identifying the difference comprises determining a similarity index based on a distance between the pattern of the disturbance and the pattern of the previously stored disturbance event.

14. The method of claim 10, wherein the extracting comprises extracting a signature in the pattern of the signal based on changes in a waveform of the pattern between the start and the end of the disturbance.

15. The method of claim 10, wherein the extracting comprises extracting one or more of a peak value, a rising time, a settling time, a damping ratio, an energy function, and a cumulative deviation in energy, from the signal between the start and the end of the disturbance.

16. The method of claim 10, wherein the identifying comprises identifying the magnitude and the diversity of the disturbance via execution of an auto-associative model which receives the signal as input.

17. A non-transitory computer-readable medium comprising program instructions which when executed cause a computer to perform a method comprising:

receiving a disturbance which is detected by a sensor of a power grid;

18. The non-transitory computer-readable medium of claim 17, wherein the method further comprises identifying a previously stored disturbance event type having a pattern that is most similar to the dynamic changes in the pattern of the signal of the disturbance.

19. The non-transitory computer-readable medium of claim 18, wherein the method further comprises determining whether to perform power system model validation and calibration based on the identified magnitude and diversity of the disturbance and the identified previously stored disturbance event type.

20. The non-transitory computer-readable medium of claim 17, wherein the extracting comprises extracting a rate of change of frequency (ROCOF) of the disturbance based on the dynamic changes in the pattern of the signal between the start and the end of the detected disturbance.