WO2021235594A1 - Système de surveillance à distance de générateur de secours - Google Patents

Système de surveillance à distance de générateur de secours Download PDF

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Publication number
WO2021235594A1
WO2021235594A1 PCT/KR2020/009960 KR2020009960W WO2021235594A1 WO 2021235594 A1 WO2021235594 A1 WO 2021235594A1 KR 2020009960 W KR2020009960 W KR 2020009960W WO 2021235594 A1 WO2021235594 A1 WO 2021235594A1
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data
emergency generator
server
database
monitoring system
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PCT/KR2020/009960
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English (en)
Korean (ko)
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하능교
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하능교
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J9/00—Circuit arrangements for emergency or stand-by power supply, e.g. for emergency lighting
    • H02J9/04—Circuit arrangements for emergency or stand-by power supply, e.g. for emergency lighting in which the distribution system is disconnected from the normal source and connected to a standby source
    • H02J9/06—Circuit arrangements for emergency or stand-by power supply, e.g. for emergency lighting in which the distribution system is disconnected from the normal source and connected to a standby source with automatic change-over, e.g. UPS systems
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02B90/20—Smart grids as enabling technology in buildings sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/12—Energy storage units, uninterruptible power supply [UPS] systems or standby or emergency generators, e.g. in the last power distribution stages
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/248—UPS systems or standby or emergency generators
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
    • Y04S40/12—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them characterised by data transport means between the monitoring, controlling or managing units and monitored, controlled or operated electrical equipment
    • Y04S40/126—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them characterised by data transport means between the monitoring, controlling or managing units and monitored, controlled or operated electrical equipment using wireless data transmission

Definitions

  • the present invention relates to a remote monitoring system for an emergency generator, and more specifically, for systematic management, attaching an ICT-based sensor to a generator, which is a core part of an emergency generator, for constant monitoring of an ICT-based emergency generator, and real-time collection of usage data;
  • a remote monitoring system for an emergency generator and more specifically, for systematic management, attaching an ICT-based sensor to a generator, which is a core part of an emergency generator, for constant monitoring of an ICT-based emergency generator, and real-time collection of usage data;
  • it is possible to not only diagnose and predict failures of emergency generators in advance, but also to accumulate data stored in the database and make them into big data to create patterns of data and apply machine learning that learns them by itself.
  • It relates to an emergency generator remote monitoring system for
  • An emergency generator is a power generation device and facility for supplying power to important facilities or facilities that require power supply when the regular power (power supplied through the general power grid) cannot be supplied due to a disaster, accident, or breakdown in a normal state.
  • Diesel engine type generators are most commonly used.
  • Emergency power sources including emergency generators are [Act on Fire Prevention, Installation/Maintenance and Safety Management of Firefighting Facilities] (Article 9, Paragraph 1), the same Act [Enforcement Decree] and the National Fire Service [Fire safety standards for indoor fire hydrant facilities (NFSC 102)] ( Article 8), it is mandatory to install (a building with 7 or more floors and a total area of 2000 m2 or more, a specific firefighting object with a total floor area of 3,000 m2 or more).
  • the [Electricity Business Act] and [Building Act] also stipulate the mandatory provisions for installing a ‘reserve power supply’.
  • the present invention is to solve the above problems, and the traditional function of an emergency generator was to supply emergency power in case of a power outage, etc., but its function is varied according to the recent government's DR: Demand Response (DR) policy promotion and expansion.
  • DR Demand Response
  • the manager performs no-load operation within 5 minutes or so once or twice a month and checks the degree of basic matters, so for systematic management, regular monitoring of the ICT-based emergency generator is required.
  • This is to provide an emergency generator remote monitoring system to support the attachment of ICT-based sensors to the generator, which is a key component of the emergency generator, and real-time collection, analysis, and monitoring of usage data.
  • the present invention can apply machine learning to create a pattern of data by accumulating data stored in a database and turning it into big data, and machine learning to learn it by itself. To provide a remote monitoring system.
  • the emergency generator remote monitoring system is composed of an embedded board 100 , a server 200 , and a client 300 , and an embedded board 100 , a server 200 .
  • the client 300 is connected through a network
  • the embedded board 100 is an emergency generator remote monitoring system 1 that performs access to the network via an AP via wireless
  • the embedded board 100 performs RS485 communication Data is received from the emergency generator 10 through
  • the power (option) and operation status (option) are provided to the server 200 through the network after being provided
  • the data obtained from the emergency generator 10 is converted into a DB in the database 200b and stored, as well as the database ( 200b) by accumulating the data stored in the big data to make a pattern of data, and applying machine learning to learn by itself about the generated pattern, as well as diagnosing the failure of the emergency generator 100, and predicting the failure in advance.
  • the server program 200a is a method of accumulating data in the database 200b, any user of the client 300 registered in the server 200 can check the data in an N:N manner rather than a 1:1 method.
  • it may be characterized in that the data and previous data of the N units of the emergency generator 10, data according to the installation environment of the emergency generator 10 are accumulated, compared and analyzed with each other, and patterned.
  • the embedded board 100 integrates vibration, power, and temperature in addition to the emergency generator data (GCP Data) as collected data from the emergency generator 10, and then transmits it to the server 200 through the cloud-type network.
  • GCP Data emergency generator data
  • the client program 300a as the main monitoring UI screen, enables monitoring of the entire state data of the emergency generator 10, and may be characterized in that the data that can be checked according to the user's authority is differently limited. .
  • the server 200 applies the verification standards of field experts in securing the optimal operating characteristics and proceeds with machine learning using the "data set" using the emergency generator 10 capacity-specific and load-specific tests. Based on the data obtained during the shipment test stage of the emergency generator 10 can
  • the emergency generator remote monitoring system is to supply emergency power in case of an emergency such as a power outage as a traditional function of the emergency generator, but according to the recent government power demand management (DR: Demand Response) policy promotion and expansion, its function As this is being diversified, it provides the effect of enabling remote monitoring of each emergency generator by remotely accessing the data on the emergency generator into big data and accessing it from the client.
  • DR Demand Response
  • the manager performs a no-load operation within 5 minutes per 1-2 times a month and checks the degree of basic matters. For systematic management, it provides the effect of attaching ICT-based sensors to the generator, which is a core part of emergency generators, for constant monitoring of ICT-based emergency generators, and supporting real-time collection, analysis, and monitoring of usage data.
  • the emergency generator remote monitoring system can apply machine learning to create a pattern of data by accumulating data stored in the database and turning it into big data, and machine learning can be applied to diagnose the failure of the emergency generator. In addition, it provides the effect of being able to predict failures in advance.
  • FIG. 1 is a view showing an emergency generator remote monitoring system 1 according to an embodiment of the present invention.
  • FIG. 2 is a hardware configuration diagram of the embedded board 100 of the emergency generator remote monitoring system 1 according to an embodiment of the present invention.
  • FIG 3 is a view showing a UI screen of the MySQL Workbench program used as a server program 200a among the emergency generator remote monitoring system 1 according to an embodiment of the present invention.
  • FIG. 4 is a diagram illustrating a UI screen when the server program 200a of the emergency generator remote monitoring system 1 is driven according to an embodiment of the present invention.
  • 5 to 10 are diagrams illustrating a UI screen provided by the client program 300a of the emergency generator remote monitoring system 1 according to an embodiment of the present invention.
  • FIG. 11 is a view showing an expanded form of the emergency generator remote monitoring system 1 according to an embodiment of the present invention.
  • FIG. 12 is a diagram for explaining the systemization of the big data analysis process for the emergency generator 10 on the database 200b by the server 200 of the emergency generator remote monitoring system 1 according to an embodiment of the present invention .
  • FIG. 13 is a diagram showing data science methodology planning and design by the server 200 of the emergency generator remote monitoring system 1 according to an embodiment of the present invention.
  • FIG. 14 is a diagram illustrating a procedure for performing data analysis of the emergency generator 10 by the server 200 of the emergency generator remote monitoring system 1 according to an embodiment of the present invention.
  • 15 is a view showing prediction verification visualization by the server 200 of the emergency generator remote monitoring system 1 according to an embodiment of the present invention, and the RNN and LSTM configuration diagrams and RNN data analysis structure.
  • 16 is a view for explaining the data processing function by the server 200 of the emergency generator remote monitoring system 1 according to an embodiment of the present invention.
  • the component when one component 'transmits' data or signal to another component, the component may directly transmit the data or signal to another component, and through at least one other component This means that data or signals can be transmitted to other components.
  • FIG. 1 is a view showing an emergency generator remote monitoring system 1 according to an embodiment of the present invention.
  • 2 is a hardware configuration diagram of the embedded board 100 of the emergency generator remote monitoring system 1 according to an embodiment of the present invention.
  • the emergency generator remote monitoring system 1 includes an embedded board 100 , a server program 200a and a database 200b installed in the server 200 , and a client program 300a installed in the client 300 .
  • the embedded board 100 , the server 200 , and the client 300 are connected through a network, and in particular, the embedded board 100 can be accessed through the network via an AP via wireless.
  • the network is a high-speed backbone network of a large-scale communication network capable of large-capacity and long-distance voice and data services, and may be a next-generation wired or wireless network for providing the Internet or high-speed multimedia services.
  • the network device is a mobile communication network, it may be a synchronous mobile communication network or an asynchronous mobile communication network.
  • the asynchronous mobile communication network there may be a wideband code division multiple access (WCDMA) type communication network.
  • WCDMA wideband code division multiple access
  • the network may include a Radio Network Controller (RNC).
  • RNC Radio Network Controller
  • the WCDMA network is taken as an example, it may be a 3G LTE network, a 4G network, other next-generation communication networks such as 5G, and other IP-based IP networks.
  • the embedded board 100 performs DC 24V power input and DC 5V, DC 3.3V generation, ADC 4 channels (voltage and current type selectable), digital input 2 channels, digital relay output 2 channels, 2 channels of digital transistor output, RS485 communication can be performed.
  • the embedded board 100 receives data from the IoT sensor attached to the emergency generator 10 through RS485 communication, and the data types include battery voltage (basic), coolant temperature (basic), oil temperature (basic), oil pressure ( Basic), power factor (default), heater temperature (option), power (option), and operation status (option) can be provided.
  • the data types include battery voltage (basic), coolant temperature (basic), oil temperature (basic), oil pressure ( Basic), power factor (default), heater temperature (option), power (option), and operation status (option) can be provided.
  • the server program 200a installed in the server 200 uses MySQL Server, which is an open source database management system, and may be replaced with a local database in some cases. Referring to FIG. 3 , a UI screen of the MySQL Workbench program used as the server program 200a is shown.
  • the function of the database 200b is to receive various information (data) obtained from the emergency generator 10 by the server 200 and store it in a DB, so that it is processed into various types of data required by the user. to be able to provide it.
  • Data obtained through the server program 200a of the server 200 is stored in the database 200b, and data is provided to the user through the client program 300a installed in the client 300 .
  • the database 200b is designed to store various data of user registration and emergency generator.
  • the data of the emergency generator 10 stored in the database 200b is shown in Table 1 below. Other additional data can be adjusted in consultation with the user.
  • the server program 200a is responsible for periodically acquiring data from the embedded board 100 and storing it in the database 200b, and may be manufactured to output a UI screen as shown in FIG. 4 when driven.
  • the server 200 When the server 200 is driven, it is automatically executed together with the database 200b to prepare to receive data from the embedded board 100 connected to the emergency generator 10 .
  • the server 200 is a TCP/IP (or UDP) Server, and transmits and receives data with the embedded board 100 through TCP/IP (or UDP) communication, and the communication protocol uses a dedicated protocol to enhance complementarity.
  • the server 200 stores the motorized data in the database 200b, manages the database 200b, and automatically starts the server program 200a when driven, as well as through an input device connected to the server 200 The user may manually start and end the operation of the server 200 by clicking the start/end button as shown in FIG. 4 .
  • the client program 300a installed in the client 300 may monitor data of all emergency generators 10 currently registered in the server 200 wherever a network such as the Internet is connected.
  • the client program 300a may bring necessary data while communicating directly with the database 200b and provide it to the user.
  • the UI and functions of the client program 300a are as follows.
  • FIG. 5 shows the Login UI screen, and the client program 300a is executed only when the registered user logs in normally on the client 300 .
  • Figure 6 is a main monitoring UI screen, so that the entire state data of the emergency generator 10 can be monitored. Depending on the user's authority, the data that can be checked may be restricted differently.
  • FIG. 7 is an individual generator monitoring UI screen so that data of the individual emergency generator 10 can be monitored. Data can be checked through selection on the main monitoring UI screen for the individual emergency generator 10 that the user wants to check.
  • an emergency generator status UI screen in which the registered emergency generator status outputs the client 300 when the installation status menu is selected on the main monitoring UI screen, and the emergency generator currently registered in the database 200b by the server 200 (10) can be displayed on the screen.
  • 9 is an alarm information UI screen showing that alarm information of an installed emergency generator is displayed on the client 300 when the alarm log menu is selected on the main monitoring UI screen.
  • 10 is an administrator-only screen that is a user registration UI screen, and only an administrator can register a user.
  • the server 200 can apply machine learning to create a pattern of data by accumulating the data stored in the database 200b and turning it into big data, so that the machine learns it by itself.
  • failures can be predicted in advance.
  • the server 200 analyzes the collected data distributed and stored for each category such as each region and use in the divided section DB in which the database 200b is divided by the distributed file program installed in the database 200b through a machine learning algorithm and malfunctions. prediction can be made.
  • the machine learning algorithm used in the server program 200a of the server 200 may be one of a decision tree (DT) classification algorithm, a random forest classification algorithm, and a support vector machine (SVM) classification algorithm. .
  • the server program 200a analyzes the collected data distributed and stored in the divided section DB by the distributed file program, extracts a plurality of feature data as a result of the analysis, and uses at least one or more of the extracted feature data among a plurality of machine learning algorithms Thus, it is possible to determine whether there is an abnormal state as a result of learning.
  • the server program 200a may apply an ensemble structure composed of a plurality of complementary machine learning algorithms to improve the accuracy of the state determination result.
  • the decision tree classification algorithm is a method of deriving results by learning in a tree structure, which makes it easy to interpret and understand the results, and the data processing speed is fast, and it may be possible to derive rules based on the search tree.
  • RF can be applied as a method to improve the low classification accuracy of DT.
  • the random forest classification algorithm is a method of slaughtering the results of learning multiple DTs as an ensemble.
  • SVM can be applied as a method to improve overfitting that may occur through DT or RF learning.
  • the SVM classification algorithm classifies data belonging to different classifications on a plane-based basis, and generally has high accuracy and may have low sensitivity to structural overfitting.
  • any user registered in the N:N method can check the data, and at the same time the data and previous data of the N emergency generators 10, and Data according to the installation environment of the emergency generator 10 can be accumulated, compared/analyzed with each other, and patterned.
  • the server program 200a In the data part collected by the server program 200a, not only the engine, but also the body part, the panel part, etc. are integrated to receive data.
  • the patterned data can be visualized and compared/analyzed for optimal values.
  • the embedded board 100 receives vibration, power, temperature, etc. in addition to the emergency generator data (GCP Data) as collected data from the emergency generator 10, and then transmits it to the server 200 through the cloud network.
  • GCP Data emergency generator data
  • the server 200 is a cloud server, divided application of data storage for analysis of vibration data, raw data edge computer storage (Data estimating: 50 GB/day), and cloud storage of Max 20 Parameter Data (Data estimating: 25) ⁇ 50MB/day).
  • the embedded board 100 receives data as IoT-based data for the emergency generator 10, but in the case of IoT data for data integration and transmission for securing reliability of IoT-based data, it is a large amount of data generated every second.
  • data collected from various sensors is integrated to reduce the loss rate through Flow Control and Error Control during cloud transmission, and to improve the security of data transmitted through the network.
  • the embedded board 100 can maintain security through secondary encryption to strengthen the security of data, that is, the embedded board 100 includes category data (each division area, use, etc.) of the emergency generator 10 . ), after receiving the unique identification number of the emergency generator 10, binarizing the category data and the unique identification number to perform a series of plaintext block data conversion, and then performing secondary encryption using a preset public key, an encryption key. . That is, after performing one-stage encryption to generate an intermediate constant by applying block encryption to the plaintext block information by the encryption key, a plurality of plaintext blocks for the generated intermediate constant and each individual data (category information and unique identification number) Complementation can be strengthened by performing an exclusive OR on each to generate a second encrypted ciphertext block.
  • category data each division area, use, etc.
  • the server 200 when receiving the unique identification number of the emergency generator 10 in addition to the category data to decrypt the public key with the embedded board 100 for the secondary encrypted data, the server 200 decrypts the data in the reverse order of encryption. It can be used to perform access to
  • server program 200a of the server 200 may "perform a multidimensional data analysis algorithm and real-time monitoring visualization" using data received through the embedded board 100 .
  • the server 200 analyzes the gap between the issue and the improvement goal according to the problem for each analysis requirement to support the integrated management of the emergency generator 10, and based on the quality standard of the emergency generator 10 As a result, it is possible to determine the critical value for a problem by applying the verification standards of field experts and continuously analyze the difference from the target value to satisfy it.
  • the server program 200a of the server 200 is an issue according to the problem through comparative analysis of various factors affecting the quality standard of the emergency generator 10, time series analysis to ensure the continuity of the stable state, etc.
  • the emergency generator 10
  • it is important to monitor the status of one unit in real time it is possible to perform an analysis for securing unexpected insights by integrating and analyzing the status of various emergency generators 10 .
  • the server 200 collects and analyzes data of the emergency generator 10 in various states to secure a factor value for the optimal state of the emergency generator 10, and analyzes the IoT sensing emergency generator 10 state data in real time.
  • Cube model / star schema that is easy for various analysis by applying multidimensional big data analysis techniques based on , unstructured / structured, sensor data, off-line cheek sheet data, machine learning preprocessing and standardization, and securing decision-making requirements and related data
  • online and multi-dimensional analysis techniques may be applied first.
  • the server 200 applies a data science-based analysis methodology for systematization of the data analysis process, establishes a data science method analysis execution procedure ⁇ planning ⁇ design ⁇ analysis ⁇ results (feedback) ⁇ , data science method planning and Design ⁇ APAA (Acquire ⁇ Prepare ⁇ Analyze ⁇ Act) ⁇ can be performed.
  • FIG. 12 is a diagram for explaining the systemization of a big data analysis process for the emergency generator 10 on the database 200b by the server 200 .
  • the server program 200a of the server 200 introduces and utilizes the concept of an agile methodology as an analysis methodology of data science.
  • Agile methodology is carried out focusing on the quality of the analysis model rather than project execution and output, and the analysis model is completed through continuous consultation with the customer, so it corresponds to an efficient analysis model suitable for the characteristics of the customer's business.
  • the server program 200a of the server 200 applies the data science methodology when an analysis task is selected as the analysis procedure of the data analysis methodology to plan ⁇ design ⁇ analysis ⁇ result It is possible to perform a pilot analysis in a total of four steps, complete the analysis model through continuous feedback, and provide architectural design support that is resilient to change through business architecture, application architecture, and technology architecture as architecture-oriented development support.
  • the server program 200a of the server 200 utilizes the framework, which is a commercialized software architecture, because it is activated, and provides an application modeling technique based on the MVC architecture, as well as data science methodology planning and design. can be done like
  • Booz Allen Hamilton's data science process is applied, and the data is based on the iterative execution technique of the agile methodology.
  • the analysis is performed in the order of collection ⁇ purification ⁇ analysis ⁇ utilization.
  • Table 2 is a table showing the data science methodology step-by-step procedure and details.
  • FIG. 14 is a diagram illustrating a procedure for performing data analysis of the emergency generator 10 by the server program 200a of the server 200 .
  • the server program 200a of the server 200 provides a state diagnosis and prediction algorithm of the emergency generator 10 based on sensor data in the analysis.
  • the server program 200a of the server 200 performs a machine vibration data / fault learning technique / emergency generator 10 self-state diagnosis interlocking test, and includes experts in each field to plan the entire project (Work Breakdown Structure) ) and apply the verification criteria of field experts to determine the critical value for the problem.
  • the server program 200a of the server 200 in providing an algorithm through correlation analysis of collected data, the results obtained through machine learning are organized into TP, FP, FN, and TN error matrix (confusion matrix)
  • TP TP
  • FP FP
  • FN FN
  • TN error matrix confusion matrix
  • the server program 200a of the server 200 performs verification and feedback of the machine learning algorithm through the emergency generator 10 state information and sensor data-based diagnosis/prediction algorithm through machine learning, and as a result, the following table It performs the same function as 3.
  • the server program 200a of the server 200 uses the "data set" using the emergency generator 10 capacity-specific and load-specific tests to proceed with machine learning and secure optimal driving characteristics, verification standards of field experts Based on the data obtained in the shipment test stage of the host company by applying can do.
  • the server program 200a of the server 200 provides an algorithm for optimal state and predictive maintenance by using GCP data and sensor data for the emergency generator 10, and the emergency generator 10 GCP data and It provides an algorithm for optimal state and predictive maintenance using sensor data, and develops a machine learning module through RNN and LSTM analysis, and uses this to develop a diagnosis/prediction algorithm based on emergency generator (10) status information and sensor data.
  • FIG. 15A shows a prediction validation visualization (an example)
  • FIG. 15B is a diagram showing an RNN and LSTM configuration diagram and an RNN data analysis structure.
  • the server program 200a of the server 200 has a built-in statistical processing function to establish an automatic baseline. More specifically, the server program 200a of the server 200 may calculate the average value and standard deviation of the input signal to generate a baseline, and evaluate the data by applying several signal processing values for each parameter.
  • the server program 200a of the server 200 may provide a hardware pre-process for big data analysis as well as provide a data processing function once per second as shown in FIG. 16 .
  • the server program (200a) of the server 200 is an integrated sensor in the emergency generator 10, Off-Line (LCD displayed on the energy controller) information, a monitoring module that collects history management information, the collected A state analysis module that analyzes the state of the emergency generator in real time using state information and a communication module that transmits the analyzed state of the target device to an external terminal, wherein the state analysis module is an IoT installed in the emergency generator 10 A state prediction module for predicting the state of the emergency generator by using the vibration data and thermal data collected from the equipment and predicting the failure time of the emergency generator 10 using at least one data among various data collected from the equipment It may include a failure prediction module that
  • the server program 200a of the server 200 compares the collected data with each reference pattern data, and when there is data that deviates by more than a preset value from the reference pattern, the emergency generator 10 state is abnormal. It can be predicted that
  • the server program 200a of the server 200 receives the information of the device and the energy controller collecting the state data of the emergency generator 10 every preset period, and the engine GCP Data and vibration/temperature data of the body.
  • Each of the reference pattern data is corrected by matching, and thereafter, the quality of the product can be predicted using the corrected reference pattern data, and the failure time of the equipment can be predicted.
  • the monitoring module and the status analysis module may further include a backup server provided in the cloud-based server and backing up the status information collected by the monitoring unit and the status of the equipment analyzed by the status analysis module at a preset period.
  • the server 200 collects and analyzes facility information using an integrated sensor provided in the facility, so that the state of a plurality of facilities located in remote locations can be integrated and monitored, and data such as vibration and temperature collected from the facility are used to collect and analyze facility information. It is possible to predict the state and failure time of the emergency generator 10 so that the maintenance of the device can be made.
  • the present invention can also be implemented as computer-readable codes on a computer-readable recording medium.
  • the computer-readable recording medium includes all kinds of recording devices in which data readable by a computer system is stored.
  • Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. also includes
  • the computer-readable recording medium is distributed in a computer system connected through a network, so that the computer-readable code can be stored and executed in a distributed manner.
  • functional programs, codes, and code segments for implementing the present invention can be easily inferred by programmers in the technical field to which the present invention pertains.
  • the emergency generator remote monitoring system is composed of an embedded board 100 , a server 200 , and a client 300 , and the embedded board 100 , the server 200 , and the client 300 are network
  • the embedded board 100 is connected to the embedded board 100 from the emergency generator 10 through RS485 communication.
  • Receives data, and data types include battery voltage (default), coolant temperature (default), oil temperature (default), oil pressure (default), power factor (default), heater temperature (option), power (option), operation status
  • the (optional) is provided to the server 200 through the network after being provided, the data obtained from the emergency generator 10 is converted into a DB in the database 200b and stored, and the data stored in the database 200b is integrated.
  • the data is processed in a way that not only diagnoses the failure of the emergency generator 100, but also predicts the failure in advance by applying machine learning to create a pattern of data by turning it into big data, and applying machine learning to learn by itself about the generated pattern, and the server 200
  • the program for the server installed in (200a); and a client program (300a) installed in the client (300) and providing data and processing data on the database (200b) to the user after access to the database (200b) through the network; It may be characterized in that it comprises a.
  • the server program 200a is a method of accumulating data in the database 200b, any user of the client 300 registered in the server 200 can check the data in an N:N manner rather than a 1:1 method.
  • it may be characterized in that the data and previous data of the N units of the emergency generator 10, data according to the installation environment of the emergency generator 10 are accumulated, compared and analyzed with each other, and patterned.
  • the embedded board 100 integrates vibration, power, and temperature in addition to the emergency generator data (GCP Data) as collected data from the emergency generator 10, and then transmits it to the server 200 through the cloud-type network.
  • GCP Data emergency generator data
  • the client program 300a as the main monitoring UI screen, enables monitoring of the entire state data of the emergency generator 10, and may be characterized in that the data that can be checked according to the user's authority is differently limited. .
  • the server 200 applies the verification standards of field experts in securing the optimal operating characteristics and proceeds with machine learning using the "data set" using the emergency generator 10 capacity-specific and load-specific tests. Based on the data obtained during the shipment test stage of the emergency generator 10 can
  • the emergency generator remote monitoring system makes it possible to remotely monitor each emergency generator by remotely accessing it from a client by converting data on the emergency generator into big data.
  • it is expected to be widely used in industry by attaching ICT-based sensors to the generator, a key component of emergency generators, and supporting real-time collection, analysis, and monitoring of usage data.

Abstract

La présente invention porte sur un système de surveillance à distance d'un générateur de secours. Le système (1) de surveillance à distance d'un générateur de secours comprend une carte intégrée (100), un serveur (200) et un client (300), la carte intégrée (100), le serveur (200) et le client (300) étant connectés par l'intermédiaire d'un réseau, et la carte intégrée (100) effectuant un accès au réseau de manière sans fil par l'intermédiaire d'un AP. Le système (1) de surveillance à distance du générateur de secours comprend : un programme (200a) de serveur destiné, lorsque la carte intégrée (100) reçoit des données à partir d'un générateur de secours (10) par l'intermédiaire d'une communication RS485 dans laquelle les données comprennent, en tant que types de données, une tension de batterie (par défaut), une température d'eau de refroidissement (par défaut), une température d'huile (par défaut), une pression d'huile (par défaut), un facteur de puissance (par défaut), une température d'appareil de chauffage (facultatif), une puissance (facultatif) et une possibilité de fonctionnement (facultatif), et fournit ensuite lesdites données au serveur (200) par l'intermédiaire du réseau, non seulement à stocker les données obtenues à partir du générateur de secours (10) dans une base de données (200b), mais également à traiter les données de manière à diagnostiquer un dysfonctionnement du générateur de secours (100a) et à prédire le dysfonctionnement à l'avance au moyen de l'intégration des données stockées dans la base de données (200b), afin de produire des mégadonnées, ce qui permet de produire un motif des données et d'appliquer le motif produit à un apprentissage automatique de la machine, et ledit programme est installé dans le serveur (200) ; et un programme (300a) de client installé dans le client (300) et fournissant, à un utilisateur, les données dans la base de données (200b) et les données traitées après l'accès à la base de données (200b) par l'intermédiaire du réseau.
PCT/KR2020/009960 2020-05-19 2020-07-29 Système de surveillance à distance de générateur de secours WO2021235594A1 (fr)

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