Power supply monitoring system and power supply monitoring method

Abstract

To qualitatively improve power supply service by early detecting abnormality caused on a consumer side and enabling appropriate and quick coping with the abnormality.SOLUTION: A power supply monitoring system communicably connects with one or more smart meters installed on the consumer side and having a function for measuring a voltage value of a distribution line; acquires time series data on voltage values of the distribution line measured by the smart meters; stores an abnormality determination model, a machine learning model that learns time series data acquired in the past as learning data and outputs a first probability indicating the degree of abnormality of time series data; inputs time series data newly acquired from the smart meters to the abnormality determination model to acquire a probability of existence of abnormality in the time series data; and outputs information based on the acquired first probability. For example, the power supply monitoring system determines presence / absence of abnormality in the time series data by comparing the first probability with a preset threshold and outputs a result of the determination.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a power supply monitoring system and a power supply monitoring method. [Background technology]

[0002] In recent years, smart meters have been installed in a considerable number of consumer premises, such as homes, office buildings, factories, and schools, and various mechanisms that utilize information acquired by smart meters have been proposed.

[0003] For example, Patent Document 1 describes a power data processing system that processes power data communicated from a smart meter, in which power attribute data relating to the attributes of the power amount when the power amount constituting the power data for a certain period is measured is added to the power data to generate processed data, the user's activity state is acquired as activity state data for a specific period arbitrarily set within the certain period corresponding to the generated certain period of processed data, and the user's activity state for the remaining period obtained by subtracting the specific period from the certain period is estimated based on a trained model that has been machine-learned to estimate the user's activity state corresponding to the processed data using learning data generated from the acquired activity state data and the processed data for the specific period corresponding to the specific period in which the activity state data was acquired.

[0004] Furthermore, for example, Patent Document 2 describes a distribution line abnormality monitoring system configured for the purpose of monitoring abnormalities in distribution lines from a power supply lead-in point to a distribution board. The distribution line abnormality monitoring system includes a smart meter for measuring the amount of power supplied from a pole transformer via a lead-in line, a distribution board having a main breaker connected to the smart meter via the distribution line, and a sensor for measuring the current and voltage flowing through the main breaker, and determines abnormalities in the distribution line based on the difference between the primary measurement value measured by the smart meter and the secondary measurement value measured by the sensor. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Publication No. 2020-28208 [Patent Document 2] Japanese Patent Application Publication No. 2017-17794 Summary of the Invention [Problem to be solved by the invention]

[0006] Currently, power suppliers such as electric power companies do not have a system to directly detect abnormalities in the power supply to consumers, and instead take a passive approach, such as starting an on-site investigation when a consumer reports that the electricity is flickering. Therefore, from the perspective of improving the quality of power supply services, it is necessary for power suppliers to build a system that enables them to actively and early detect abnormalities occurring on the consumer side and take appropriate measures.

[0007] The present invention has been made in light of the above background, and aims to provide a power supply monitoring system and a power supply monitoring method that enable the power supplier to actively and early detect abnormalities occurring on the consumer side. [Means for solving the problem]

[0008] One of the present inventions for achieving the above-mentioned object is a power supply monitoring system, which is configured using an information processing device having a processor and a storage device, and is communicatively connected to one or more smart meters installed on the consumer side and measuring voltage values ​​of distribution lines, acquires time series data of the voltage values ​​of the distribution lines measured by the smart meters, stores an abnormality determination model, which is a machine learning model that learns from the time series data acquired in the past as learning data and outputs a first probability indicating the degree of abnormality in the time series data, inputs the time series data newly acquired from the smart meter into the abnormality determination model, acquires the probability that there is an abnormality in the time series data, and outputs information based on the acquired first probability.

[0009] For example, the power supply monitoring system compares the first probability with a preset threshold to determine whether or not there is an abnormality in the time-series data, and outputs the result.

[0010] According to the present invention, abnormalities occurring on the consumer side can be detected early based on the time-series data of voltage values ​​sent from the smart meter. This allows the power supplier to respond quickly and appropriately to abnormalities occurring on the consumer side, resulting in thorough voltage management and improved quality of power supply services.

[0011] Another aspect of the present invention is the power supply monitoring system described above, which stores an anomaly determination model, which is a machine learning model that learns the time series data as learning data and outputs the first probability and a second probability, which is the probability that the type of abnormality in the time series data is a predetermined type, and by inputting the time series data newly acquired from the smart meter into the anomaly determination model, acquires the first probability and the second probability, and outputs information based on the acquired first probability and second probability.

[0012] For example, the power supply monitoring system determines the type of abnormality in the time-series data by comparing the second probability with a preset threshold value and outputs the result.

[0013] According to the present invention, it is possible to obtain information on the type of abnormality occurring on the consumer side based on the time-series data of voltage values ​​transmitted from the smart meter, thereby enabling the power supplier to identify the type of abnormality occurring on the consumer side and take appropriate action.

[0014] Another aspect of the present invention is the above-mentioned power supply monitoring system, which stores an abnormality determination model, which is a machine learning model that learns the time series data as learning data and outputs the first probability and the second probability, as well as a third probability, which is the probability that the cause of the abnormality in the time series data is a predetermined cause, and by inputting the time series data newly acquired from the smart meter into the abnormality determination model, acquires the first probability, the second probability, and the third probability, and outputs information based on the acquired first probability, second probability, and third probability.

[0015] For example, the power supply monitoring system determines the cause of the abnormality in the time-series data by comparing the third probability with a preset threshold value and outputs the result.

[0016] According to the present invention, it is possible to obtain information on the cause of an abnormality occurring on the consumer side based on the time-series data of voltage values ​​sent from the smart meter, which allows the power supplier to identify the cause of the abnormality and take appropriate action.

[0017] Other problems and solutions disclosed in the present application will be made clear in the detailed description and drawings. [Effects of the Invention]

[0018] According to the present invention, it becomes possible for the power supplier side to actively and early detect an abnormality occurring on the consumer side. [Brief explanation of the drawings]

[0019] [Figure 1] FIG. 1 is a diagram illustrating a schematic configuration of a power supply monitoring system. [Figure 2] FIG. 2 is a diagram illustrating main functions of the power supply monitoring device. [Figure 3] 1 is a diagram illustrating an example of a hardware configuration of an information processing device used to realize a power supply monitoring device. [Figure 4] 1 is an example of the hardware configuration of a smart meter (SM). [Figure 5] 10 is an example of smart meter information. [Figure 6] 10 is an example of measurement value information. [Figure 7A] 10 is a graph showing time series changes in measured voltage values ​​under normal conditions. [Figure 7B] 10 is a graph showing time series changes in measured voltage values ​​when an abnormality occurs. [Figure 7C] 10 is a graph showing time series changes in measured voltage values ​​when an abnormality occurs. [Figure 8] FIG. 1 is a diagram (neural network) for explaining an anomaly determination model. [Figure 9] 10 is a flowchart illustrating an abnormality determination model learning process. [Figure 10A] 10 is an example of a learning data setting screen. [Figure 10B] 10 is an example of a learning data setting screen. [Figure 10C] 10 is an example of a learning data setting screen. [Figure 11] 10 is a flowchart illustrating an abnormality determination process. [Figure 12] 10 is an example of a determination result presentation screen. DETAILED DESCRIPTION OF THE INVENTION

[0020] Hereinafter, an embodiment will be described with reference to the drawings. In the following description, the same or similar components will be denoted by the same reference numerals and the description thereof will be omitted.

[0021] 1 shows a schematic configuration of an information processing system (hereinafter referred to as "power supply monitoring system 1") that uses smart meters to monitor the power supply status at consumers, which will be described as one embodiment. As shown in the figure, the power supply monitoring system 1 includes a power supply monitoring device 100 and a number of smart meters (hereinafter referred to as "SM200") installed at consumers 2 (homes, public facilities, office buildings, various public facilities, factories, infrastructure facilities, etc.) that receive power supply through a power transmission and distribution system.

[0022] The power supply monitoring device 100 is installed, for example, in a monitoring station of a power supplier such as a power company or in a control center of a smart grid (hereinafter collectively referred to as "power supply monitoring center 3"). The power supply monitoring device 100 is configured using an information processing device (computer) and is communicatively connected to an SM 200 installed in each consumer 2 via a communication network 5 by wireless or wired communication. The communication network 5 is configured using a communication infrastructure such as a WAN (Wide Area Network), a concentrator, a 920 Hz band communication network, or a PLC (Power Line Communication).

[0023] The SM200 of each consumer 2 transmits various measurement values ​​(voltage value, current value, active power, reactive power, power factor, temperature, etc.) related to the power supplied from the power transmission and distribution system to the consumer 2 where it is installed to the power supply monitoring device 100 of the power supply monitoring center 3 via the communication network 5. The power supply monitoring device 100 monitors whether or not there is an abnormality in the power supply at the consumer 2 by analyzing, using a machine learning model, the time series data of the measurement values ​​transmitted from the SM200 at regular intervals (for example, every 5 minutes, every 30 minutes, etc.). In the following description, the term "measurement value" refers to the voltage value (standard voltage 100V (tolerance range 101V±6V), standard voltage 200V (tolerance range 202V±20V)) of the distribution line (single-phase two-wire, single-phase three-wire, three-phase three-wire, etc.) supplying power to the consumer 2.

[0024] 2 shows the main functions of the power supply monitoring device 100. As shown in the figure, the power supply monitoring device 100 has the following functions: a storage unit 110, a smart meter information management unit 120, a measurement value acquisition management unit 130, a learning data generation unit 135, a model learning unit 140, an abnormality determination unit 145, and a determination result presentation unit 150.

[0025] Of the above functions, the storage unit 110 stores the following information (data): smart meter information 111, measurement value information 112, learning data 113, an abnormality determination model 114, and a determination result 115.

[0026] Among these, the smart meter information 111 includes various types of information about each of the SMs 200 provided in each of the customers 2.

[0027] The measurement value information 112 includes time-series data of measurement values ​​acquired from the SM 200 of each consumer 2 via the communication network 5.

[0028] The learning data 113 is learning data (teaching data) used to learn a machine learning model (hereinafter referred to as an "abnormality determination model") used to determine whether or not there is an abnormality in the power supply at the consumer 2 and to identify the type of abnormality.

[0029] The abnormality determination model 114 is an entity of the abnormality determination model (for example, expressed as a determinant, a mathematical expression, a vector, or the like including adjustable parameters). In this embodiment, the abnormality determination model 114 is a DNN (Deep Neural Network), but may be realized by other types of models such as gradient boosting (GBDT (Gradient Boosting Decision Tree)), and the type of the abnormality determination model 114 is not necessarily limited.

[0030] The determination result 115 includes information about the analysis result of whether or not there is an abnormality in the power supply to the customer 2, which is performed using the abnormality determination model 114.

[0031] The measurement value acquisition management unit 130 shown in the same figure receives measurement values ​​sent from the SM 200 of each consumer 2 via the communication network 5, and stores time series data based on the received measurement values ​​as measurement value information 112. In this embodiment, the measurement value acquisition management unit 130 acquires measurement values ​​from the SM 200 of each consumer 2 at predetermined time intervals (for example, every 5 minutes, every 30 minutes, etc.) and manages them as time series data. The measurement value acquisition management unit 130 manages both the time series data of measurement values ​​newly acquired from the SM 200 and the time series data of measurement values ​​previously acquired from the SM 200 as measurement value information 112.

[0032] The learning data generation unit 135 generates learning data to be used for training the anomaly determination model 114. The learning data generation unit 135 presents time-series data of measurement values ​​acquired in the past for a predetermined period (half a day, a full day, a week, etc., hereinafter referred to as a "time interval") to the user as explanatory variables (features), and accepts from the user a setting of a label (such as abnormal or normal, or the type of abnormality if abnormal) that is a target variable (correct answer data) for the measurement values. The learning data generation unit 135 then generates data in which the time-series data of the measurement values ​​is associated with the accepted label, and manages the data as learning data 113.

[0033] The time interval is set empirically, for example, based on time-series changes in measurement values ​​when abnormalities occurred in the past. Alternatively, a plurality of abnormality determination models 114 may be generated for each of a plurality of time intervals, and the presence or absence of an abnormality in the power supply at the consumer 2 and the type of abnormality may be determined from various perspectives using the generated plurality of abnormality determination models 114. The time interval is set to a length that improves the accuracy of detecting an abnormality, for example, based on past cases.

[0034] The model learning unit 140 uses the learning data 113 to learn the anomaly determination model 114. For example, the model learning unit 140 inputs the learning data 113 to the anomaly determination model 114, and adjusts parameters that define the anomaly determination model 114 based on the difference between the result output by the anomaly determination model 114 and the input learning data 113, using a method such as backpropagation, thereby learning the anomaly determination model 114.

[0035] The abnormality determination unit 145 determines whether or not there is an abnormality in the power supply at the consumer 2 by inputting the measurement values ​​for a specified period for which the presence or absence of an abnormality is to be determined (for example, the measurement values ​​for the latest specified period newly acquired from SM200) into the abnormality determination model 114, and manages the result as the determination result 115.

[0036] The judgment result presentation unit 150 generates a screen (a judgment result presentation screen 1200 described later) for presenting the contents of the judgment result 115 to the user, and presents (displays) the generated judgment result presentation screen to the user via a user interface.

[0037] FIG. 3 shows an example of the hardware configuration of an information processing device used to implement the power supply monitoring device 100. The illustrated information processing device 10 includes a processor 11, a main memory device 12, an auxiliary memory device 13, an input device 14, an output device 15, and a communication device 16. Specific examples of the information processing device 10 include personal computers, office computers, various server devices, and general-purpose computers. All or part of the information processing device 10 may be implemented using virtual information processing resources provided using virtualization technology, process space separation technology, or the like, such as a virtual server provided by a cloud system. The power supply monitoring device 100 may be implemented using multiple information processing devices 10 connected to each other so that they can communicate with each other.

[0038] In the same figure, the processor 11 is configured using, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), a GPU (Graphics Processing Unit), an FPGA (Field Programmable Gate Array), an ASIC (Application Specific Integrated Circuit), an AI (Artificial Intelligence) chip, etc.

[0039] The main memory device 12 is a device that stores programs and data, and is, for example, a read-only memory (ROM), a random access memory (RAM), or a non-volatile memory (NVRAM (Non-Volatile RAM)).

[0040] The auxiliary storage device 13 is, for example, an SSD (Solid State Drive), a hard disk drive, an optical storage device (CD (Compact Disc), DVD (Digital Versatile Disc), etc.), a storage system, a reading / writing device for a recording medium such as an IC card, an SD card, or an optical recording medium, a storage area of ​​a cloud server, etc. Programs and data can be read into the auxiliary storage device 13 via a recording medium reading device or a communication device 16. The programs and data stored (memorized) in the auxiliary storage device 13 are read into the main storage device 12 as needed.

[0041] The input device 14 is an interface that accepts input from the outside, and is, for example, a keyboard, a mouse, a touch panel, a card reader, a pen-input tablet, a voice input device, or the like.

[0042] The output device 15 is an interface that outputs various information such as the progress of processing and the results of processing. The output device 15 is, for example, a display device (LCD (Liquid Crystal Display), graphic card, etc.) that visualizes the various information, a device that converts the various information into audio (audio output device (speaker, etc.)), or a device that converts the various information into text (printer, etc.). Note that, for example, the information processing device 10 may be configured to input and output information to and from other devices via the communication device 16.

[0043] The input device 14 and the output device 15 constitute a user interface for receiving information from the user and presenting information.

[0044] The communication device 16 is a device that realizes communication (wired communication or wireless communication) with other devices via a communication infrastructure such as the communication network 5, and is configured using, for example, a NIC (Network Interface Card), a wireless communication module, a USB module, etc. The communication device 16 communicates with the SM 200 installed in each consumer 2 via the communication network 5. The communication device 16 also Power Supply Monitoring Center 3 The SM 200 communicates with other information processing systems that manage information relating to the power transmission and distribution system and the SM 200 installed in each consumer 2, such as a power distribution control system and a customer management system that operate in the SM 200.

[0045] The information processing device 10 may be equipped with, for example, an operating system, a file system, a DBMS (DataBase Management System) (relational database, NoSQL, etc.), a KVS (Key-Value Store), etc.

[0046] The functions of the power supply monitoring device 100 are realized by the processor 11 of the information processing device 10 reading and executing a program stored in the main memory device 12, or by the functions of the hardware (FPGA, ASIC, AI chip, etc.) that constitutes the power supply monitoring device 100. The power supply monitoring device 100 stores the various pieces of information (data) described above, for example, as tables in a database or files managed by a file system.

[0047] 4 shows an example of the hardware configuration of the SM 200. As shown in the figure, the SM 200 includes a processor 201, a memory 202, a timing device 203, a measuring device 204, an input device 205, an output device 206, a communication device 207, and a power supply device 208.

[0048] The processor 201 is configured using a CPU, MPU, power calculation engine (PCE), etc., and realizes various functions provided by the SM 200 by reading and executing programs stored in the memory 202. The memory 202 is configured using a ROM, RAM, non-volatile memory, etc., and stores programs (including firmware) and data. The programs stored in the memory 202 can be updated as needed, for example, by updating firmware, etc.

[0049] The timing device 203 is configured using, for example, a temperature-compensated RTC (Real Time Clock) or the like, and provides time information such as the current date and time. The time information provided by the timing device 203 is used as information indicating the measurement date and time of measurement information such as voltage measurement values. The timing device 203 may have a function of acquiring date and time information via the communication network 5 using NTP (Network Time Protocol) or the like.

[0050] The measuring device 204 is equipped with various sensors (voltage sensor, current sensor, temperature sensor, etc.), a ΔΣ type A / D converter, a gain amplifier, etc., and acquires various measurement values ​​measured at the consumer 2 where the SM200 is installed and stores them in the memory 202.

[0051] The input device 205 and the output device 206 provide a user interface for a worker or the like to directly operate the SM 200 at the site where the SM 200 is installed. The input device 205 is, for example, a keyboard, a touch panel, an operation button, etc. The output device 206 is, for example, various display devices such as an LCD, an organic EL panel (organic electro-luminescence panel), an LED (light emitting diode), etc.

[0052] The communication device 207 realizes communication with the power supply monitoring device 100 via the communication network 5. The communication device 207 includes, for example, a wireless communication module (a 920 MHz band wireless module, a 1:N wireless module, etc.) and a wired communication module (a PLC module (PLC: Power Line Communication), etc.).

[0053] The power supply device 208 is a device that supplies driving power to each component of the SM200, and includes, for example, an A / D converter that converts AC power from the power distribution line into DC power, a battery that enables the SM200 to operate independently when the power supply from the power distribution line is stopped, and a charging circuit for the battery.

[0054] Fig. 5 shows an example of the smart meter information 111. As shown in the figure, the smart meter information 111 is made up of multiple records, each of which includes items such as a meter ID 1111, a consumer ID 1112, and various information 1113. One record of the smart meter information 111 corresponds to one smart meter (SM200) installed in the consumer 2. The smart meter information 111 is acquired, for example, from a power distribution control system, a customer management system, or the like.

[0055] Of the above items, the meter ID 1111 stores an identifier of the smart meter (hereinafter referred to as "meter ID"). The consumer ID 1112 stores an identifier of the consumer (hereinafter referred to as "consumer ID"). The various information 1113 stores various information related to the SM 200, such as the type of power supply contract (contracted amperes (10A, 30A, 50A, etc.)) of the consumer 2 in which the SM 200 is installed, the section of the distribution line in which the SM 200 is installed, identification information of the distribution line (line name, service pole number), and the date and time of installation of the SM 200 at the consumer 2.

[0056] 6 shows an example of the measurement value information 112. As shown in the figure, the measurement value information 112 is made up of multiple records each having items such as a meter ID 1121, a measurement date and time 1122, a voltage value (1) 1123, and a voltage value (2) 1124. One record of the measurement value information 112 corresponds to a measurement value at a certain measurement date and time sent from a certain SM 200.

[0057] The meter ID of the SM 200 that measured the measurement value of the record is stored in the meter ID 1121. The date and time when the measurement value was measured is stored in the measurement date and time 1122. The measurement values ​​of each measurement point of the distribution line that is the measurement target of the SM 200 are stored in the voltage value (1) 1123, voltage value (2) 1124, ....

[0058] 7A to 7C are graphs each showing time series data of measurement values ​​(time series data of voltage value (1) 1123 and time series data of voltage value (2) 1124) in a certain time interval of measurement value information 112. Fig. 7A is an example of time series data of voltage values ​​in a time interval during which power supply to consumer 2 is normal, and Figs. 7B and 7C are both examples of time series data of voltage values ​​in a time interval during which an abnormality occurs in power supply to consumer 2.

[0059] As shown in FIG. 7A, during a time period when power is being supplied normally, the voltage values ​​(voltage value (1) 1123 and voltage value (2) 1124) fall within the allowable range of 101 V ± 6 V. On the other hand, as shown in FIGS. 7B and 7C, during a time period when an abnormality is occurring in the power supply, there is a period when the voltage value (1) 1123 and the voltage value (2) 1124 do not fall within the allowable range of 101 V ± 6 V. For example, in FIG. 7B, there is a period when the voltage value temporarily drops to 90 V or less. Also, for example, in FIG. 7C, there are periodic periods when the voltage value temporarily drops to 90 V or less. By learning such time-series data of voltage values ​​during normal times and time-series data of voltage values ​​during abnormal times, the abnormality determination model 114 determines whether power is being supplied normally and, if an abnormality occurs, identifies the type and cause of the abnormality from the newly acquired time-series data of voltage values.

[0060] FIG. 8 shows an example (neural network structure) of the anomaly determination model 114. As shown in the figure, time-series data of measurement values ​​for a predetermined time interval, acquired from the measurement value information 112, is input to an input layer 811 of the anomaly determination model 114. The intermediate layer 812 includes one or more hidden layers made up of one or more nodes including parameters adjusted by learning. The intermediate layer 812 calculates one or more predicted values ​​(probabilities) for the output layer 813 based on the time-series data provided to the input layer 811. In the anomaly determination model 114 of this embodiment, the output layer 813 includes a plurality of predicted values ​​(probability that an anomaly has occurred in the power supply, probabilities indicating the possibility of each type of anomaly (probability that the occurring anomaly is A, probability that the occurring anomaly is B, probability that the occurring anomaly is caused by α, probability that the occurring anomaly is caused by β, ...)).

[0061] Although this embodiment illustrates a case where time series data of one measurement value acquired from a certain SM 200 in a predetermined time interval is provided to the input layer 811 of the anomaly determination model 114, for example, time series data of a plurality of measurement values ​​measured from a certain SM 200 at different measurement points on a distribution line in a predetermined time interval may be provided to the input layer 811. By providing time series data of a plurality of measurement values ​​acquired at different measurement points on a distribution line to the input layer 811, an improvement in the accuracy of the anomaly determination model 114 can be expected.

[0062] 9 is a flowchart illustrating the processing (hereinafter referred to as "abnormality determination model learning processing S900") that the learning data generation unit 135 and the model learning unit 140 of the power supply monitoring device 100 perform when generating learning data and learning the abnormality determination model 114 using the generated learning data. The timing for executing the abnormality determination model learning processing S900 is not necessarily limited. For example, the power supply monitoring device 100 executes the abnormality determination model learning processing S900 when new learning data 113 is generated or when an execution instruction is received from a user via a user interface. The abnormality determination model learning processing S900 will be described below with reference to FIG.

[0063] First, the learning data generation unit 135 of the power supply monitoring device 100 presents the user with measurement values ​​(explanatory variables) to be used as learning data 113, and accepts the user's setting of labels (objective variables) for the measurement values, and generates data that associates the measurement values ​​with the labels as learning data 113 (S911).

[0064] 10A to 10C show examples of screens (hereinafter referred to as "learning data setting screen 1000") that the learning data generation unit 135 presents to the user via a user interface when accepting the presentation of the measurement values ​​(explanatory variables) and the setting of labels (target variables). FIG. 10A is an example of the learning data setting screen 1000 when the power supply is normal, and FIGS. 10B and 10C are examples of the learning data setting screen 1000 when the power supply is abnormal. As shown in these screens, the learning data setting screen 1000 has a display field 1010 for the measurement values ​​(explanatory variables) and a field 1020 for setting labels.

[0065] 9, next, the model learning unit 140 of the power supply monitoring device 100 learns the anomaly determination model 114 based on the learning data 113 (S912). Note that the model learning unit 140 may, for example, verify the prediction accuracy of the learned anomaly determination model 114. In this case, for example, the learning data 113 is classified in advance into learning data and verification data, and the anomaly determination model 114 is learned using the learning data, and the anomaly determination model 114 is verified using the verification data.

[0066] 11 is a flowchart illustrating a process (hereinafter referred to as "abnormality determination process S1100") in which the abnormality determination unit 145 of the power supply monitoring device 100 inputs explanatory variables to be determined (for example, newly acquired time-series data of measurement values ​​(voltage values) in a predetermined time interval) into the abnormality determination model 114 to determine objective variables (probability that an abnormality has occurred, probability that the type of abnormality is A, probability that the type of abnormality is B, ∼, probability that the cause of abnormality A is α, probability that the cause of abnormality B is β, ∼), and the determination result presenting unit 150 of the power supply monitoring device 100 presents the determined objective variables to the user. The abnormality determination process S1100 is executed at a preset timing (periodically, at a scheduled date and time, when an execution instruction is received from a user, etc.). The abnormality determination process S1100 will be described below with reference to FIG.

[0067] First, the abnormality determination unit 145 obtains, from the measurement value information 112, time-series data of newly acquired measurement values ​​(voltage values), which are explanatory variables of the determination target (S1111).

[0068] Next, the abnormality determination unit 145 inputs the acquired explanatory variables to the abnormality determination model 114, and acquires the output of the abnormality determination model 114 as the determination result 115 (S1112).

[0069] Next, the judgment result presentation unit 150 generates a screen (hereinafter referred to as the "judgment result presentation screen 1200") that displays the judgment result 115, and outputs (displays) the generated judgment result presentation screen 1200 to the output device 15 (display device) (S1113).

[0070] 12 shows an example of a determination result presentation screen 1200. The illustrated determination result presentation screen 1200 has a display field 1210 for the time interval to be determined, and a display field 1220 for the determination result. The display field 1210 for the time interval to be determined displays information indicating the time interval of the time-series data of the measurement value to be determined. The display field 1220 for the determination result displays the contents of the determination result 115 acquired in S1112 of FIG. 11. As shown in the figure, the display field 1220 for the determination result displays a table consisting of multiple rows having the following items: meter ID 1221, normal / abnormal 1222, abnormality type 1223, and cause prediction 1224. One row in the display field 1220 for the determination result corresponds to the determination result of one SM 200.

[0071] The meter ID 1221 displays the meter ID.

[0072] Normal / Abnormal 1222 displays information (normal / abnormal) indicating whether or not an abnormality has occurred in the SM 200. For example, if the probability indicating the degree of abnormality in the time-series data of the measurement value (hereinafter referred to as the "first probability") is equal to or less than a preset threshold, "normal" is displayed in Normal / Abnormal 1222. Furthermore, if the first probability exceeds the threshold, "abnormal" is displayed in Normal / Abnormal 1222.

[0073] When an abnormality occurs, the predicted type of the abnormality is displayed in the abnormality type 1223. For example, if the probability that the type of abnormality in the time-series data of the measurement values ​​is a predetermined type exceeds a preset threshold, information indicating the type, such as "temporary voltage drop" or "periodic voltage drop," is displayed in the abnormality type 1223.

[0074] When an abnormality occurs, the result of predicting the cause of the abnormality is displayed in cause prediction 1224. For example, if the probability that the cause of the abnormality in the time-series data of the measurement values ​​is a predetermined cause exceeds a preset threshold, information indicating the cause, such as "poor contact" or "transformer failure," is displayed in cause prediction 1224. The abnormality determination process S1100 is performed as described above.

[0075] As described above in detail, the power supply monitoring system 1 of this embodiment can detect abnormalities occurring on the consumer side at an early stage based on the time-series data of voltage values ​​sent from the SM 200. This enables the power supplier to respond quickly and appropriately to abnormalities occurring on the consumer side.

[0076] Furthermore, the power supply monitoring system 1 of this embodiment can obtain information about the type of abnormality occurring on the consumer side based on the time-series data of voltage values ​​sent from the smart meter. This allows the power supplier to identify the type of abnormality occurring on the consumer side and take appropriate action.

[0077] Furthermore, the power supply monitoring system 1 of this embodiment can obtain information on the cause of an abnormality occurring on the consumer side based on the time-series data of voltage values ​​sent from the smart meter. This allows the power supplier to identify the cause of the abnormality occurring on the consumer side and take appropriate action.

[0078] Although the embodiments of the present invention have been described in detail above, the above description is intended to facilitate understanding of the present invention and is not intended to limit the present invention. The present invention may be modified or improved without departing from the spirit thereof, and the present invention naturally includes equivalents thereof. For example, the above embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations. Furthermore, it is possible to add, delete, or replace some of the configurations of the above embodiments with other configurations.

[0079] For example, in the above embodiment, the learning data 113 is generated using time-series data of voltage values, which are measurement values, as explanatory variables. However, the learning data 113 may be generated using, for example, other measurement values ​​(time-series data of current values, time-series data of active power, time-series data of reactive power, time-series data of power factor, time-series data of temperature, etc.) acquired from the measurement values ​​of the SM 200 as explanatory variables (features). Furthermore, for example, measurement values ​​acquired from other SMs 200 (e.g., other SMs 200 connected to the same transformer) around the SM 200 from which the target measurement value was acquired, as understood from the smart meter information 111, may be used as explanatory variables (features). In this way, it is possible to generate learning data 113 that takes into account the surrounding conditions of the SM 200 from which the target measurement value was acquired, and further improvement in the accuracy of the anomaly determination model 114 can be expected.

[0080] Furthermore, in the above embodiment, an example has been described in which the abnormality determination model 114 is learned by "supervised learning." However, for example, the abnormality determination model 114 may be generated by inputting a large amount of normal measurement value information 112 into a machine learning model and learning a steady state. [Explanation of symbols]

[0081] 1 Power supply monitoring system, 2 Consumer, 3 Power supply monitoring center, 5 Communication network, 100 Power supply monitoring device, 110 Memory unit, 111 Smart meter information, 112 Measurement value information, 113 Learning data, 114 Abnormality determination model, 115 Determination result, 120 Smart meter information management unit, 130 Measurement value acquisition management unit, 135 Learning data generation unit, 140 Model learning unit, 145 Abnormality determination unit, 150 Determination result presentation unit, 200 SM (smart meter), 1000 Learning data setting screen, 1200 Determination result presentation screen, S900 Abnormality determination model learning process, S1100 Abnormality determination process

Claims

1. The information processing device is provided on the side of an electric power supplier that supplies electric power to individual consumers through a power transmission and distribution system, and is configured using an information processing device having a processor and a storage device, A plurality of smart meters are installed on the side of each of the consumers and are communicably connected to measure voltage values ​​of the distribution line, acquiring time-series data of distribution line voltage values ​​measured at each of the consumers, the data being transmitted from each of the smart meters as needed; storing an anomaly determination model that is a machine learning model that learns the time series data acquired in the past as learning data and outputs a first probability that indicates the degree of anomaly in the time series data; inputting the time-series data newly acquired from the smart meter into the anomaly determination model to acquire a probability that the time-series data has an anomaly, and outputting information based on the acquired first probability; storing an anomaly determination model that is a machine learning model that has learned the time series data as learning data and that outputs the first probability and a second probability that the type of anomaly in the time series data is a predetermined type; inputting the time-series data newly acquired from the smart meter into the anomaly determination model to acquire the first probability and the second probability, and outputting information based on the acquired first probability and second probability; storing an anomaly determination model that is a machine learning model that has learned the time-series data as learning data and that outputs the first probability, the second probability, and a third probability that is a probability that the cause of each type of anomaly in the time-series data is a predetermined cause; inputting the time series data newly acquired from the smart meter into the anomaly determination model to acquire the first probability, the second probability, and the third probability, and outputting information based on the acquired first probability, the second probability, and the third probability; Power supply monitoring system.

2. 2. The power supply monitoring system according to claim 1, determining whether or not there is an abnormality in the time-series data by comparing the first probability with a preset threshold value, and outputting the result of the comparison; Power supply monitoring system.

3. 2. The power supply monitoring system according to claim 1, determining whether or not an abnormality exists in the time-series data and the type of the abnormality by comparing the first probability and the second probability with a predetermined threshold value, and outputting the result of the comparison; Power supply monitoring system.

4. 2. The power supply monitoring system according to claim 1, determining whether or not an abnormality exists in the time-series data, the type of the abnormality, and the cause of the abnormality by comparing the first probability, the second probability, and the third probability with a predetermined threshold value, and outputting the results; Power supply monitoring system.

5. An information processing device provided on an electric power supplier side that supplies electric power to individual consumers through an electric power transmission and distribution system, the information processing device having a processor and a memory, acquiring time-series data of the voltage values ​​of the distribution line measured at each of the consumers, the data being sent from each of a plurality of smart meters installed on the side of each of the consumers and measuring the voltage values ​​of the distribution line; storing an anomaly determination model that is a machine learning model that learns the time series data acquired in the past as learning data and outputs a first probability that indicates the degree of anomaly in the time series data; inputting the time-series data newly acquired from the smart meter into the anomaly determination model to acquire a probability that the time-series data has an anomaly, and outputting information based on the acquired first probability; storing an anomaly determination model that is a machine learning model that has learned the time series data as learning data and that outputs the first probability and a second probability that the type of anomaly in the time series data is a predetermined type; acquiring the first probability and the second probability by inputting the time-series data newly acquired from the smart meter into the abnormality determination model, and outputting information based on the acquired first probability and second probability; storing an anomaly determination model that is a machine learning model that has learned the time series data as learning data and that outputs the first probability, the second probability, and a third probability that is a probability that the cause of each type of anomaly in the time series data is a predetermined cause; acquiring the first probability, the second probability, and the third probability by inputting the time-series data newly acquired from the smart meter into the abnormality determination model, and outputting information based on the acquired first probability, the second probability, and the third probability; A power supply monitoring method for performing the above.

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