Power distribution automation system, monitoring method and program

Abstract

In an automated power distribution system equipped with a power generation device such as a solar power plant, the discharge time of a substation is estimated. [Solution] The slave station 2 and master station 3 that constitute the power distribution automation system 1 are equipped with a solar power generation unit 220, a power storage unit 230, and a sunshine measurement unit 240 as functions of the slave station 2, and a prediction unit 342 that predicts the amount of power generated by the solar power generation unit 220 based on the sunshine duration and amount of solar radiation as functions of the master station 3, an estimation unit 343 that estimates the dischargeable time of the power storage unit 230 based on the predicted amount of power generated and the amount of power stored in the power storage unit 230, and a display unit 320 that displays the estimated dischargeable time of the power storage unit 230.

Description

Technical Field

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[0001] The present invention relates to a distribution automation system, a monitoring method, and a program including a plurality of slave stations installed in a power distribution system and a master station for monitoring and controlling the slave stations.

Background Art

[0002] Conventionally, there has been installed and operated a distribution automation system that monitors a power distribution system, controls switches, etc., identifies a power outage area on the power distribution system when a power outage occurs, and generates power outage information such as the occurrence of a power outage and the restoration of power supply based on the results of monitoring and control.

[0003] In such a power distribution system, a distribution automation system for managing and controlling a plurality of management areas is known (see, for example, Patent Document 1). In this distribution automation system, a remote master station (master station) is provided for each management area. A plurality of remote slave stations (slave stations) are provided, for example, on utility poles, etc., and are communicably connected to and controlled by the master station.

[0004] Also, in a distribution automation system, in order to shorten the restoration time even during a disaster where the power outage time extends for several hours or more, a distribution automation system provided with a power generation device using natural energy for each slave station is known (see, for example, Patent Document 2).

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0006] Incidentally, in the event of a power outage due to an accident or other reason in an automated power distribution system, the discharge time of energy storage devices such as batteries installed in the slave stations is limited, so it is necessary to take action at the slave stations within a predetermined time. However, since the discharge time of the energy storage devices differs from one slave station to another, it has been difficult to take action in the appropriate order and within the limited time. Even with an automated power distribution system like the one described in Patent Document 2, the amount of power generated by the power generation device differs from one slave station to another, making it difficult to take action in the appropriate order and within the limited time.

[0007] Therefore, the present invention aims to provide a power distribution automation system equipped with a power generation device such as a solar power generation device that enables the estimation of the dischargeable time of a substation, a monitoring method for monitoring the power distribution automation system, and a program. [Means for solving the problem]

[0008] To solve the above problems, the invention of claim 1 is a power distribution automation system including a plurality of substations installed in a power distribution system and a master station that monitors and controls the substations, wherein each substation includes a power storage unit that stores electricity and supplies power to the devices of the substation, a solar power generation unit that generates electricity to charge the power storage device using sunlight, and a solar radiation measurement unit that measures the duration of sunlight and the amount of solar radiation at the installation location of the substation, and the master station includes a prediction unit that predicts the amount of power generated by the solar power generation unit based on the duration of sunlight and the amount of solar radiation measured by the solar radiation measurement unit, an estimation unit that estimates the dischargeable time of the power storage unit based on the amount of power generated, which is the prediction result of the prediction unit, and the amount of electricity stored in the power storage unit, and a display unit that displays the dischargeable time of the power storage unit, which is the estimation result of the estimation unit.

[0009] The invention of claim 2 is characterized in that, in the power distribution automation system described in claim 1, the master station further comprises a determination unit that determines whether the installation location of the slave station is suitable for predetermined conditions based on the measurement results of the sunshine duration and solar radiation from the sunshine measurement unit.

[0010] The invention of claim 3 is characterized in that, in the power distribution automation system described in claim 2, the determination unit determines whether or not the installation location of the substation is a suitable location for power generation by the solar power generation unit.

[0011] The invention of claim 4 is characterized in that, in the power distribution automation system according to any one of claims 1 to 3, the estimation unit estimates the discharge time of the energy storage unit based on a learning model that uses the past amount of power generated by the solar power generation unit and the past discharge time of the energy storage unit as training data, taking the amount of power generated by the solar power generation unit and the amount of energy stored in the energy storage unit as input.

[0012] The invention of claim 5 is a monitoring method for monitoring a power distribution automation system, which is executed on a computer having a processor and memory, and includes a plurality of slave stations installed in a power distribution system and a master station that monitors and controls the slave stations, characterized in that the processor performs the following steps: predicts the amount of power generated by a solar power generation unit that generates power to charge a power storage unit that stores power using sunlight and supplies power to the devices of the slave stations, based on the measurement results of the amount of sunlight and solar radiation of sunlight at the installation locations of the slave stations; estimates the dischargeable time of the power storage unit based on the predicted amount of power generated and the amount of energy stored in the power storage unit; and displays the estimated dischargeable time of the power storage unit.

[0013] The invention of claim 6 is a program for monitoring a power distribution automation system, which includes a plurality of slave stations installed in a power distribution system and a master station that monitors and controls the slave stations, and which is executed by a computer having a processor and memory, characterized in that the program causes the processor to perform the following steps: predict the amount of power generated by a solar power generation unit that generates power to charge a power storage unit that stores power using sunlight and supplies power to the devices of the slave stations, based on the measurement results of the amount of sunlight and solar radiation at the installation locations of the slave stations; estimate the dischargeable time of the power storage unit based on the predicted amount of power generated and the amount of power stored in the power storage unit; and display the dischargeable time of the power storage unit as an estimate. [Effects of the Invention]

[0014] According to the inventions described in claims 1, 5, and 6, the amount of power generated by the solar power generation unit is predicted based on the measurement results of the amount of sunlight and solar radiation, and the discharge time of the energy storage unit is estimated based on the predicted amount of power generated and the amount of energy stored in the energy storage unit. The estimated discharge time of the energy storage unit is then displayed on the master station. Therefore, it becomes possible to understand the different discharge times of each energy storage unit, which vary depending on the geographical conditions of the installation location of the slave station and the aging conditions of the energy storage unit, which is composed of batteries, etc. This can be used to perform power outage restoration operations up to the discharge time (operation time of the slave station), making operations more efficient.

[0015] According to the invention described in claim 2, the system includes a determination unit that determines whether the installation location of the substation is suitable for predetermined conditions based on the measurement results of the duration of sunlight and the amount of solar radiation. This makes it possible to provide information for determining the suitability of the installation location of the substation for predetermined conditions, such as the suitability of performing solar power generation.

[0016] According to the invention described in claim 3, based on the measurement results of the duration of sunlight and the amount of solar radiation, it is determined whether the installation location of the substation is suitable for power generation by the solar power generation unit. This makes it possible to provide information for making decisions regarding the installation, expansion, removal, etc., of the solar power generation unit (power generation device).

[0017] According to the invention described in claim 4, the discharge time of the energy storage unit is estimated based on a learning model that uses the past power generation amount of the solar power generation unit and the past discharge time of the energy storage unit as training data, taking the power generation amount of the solar power generation unit and the amount of energy stored in the energy storage unit as input. This makes it possible to estimate the discharge time of the energy storage unit more accurately using a learning model such as machine learning. [Brief explanation of the drawing]

[0018] [Figure 1]It is a schematic diagram showing the overall configuration of the distribution automation system 1 according to Embodiment 1 of the present invention. [Figure 2] It is a block diagram showing the functional configuration of the slave station 2 in FIG. 1. [Figure 3] It is a block diagram showing the functional configuration of the master station 3 in FIG. 1. [Figure 4] It is a screen diagram showing an example of the display of the dischargeable time displayed on the display unit 320 in FIG. 3. [Figure 5] It is a flowchart showing the procedure of the estimation process by the control unit 340 in FIG. 3. [Figure 6] It is a flowchart showing the procedure of the learning process by the control unit 340 in FIG. 3. [Figure 7] It is a block diagram showing the procedure of machine learning and estimation by the learning unit 346 in FIG. 3. [Figure 8] It is a block diagram showing the functions of the computer 700 according to Embodiment 2 of the present invention.

Mode for Carrying Out the Invention

[0019] Hereinafter, this invention will be described based on the illustrated embodiments.

[0020] (Overview) The power distribution automation system according to an embodiment of the present invention is a system that monitors the power distribution system and controls switches, identifies the power outage area on the power distribution system when a power outage occurs, and generates power outage information such as the occurrence of a power outage and the restoration of power supply based on the results of monitoring and control. In this power distribution automation system, a remote control master station (master station) and a number of remote control slave stations (slave stations) are provided to manage and control multiple management areas, and the master station and slave stations are configured to communicate with each other. In a specific embodiment, this power distribution automation system is configured to include a power storage unit that stores electricity in the slave station, a solar power generation unit that generates electricity using sunlight, and a solar radiation measurement unit that measures the amount of sunlight and solar radiation at the installation location of the slave station. Furthermore, the master station predicts the amount of power generated by the solar power generation unit based on the measurement results of the amount of sunlight and solar radiation, estimates the discharge time of the power storage unit based on the predicted amount of power generated and the amount of power stored in the power storage unit, and displays the estimated discharge time of the power storage unit.

[0021] Furthermore, the power distribution automation system according to the embodiment of the present invention determines whether the installation location of the substation is suitable for predetermined conditions, specifically whether it is suitable for power generation by the solar power generation unit, based on the measurement results of the duration of sunlight and the amount of solar radiation.

[0022] Furthermore, the power distribution automation system according to an embodiment of the present invention estimates the discharge time of the energy storage unit based on a learning model that uses the past power generation amount of the solar power generation unit and the past discharge time of the energy storage unit as training data, taking the power generation amount and the amount of stored energy as input.

[0023] This configuration makes it possible to determine the discharge time of each energy storage unit, which varies depending on the geographical conditions of the installation location of the substation and the aging conditions of the energy storage unit, such as batteries. Furthermore, it makes it possible to provide information for determining the appropriateness of the installation location of the substation, such as whether it is suitable for solar power generation.

[0024] (Embodiment 1) <Structure> Figures 1 to 7 illustrate this embodiment, with Figure 1 being a schematic diagram showing the overall configuration of the power distribution automation system 1 according to Embodiment 1 of the present invention. This power distribution automation system 1 is a system composed of devices for monitoring the power distribution system and controlling switches. The power distribution automation system 1 comprises a plurality of slave stations (remote control slave stations) 2 and a master station (remote control master station) 3. The slave stations 2 and master station 3 are connected to a communication device via a wired or wireless network and are connected to each other in a manner that enables communication. When the slave stations 2 and master station 3 are connected via a wireless network, they are connected to the network by communicating with communication devices such as a wireless base station compatible with communication standards such as 4G, 5G, and LTE (Long Term Evolution), and a wireless LAN router compatible with wireless LAN (Local Area Network) standards such as IEEE (Institute of Electrical and Electronics Engineers) 802.11.

[0025] As shown in Figure 1, the substation 2 is, for example, attached to a utility pole D in a power distribution network and is a device for controlling the opening and closing of a switch (remote control switch) on the utility pole D. The substation 2 may be composed of a device including, for example, various computers (laptops, tablets, etc.).

[0026] The master station 3 is a device installed in each branch office of, for example, a power supply company, and monitors and controls the slave stations 2. The master station 3 may be composed of, for example, various computers (servers, desktops, laptops, etc.).

[0027] Figure 2 is a block diagram showing the functional configuration of the slave station 2 in Figure 1.

[0028] Substation 2 comprises, functionally, a communication unit 210, a photovoltaic power generation unit 220, a power storage unit 230, a sunshine measurement unit 240, a memory unit 250, and a control unit 260. As shown in Figure 2, the communication unit 210, photovoltaic power generation unit 220, power storage unit 230, sunshine measurement unit 240, memory unit 250, and control unit 260 are electrically connected by a bus or the like.

[0029] The communication unit 210 is a communication interface for wired or wireless communication, and any communication protocol may be used as long as communication between the units is possible. This communication unit 210 communicates using a communication protocol such as TCP / IP (Transmission Control Protocol / Internet Protocol).

[0030] The solar power generation unit 220 is a device that generates electricity using sunlight. The solar power generation unit 220 may be composed of devices such as solar panels that generate electricity by converting solar energy into electricity. The solar power generation unit 220 transmits the generated electricity to the energy storage unit 230.

[0031] The energy storage unit 230 is a device that stores electricity and discharges it by supplying power to each device of the slave station 2. The energy storage unit 230 is configured to store the electricity generated by the solar power generation unit 220 and discharge it through the operation of the communication unit 210, the sunlight measurement unit 240, the memory unit 250, and the control unit 260, and may be composed of a device such as a replaceable battery.

[0032] The solar radiation measurement unit 240 is a device that measures the duration of sunlight and the amount of solar radiation at the installation location of the sub-station 2. The solar radiation measurement unit 240 may be composed of a solar sun meter or other device. The solar radiation measurement unit 240 transmits the measurement data of the duration of sunlight and the amount of solar radiation to the control unit 260. The duration of sunlight is the number of hours (hours, minutes) that sunlight irradiates the installation location of the sub-station 2, and the amount of solar radiation is the amount of solar radiation energy per unit area (kW / m^2) at the installation location of the sub-station 2.

[0033] The memory unit 250 stores programs and input data for executing various control processes and functions within the control unit 260, and consists of memory including RAM (Random Access Memory) and ROM (Read Only Memory), and storage including HDD (Hard Disk Drive), SSD (Solid State Drive), and DRAM (Dynamic Random Access Memory). The memory unit 250 also temporarily or for a long period stores operational data such as the opening and closing of switches, the amount of energy stored in the energy storage unit 230, and measurement data of sunlight duration and solar radiation from the sunlight measurement unit 240, linked to data indicating the date and time.

[0034] The control unit 260 controls the overall operation of the slave station 2 by executing a program stored in the memory unit 250, and is composed of devices including a CPU (Central Processing Unit), MPU (Micro Processing Unit), GPU (Graphics Processing Unit), microprocessor, processor core, multiprocessor, ASIC (Application-Specific Integrated Circuit), and FPGA (Field Programmable Gate Array). The functions of the control unit 260 include controlling the opening and closing of switches in response to operation instructions from the master station 3, and transmitting operation data of the opened and closed switches, storage amount data from the energy storage unit 230, and measurement data of sunlight duration and solar radiation from the solar radiation measurement unit 240 to the master station 3 via the communication unit 210. These functions are activated by a program stored in the memory unit 250 and executed by the slave station 2.

[0035] Figure 3 is a block diagram showing the functional configuration of the master station 3 in Figure 1.

[0036] The master station 3 comprises a communication unit 310, a display unit 320, a storage unit 330, and a control unit 340. As shown in Figure 3, the communication unit 310, display unit 320, storage unit 330, and control unit 340 are electrically connected by a bus or the like.

[0037] The communication unit 310 is a communication interface for wired or wireless communication, and any communication protocol may be used as long as communication between the units is possible. This communication unit 310 communicates using a communication protocol such as TCP / IP.

[0038] The display unit 320 displays data such as images, videos, and text in accordance with the control of the control unit 340. The display unit 320 is implemented by, for example, a liquid crystal display (LCD), an organic electro-luminescence (EL) display, or the like.

[0039] The memory unit 330 stores programs and input data for executing various control processes and functions within the control unit 340, and consists of memory including RAM and ROM, and storage including HDD, SSD, and DRAM. The memory unit 330 also stores operation data for opening and closing switches, stored energy data, and measurement data for sunlight duration and solar radiation received from the slave station 2 (not shown in the figure). Furthermore, the memory unit 330 stores the learning model 331.

[0040] The learning model 331 is a learning model that estimates the discharge time of the energy storage unit 230 by taking data on the amount of power generated by the solar power generation unit 220 and data on the amount of energy stored by the energy storage unit 230 as input. It is an inference algorithm that has been trained (learned) using data showing past power generation amounts of the solar power generation unit 220 and past discharge times of the energy storage unit 230 as training data. The learning model 331 can be any machine learning model, deep learning model, or artificial intelligence model that has undergone appropriate training. For example, the learning model 331 is a learning model that extracts features from data showing past power generation amounts of the solar power generation unit 220 and past discharge times of the energy storage unit 230, recognizes patterns such as correlations between them, and learns from them. Alternatively, the learning model 331 may be a learning model that takes measurement data of sunlight duration and solar radiation as input and determines whether the installation location of the slave unit is suitable for predetermined conditions. The learning model 331 may be generated by learning performed as a function of the learning unit 346, which will be described later, or it may be a learning model generated as a result of learning performed by another device. The learning algorithm for generating the learning model will be described later.

[0041] The control unit 340 controls the overall operation of the slave station 2 by executing a program stored in the memory unit 330, and is composed of a CPU, MPU, GPU, microprocessor, processor core, multiprocessor, ASIC, FPGA, and other devices. The functions of the control unit 340 include an acquisition unit 341, a prediction unit 342, an estimation unit 343, a determination unit 344, a display control unit 345, and a learning unit 346. The acquisition unit 341, prediction unit 342, estimation unit 343, determination unit 344, display control unit 345, and learning unit 346 are activated by a program stored in the memory unit 330 and executed by the master station 3.

[0042] The acquisition unit 341 controls the process of acquiring data transmitted from the slave station 2, such as operation data for opening and closing switches, data on the amount of stored energy in the energy storage unit 230, and measurement data of sunlight duration and solar radiation amount, which are the measurement results of the solar radiation measurement unit 240. The acquisition unit 341 acquires this data from the slave station 2 via the communication unit 310.

[0043] The acquisition unit 341 may acquire this data from the slave station 2 by accessing the slave station 2 at a predetermined time, by having the slave station 2 transmit data at a predetermined time, or by maintaining a constant connection with the slave station 2.

[0044] The prediction unit 342 controls the process of predicting the amount of power generated by the photovoltaic power generation unit 220 based on the sunshine duration and solar radiation, which are measurement results of the sunshine measurement unit 240 acquired by the acquisition unit 341. For example, the prediction unit 342 predicts the amount of power generated by the photovoltaic power generation unit 220 from past sunshine duration and solar radiation for each installation location of the slave station 2. In this case, for example, weather data for the installation location of the slave station 2 may be acquired from an external server to predict the amount of power generated by the photovoltaic power generation unit 220.

[0045] The estimation unit 343 controls the process of estimating the discharge time of the energy storage unit 230 based on the power generation amount, which is the prediction result of the prediction unit 342, and the amount of energy stored in the energy storage unit 230, which is acquired by the acquisition unit 341. For each slave station 2, if the power supply to the slave station 2 is stopped due to a power outage or the like, the estimation unit 343 estimates the discharge time of the energy storage unit 230, i.e., the operating time of the slave station 2, based on the power generation amount of the solar power generation unit 220 predicted by the prediction unit 342 and the amount of energy stored in the energy storage unit 230.

[0046] In a given scenario, the estimation unit 343 may estimate the dischargeable time of the energy storage unit 230 for each slave station 2 based on the learning model 331. In this case, the estimation unit 343 performs the estimation based on the inference algorithm of the learning model 331, which uses data showing the past power generation amount of the solar power generation unit 220 and the past dischargeable time of the energy storage unit 230 as training data.

[0047] Specifically, the estimation of the dischargeable time of the energy storage unit 230 for each slave station 2 by the estimation unit 343 based on the learning model 331 is performed as follows: The estimation unit 343 recognizes patterns such as correlations between past power generation data and energy storage data stored in the learning model 331 from the predicted power generation data and acquired energy storage data. At this time, the estimation unit 343 extracts features from the predicted power generation data and acquired energy storage data and recognizes patterns such as correlations between past power generation data and energy storage data. Then, the estimation unit 343 estimates the dischargeable time of the energy storage unit 230 from the recognized patterns of power generation data and energy storage data.

[0048] The determination unit 344 controls the process of determining whether the installation location of the sub-station 2 is suitable for predetermined conditions, based on the data of sunshine duration and solar radiation, which are measurement results of the sunshine measurement unit 240 and acquired by the acquisition unit 341. For example, the determination unit 344 may determine, based on the sunshine duration and solar radiation data, for each sub-station 2 whether it is a suitable location for power generation by the photovoltaic power generation unit 220, or whether installation, expansion, removal, etc., of the photovoltaic power generation unit 220 is necessary.

[0049] Furthermore, the determination unit 344 may determine, for example, whether the installation location of the substation 2 is suitable for a predetermined use such as agriculture (e.g., whether it is suitable for growing a specific crop) based on the sunshine duration and solar radiation data acquired by the acquisition unit 341.

[0050] The display control unit 345 controls the process of displaying the dischargeable time of the energy storage unit 230, which is the estimation result of the estimation unit 343. The display control unit 345 causes the display unit 320 to display, for example, the dischargeable time of the energy storage unit 230 for each slave station 2. At this time, the display control unit 345 may display the entire power distribution system managed by the power distribution automation system 1, show the slave stations 2 and the master station 3, and display the dischargeable time for each slave station 2.

[0051] Figure 4 is a screen view showing an example of the display of the dischargeable time shown on the display unit 320 in Figure 3.

[0052] The screen diagram shown in Figure 4 is a display screen 40 that shows an example of the display of the dischargeable time displayed on the display unit 320. As shown in Figure 4, the display screen 40 displays a system diagram 41 showing the power distribution system. The display screen 40 also displays a slave station display 42 that represents slave station 2 in the system diagram 41, and a time display 43 showing the dischargeable time is displayed for each slave station 2 (slave station display 42). With this configuration of the display screen 40, the dischargeable time of the energy storage unit 230 can be determined for each slave station 2, so it is possible to use this information for power restoration operations up to the dischargeable time (operable time for the slave station) and perform operations efficiently.

[0053] The learning unit 346 trains (learns) the learning model 331 using data indicating the past power generation amount of the solar power generation unit 220 and the past dischargeable time of the energy storage unit 230 as training data, and controls the process of generating or updating the learning model 331 based on the learning results. At this time, the learning unit 346 may update the existing learning model 331 by retraining, or it may generate a new learning model.

[0054] Learning in the learning unit 346 may be performed using any machine learning algorithm, deep learning algorithm, etc., with data showing the past power generation amount of the solar power generation unit 220 and the past discharge time of the energy storage unit 230 as training data. In addition, in the learning in the learning unit 346, data processing (annotation) may be performed on the training data, which is the past power generation amount data and the data showing the discharge time of the energy storage unit 230, for example, by adding the interpretation of the data, for example, the labeling result indicating the weather conditions at the installation location of the slave station 2, as tag information. Furthermore, when updating the learning model 331, the learning unit 346 may update it by retraining, or it may be configured to update only when the performance of the learning model 331 has improved after a performance evaluation.

[0055] <Processing flow> Referring to Figures 5 through 7, an example of the control processing (monitoring method) flow by the power distribution automation system 1 will be explained. Figure 5 is a flowchart showing the estimation processing procedure by the control unit 340 in Figure 3.

[0056] As part of the process in step S101, the acquisition unit 341 of the control unit 340 acquires data transmitted from the slave station 2, such as operation data of opening and closing the switch, the amount of stored energy in the energy storage unit 230, and measurement data of sunlight duration and solar radiation amount, which are the measurement results of the sunlight measurement unit 240.

[0057] In step S102, the prediction unit 342 of the control unit 340 predicts the amount of power generated by the photovoltaic power generation unit 220 based on the sunshine duration and solar radiation, which are the measurement results of the sunshine measurement unit 240 obtained in step S101. Specifically in step S102, the amount of power generated by the photovoltaic power generation unit 220 is predicted for each installation location of the slave station 2 based on past sunshine duration and solar radiation.

[0058] In step S103, the estimation unit 343 of the control unit 340 estimates the discharge time of the energy storage unit 230 based on the predicted amount of power generated in step S102 and the amount of energy stored in the energy storage unit 230 acquired in step S101. Specifically in step S103, for each slave station 2, if the power supply to the slave station 2 is stopped due to a power outage or the like, the estimation unit 342 estimates the discharge time of the energy storage unit 230, i.e., the operating time of the slave station 2, based on the predicted amount of power generated by the solar power generation unit 220 and the amount of energy stored in the energy storage unit 230.

[0059] In step S104, the determination unit 344 of the control unit 340 determines whether the installation location of the sub-station 2 is suitable for predetermined conditions, based on the data of sunshine duration and solar radiation, which are the measurement results of the sunshine measurement unit 240 obtained in step S101. Specifically in step S104, for example, a determination may be made for each sub-station 2 based on the data of sunshine duration and solar radiation to determine whether the location is suitable for power generation by the photovoltaic power generation unit 220, and whether installation, expansion, removal, etc., of the photovoltaic power generation unit 220 is necessary.

[0060] As part of the process in step S105, the display control unit 345 of the control unit 340 displays the estimated discharge time of the energy storage unit 230, which was determined in step S103, on the display unit 320.

[0061] Figure 6 is a flowchart showing the learning process performed by the control unit 340 in Figure 3. The learning process shown in Figure 6 may be performed automatically at predetermined intervals (e.g., quarterly, annually), or it may be performed at any time by user instruction.

[0062] As part of the process in step S201, the acquisition unit 231 of the control unit 340 acquires data transmitted from the slave station 2, such as operation data of opening and closing the switch, the amount of stored energy in the energy storage unit 230, and measurement data of the amount of sunlight and solar radiation, which are the measurement results of the solar radiation measurement unit 240, similar to the process in step S101.

[0063] As part of the process in step S202, the control unit 340 acquires data indicating the dischargeable time of the energy storage unit 230, which is the estimated result in step S103.

[0064] As part of step S203, the learning unit 346 of the control unit 340 learns data showing the past power generation amount of the solar power generation unit 220 and the past dischargeable time of the energy storage unit 230 as training data, and generates or updates the learning model 331 based on the learning results.

[0065] As part of step S204, the learning unit 346 of the control unit 340 updates the learning model 331 based on the learning results performed in step S203. Note that if a new learning model different from the learning model 331 is to be generated, the process in step S204 does not need to be performed. As a result, the learning model 331 is updated to the latest learning state.

[0066] The learning and inference procedures by the learning unit 346 will be explained with reference to Figure 7. Figure 7 is a block diagram showing the machine learning and inference procedures by the learning unit 346 in Figure 3.

[0067] As part of the process in step S301, the acquisition unit 341 of the control unit 340 collects training data for learning. Examples of training data include data showing the past power generation amount of the solar power generation unit 220 and the past dischargeable time of the energy storage unit 230, and may also include weather data for the installation location of the slave station 2.

[0068] As part of step S302, the learning unit 346 of the control unit 340 performs learning using an arbitrary machine learning algorithm, deep learning algorithm, etc., with the training data collected in step S301, and generates or updates the learning model 331.

[0069] As part of the process in step S303, the acquisition unit 341 of the control unit 340 acquires power generation data, which is the prediction result of the prediction unit 342, and energy storage data from the energy storage unit 230, which are input data for performing inference.

[0070] As part of the process in step S304, the estimation unit 343 of the control unit 340 takes the power generation amount data and the energy storage amount data acquired in step S303 as input and estimates the dischargeable time of the energy storage unit 230 for each slave station 2.

[0071] As part of the processing in step S305, the estimation unit 343 of the control unit 340 outputs the estimation result from step S304.

[0072] <Effects> According to Embodiment 1 of the power distribution automation system 1 and monitoring method, the slave station 2 is configured to include a power storage unit 230 for storing electricity, a solar power generation unit 220 for generating electricity using sunlight, and a solar radiation measurement unit 240 for measuring the duration of sunlight and the amount of solar radiation at the installation location of the slave station 2. The master station 3 is configured to include a prediction unit 342 for predicting the amount of power generated by the solar power generation unit 220 based on data showing the measurement results of the duration of sunlight and the amount of solar radiation, an estimation unit 343 for estimating the dischargeable time of the power storage unit 230 (operable time of the slave station 2) based on the predicted amount of power generated and the amount of energy stored in the power storage unit 230, and a display control unit 345 for displaying the estimated dischargeable time of the power storage unit 230 on a display unit 320. Therefore, it is possible to understand the different dischargeable times of the power storage unit 230 based on different amounts of energy stored due to geographical conditions of the installation location of the slave station 2, the aging conditions of the power storage unit 230 which is composed of batteries, etc. This allows for efficient operation of power restoration during the dischargeable time (the working time of sub-station 2) after a power outage.

[0073] Furthermore, the power distribution automation system 1 and monitoring method include a determination unit 344 that determines whether the installation location of the sub-station 2 is suitable for predetermined conditions, specifically whether it is suitable for power generation by the solar power generation unit, based on the measurement results of the solar irradiance measurement unit 240 for solar irradiance duration and solar radiation. Therefore, it becomes possible to provide information for determining the appropriateness of the installation location of the sub-station 2, such as the suitability of performing solar power generation.

[0074] Furthermore, according to the power distribution automation system 1 and monitoring method, based on a learning model 331 that uses past power generation amounts from the solar power generation unit and past dischargeable times from the energy storage unit as training data, the dischargeable time of the energy storage unit 230 is estimated by taking the power generation amount from the solar power generation unit 220 and the amount of energy stored in the energy storage unit 230 as input. This makes it possible to estimate the dischargeable time of the energy storage unit more accurately using a learning model such as machine learning.

[0075] (Embodiment 2 (Program)) Figure 8 is a block diagram showing an example of the configuration of a computer (electronic computer) 700 according to Embodiment 2 of the present invention. The computer 700 comprises a CPU 701, a main memory 702, an auxiliary memory 703, and an interface 704.

[0076] Here, we will describe in detail the programs for realizing each function that constitutes the acquisition unit 341, prediction unit 342, estimation unit 343, determination unit 344, display control unit 345, and learning unit 346 according to Embodiment 1. These functional blocks are implemented in the computer 700. The operation of each of these components is stored in auxiliary storage device 703 in the form of a program. The CPU 701 reads the program from auxiliary storage device 703, loads it into main memory device 702, and executes the above processing according to the program. The CPU 701 also allocates a memory area in main memory device 702 corresponding to the above-mentioned memory unit according to the program.

[0077] Specifically, the program is a program that uses a computer 700 to perform the following steps: predict the amount of power generated by a solar power generation unit, which generates power to charge a power storage unit that stores electricity using sunlight and supplies power to the device of the slave station, based on the measurement results of the amount of sunlight and solar radiation at the installation location of the slave station; estimate the discharge time of the power storage unit based on the predicted amount of power generated and the amount of electricity stored in the power storage unit; and display the estimated discharge time of the power storage unit.

[0078] The CPU 701 is hardware for executing the instruction set described in the program, and consists of an arithmetic unit, registers, peripheral circuits, etc. The CPU 701 consists of at least one processor, which is typically a microprocessor, but may be other types of processors including MPUs, GPUs, microprocessors, processor cores, and multiprocessors. At least one processor may be single-core or multi-core. Also, at least one processor may be a broader processor such as a hardware circuit that performs some or all of the processing (e.g., FPGA or ASIC).

[0079] The main memory 702 is for temporarily storing programs and data processed by programs, etc., and is, for example, a memory such as DRAM. The main memory 702 consists of one or more memories, which are typically composed of main memory devices, and at least one of the memories may be volatile memory or non-volatile memory.

[0080] The auxiliary storage device 703 is an example of a non-temporary tangible medium. Other examples of non-temporary tangible media include magnetic disks, magneto-optical disks, CD-ROMs, DVD-ROMs, and semiconductor memory connected via the interface 704. Furthermore, if this program is distributed to computer 700 via a network, the receiving computer 700 may expand the program into main memory 702 and execute the above processing.

[0081] Furthermore, the program may be intended to implement some of the functions described above. In addition, the program may be a so-called differential file (differential program) that implements the functions described above in combination with other programs already stored in the auxiliary storage device 703.

[0082] Although embodiments of this invention have been described in detail above, the specific configuration is not limited to these embodiments, and any design changes, etc., that do not depart from the spirit of this invention are also included. [Explanation of Symbols]

[0083] 1: Power distribution automation system 2: Substation 3: Master station 210: Communications Department 220: Solar Power Generation Department 230: Energy storage unit 231: Acquisition Department 240: Sunlight Measurement Unit 250: Storage section 260: Control Unit 310: Communications Department 320:Display section 330: Storage section 331: Learning Model 340: Control Unit 341: Acquisition Department 342: Prediction Department 343:Estimation Department 344: Judgment section 345: Display Control Unit 346: Learning Department 700: Computer 701: CPU 702: Main memory 703 :Auxiliary storage device 704: Interface

Claims

1. A power distribution automation system including a plurality of substations installed in a power distribution system and a master station that monitors and controls the substations, The substation comprises a power storage unit that stores electricity and supplies it to the substation's equipment, a solar power generation unit that generates electricity to charge the power storage unit using sunlight, and a solar radiation measurement unit that measures the duration of sunlight and the amount of solar radiation at the installation location of the substation. The master station comprises: a prediction unit that predicts the amount of power generated by the photovoltaic power generation unit based on the measurement results of the sunshine duration and solar radiation of the sunshine measurement unit; an estimation unit that estimates the dischargeable time of the energy storage unit based on the amount of power generated, which is the prediction result of the prediction unit, and the amount of energy stored in the energy storage unit; and a display unit that displays the dischargeable time of the energy storage unit, which is the estimation result of the estimation unit. A power distribution automation system characterized by the following features.

2. The aforementioned base station further, The system includes a determination unit that determines whether the installation location of the substation is suitable for predetermined conditions, based on the measurement results of the sunshine duration and solar radiation from the sunshine measurement unit. The power distribution automation system according to claim 1.

3. The determination unit determines whether the installation location of the substation is suitable for power generation by the solar power generation unit. The power distribution automation system according to claim 2.

4. The estimation unit estimates the discharge time of the energy storage unit based on a learning model that uses the past power generation amount of the solar power generation unit and the past discharge time of the energy storage unit as training data, taking the power generation amount of the solar power generation unit and the amount of energy stored in the energy storage unit as input. The power distribution automation system according to any one of claims 1 to 3.

5. A monitoring method for monitoring a power distribution automation system, which is executed on a computer having a processor and memory, and includes a plurality of slave stations installed in a power distribution system, and a master station that monitors and controls the slave stations, The aforementioned processor, The steps include: predicting the amount of power generated by a solar power generation unit, which generates power to charge a power storage unit that stores electricity using sunlight and supplies power to the device of the aforementioned substation, based on the measurement results of the duration of sunlight and the amount of solar radiation at the installation location of the aforementioned substation; A step of estimating the discharge time of the energy storage unit based on the predicted amount of power generated and the amount of energy stored in the energy storage unit, The steps include: displaying the estimated dischargeable time of the energy storage unit; and performing the following steps. A monitoring method characterized by the following features.

6. A program for monitoring a power distribution automation system, which includes a plurality of slave stations installed in a power distribution system and a master station that monitors and controls the slave stations, which is executed on a computer having a processor and memory, The aforementioned processor, The steps include: predicting the amount of power generated by a solar power generation unit, which generates power to charge a power storage unit that stores electricity using sunlight and supplies power to the device of the aforementioned substation, based on the measurement results of the duration of sunlight and the amount of solar radiation at the installation location of the aforementioned substation; A step of estimating the discharge time of the energy storage unit based on the predicted amount of power generated and the amount of energy stored in the energy storage unit, The steps include: displaying the estimated dischargeable time of the energy storage unit; and performing the following steps. A program characterized by the following features.

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