Management methods, servers, programs, and power management systems for tapped transformers.

Abstract

To provide a method of managing a tapped transformer, a server, a program, and a power supply management system in which the tapped transformer is appropriately and easily managed.SOLUTION: A method of managing a tapped transformer includes: selecting a target parameter mutually related to the number of tap changing of the tapped transformer among a plural types of parameters related to a power supplied from a power system to the tapped transformer (S12 and S13); acquiring a predicted value of the target parameter in a future period (S14); and acquiring a first probability density function of a usable period of the tapped transformer by using the predicted value of the target parameter (S15-S19).SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present disclosure relates to a method for managing a tapped transformer, a server, a program, and a power management system.

Background Art

[0002] A tapped transformer is used for voltage adjustment in a substation of a power system. The tapped transformer adjusts the voltage by tap switching. As the number of tap switches increases, the deterioration of the tapped transformer tends to progress. Therefore, a monitoring device is provided for the maintenance of the tapped transformer. The monitoring device calculates the number of tap switches of the tapped transformer and displays the obtained number of tap switches. An operator (TSO: system operator) of the power system visually confirms the number of tap switches displayed by the monitoring device regularly (for example, once every few months or once every six months), and replaces the parts of the tapped transformer before the number of tap switches exceeds a predetermined durable switching number. An example of a system operator is a power company.

[0003] Japanese Patent Application Laid-Open No. 2017-158341 (Patent Document 1) discloses a technique for calculating an amount corresponding to the operable number of times of a tapped transformer and displaying the obtained amount corresponding to the operable number of times. The amount corresponding to the operable number of times is an amount corresponding to the remaining number of tap switches until the durable switching number. An operator (system operator) regularly checks the amount corresponding to the operable number of times, and replaces the parts (for example, a switching disconnector) of the tapped transformer when the displayed amount corresponding to the operable number of times approaches 0 (zero).

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] Patent Document 1 states the formula "NIm=(Im 2 / 200000In 2 ) × 600000" or "NIm = (Im 2 / 100000In 2 The number of possible operating cycles is calculated using "(x) × 700000". In each equation, "NIm" represents the number of cycles of mechanical degradation due to electrical degradation, "Im" represents the load current at the mth switching cycle, and "In" represents the rated current. However, these equations do not necessarily hold true for all tapped transformers. Depending on the tapped transformer, the above equations may not hold true due to individual differences in the device.

[0006] Furthermore, the number of tap changes per day can vary depending on the daily power grid conditions. Therefore, even if the technology described in Patent Document 1 is applied to a power management system equipped with a tapped transformer, it is difficult for the user to know how many more days the tapped transformer will be usable before it becomes unusable.

[0007] Due to the factors described above, the technology described in Patent Document 1 may not be able to properly manage tapped transformers.

[0008] Therefore, the primary purpose of this disclosure is to provide a method for managing tapped transformers, a server, a program, and a power management system that facilitates the proper management of tapped transformers. [Means for solving the problem]

[0009] The tapped transformer management method of this disclosure includes selecting a target parameter from among several types of parameters relating to the power supplied to the tapped transformer from the power system, which is interrelated with the number of tap changes of the tapped transformer; obtaining a predicted value of the target parameter over a future period; and obtaining a probability density function of the usable period of the tapped transformer using the predicted value of the target parameter.

[0010] The power management system disclosed herein comprises one or more tapped transformers installed in a power grid and a server that manages one or more tapped transformers. The server is configured to select target parameters from among several types of parameters relating to the power supplied from the power grid to the tapped transformers that are interrelated with the number of tap changes of the tapped transformers, to obtain predicted values ​​of the target parameters over a future period, and to obtain a probability density function of the usable period of the tapped transformers using the predicted values ​​of the target parameters. [Effects of the Invention]

[0011] This disclosure makes it easier to properly manage tapped transformers. [Brief explanation of the drawing]

[0012] [Figure 1] This figure shows a schematic configuration of a power management system according to an embodiment of the present disclosure. [Figure 2] This flowchart shows the procedure for a method of managing a tapped transformer according to an embodiment of the present disclosure. [Figure 3] This figure shows the history data of a tapped transformer in the embodiment of the disclosure. [Figure 4] This figure illustrates clustering and target parameter selection in the embodiments of this disclosure. [Figure 5] This figure illustrates the process for obtaining a second probability density function in an embodiment of the present disclosure. [Figure 6] This figure illustrates the process for obtaining a first probability density function from a second probability density function in an embodiment of the present disclosure. [Figure 7] This is a flowchart illustrating the first control using the first probability density function in an embodiment of the present disclosure. [Figure 8] This flowchart shows a second control using a first probability density function in an embodiment of the present disclosure. [Modes for carrying out the invention]

[0013] Hereinafter, embodiments will be described in detail with reference to the drawings. In the following, the same or corresponding parts in the drawings are denoted by the same reference numerals, and the description thereof will not be repeated in principle.

[0014] FIG. 1 is a diagram showing a schematic configuration of a power management system according to the present embodiment. As shown in FIG. 1, the power management system 1 includes a server 100, a HMI (Human Machine Interface) 210, a mobile terminal 220, transformers 10A to 10C, monitoring devices 20A to 20C, and power generation facilities 30A to 30C. A power system for supplying power to each consumer (such as a house or a factory) is constructed by a power plant (including power generation facilities 30A to 30C) and a power transmission and distribution network (including transformers 10A to 10C).

[0015] The server 100 is configured to be communicable with each of the monitoring devices 20A to 20C via the cloud CL. The server 100 is configured to collect information regarding the transformers 10A, 10B, and 10C from the monitoring devices 20A, 20B, and 20C via the cloud CL. Note that the communication system is not limited to the mode shown in FIG. 1. The server 100 may be connected to each of the monitoring devices 20A to 20C via a communication network (for example, the Internet) without going through the cloud CL. The connection mode may be either wired or wireless.

[0016] The server 100 includes a processor 110, a RAM (Random Access Memory) 120, and a storage device 130. Examples of the processor 110 include a CPU (Central Processing Unit). The storage device 130 is configured to be able to store the stored information. In the present embodiment, various controls (for example, the control shown in FIG. 2 described later) are executed by the processor 110 executing the program stored in the storage device 130. The number of processors included in the server 100 is arbitrary and may be one or more.

[0017] The HMI 210 functions as an interface between the user and the server 100. The HMI 210 includes an input device and a display device. The display device is controlled by the server 100. The HMI 210 may include a touch panel display. The HMI 210 may further include a smart speaker that receives voice input, or a display device that performs AR (augmented reality) display.

[0018] The mobile terminal 220 is a terminal that can be carried by a user (e.g., an administrator of a tapped transformer). The mobile terminal 220 is configured to be communicable with the server 100. In this embodiment, a smartphone equipped with a touch panel display is adopted as the mobile terminal 220. The smartphone incorporates a computer and has a speaker function. However, it is not limited thereto, and any mobile terminal can be adopted as the mobile terminal 220. For example, a laptop, a tablet terminal, a portable game machine, a wearable device (e.g., a smartwatch, smart glasses, or smart gloves), an electronic key, etc. can also be adopted as the mobile terminal 220.

[0019] The transformers 10A to 10C are provided in a power transmission and distribution network for supplying predetermined power to consumers. The transformers 10A, 10B, and 10C receive power supply from the power generation facilities 30A, 30B, and 30C via the power transmission network, respectively. And each of the transformers 10A to 1ZC steps down the power from the power generation facility side (e.g., power with a voltage of 66 kV) and outputs the stepped-down power (e.g., power with a voltage of 6.6 kV) to the consumer side. The stepped-down power is sent to the consumers through the distribution network. In this embodiment, the power generation facility 30A is a renewable energy power plant, the power generation facility 30B is a nuclear power plant, and the power generation facility 30C is a thermal power plant. The renewable energy power plant is configured to generate electricity using renewable energy (RE: Renewable Energy). Examples of the renewable energy power plant include a solar power plant, a wind power plant, a hydroelectric power plant, a geothermal power plant, or a biomass power plant. However, it is not limited thereto, and the power generation facilities 30A to 30C may be power plants with the same power generation method.

[0020] Each of the transformers 10A to 10C is an on-load tap changer (LTC). In this embodiment, transformers 10A, 10B, and 10C have the same structure, so below, unless distinguished, they will be referred to as "transformer 10". In this embodiment, transformer 10 is a vacuum valve type LTC. An LTC switches winding connections (taps) to adjust the voltage (change the turns ratio) in the operating state of the transformer. Specifically, transformer 10 includes a changeover switch 11 that switches the energized current while the transformer is operating, and a tap selector 12 connected to the transformer windings. The changeover switch 11 includes a vacuum valve with contacts sealed inside a vacuum container. The tap selector 12 selects the tap to operate from a plurality of terminals (taps) connected to the transformer windings, and the changeover switch 11 switches the circuit while energized to the selected tap. The vacuum valve described above suppresses localized heating caused by arcing when energized contacts open. It is not necessary for transformers 10A to 10C managed by server 100 to have the same structure; these transformers may have different structures.

[0021] Monitoring devices 20A, 20B, and 20C are configured to monitor transformers 10A, 10B, and 10C, respectively. In this embodiment, since monitoring devices 20A, 20B, and 20C have the same structure, they will be referred to as "monitoring device 20" below unless otherwise distinguished. The monitoring device 20 is installed near the corresponding transformer 10. The monitoring device 20 includes an input sensor 21 that detects parameters (voltage, current, phase, frequency, etc.) related to the power supplied to the transformer 10 from the power grid, an output sensor 22 that detects parameters (voltage, current, phase, frequency, etc.) related to the power output from the transformer 10 to the consumer (power load) side, a counter 23 that measures the number of tap changes of the transformer 10, and a controller 25 that controls the transformer 10. The controller 25 may be a control circuit or a computer. The controller 25 is configured to communicate with the server 100. The controller 25 controls the transformer 10 so that a stable power having a predetermined voltage is supplied to the consumer side, for example, based on the detection results from the input sensor 21 and the output sensor 22. Hereinafter, parameters related to the power supplied to the transformer 10 from the power system will be referred to as "system parameters." The values ​​of the system parameters measured using the detection results from the input sensor 21 will be referred to as "system measured values." The controller 25 may also calculate the active power using the detection results from the input sensor 21 and the output sensor 22. Active power is an example of a system parameter, and the calculated value of active power is an example of a system measured value.

[0022] The monitoring device 20 sequentially uploads (transmits) data related to the corresponding transformer 10 to the cloud CL (storage device). In this embodiment, the monitoring device 20 measures (counts) the number of tap changes of the transformer 10 and records the obtained measured value of the tap change count (first measured value) in the cloud CL (storage device) linked to the measurement date (date). Specifically, as shown in Figure 1, monitoring devices 20A, 20B, and 20C record the measured values ​​Na, Nb, and Nc of the tap change count for transformers 10A, 10B, and 10C, respectively, in the cloud CL. In addition, the monitoring device 20 measures several types of system parameters, including voltage, current, phase, frequency, and active power, and records these system measured values ​​(second measured values) in the cloud CL (storage device) linked to the measurement time. Specifically, as shown in Figure 1, monitoring devices 20A, 20B, and 20C record the measured system parameter values ​​Ga, Gb, and Gc for transformers 10A, 10B, and 10C, respectively, to the cloud CL. Server 100 can download (receive) data for each of the transformers 10A to 10C (including measured values ​​Na, Nb, Nc and Ga, Gb, Gc) from the cloud CL.

[0023] Figure 2 is a flowchart showing the procedure for managing a tapped transformer according to this embodiment. The series of processes shown in this flowchart are executed for each transformer managed by the server 100. Hereinafter, the transformer subject to processing (for example, any of the transformers 10A to 10C shown in Figure 1) will be referred to as the "target transformer". In this embodiment, when a predetermined usage period has elapsed since the start of use of the target transformer, the server 100 executes the series of processes shown in Figure 2, and thereafter executes the series of processes shown in Figure 2 each time a predetermined unit period has elapsed. That is, the series of processes shown in Figure 2 are executed repeatedly at regular intervals. In this embodiment, the usage period is set to one year, and the unit period is set to one month. However, these periods can be set arbitrarily.

[0024] As shown in Figure 2, in step S11, the server 100 first acquires historical data of the target transformer for a past period (hereinafter referred to as the "evaluation period"). For example, if the target transformer is transformer 10A, the historical data of the target transformer includes the measured values ​​Na and Ga (Figure 1) during the evaluation period. The evaluation period can be set arbitrarily. In this embodiment, the evaluation period is defined as the period from the present to a predetermined time (more specifically, 6 months) prior to the present. However, the length of the evaluation period is not limited to 6 months and can be changed as appropriate.

[0025] Figure 3 shows an example of the historical data of the target transformer acquired in step S11. As shown in Figure 3, the historical data D10 of the target transformer in this embodiment includes daily measurements of the number of tap changes of the target transformer and several types of system parameters during the evaluation period (the most recent 6 months). Each measurement is linked to a date. These measurements are taken by the monitoring device 20 and uploaded to the cloud CL. The server 100 acquires the historical data D10 from the cloud CL. The several types of system parameters in the historical data D10 include, for example, voltage, current, phase, frequency, and active power. Note that the historical data of the target transformer acquired in step S11 is not limited to the example shown in Figure 3. The data interval is not limited to one day and can be arbitrary. For example, the data interval may be set to two days and the average measurement value over two days may be linked to the dates of those two days.

[0026] In the series of processes shown in Figure 2, once the process in step S11 is executed, the process proceeds to step S12. In step S12, the server 100 sequentially assigns each system parameter in the history data D10 and sequentially performs data clustering that shows the relationship between one system parameter and the number of tap changes. Subsequently, in step S13, the server 100 selects a system parameter (hereinafter referred to as the "target parameter") that is interrelated with the number of tap changes of the target transformer from among multiple types of system parameters in the history data D10. Figure 4 is a diagram illustrating the above clustering (step S12) and target parameter selection (step S13).

[0027] As shown in Figure 4, in this embodiment, the server 100 sequentially assigns system parameters in the historical data D10, such as the first parameter, second parameter, third parameter, etc., and plots data on a coordinate plane showing the relationship between the measured values ​​of the system parameters and the measured values ​​of the number of tap changes per day. For example, the voltage, current, phase, etc. of the power supplied from the power system to the target transformer correspond to the first parameter, second parameter, third parameter, etc., respectively. In this embodiment, a number of data points corresponding to the number of days in six months are plotted.

[0028] In the example shown in Figure 4, the first data set, which contains multiple data points showing the relationship between the measured value of the first parameter and the measured value of the tap switching count, is classified into three groups (clusters) by clustering. Specifically, the first data set is divided into data P11 belonging to the first cluster, data P12 belonging to the second cluster, and data P13 belonging to the third cluster. In the example shown in Figure 4, the first data set is clearly divided into three clusters. Moreover, the first parameter and the tap switching count have a relationship in which the first parameter increases as the tap switching count increases. This means that there is a high correlation between the first parameter and the tap switching count.

[0029] Furthermore, the second data set, which includes multiple data points showing the relationship between the measured value of the second parameter and the measured value of the tap switching count, is also divided into data points P21, P22, and P23 belonging to the first, second, and third clusters, respectively, through clustering. However, the division of the second data set into three clusters is insufficient, and the relationship between the second parameter and the tap switching count remains unclear. This means that the correlation between the second parameter and the tap switching count is low.

[0030] In step S13 of Figure 2, Server 100 uses the clustering results described above to select the system parameter with the highest correlation to the tap switching count from among multiple system parameters in the historical data D10 as the target parameter. For example, Server 100 may determine that the clearer the cluster division, the higher the correlation. Alternatively, Server 100 may determine that the more uniform the change in system parameters with increasing tap switching count, the higher the correlation between the two. For example, if system parameters increase as the number of tap switching counts increase, the correlation is high. Similarly, if system parameters decrease as the number of tap switching counts increases, the correlation is also high. If system parameters increase or decrease independently of the trend in the change in the number of tap switching counts, the correlation is low. Note that the number of clusters in clustering is not limited to three and can be changed as appropriate.

[0031] In the series of processes shown in Figure 2, once the process in step S13 is executed, the process proceeds to step S14. In step S14, the server 100 obtains predicted values ​​of the target parameters for a future period (hereinafter referred to as the "prediction period"). The server 100 may predict the daily values ​​of the target parameters during the prediction period based on weather forecast information showing daily weather conditions during the prediction period (e.g., weather, temperature, solar radiation intensity, and wind speed) and historical target parameter information showing past values ​​of the target parameters (e.g., values ​​from the previous year of the prediction period). The server 100 may also predict the amount of power demanded to be consumed on the output side of the target transformer and predict the values ​​of the target parameters based on the predicted amount of power demand. The server 100 may also predict the amount of power generated on the input side of the target transformer and predict the values ​​of the target parameters based on the predicted amount of power generated. For predicting the target parameters, the server 100 may use a prediction program optimized for each target transformer, or it may use a trained model generated by AI (artificial intelligence) machine learning. Big data stored in the cloud CL may be used to train the model. The prediction period can be set arbitrarily. In this embodiment, the prediction period is defined as the period from the present time to a predetermined time (more specifically, 6 months) after that time has elapsed. However, the length of the prediction period is not limited to 6 months and can be changed as appropriate.

[0032] Next, in step S15, when the value of the target parameter at a certain timing is input, the server 100 generates an estimation model that outputs the number of tap switches at that timing. The server 100 may also generate an estimation model (regression model) that defines the relationship between the target parameter and the number of tap switches by regression analysis using the historical data D10. A high correlation between the target parameter and the number of tap switches makes it easier to obtain an estimation model with high accuracy. The generated estimation model is stored, for example, in the memory device 130. Note that the estimation model is not limited to a regression model, but may also be a trained model generated by machine learning using AI (artificial intelligence).

[0033] In the subsequent step S16, the server 100 uses the generated estimation model to obtain a predicted value for the number of tap changes during the prediction period from the predicted values ​​of the target parameter during the prediction period. Subsequently, in step S17, the server 100 obtains a predetermined number of bootstrap samples for the predicted number of tap changes during the prediction period using the bootstrap method. Subsequently, in step S18, the server 100 uses the predetermined number of bootstrap samples to obtain the probability density function (second probability density function) of the future number of tap changes per day for the target transformer.

[0034] Figure 5 is a diagram illustrating the process (steps S14 to S18) for obtaining the second probability density function described above. As shown in Figure 5, in this embodiment, prediction data D21 of the target parameter is obtained in step S14. The prediction data D21 of the target parameter includes the daily measured values ​​of the target parameter during the prediction period (the next 6 months). Server 100 inputs the prediction data D21 of the target parameter into the estimation model 150 generated in step S15. As a result, prediction data D22 of the tap switching count is output from the estimation model 150. The prediction data D22 of the tap switching count includes the daily measured values ​​of the tap switching count during the prediction period (the next 6 months). Server 100 obtains 5000 random bootstrap samples from the prediction data D22 of the tap switching count using the bootstrap method, allowing for 5000 overlaps. Server 100 obtains the probability density function L1 of the future daily tap switching count for the target transformer by creating a histogram of the mean values ​​of 5000 bootstrap samples.

[0035] In the series of processes shown in Figure 2, once the process in step S18 is executed, the process proceeds to step S19. In step S19, the server 100 uses the probability density function of the number of tap changes per day (second probability density function) obtained in step S18 to obtain the probability density function of the number of usable days of the target transformer (first probability density function). Figure 6 is a diagram illustrating the process (step S19) for obtaining the first probability density function. As shown in Figure 6, in this embodiment, the server 100 converts the probability density function L1 of the future number of tap changes per day of the target transformer into the probability density function L2 of the number of usable days of the target transformer. The number of usable days is the usable period expressed in "days" and is an example of the usable period. The usable period of the target transformer corresponds to the period from the current number of tap changes of the target transformer until the number of tap changes reaches the service life change count due to future use of the target transformer. For example, if the current number of tap changes is 10,000, the service life of the transformer is 200,000, and the number of tap changes per day is 10, then the usable days of the transformer can be calculated as follows: "Usable days = (200,000 - 10,000) / 10 = 19,000 days". Because the number of tap changes per day of the transformer and the usable days of the transformer have this relationship, the probability density function L2 can be obtained from the probability density function L1. If the probability density function L2 (first probability density function) is expressed as "f(x)" as a function of usable days (x), then equation (1) in Figure 6 holds. Therefore, the probability R that the usable days are between a and b days can be obtained by the definite integral of f(x) from a to b.

[0036] The probability density function L2 (first probability density function) for the number of usable days of the target transformer may be used, for example, in the control described below.

[0037] Figure 7 is a flowchart of the first control using the probability density function L2 obtained by the series of processes shown in Figure 2. The series of processes shown in this flowchart are executed by the server 100 each time a new probability density function L2 is obtained for the target transformer.

[0038] As shown in Figure 7, in step S21, the server 100 first uses the latest probability density function L2 to determine the probability that the target transformer will become unusable within a future period (hereinafter referred to as the "replacement period"), and determines whether the obtained probability is equal to or greater than the first reference value (hereinafter referred to as "Th1"). Reaching the service life number of tap changes for the target transformer means that the target transformer will become unusable. The replacement period can be set arbitrarily. In this embodiment, the period from the present to a predetermined time (more specifically, 4 weeks) is defined as the replacement period. However, the length of the replacement period is not limited to 4 weeks and can be changed as appropriate. Th1 can also be set arbitrarily. Th1 may be around 50%.

[0039] If the probability obtained in step S21 is less than Th1 (NO in step S21), the series of processes shown in Figure 7 ends. On the other hand, if the probability obtained in step S21 is Th1 or greater (YES in step S21), the process proceeds to step S22.

[0040] In step S22, the server 100 uses the latest probability density function L2 to determine the probability that the target transformer will become unusable for each of the multiple divisions into which the replacement period is divided, and displays the obtained probability for each division on the first display device (for example, the HMI 210 or mobile terminal 220 shown in Figure 1). In this embodiment, the replacement period (4 weeks) is divided into four divisions of one week each (first to fourth divisions). However, the number and length of the divisions can be changed as appropriate. The number of divisions may be two, three, or five or more.

[0041] The first display device displays, for example, screen Sc1, according to instructions from server 100. Screen Sc1 includes a display unit M11 that displays identification information of the target transformer (e.g., a unique number) and a display unit M12 that displays the probability for each category. By looking at screen Sc1, the user can estimate how many more days the target transformer will be used before it becomes unusable. In the example shown in Figure 7, the first display device displays the probability calculated based on the probability density function L2. However, it is not limited to this, and server 100 may also display the probability density function L2 itself on the first display device, for example, in the form of a graph.

[0042] In the following step S23, the server 100 determines whether at least one of the probabilities for each category obtained in step S22 is equal to or greater than the second criterion value (hereinafter referred to as "Th2"). Th2 can be set arbitrarily. Th2 is, for example, a lower value than Th1. Th2 may be around 25%. If the probabilities for all categories are less than Th2 (NO in step S23), the series of processes shown in Figure 7 ends. On the other hand, if the probability of any category is Th2 or greater (YES in step S23), the process proceeds to step S24.

[0043] In step S24, the server 100 requests the administrator to replace the target transformer. Specifically, the server 100 displays a message requesting the replacement of the target transformer on a second display device (for example, the HMI 210 or mobile terminal 220 shown in Figure 1). The first and second display devices may be the same or different. The second display device displays, for example, screen Sc2, according to instructions from the server 100. Screen Sc2 includes a display unit M21 that displays identification information of the target transformer and a display unit M22 that displays a message requesting the replacement of the target transformer. Screen Sc2 (display unit M22) shown in Figure 7 requests the replacement of the target transformer by specifying a time. In the example shown in Figure 7, the probability of the fourth category (October 22nd to October 28th) exceeds Th2. Therefore, the server 100 requests the administrator to replace the target transformer before the fourth category. This makes it easier to replace the target transformer before it becomes unusable. The methods for requesting an exchange are not limited to those described above. For example, one could request an exchange by voice.

[0044] Figure 8 is a flowchart of the second control using the probability density function L2 obtained by the series of processes shown in Figure 2. The control shown in Figure 8 is the same as the control shown in Figure 7, except that step S24A is used instead of step S24.

[0045] In step S24A, the server 100 requests the controller 25 of the target transformer to restrict the operation of the target transformer. The server 100 may specify a time when requesting the restriction of the target transformer's operation. The server 100 may determine when to start the restriction based on the latest probability density function L2. The server 100 may also determine the degree of the restriction based on the latest probability density function L2. Upon receiving the request from the server 100, the controller 25 modifies the control mode of the target transformer so that the number of tap changes of the target transformer is less than in the unrestricted state. The controller 25 may reduce the number of tap changes of the target transformer by, for example, reducing the sensitivity of the target transformer to voltage changes. Such a restriction prevents the number of tap changes of the target transformer from reaching the service life before the target transformer is replaced.

[0046] As described above, in the above embodiment, the server 100 obtains the probability density function (probability density function L2) of the number of usable days for the tapped transformer. Since the server 100 obtains the probability density function L2 for each tapped transformer, it can obtain a probability density function L2 that corresponds to the individual characteristics of the tapped transformer. The server 100 then notifies the user of the obtained probability density function L2 or changes the control mode of the tapped transformer based on the obtained probability density function L2. By using the probability density function L2 in this way, it becomes easier to properly manage the tapped transformer.

[0047] The method for managing a tapped transformer according to this embodiment includes the processes shown in Figure 2 and the processes shown in Figure 7 or Figure 8. Each of these processes is executed by the server 100 (computer). Specifically, each process is executed by one or more processors executing programs stored in one or more storage devices. However, these processes may be executed by hardware (electronic circuits) instead of software.

[0048] The processing flows shown in Figures 2, 7, and 8 can be modified as needed. For example, the order of processing may be changed, or unnecessary steps may be omitted depending on the purpose. Furthermore, the content of any of the processes may be changed.

[0049] In the above embodiment, an LTC (On-Load Tap-Changing Transformer) is used as the tapped transformer. However, the tapped transformer is not limited to an LTC; it may also be a Step Voltage Regulator (SVR). The server 100 may manage two or more types of tapped transformers (for example, an LTC and an SVR). The number of tapped transformers managed by the server 100 is arbitrary and may be as few as one.

[0050] In the above embodiment, one server 100 performs data collection related to the transformer and calculations to determine the probability density distribution. However, it is not limited to this, and the data collection device and the calculation device may be provided separately.

[0051] In the above embodiment, an on-premises server is given as an example of server 100 (see Figure 1). However, it is not limited to this, and the functions of server 100 (for example, the functions related to data collection and computation described above) may be implemented on the cloud through cloud computing. The estimation model may also be implemented on the cloud. The estimation model may be updated sequentially through learning on the cloud.

[0052] Within the scope of this disclosure, it is possible to combine the embodiments, or to modify or omit the embodiments as appropriate.

[0053] The various aspects of this disclosure are summarized below as an appendix.

[0054] (Note 1) From among several parameters relating to the power supplied from the power system to the tapped transformer, select the target parameter that is interrelated with the number of tap changes of the tapped transformer (for example, steps S12 and S13 in Figure 2), Obtaining predicted values ​​of the target parameter over a future period (for example, step S14 in Figure 2), Using the predicted values ​​of the target parameters, obtain a first probability density function for the usable period of the tapped transformer (for example, steps S15 to S19 in Figure 2), Management methods for tapped transformers, including those mentioned above.

[0055] The usable period of a tapped transformer can vary depending on the power system conditions. Therefore, even if the accuracy of predicting the usable period of a tapped transformer is high, there is still a possibility that the prediction will be wrong to some extent. If some work or control is performed based on the results of such a prediction, an unforeseen situation may occur if the prediction is wrong. Therefore, in the configuration described in Appendix 1 above, a probability density function (first probability density function) of the usable period of the tapped transformer is obtained. The user (person) or computer can know in advance the probability of the prediction being wrong using the first probability density function, thus making unforeseen situations less likely to occur. For this reason, obtaining the first probability density function makes it easier to properly manage the tapped transformer.

[0056] The aforementioned multiple types of parameters may include at least one of current, voltage, and active power.

[0057] (Note 2) Selecting the aforementioned target parameters is For each of the aforementioned multiple types of parameters, clustering of data showing the relationship with the number of tap switching operations is performed (for example, step S12 in Figure 2), Using the results of the clustering, the parameter with the highest correlation to the number of tap switches is selected from among the multiple types of parameters as the target parameter (for example, step S13 in Figure 2), The management method for tapped transformers, including the method described in Appendix 1.

[0058] According to the configuration described in Appendix 2 above, it becomes easier to select the appropriate target parameters for each tapped transformer.

[0059] (Note 3) Obtaining the first probability density function means When the value of the target parameter at a certain timing is input, an estimation model is used that outputs the number of tap switches at that timing to obtain a predicted value of the number of tap switches in the future period from the predicted value of the target parameter in the future period (for example, step S16 in Figure 2), Using the predicted value of the number of tap changes, obtain a second probability density function for the number of tap changes per day of the tapped transformer (for example, steps S17 and S18 in Figure 2), Converting the second probability density function to the first probability density function (for example, step S19 in Figure 2), A method for managing tapped transformers, including the methods described in Appendix 1 or 2.

[0060] According to the configuration described in Appendix 3 above, it becomes easier to obtain an appropriate first probability density function for each tapped transformer.

[0061] (Note 4) Obtaining the aforementioned second probability density function means Regarding the predicted value of the number of tap switching cycles, a bootstrap sample is obtained using the bootstrap method (for example, step S17 in Figure 2), Obtaining the second probability density function using the bootstrap sample (for example, step S18 in Figure 2), The management method for tapped transformers, including the method described in Appendix 3.

[0062] The configuration described in Appendix 4 above makes it easier to obtain the second probability density function easily and accurately.

[0063] (Note 5) A server that performs the management method for tapped transformers described in one of the appendices 1 to 4.

[0064] (Note 6) A program that causes a computer to execute the management method for a tapped transformer described in one of the appendices 1 to 4.

[0065] The above server and program make it easier to properly implement the tapped transformer management method described above.

[0066] In one configuration, a server is provided that includes a storage device for storing the above-mentioned program and a processor for executing the program stored in the storage device. In another configuration, a server is provided that distributes the above-mentioned program.

[0067] (Note 7) A power management system comprising one or more tapped transformers installed in a power system and a server that manages the one or more tapped transformers, The aforementioned server, From among several types of parameters relating to the power supplied from the power system to the tapped transformer, select the target parameter that is interrelated with the number of tap changes of the tapped transformer, Obtaining predicted values ​​of the aforementioned target parameters over a future period, Using the predicted values ​​of the target parameters, the probability density function of the usable period of the tapped transformer is obtained, A power management system configured to perform the following actions.

[0068] According to the power management system described above, similar to the previously mentioned method for managing tapped transformers, obtaining the probability density function of the usable period of the tapped transformer makes it easier to properly manage the tapped transformer.

[0069] (Note 8) The power management system according to Appendix 7, further comprising a display device that displays the probability density function or a probability calculated based on the probability density function.

[0070] In the configuration described in Appendix 8 above, displaying the probability density function of the usable period of the tapped transformer, or the probability calculated from this function, makes it easier to accurately inform the user how much longer the tapped transformer can be used before it becomes unusable.

[0071] (Note 9) The one or more tapped transformers include multiple on-load tap-changing transformers, The power management system further comprises a counter, a sensor, and a controller provided for each of the plurality of on-load tap-changing transformers. The counter is configured to measure the number of tap changes of the corresponding on-load tap-changing transformer. The sensor is configured to measure the values ​​of the multiple parameters relating to the power supplied to the corresponding on-load tap-changing transformer. The controller is configured to control the corresponding on-load tap-changing transformer using the measurement results from the sensor. The controller is configured to transmit a first measured value, which is the measured value of the number of tap switching operations obtained by the counter, and a second measured value, which is the measured value of the multiple types of parameters obtained by the sensor. The power management system described in Appendix 7 or 8, wherein the server is configured to receive the first measured value and the second measured value transmitted by the controller.

[0072] In the configuration described in Appendix 9 above, a controller provided for each on-load tap-changing transformer acquires a first measurement value and a second measurement value, and transmits the obtained first and second measurement values. The server can use the first and second measurement values ​​to obtain the probability density function of the usable period for each tapped transformer. According to the configuration described in Appendix 9 above, it becomes easier to manage multiple on-load tap-changing transformers individually.

[0073] The embodiments disclosed herein should be considered in all respects to be illustrative and not restrictive. The claims are defined by the claims themselves and not by the foregoing description, and all modifications within the meaning and scope of equivalents to the claims are intended. [Explanation of Symbols]

[0074] 1 Power management system, 10A, 10B, 10C transformers, 11 Changeover switches, 12 Tap selectors, 20A, 20B, 20C monitoring devices, 21 Input sensors, 22 Output sensors, 23 Counters, 25 Controllers, 30A, 30B, 30C power generation equipment, 100 Servers, 110 Processors, 120 RAM, 130 Storage devices, 150 Estimation models, 210 HMIs, 220 Mobile terminals, CL Cloud.

Claims

1. From among several parameters related to the power supplied from the power system to the tapped transformer, select the target parameter that is interrelated with the number of tap changes of the tapped transformer, Obtaining predicted values ​​of the aforementioned target parameters over a future period, Using the predicted values ​​of the target parameters, a first probability density function of the usable period of the tapped transformer is obtained, Management methods for tapped transformers, including those mentioned above.

2. Selecting the aforementioned target parameters is For each of the aforementioned multiple types of parameters, clustering of data showing the relationship with the number of tap switching operations is performed, Using the results of the clustering, the parameter with the highest correlation to the number of tap switches is selected from among the multiple types of parameters as the target parameter. A method for managing a tapped transformer according to claim 1, including the method described in claim 1.

3. Obtaining the first probability density function means When the value of the target parameter at a certain timing is input, an estimation model that outputs the number of tap switches at that timing is used to obtain a predicted value of the number of tap switches in the future period from the predicted value of the target parameter in the future period. Using the predicted value of the number of tap changes, a second probability density function of the number of tap changes per day for the tapped transformer is obtained, Converting the second probability density function to the first probability density function, A method for managing a tapped transformer according to claim 1, including the method described in claim 1.

4. Obtaining the second probability density function means Regarding the predicted value of the number of tap switching cycles, a bootstrap sample is obtained using the bootstrap method, Obtaining the second probability density function using the bootstrap sample, A method for managing a tapped transformer according to claim 3, including the method described in claim 3.

5. A server that performs the method for managing a tapped transformer according to any one of claims 1 to 4.

6. A program that causes a computer to execute the method for managing a tapped transformer described in any one of claims 1 to 4.

7. A power management system comprising one or more tapped transformers installed in a power system and a server that manages the one or more tapped transformers, The aforementioned server, From among several types of parameters relating to the power supplied from the power system to the tapped transformer, select the target parameter that is interrelated with the number of tap changes of the tapped transformer, Obtaining predicted values ​​of the aforementioned target parameters over a future period, Using the predicted values ​​of the target parameters, the probability density function of the usable period of the tapped transformer is obtained, A power management system configured to perform the following actions.

8. The power management system according to claim 7, further comprising a display device that displays the probability density function or a probability calculated based on the probability density function.

9. The one or more tapped transformers include multiple on-load tap-changing transformers, The power management system further comprises a counter, a sensor, and a controller provided for each of the plurality of on-load tap-changing transformers. The counter is configured to measure the number of tap changes of the corresponding on-load tap-changing transformer. The sensor is configured to measure the values ​​of the multiple parameters relating to the power supplied to the corresponding on-load tap-changing transformer. The controller is configured to control the corresponding on-load tap-changing transformer using the measurement results from the sensor. The controller is configured to transmit a first measured value, which is the measured value of the number of tap switching operations obtained by the counter, and a second measured value, which is the measured value of the multiple types of parameters obtained by the sensor. The power management system according to claim 7, wherein the server is configured to receive the first measured value and the second measured value transmitted by the controller.

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