JP7647168B2 - Distribution line accident cause estimation device and distribution line accident cause estimation program - Google Patents

Description

This invention relates to a power distribution line accident cause estimation device and a power distribution line accident cause estimation program, and more specifically, to a technology for estimating the cause and location of a power distribution line accident when the accident occurs.

A conventional technology to support facility management is a facility management support system that allows the inspection content to be changed depending on the condition of the on-site facility, and is known to provide more convenient operation by revising the content of the initial plan template for inspection and maintenance work on power transmission and distribution facilities based on the work knowledge of veterans (see Patent Document 1).

JP 2016-189088 A

Conventionally, to analyze and determine the cause of power distribution line accidents, visual inspections are conducted and measurements are made using fault location detection devices. However, there are many cases where the cause of a power distribution line accident cannot be identified immediately, and recovery takes time. Furthermore, if it takes a long time to determine the cause of a power distribution line accident, there is also the problem that a similar accident will occur again.

In addition, the values of the output voltage meter and earth fault indicator meter of the fault location detection device used to detect the fault location of a distribution line fluctuate depending on rainy weather or the zero-phase voltage value, and show various variations/movements depending on the type of accident cause. Therefore, the analysis and investigation of the cause of distribution line accidents is actually done blindly based on past experience and knowledge, which is not efficient and requires a great deal of effort and time.

The present invention aims to provide a power distribution line accident cause estimation device and a power distribution line accident cause estimation program that can assist in quickly and accurately identifying the details of a power distribution line accident.

In order to solve the above problem, the invention of claim 1 is a device for estimating the content of a distribution line accident when the accident occurs, comprising: a means for acquiring accident situation information and accident characteristic information; and a means for estimating at least one of the cause, location, and type of distribution equipment of the distribution line accident based on the accident situation information and the accident characteristic information, wherein the accident situation information includes a line configuration, a district characteristic, an output voltage meter value, a ground fault indication meter value, and a zero-phase voltage value, the accident characteristic information includes a state of the distribution line accident and a dynamic state related to a change in the state, and the dynamic state of the distribution line accident includes, as types of dynamic state, a power outage occurring multiple times in succession, a transition from a micro-ground fault to a short-term power outage, a short-term power outage being recovered and then immediately transitioning to a long-term power outage, natural resolution, and no change. This is a distribution line accident cause estimation device characterized in that

The invention of claim 2 is characterized in that in the distribution line accident cause estimation device described in claim 1, the type of the distribution line accident includes the occurrence of a long-term power outage, the occurrence of a short-term power outage, and the occurrence of a micro-ground fault.

The invention of claim 3 is characterized in that, in the distribution line accident cause estimating device according to claim 1 or 2 , the process of estimating at least one of the cause, location, and type of distribution equipment of the distribution line accident is performed using a trained model that has been machine-learned based on past performance data so that, when the accident situation information and the accident characteristic information are input, the at least one of the cause, location, and type of distribution equipment of the distribution line accident is output.

The invention of claim 4 is a program for estimating the contents of a power distribution line accident when the accident occurs, which causes a computer to function as a means for acquiring accident situation information and accident characteristic information, and a means for estimating at least one of the cause, location, and type of power distribution equipment of the power distribution line accident based on the accident situation information and the accident characteristic information, wherein the accident situation information includes a line configuration, a district characteristic, an output voltage meter value, a ground fault indication meter value, and a zero-phase voltage value, the accident characteristic information includes a state of the power distribution line accident and a state of dynamics related to a change in the state, and the state of dynamics of the power distribution line accident includes, as types of dynamics, a power distribution line accident occurring multiple times in succession, a transition from a micro-ground fault to a short-term power outage, a short-term power outage being restored and then immediately transitioning to a long-term power outage, natural resolution, and no change .

The invention of claim 5 is characterized in that, in the distribution line accident cause estimating program described in claim 4 , the modes of the distribution line accident include, as types of modes, the occurrence of a long-term power outage, the occurrence of a short-term power outage, and the occurrence of a micro-earth fault.

The invention of claim 6 is characterized in that, in the distribution line accident cause estimating program described in claim 4 or 5 , the process of estimating at least one of the cause, location, and type of distribution equipment of the distribution line accident is performed using a learned model that has been machine-learned based on past performance data so that when the accident situation information and the accident characteristic information are input, the at least one of the cause, location, and type of distribution equipment of the distribution line accident is output.

According to the inventions of claims 1 and 4 , since the line configuration and area characteristics are taken into consideration as the accident situation information and the mode and dynamics of the distribution line accident are taken into consideration as the accident characteristic information, it is possible to accurately estimate the content of the distribution line accident, and in turn, it is possible to improve the reliability of the estimation technology for the content of the distribution line accident. And, according to the inventions of claims 1 and 4 , since the content of the distribution line accident can be accurately estimated, it is possible to support the rapid and accurate identification of the content of the distribution line accident, which in turn makes it possible to significantly reduce and shorten the effort and time required to analyze and investigate the content of the distribution line accident, and to significantly shorten the time until the accident is restored, thereby suppressing the impact on consumers.

According to the inventions of claims 2 and 5 , specific types of distribution line accidents include (in other words, are taken into consideration) the occurrence of long-term power outages, short-term power outages, and micro-earth faults, making it possible to more accurately estimate the content of the distribution line accident.

In addition, according to the inventions of claims 1 and 4 , the conductors of a distribution line accident specifically include (in other words, take into consideration) the occurrence of multiple consecutive power outages, a transition from a micro-ground fault to a short-term power outage, a short-term power outage that is restored and then immediately transitions to a long-term power outage, natural resolution, and no change, making it possible to more accurately estimate the content of the distribution line accident.

According to the inventions of claims 3 and 6 , the content of the power distribution line accident is estimated according to a trained model constructed by machine learning using accident situation information and accident characteristic information as input and the content of the power distribution line accident (specifically, the cause, location, and type of distribution equipment of the power distribution line accident) as output, so that it is possible to estimate a more realistic content of the power distribution line accident that reflects actual information on past power distribution line accidents.

1 is a functional block diagram showing a schematic configuration of a power distribution line accident cause estimating device according to an embodiment of the present invention; 2 is a diagram showing an example of a data layout of an accident situation database of the power distribution line accident cause estimating device of FIG. 1 . 2 is a diagram showing an example of a data layout of an accident characteristic database of the power distribution line accident cause estimating device of FIG. 1 . 2 is a diagram showing an example of a data layout of an accident cause database of the power distribution line accident cause estimating device of FIG. 1 . FIG. 2 is a diagram showing an example of information output by a trained model of the power distribution line accident cause estimation device of FIG. 1 .

The present invention will be described below based on the illustrated embodiment.

Figure 1 is a functional block diagram showing the schematic configuration of a distribution line accident cause estimation device 1 according to an embodiment of the present invention. The distribution line accident cause estimation device 1 is a mechanism for estimating the cause of an accident based on the situation at the time of the accident on the distribution line, and mainly comprises an input unit 11, a display unit 12, a storage unit 13, a memory 14, a main task 15, a communication unit 16, and a central processing unit 17 that controls these.

The power distribution line accident cause estimation device 1 is configured by installing and executing a control program for the entire device and an application (power distribution line accident cause estimation program 18) for performing various processes related to estimating the details of the power distribution line accident on, for example, a tablet terminal, a personal digital assistant (PDA), or various types of computers such as notebook computers or desktop computers.

The input unit 11 is an interface that has the function of receiving commands from a user and inputting information to the power distribution line accident cause estimation device 1, and is composed of, for example, a keyboard, a mouse, and a touch panel.

The display unit 12 has functions such as displaying information input via the input unit 11 and displaying the processing results of the power distribution line accident cause estimation device 1, and is configured, for example, by a liquid crystal display.

The storage unit 13 is a storage area/storage device that has the function of storing various information, programs, data, etc., and is composed of, for example, a hard disk (HDD: short for Hard Disk Drive) and ROM (short for Read Only Memory).

The storage unit 13 stores a control program for the entire distribution line accident cause estimation device 1 and a distribution line accident cause estimation program 18. The storage unit 13 also stores a trained model 19 for estimating the details of a distribution line accident (specifically, the cause, location, and type of distribution equipment) based on information related to the distribution line accident when it occurs (referred to as "occurring accident information"), and an accident situation database 131, an accident characteristics database 132, and an accident cause database 133 in which teacher data used for machine learning of the trained model 19 are recorded and accumulated.

The databases 131 to 133 may be stored in an external storage device such as a server. In this case, the power distribution line accident cause estimation device 1 may access the external storage device via the communication unit 16 to acquire various data and information. Alternatively, a connection interface may be provided for transmitting/receiving/inputting/outputting signals such as data and control commands to/from the external storage device via various signal lines, and the external storage device may be accessed via the connection interface to acquire various data and information.

The memory 14 is a storage area/storage device that functions as a working area for temporarily storing data and information generated when the central processing unit 17 executes calculations related to estimating the details of the power distribution line accident, and is configured, for example, with RAM (short for Random Access Memory).

The main task 15 is a group of tasks for executing various processes related to estimating the details of a power distribution line accident, which are realized by executing the power distribution line accident cause estimation program 18 stored in the memory unit 13.

The communication unit 16 is a communication interface that has the function of transmitting/receiving/inputting/outputting signals and information transmitted via various wireless/wired communication networks, including, for example, LAN (short for Local Area Network) and WAN (short for Wide Area Network).

The central processing unit 17 has the function of coordinating and controlling each part that constitutes the power distribution line accident cause estimation device 1, and is configured to include, for example, a central processing unit (CPU: abbreviation for Central Processing Unit), and realizes each function according to the control program stored in the memory unit 13 and the power distribution line accident cause estimation program 18.

The power distribution line accident cause estimation device 1 is a device that estimates the details of a power distribution line accident when the accident occurs, and includes a means for acquiring accident situation information and accident characteristic information (power outage information acquisition task 151), and a means for estimating the details of the power distribution line accident based on the accident situation information and accident characteristic information (accident cause estimation task 152), where the accident situation information includes the line configuration, area characteristics, output voltage meter value, earth fault display meter value, and zero-phase voltage value, and the accident characteristic information includes the state of the power distribution line accident and the dynamics related to the change in state.

The distribution line accident cause estimation program 18 is a program for estimating the details of a distribution line accident when the accident occurs, and causes the computer to function as a means for acquiring accident situation information and accident characteristic information (power outage information acquisition task 151), and as a means for estimating the details of the distribution line accident based on the accident situation information and accident characteristic information (accident cause estimation task 152), where the accident situation information includes the line configuration, area characteristics, output voltage meter value, earth fault display meter value, and zero-phase voltage value, and the accident characteristic information includes the state of the distribution line accident and the dynamics of how the state of the accident is changing.

The power distribution line accident cause estimation device 1 and the power distribution line accident cause estimation program 18 perform the process of estimating the details of the power distribution line accident using a trained model 19 that has been machine-learned based on past performance data, so that when accident situation information and accident characteristic information are input, the details of the power distribution line accident are output.

To execute various processes related to estimating the details of a power distribution line accident, a power distribution line accident cause estimation program 18 stored in the memory unit 13 is executed, thereby forming a main task 15. The main task 15 includes a power outage information acquisition task 151, an accident cause estimation task 152, and a learning task 153.

When a power distribution line accident occurs, the power outage information acquisition task 151 acquires information related to the power distribution line accident (i.e., accident information), which is input by a worker via the input unit 11 or information transmitted from equipment installed in the power distribution system and input via the communication unit 16.

Then, the power outage information acquisition task 151 transfers the accident information to the accident cause estimation task 152.

The accident cause estimation task 152 estimates the details of the power distribution line accident based on the accident information transferred from the power outage information acquisition task 151.

In the accident cause estimation task 152, when information about an accident that has occurred is input, a trained model 19 that has been trained using machine learning (including techniques also known as deep learning) is used to output information about the details of the power distribution line accident.

The trained model 19 is created by the learning task 153. The learning task 153 uses as training data the historical information/historical data on past power distribution line accidents recorded and accumulated in the accident situation database 131, the accident characteristics database 132, and the accident cause database 133 stored in the memory unit 13, and performs machine learning on various situations that may be related to or affect a power distribution line accident at the time of the accident, and the relationship between the specific characteristics of the power distribution line accident and the content of the power distribution line accident, to create the trained model 19.

In machine learning, it is considered that a machine learning method that is particularly suitable for classification may be used, such as a decision tree, an extreme learning machine (ELM), a neural network/deep neural network (DNN), or a support vector machine (SVM).

As input for machine learning, historical information related to various conditions that may be related to or affect a distribution line accident at the time of its occurrence is recorded and stored in an accident situation database 131, historical information related to the specific characteristics of the distribution line accident is recorded and stored in an accident characteristics database 132, and historical information related to the content of the distribution line accident is recorded and stored in an accident cause database 133.

In the accident situation database 131, the accident characteristics database 132, and the accident cause database 133, an accident number common to the databases 131 to 133 is used as identification information (in other words, an ID) for distinguishing and individually identifying each past power distribution line accident from each other, and the information recorded and stored in each of the databases 131 to 133 is configured to be combined and available via the accident number.

In addition, the various information recorded and stored in the accident situation database 131, the accident characteristics database 132, and the accident cause database 133 may be converted into a code, which is a sequence of numbers or letters defined for each item, and used, or may be represented by a predetermined keyword/character sequence and used.

In the accident situation database 131, information on various circumstances that may be related to or affect a past power distribution line accident at the time of the accident (called "accident situation information") is recorded and accumulated for each past power distribution line accident. Specifically, the following information is recorded and accumulated in the accident situation database 131 as accident situation information (see Figure 2):

a) Date: Information on the date on which the power distribution line accident occurred. This item is for reflecting the causal relationship between the season as the background of the power distribution line accident and the accident content in the estimation of the content of the power distribution line accident, and may be only the month or the four seasons.

b) Time: Information on the time when the power distribution line accident occurred. This item is for reflecting the causal relationship between the time as the background of the power distribution line accident and the accident content in the estimation of the content of the power distribution line accident, and may be, for example, a time period divided into about three hours, or a division of a day divided into morning, afternoon, night, late night, early morning, etc.

c) Weather Information about the weather when the power distribution line accident occurred. Specific examples of the types of weather include sunny, cloudy, rain, thunderstorm, and snow. The weather may also include temperature, humidity, and wind speed.

d) Power Distribution Line Information for identifying the power distribution line in which the accident occurred (for example, an identification number/ID that is given in advance to each power distribution line or each section of the power distribution line).
As the information for this item, for example, information on the power outage section specified by the power distribution automation system may be used. The power distribution automation system remotely monitors and controls sectionalizing switches installed in the power distribution system, thereby performing planned switching of the power distribution system and automatic switching of the power distribution system so as to transmit power to a section other than the accident section when a sudden power outage occurs due to a power distribution line accident or the like. When a power outage occurs due to a power distribution line accident, the power distribution automation system transmits high-voltage power distribution line power outage information including information on the power outage section to the power distribution line accident cause estimation device 1.

e) Line Type: Information about the type of the power distribution line where the accident occurred. Specific examples of line types include mountain areas, coastal areas, urban areas (residential areas, commercial areas), factory areas, and farmland areas.

f) Line Type: Information on the type of the distribution line where the accident occurred. Specific examples of the line type include an overhead line, an underground line (cable), and an underwater line (cable).

g) Area Characteristics: Information on the characteristics of the natural environment of the area where the power distribution line where the accident occurred is located. Specific examples of the types of area characteristics include salt damage area, lightning damage area, strong wind area, and no characteristics.

h) Output voltage meter value This is combined data of a measurement point ID and the value of the output voltage meter in the fault point detection device when an accident occurs on a power distribution line. The measurement point ID is identification information for distinguishing and individually identifying each measurement point. Note that when multiple output voltage meter values are measured for one power distribution line accident, for example, a predetermined number of output voltage meter values are recorded from a measurement point close to the location of the power distribution line accident.

An accident point detection device is a device used when a ground fault or other accident occurs on a distribution line. Specifically, for example, the accident section (in other words, the blackout section) is identified by a distribution automation system that manages the distribution line where the accident occurred, and then a worker goes to the identified section and applies a voltage to the distribution line (specifically, injects a high-voltage DC pulse), and uses a current detector to repeatedly detect the current flowing between the injection point and the accident point in sequence from the high-voltage pulse injection point to the accident point, thereby detecting the accident point. The accident point detection device is equipped with an output voltage display meter that displays the pulse voltage value, and a ground fault display meter that displays an indication value proportional to the magnitude of the current flowing between the injection point and the accident point.

Since the fault point detection device is usually connected to a utility pole in the outage section to detect the fault point, the measurement point ID specifically uses, for example, identification information to distinguish and individually identify each utility pole from each other.

i) Earth fault meter value This is combined data of the measurement point ID and the value of the earth fault meter in the fault point detection device when investigating the occurrence of a distribution line accident. Note that when multiple earth fault meter values are measured for one distribution line accident, for example, a predetermined number of earth fault meter values are recorded from the measurement point closest to the location of the distribution line accident.

j) Zero-phase voltage value: This is a combination of the measurement device ID and the measurement value of the zero-phase voltage in the distribution line when a distribution line accident occurs. The measurement device ID is identification information for individually identifying each measurement device by distinguishing them from each other.
The zero-phase-sequence voltage is measured by a measuring device installed on a utility pole, for example. The measurement data of the zero-phase-sequence voltage is periodically transmitted to the power distribution line accident cause estimation device 1 via various wireless/wired communication networks including, for example, a LAN or a WAN.

k) Overcurrent Value This is a combination of the measurement device ID and the measurement value of the overcurrent (i.e., a current larger than the current supplied to the load) when a distribution line accident occurs.
The overcurrent is measured by a device for detecting the overcurrent, such as various relays including an overcurrent relay, etc. The measurement data of the overcurrent is periodically transmitted to the power distribution line accident cause estimation device 1 via various wireless/wired communication networks including, for example, a LAN and a WAN.

In the accident characteristics database 132, information related to the specific characteristics of each past power distribution line accident (called "accident characteristics information") is recorded and accumulated. Specifically, the following information is recorded and accumulated as accident characteristics information in the accident characteristics database 132 (see FIG. 3):

l) Status: Information on the status of the power distribution line accident. Specific examples of the types of status include a long-term power outage, a short-term power outage, and a micro-ground fault, as conditions caused by the power distribution line accident.

m) Status: Information on changes in the state of the power distribution line accident. Specific types of status include, for example, multiple consecutive power outages, a transition from a micro-ground fault to a short-term power outage, a short-term power outage that is restored and then immediately transitions to a long-term power outage, spontaneous resolution, and no change.

Here, the "state" in l) above is information about the initial state/situation when a power distribution line accident occurs, and this "dynamics" is information about how things have changed from the initial state/situation.

Regarding "status," if the initial state/condition at the time the distribution line accident occurred remains unchanged, and the accident naturally resolves/recovers before any specific restoration measures are taken for the distribution line accident, it is considered to have been "naturally resolved." Conversely, if the initial state/condition at the time the distribution line accident occurred remains unchanged, and the accident is resolved by taking restoration measures for the distribution line accident (i.e., it is not naturally resolved), it is considered to have been "no change."

The above accident situation information and accident characteristic information correspond to accident information that occurred in past power distribution line accidents.

In the accident cause database 133, information related to the details of each past power distribution line accident is recorded and accumulated. Specifically, the accident cause database 133 records and accumulates the following information as information related to the details of power distribution line accidents (see FIG. 4):

X) Cause: Information on the cause of a power distribution line accident, which is the cause of the accident confirmed by an operator at the accident site when the power distribution line accident occurred. Specific types of causes include, for example, wind, rain, lightning, corrosion, natural deterioration, poor maintenance, overload, contact with trees, contact with birds and animals, and man-made accidents (for example, operator negligence or vehicle contact).

Y) Location: Information about the accident point in a power distribution line, which is the accident point confirmed by an operator at the accident site when a power distribution line accident occurs. Specific ways of identifying the location include, for example, the location of the power distribution line where the accident occurred (for example, a combination of pole identification information for identifying the power distribution line section divided by two adjacent utility poles) and the facility or equipment where the malfunction occurred (for example, identification information for identifying the equipment installed on the utility pole or power distribution system).

Z) Type: Information on the type of the distribution facility that was the subject of the accident, which is the type of the faulty distribution facility/device that was confirmed by the worker at the accident site when the distribution line accident occurred. Specific examples of the types of distribution facilities include overhead lines, underground/underwater wires (cables), utility poles, insulators, transformers, switches, and circuit breakers.

The trained model 19 is constructed, for example, based on a multi-class classification model, and is created to estimate and output the cause, location, and type of distribution equipment of a power distribution line accident recorded and stored in the accident cause database 133 based on multiple items of input data recorded and stored in the accident situation database 131 and the accident characteristics database 132.

In other words, the learning task 153 uses the performance information/performance data group on past distribution line accidents recorded and accumulated in the accident situation database 131, the accident characteristics database 132, and the accident cause database 133 as training data to execute a learning process on the cause, location, and type of distribution line accident according to various situations that are thought to be related to or that may affect the distribution line accident (in other words, accident situation information) and the specific characteristics of the distribution line accident (in other words, accident characteristics information), and constructs a learned model 19 based on the performance information/performance data group, so that an accurate estimation result on the cause, location, and type of distribution line accident can be obtained when accident information is input.

The learning model used by the learning task 153 uses, for example, machine learning/deep learning using a neural network/deep neural network to create a neural network/deep neural network including an intermediate layer, in which various situations that are thought to be related to or that may affect a distribution line accident (in other words, accident situation information) and specific characteristics of the distribution line accident (in other words, accident characteristic information) are used as an input layer based on historical data on distribution line accidents obtained from the historical information recorded and accumulated in the above databases 131 to 133, and information on the cause, location, and type of distribution equipment of the distribution line accident according to the various situations and accident characteristics is used as an output layer.

In this case, a neural network/deep neural network may be created in which the cause and location of the distribution line accident and the type of distribution equipment are each an output layer, or a neural network/deep neural network may be created in which a combination of two of the cause and location of the distribution line accident and the type of distribution equipment are each an output layer, or a neural network/deep neural network may be created in which a combination of the cause and location of the distribution line accident and the type of distribution equipment are each an output layer. In light of the above, the cause of the accident in this invention includes not only the cause of the distribution line accident itself, but also the accident point in the distribution line itself and the distribution equipment itself that was the subject of the accident.

The learning model used by the learning task 153 creates an intermediate layer using the causes, locations, and types of distribution equipment of the power distribution line accidents according to the various situations and characteristics of the accidents as actual results as learning data (teaching data), and learns various parameters in the intermediate layer. The learning model used by the learning task 153 learns various parameters in the intermediate layer so as to minimize the error between the output (i.e., the estimated result) of the causes, locations, and types of distribution equipment of the power distribution line accidents according to the various situations and characteristics of the accidents, and the causes, locations, and types of distribution equipment of the power distribution line accidents according to the various situations and characteristics of the accidents contained in the learning data (teaching data) (i.e., actual data).

The trained model 19 may output the probability of each of the cause, location, and type of distribution equipment of a power distribution line accident as the likelihood of each of them. Specifically, the trained model 19 may output information such as that shown in FIG. 5. In FIG. 5, (A) is an example of a case where the cause, location, and type of distribution equipment of a power distribution line accident are each set as an output layer, (B) is an example of a case where two combinations of the cause, location, and type of distribution equipment of a power distribution line accident are set as an output layer (note that both <Estimation result 1> and <Estimation result 2> are output), and (C) is an example of a case where the combination of the cause, location, and type of distribution equipment of a power distribution line accident are set as an output layer.

Then, the learning task 153 stores the created trained model 19 in the memory unit 13.

The accident cause estimation task 152 uses the trained model 19 created by the learning task 153 to estimate the details of a new power distribution line accident.

Specifically, when a new power distribution line accident occurs, the power outage information acquisition task 151 first acquires information and data on items similar to those recorded and stored in the accident situation database 131 and the accident characteristics database 132 (in other words, items adopted by the trained model 19) as accident information.

The power outage information acquisition task 151 acquires, for example, information on the above items a) date, b) time, c) weather, e) line configuration, f) line type, and g) area characteristics according to the contents input by the worker via the input unit 11. Also, the above item d) information on the distribution line is acquired according to the contents transmitted from the distribution automation system and input via the communication unit 16 (specifically, high-voltage distribution line power outage information). Also, the above items h) output voltage meter value and i) earth fault display meter value are acquired according to the output voltage meter value and earth fault display meter value obtained by a (different) worker who went to the site and operated the fault point exploration device input by the worker via the input unit 11. Also, the above items j) information on the zero-phase voltage value and k) information on the overcurrent value are acquired according to the contents transmitted from a specific device installed in the distribution system and input via the communication unit 16. Also, the above items l) aspect and m) status information are acquired according to the contents input by the worker via the input unit 11.

The accident cause estimation task 152 then inputs the accident information transferred from the power outage information acquisition task 151 into the trained model 19, thereby estimating and outputting the cause, location, and type of distribution equipment of the power distribution line accident (specifically, for example, the probability that each of them applies). At this time, the accident cause estimation task 152 may list multiple causes, locations, and types of distribution equipment of the power distribution line accident in descending order of the estimated probability.

Then, the accident cause estimation task 152 displays the estimation results on the display unit 12 (see Figure 5; it is optional to also display the probability).

As described above, according to the distribution line accident cause estimation device 1 and the distribution line accident cause estimation program 18, the accident situation information and the accident characteristic information are input, and the content of the distribution line accident (specifically, the cause, location, and type of distribution equipment of the distribution line accident) is output, and the content of the distribution line accident is estimated according to the learned model 19 constructed by machine learning. Therefore, it is possible to estimate the content of the distribution line accident more realistically, which reflects the actual information on the past distribution line accidents, and it is possible to support the rapid and accurate identification of the content of the distribution line accident. In addition, it is possible to significantly reduce and shorten the labor and time required for analyzing and investigating the content of the distribution line accident, and to significantly shorten the time until the accident is restored, thereby suppressing the impact on consumers. According to the distribution line accident cause estimation device 1 and the distribution line accident cause estimation program 18, in particular, the line configuration and district characteristics are taken into account as the accident situation information, and the form and dynamics of the distribution line accident are taken into account as the accident characteristic information, so that the content of the distribution line accident can be estimated more accurately, and it is possible to improve the reliability of the estimation technology for the content of the distribution line accident.

The distribution line accident cause estimation device 1 and the distribution line accident cause estimation program 18 also estimate the details of a distribution line accident when an accident of unknown cause occurs, such as a micro-ground fault that is then naturally resolved/recovered. This makes it possible to narrow down the points that should be inspected intensively during a future patrol, and helps to discover the cause of the distribution line accident, which in turn makes it possible to operate the distribution line stably.

Although the embodiment of the present invention has been described above, the specific configuration is not limited to the above embodiment, and even if there are design changes within the scope of the invention that do not deviate from the gist of the invention, they are included in the invention.

For example, in the above embodiment, the accident cause estimation task 152 (specifically, the trained model 19) estimates three items of the cause, location, and type of distribution equipment as the details of the distribution line accident, but the accident cause estimation task 152 (trained model 19) may estimate only one or two items of the cause, location, and type of distribution equipment as the details of the distribution line accident. By estimating at least one of the cause, location, and type of distribution equipment of the distribution line accident, it is possible to narrow down the points to be checked when a distribution line accident occurs, so that the analysis and investigation of the cause of the accident on the distribution line can be performed efficiently, and ultimately it becomes possible to support the rapid and accurate identification of the details of the distribution line accident.

REFERENCE SIGNS LIST 1 Distribution line accident cause estimating device 11 Input unit 12 Display unit 13 Storage unit 131 Accident situation database 132 Accident characteristics database 133 Accident cause database 14 Memory 15 Main task 151 Power outage information acquisition task 152 Accident cause estimating task 153 Learning task 16 Communication unit 17 Central processing unit 18 Distribution line accident cause estimating program 19 Learned model

Claims (6)

A device for estimating details of a power distribution line accident when the power distribution line accident occurs,
A means for acquiring accident situation information and accident characteristic information;
and a means for estimating at least one of a cause, a location, and a type of the power distribution facility of the power distribution line accident based on the accident situation information and the accident characteristic information,
The fault condition information includes a line configuration, a district characteristic, an output voltage meter value, a ground fault indication meter value, and a zero-phase voltage value,
The fault characteristic information includes a state of the power distribution line fault and a dynamic state regarding a change in the state of the fault,
The dynamic state of the power distribution line accident includes, as types of dynamic state, a power outage occurring multiple times in succession, a transition from a micro-ground fault to a short-term power outage, a short-term power outage recovering once and then immediately transitioning to a long-term power outage, a natural resolution, and no change;
A power distribution line accident cause estimating device characterized by the above. The type of the power distribution line accident includes a long-term power outage, a short-term power outage, and a micro-ground fault,
2. The power distribution line accident cause estimating device according to claim 1 . The process of estimating at least one of the cause, location, and type of the distribution equipment of the power distribution line accident is performed using a trained model that has been machine-learned based on past performance data so that, when the accident situation information and the accident characteristic information are input, the at least one of the cause, location, and type of the power distribution equipment of the power distribution line accident is output.
3. The power distribution line accident cause estimating device according to claim 1 or 2 . A program for estimating details of a power distribution line accident when the power distribution line accident occurs,
Computer,
A means for acquiring accident situation information and accident characteristic information; and
and functioning as a means for estimating at least one of a cause, a location, and a type of the power distribution facility of the power distribution line accident based on the accident situation information and the accident characteristic information;
The fault condition information includes a line configuration, a district characteristic, an output voltage meter value, a ground fault indication meter value, and a zero-phase voltage value,
The fault characteristic information includes a state of the power distribution line fault and a dynamic state regarding a change in the state of the fault,
The dynamic state of the power distribution line accident includes, as types of dynamic state, a power outage occurring multiple times in succession, a transition from a micro-ground fault to a short-term power outage, a short-term power outage recovering once and then immediately transitioning to a long-term power outage, a natural resolution, and no change;
A power distribution line accident cause estimation program comprising: The type of the power distribution line accident includes a long-term power outage, a short-term power outage, and a micro-ground fault,
5. The power distribution line accident cause estimating program according to claim 4 . The process of estimating at least one of the cause, location, and type of the distribution equipment of the power distribution line accident is performed using a trained model that has been machine-learned based on past performance data so that, when the accident situation information and the accident characteristic information are input, the at least one of the cause, location, and type of the power distribution equipment of the power distribution line accident is output.
6. The power distribution line accident cause estimating program according to claim 4 or 5 .