KR101398412B1 - Apparatus for learning and calculating the number of received cases of electrical fault using weather data and neural networks - Google Patents

Abstract

The present invention relates to an apparatus for learning and calculating the number of received cases of electrical faults using weather data and neural networks. The purpose of the present invention is to provide an apparatus for learning and calculating the number of received cases of electrical faults using weather data and neural networks, which performs learning for calculating the predicted number of received cases of electrical faults and automatically calculates the predicted number of received cases of electrical faults on electrical equipment by date, by inputting, to neural networks, past and predicted weather data by date, and the number of past received cases of electrical faults on the electrical equipment by date. To achieve the above purpose, the present invention provides an apparatus for learning and calculating the number of received cases of electrical faults using weather data and neural networks includes: a data acquisition unit which acquires past and predicted weather data by date from a server of the weather center; a storage unit which stores the number of past received cases of electrical faults on electrical equipment by date; a neural network learning and calculating unit which normalizes the past and predicted weather data by date acquired by the data acquisition unit, inputs the past and predicted weather data to neural networks to perform learning for calculating the number of received cases of electrical faults on the electrical equipment, and calculates the predicted number of received cases of electrical faults by date; and an input unit which receives a learning control signal and a calculating control signal from a user.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an apparatus and method for detecting and calculating the number of electrical failures using weather data and a neural network,

More particularly, the present invention relates to an apparatus and method for learning and calculating the number of received electric faults using weather data and neural networks, and more particularly, The number of reception cases is calculated, and the number of forecast failure receipts per day of the electrical equipment can be automatically calculated.

Related to the apparatus for detecting the state of the electric equipment, there are many applications and disclosures in addition to Korean Patent Laid-Open No. 10-2012-0016423 (hereinafter referred to as "prior art"). In the above-mentioned prior art, an ultrasonic wave is applied from an ultrasonic sensor array remotely, an electric signal is extracted, and an abnormal state of the electric power facility is detected by analyzing a signal component.

On the other hand, faults in electrical equipment have a close relationship with precipitation. For example, in the third quarter, where rainfall is highest in one year, resistive leakage currents frequently occur in electrical equipment, which may cause electrical equipment to fail.

In addition, waiters who are waiting for dispatch by complaints about electrical equipment failure are waiting for one person in each region and dispatching them in case of an emergency electrician accident, thereby preventing electric accidents. However, if a lot of faults occur, one waiting person is dispatched and the accident is safely handled. However, there may be cases where an additional electric accidents complaints occur and the treatment is delayed or the accident can not be handled. In order to solve such problems, it is possible to process complaints by waiting for a large number of dispatched personnel. However, when a large number of daytime workers are arranged at night, the number of people who can perform weekly tasks is small, Manpower management problems arise. Therefore, since one person is selected and operated by the local dispatch work force, the summer heavy rainfall that causes many electric accidents is delayed due to a large number of notifications, the delay of proper accident response, And the like.

Although there is a technology for detecting a failure by detecting a state of an electric facility as in the above-mentioned prior art, there is no technology for predicting and calculating the number of failures received by an electric facility using weather data such as precipitation.

SUMMARY OF THE INVENTION The present invention has been conceived in view of the above problems, and it is an object of the present invention to provide a neural network in which the number of past failures received by each of the past, And to provide a device for learning and calculating the number of received electric faults using weather data and a neural network that automatically calculates the number of predicted faults received.

According to an aspect of the present invention, there is provided an apparatus and method for learning and calculating the number of received electric faults using a weather data and a neural network, comprising: a data acquisition unit for acquiring past and predicted vapor data from a weather service server; A storage unit for storing the number of past failures received by the electric equipment; A neural network learning unit for normalizing past past and predicted weather data acquired through the data acquiring unit and inputting the past past and predicted weather data to a neural network to perform learning for calculating the number of failures received by the electric equipment, And a calculating unit; And an input unit for receiving a learning control signal and a calculation control signal of a user; Wherein the neural network learning and calculation unit comprises: a normalization module for normalizing the size of the predicted vapor phase data including the past weather data including the daily precipitation amount acquired through the data acquisition unit and the forecasted precipitation amount per day; And inputting past weather data normalized by the normality module to the input of a neural network in accordance with a learning control signal of a user inputted through the input unit and storing the number of past failures received by the neural network And then recalculates the past meteorological data of each standardized by adjusting the weights for learning and applying the adjusted weights to generate the recalculated metricized past meteorological data for each day, A learning module for estimating the number of failures received per day based on the standardized past weather data and determining whether the estimated number of failures received per day is equal to the number of past failures received per day inputted to the output terminal of the neural network; And an input unit for inputting the predicted weather data normalized by the norming module to the input of the neural network in accordance with the user's control signal inputted through the input unit and outputting a weight corresponding to the inputted standardized predicted weather data To generate the recalculated standardized forecasted weather data by recalculating the normalized forecasted weather data by applying the recalculated daily forecasted weather data of the electric equipment, A calculation module for calculating and outputting; And a control unit.

The learning module terminates the learning when the estimated number of received failures per day is equal to the number of received past failures per day inputted to the output terminal of the neural network, The data recalculation and the number of fault reception counts are repeatedly performed.

Further, the learning module is characterized by recognizing the inputted normative data and the weight corresponding thereto according to the number of failures received.

And the past weather data acquired through the data acquisition unit and the number of past failure acceptances by date stored in the storage unit are data for the same period.

According to the present invention as described above, since the number of trouble receipts closely related to the weather data is calculated by using past and predicted vapor data, , It is possible to appropriately wait for the dispatching personnel according to the fault reception.

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a schematic view showing conceptually a device for learning and calculating the number of received electric faults using weather data and a neural network according to the present invention; FIG.
2 is a schematic diagram of a neural network having an input layer unit, a hidden layer unit and an output layer according to the present invention.
3 is an overall flowchart of a method for learning and calculating the number of received electrical failures using weather data and a neural network according to the present invention.

Specific features and advantages of the present invention will become more apparent from the following detailed description based on the accompanying drawings. It is to be noted that the detailed description of known functions and constructions related to the present invention is omitted when it is determined that the gist of the present invention may be unnecessarily blurred.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will now be described in detail with reference to the accompanying drawings.

1 and 2, a description will be made of an apparatus for learning and calculating the number of received electric faults using the weather data and the neural network according to the present invention.

1 is a block diagram conceptually showing an apparatus for learning and calculating the number of received electrical faults using the weather data and the neural network according to the present invention. As shown in the figure, the data acquisition unit 100, the storage unit 200, A network learning and calculating unit 300 and an input unit 400.

The data acquisition unit 100 acquires past past and predicted weather data from the weather station server 10.

At this time, the past weather data includes data of the immediately preceding one year, and the predicted weather data includes data such as the predicted precipitation amount by day for one month from now.

In the present embodiment, the past weather data is set as the data of the immediately preceding one year, and the predicted weather data is set as the data of one month from now. However, the present invention is not limited to this period, May be received and used. The past and predicted weather data may also include data such as rain probability and temperature.

The storage unit 200 stores the number of past failures received for each electric facility. At this time, the number of failure acceptances stored in the storage unit 200 may be the number of failures received per day of the immediately preceding one year like the data acquisition unit 100.

In addition, the storage unit 200 stores past past and predicted vapor data acquired through the data acquisition unit 100, data calculated through the neural network learning and calculation unit 300, and the number of calculated predictive failure acceptances. Accordingly, the neural network learning and calculation unit 300 may refer to the data stored in the storage unit 200. [

The neural network learning and calculation unit 300 normalizes the past and predicted vapor data acquired by the data acquisition unit 100 and inputs the past and predicted vapor data to the neural network 20 to calculate the number of failure acceptance And performs a function to calculate the number of forecasted failure acceptance by day, and includes a normalization module 310, a learning module 320, and a calculation module 330 as shown in FIG.

Specifically, the normalization module 310 calculates the size of the predicted weather data including the past weather data including the immediately preceding one-year precipitation amount acquired through the data acquisition unit 100 and the one-month predicted precipitation amount from the present Normalize.

The learning module 320 inputs the past weather data normalized by the norming module 310 to the input terminal of the neural network 20 according to the learning control signal of the user inputted through the input unit 400, The number of past failures received by the date stored in the storage unit 200 is input to the output terminal of the neural network 20. [

Thereafter, the learning module 320 generates the recalculated standardized past meteorological data by adjusting the weights for learning and recalculating the standardized meteorological data for each standard by applying the adjusted weights, Based on the recalculated normalized day - to - day meteorological data, we estimate the number of breakdowns by day.

Then, the learning module 320 determines whether the estimated number of received failures per day is equal to the number of received past failures per day inputted to the output terminal of the neural network 20.

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If the results of the judgment are all the same, the learning is terminated. If not identical, the weight adjustment, the recalculation of the normized data, and the estimation of the number of failures are repeated until the same, The corresponding weight value is recognized.

Through such learning, when the standardized data is input, the number of predicted failure acceptances per one day of the electric equipment can be automatically calculated and output by using the learned and recognized normalized data and corresponding weights.

That is, in accordance with the user's control signal inputted through the input unit 400, the calculation module 330 inputs the predicted vapor-phase data normalized through the normalization module 310 to the input terminal of the neural network 20 And recalculates the normalized forecasted weather data by applying the weights corresponding to the inputted standardized forecasted weather data to generate the recalculated standardized forecasted weather data for each day to generate the recalculated standardized daily data Based on the predicted meteorological data, the number of predicted failure receipts per day of the electrical equipment is calculated and output.

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FIG. 2 shows a structure of a neural network 20 having an input layer unit, a hidden layer unit and an output layer according to the present invention. As described above, signal(

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And the learning can be performed.

The input unit 400 receives the learning control signal and the calculation control signal of the user.

The input unit 400 may receive various control signals of the user regarding calculation, storage, output, and the like.

Hereinafter, a method of learning and calculating the number of received electrical failures using the weather data and the neural network using the above-described apparatus will be described with reference to FIG.

FIG. 3 is an overall flowchart of a method for learning and calculating the number of received electrical failures using the weather data and the neural network according to the present invention. As shown in FIG. 3, the data acquisition unit 100 receives data from the meteorological office server 10, Data is acquired (S10). At this time, the past weather data includes data of the immediately preceding one year, and the predicted weather data includes data such as the predicted precipitation amount by day for one month from now.

Thereafter, the normalization module 310 of the neural network learning and calculation unit 300 acquires the data acquired by the data acquisition unit 100 (S20) the past meteorological data including the immediately preceding one-year precipitation amount and the predicted meteorological data including the one-month forecasted precipitation amount from the present one.

In addition, the learning module 320 of the neural network learning and calculation unit 300 determines whether the control signal input through the input unit 400 is a learning control signal of the user (S30).

As a result of the determination in step S30, if the learning control signal is a learning control signal, the learning module 320 inputs the past weather data normalized by the normizing module 310 to the input terminal of the neural network 20, 200 to the output terminal of the neural network 20 (S40).

Thereafter, the learning module 320 adjusts a weight for learning (S50), and recalculates the recalculated past meteorological data based on the standardized meteorological data by applying the adjusted weight to the data (S60), and estimates the number of failures received per day based on the recalculated normalized past weather data (S70).

In step S80, the learning module 320 determines whether the estimated number of received failures per day is equal to the number of received past failures per day input to the output terminal of the neural network 20 (S80).

As a result of the determination in step S80, if all are the same, the storage unit 200 stores past and predicted vapor data acquired by the data acquisition unit 100, data calculated through the neural network learning and calculation unit 300, and the like (S90), and the learning module 320 ends the learning.

As a result of the determination in step S80, if not, the learning module 320 proceeds to step S50. That is, by performing the weight adjustment, the recalculation of the normalized data, and the estimation of the number of received failures repeatedly until all are the same, the inputted normative data and the corresponding weight are recognized according to the number of received failures.

Through such learning, when the standardized data is input, the number of predicted failure acceptances per one day of the electric equipment can be automatically calculated and output by using the learned and recognized normalized data and corresponding weights.

On the other hand, if it is determined in operation S30 that the control signal is not a learning control signal, the learning module 320 determines whether the input control signal is a user's control signal (S100).

As a result of the determination in step S100, in the case of the calculation control signal, the calculation module 330 of the neural network learning and calculation unit 300 calculates the predicted vapor-phase data normalized through the normalization module 310 in step S20 (S110), and the weighted value corresponding to the inputted standardized predicted vapor data for each day is applied to recalculate the normalized predicted vapor data for each day to thereby calculate the recalculated standardized day-by- (S120), calculates and outputs the number of predicted failure acceptance by day based on the recalculated normalized predicted day-by-day meteorological data (S130), and executes the procedure to step S90.

On the other hand, if the result of the determination in step S100 is negative, the learning module 320 ends the process.

While the present invention has been particularly shown and described with reference to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. It will be appreciated by those skilled in the art that numerous changes and modifications may be made without departing from the invention. Accordingly, all such appropriate modifications and changes, and equivalents thereof, should be regarded as within the scope of the present invention.

100: Data acquisition unit 200:
300: Neural network learning and calculation part 400: Input part
310: Normalizing module 320: Learning module
330: Output module 10: weather station server
20: Neural network

Claims (4)

1. A device for learning and calculating an electrical fault number using meteorological data and a neural network,
A data acquisition unit (100) for acquiring historical past and predicted vapor data from the weather station server (10);
A storage unit (200) for storing the number of past failures received by each electric equipment;
The past and predicted weather data acquired by the data acquiring unit 100 are normalized and input to the neural network 20 to perform learning for calculating the number of failures received in the electrical equipment, A neural network learning and calculating unit 300 for calculating the number of cases; And
An input unit 400 for receiving a learning control signal and a calculation control signal of a user; , ≪ / RTI &
The neural network learning and calculation unit (300)
A normalization module 310 for normalizing the size of the predicted vapor phase data including the past weather data including the daily precipitation amount acquired through the data acquisition unit 100 and the forecasted precipitation amount per day;
According to a learning control signal of a user inputted through the input unit 400, the past weather data normalized by the norming module 310 is input to an input terminal of the neural network 20, ) Is inputted to the output terminal of the neural network 20, the weight for learning is adjusted, and the adjusted weight is applied to recalculate the past meteorological data per the standardized recalculation And estimates the number of failures received per day on the basis of the recalculated standardized past weather data for each day and inputs the estimated number of failed failures per day to the output terminal of the neural network 20 A learning module 320 for judging whether or not the number of past failures received by the user is equal to the number of past failures received by the user; And
The predicted vapor data normalized by the norming module 310 is input to the input of the neural network 20 according to the user's control signal inputted through the input unit 400, Calculates the recalculated standardized forecasted weather data by recalculating the normalized forecasted weather data by applying the weights corresponding to the daily forecasted weather data, and generates the recalculated standardized forecasted weather data based on the recalculated standardized daily forecasted weather data, A calculation module 330 for calculating and outputting the number of predicted failure receipts per day of the facility; And a learning and calculating device for calculating the number of electrical failures using the weather data and the neural network.
The method according to claim 1,
The learning module (320)
If the estimated number of failures received per day is equal to the number of past failures received per day inputted to the output terminal of the neural network 20, the learning is terminated, and if not identical, the weight adjustment, And estimating the number of failures received. The apparatus for learning and calculating the number of received electrical faults using weather data and neural networks.
3. The method according to claim 1 or 2,
The learning module (320)
Wherein the input data and the weight corresponding thereto are recognized according to the number of failures received.
The method according to claim 1,
Wherein the past weather data acquired by the data acquisition unit (100) and the number of past failures received by each store stored in the storage unit (200) are data for the same period. Number Learning and Calculation Device.

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