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Power distribution network fault classification method and system based on deep learning, and medium

A distribution network fault and deep learning technology, applied in information technology support systems, photovoltaic power generation, character and pattern recognition, etc., can solve problems such as poor protection sensitivity and complicated threshold value setting process, and achieve high classification accuracy and fast Reliable identification and classification, and the effect of reducing the false action rate

Active Publication Date: 2020-05-15
HUAZHONG UNIV OF SCI & TECH +1
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide a distribution network fault classification method, system and medium based on deep learning, which can use the strong classification advantages of deep learning to classify faults in the distribution network. Fast and reliable identification and classification, which solves the technical problems of complex threshold value setting process and poor protection sensitivity when identifying DC distribution network faults in the prior art

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  • Power distribution network fault classification method and system based on deep learning, and medium
  • Power distribution network fault classification method and system based on deep learning, and medium
  • Power distribution network fault classification method and system based on deep learning, and medium

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Experimental program
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Effect test

Embodiment 1

[0064] Embodiment one, as figure 1 As shown, a distribution network fault classification method based on deep learning includes the following steps:

[0065] S1: Obtain multiple original fault waveform data sets of the DC distribution network;

[0066] S2: Process each original fault waveform data group separately to obtain target sample data corresponding to each original fault waveform data group;

[0067] S3: Make all the target sample data into a data set, divide the data set into a training set and a test set, construct a deep learning network model, use the training set to train the deep learning network model, and obtain the original fault classification model;

[0068] S4: Using the test set to perform parameter tuning on the original fault classification model to obtain an optimized fault classification model;

[0069] S5: Obtain the real-time fault waveform data group of the DC distribution network, and process the real-time fault waveform data group according to ...

Embodiment 2

[0141] Embodiment two, such as Figure 8 As shown, a distribution network fault classification system based on deep learning, including data acquisition module, data processing module, model acquisition module, parameter optimization module and fault classification module;

[0142] The data acquisition module is used to acquire a plurality of original fault waveform data groups of the DC distribution network; it is also used to obtain real-time fault waveform data groups of the DC distribution network;

[0143] The data processing module is used to process each original fault waveform data group separately to obtain target sample data corresponding to each original fault waveform data group; it is also used to process the real-time fault waveform data group, Obtaining the fault data to be tested corresponding to the real-time fault waveform data group;

[0144] The model acquisition module is used to make all target sample data into a data set, and divide the data set into a ...

Embodiment 3

[0166] Embodiment 3. Based on Embodiment 1 and Embodiment 2, this embodiment also discloses a distribution network fault classification system based on deep learning, which includes a processor, a memory, and is stored in the memory and can run on the A computer program on a processor, when said computer program runs, it realizes as figure 1 The specific steps from S1 to S5 are shown.

[0167] Through the computer program stored in the memory and run on the processor, the fault classification of the DC distribution network of the present invention is realized, and the faults in the distribution network are quickly and reliably identified and classified by using the strong classification advantages of deep learning. The efficiency is high, and the classification accuracy is high, which is beneficial to reduce the misoperation rate of the protection action of the DC distribution network according to the fault.

[0168] This embodiment also provides a computer storage medium, wh...

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Abstract

The invention relates to a power distribution network fault classification method and system based on deep learning, and a medium. The method comprises the steps: obtaining a plurality of original fault waveform data sets; respectively processing each original fault waveform data set to obtain target sample data; making all target sample data into a data set, dividing the data set into a trainingset and a test set, constructing a deep learning network model, and training the deep learning network model by using the training set to obtain an original fault classification model; performing parameter tuning on the original fault classification model by using the test set to obtain an optimized fault classification model; acquiring a real-time fault waveform data set, processing the real-timefault waveform data set to obtain to-be-detected fault data, and performing real-time identification on the to-be-detected fault data by utilizing the optimized fault classification model to obtain areal-time fault classification result. According to the method, the faults in the power distribution network are quickly and reliably recognized and classified by utilizing the strong classificationadvantage of deep learning, the recognition efficiency is high, and the classification accuracy is high.

Description

technical field [0001] The invention relates to the technical field of power system relay protection, in particular to a method, system and medium for fault classification of distribution network based on deep learning. Background technique [0002] As more and more distributed renewable energy sources are connected to the distribution network with a high penetration rate, such as wind energy, photovoltaics, etc., the structure of the distribution network is becoming more and more complex, and various high-power units, high-power electrical equipment, etc. The ground is put into the operation of the DC distribution network, which increases the harm caused by the short-circuit current of the distribution network. Unlike the AC system, the DC system, as a low-inertia system, will quickly generate a high amount of short-circuit current after a fault occurs, endangering the safety of the grid equipment. Therefore, it is necessary to quickly and accurately detect the fault in ord...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06N3/04G06Q50/06
CPCG06Q50/06G06N3/045G06F2218/10G06F2218/12Y04S10/50
Inventor 林湘宁汪光远马啸李正天曹善康
Owner HUAZHONG UNIV OF SCI & TECH
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