Application of Convolutional Neural Network in Fault Line Selection of Distribution Network

Aiming at the problem that the effect of the existing fault line selection methods is mainly determined by the fault features constructed by manual extraction, and the fault feature extraction process is complex and time-consuming, a new method based on convolutional neural network (CNN) is proposed...

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Autores principales:	Jingjing Tian, Fang Geng, Feng Zhao, Fengyang Gao, Xinqiang Niu
Formato:	article
Lenguaje:	EN
Publicado:	Tamkang University Press 2021
Materias:	fault line selection time-frequency energy matrix convolutional neural network shared weights Engineering (General). Civil engineering (General) TA1-2040 Chemical engineering TP155-156 Physics QC1-999
Acceso en línea:	https://doaj.org/article/f4f1c7caf231413f81a2527550918bbe
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spelling	oai:doaj.org-article:f4f1c7caf231413f81a2527550918bbe2021-11-23T14:59:59ZApplication of Convolutional Neural Network in Fault Line Selection of Distribution Network10.6180/jase.202202_25(1).00202708-99672708-9975https://doaj.org/article/f4f1c7caf231413f81a2527550918bbe2021-11-01T00:00:00Zhttp://jase.tku.edu.tw/articles/jase-202202-25-1-0020https://doaj.org/toc/2708-9967https://doaj.org/toc/2708-9975Aiming at the problem that the effect of the existing fault line selection methods is mainly determined by the fault features constructed by manual extraction, and the fault feature extraction process is complex and time-consuming, a new method based on convolutional neural network (CNN) is proposed. Firstly, the fault voltage and current signal data are collected, and the time-frequency energy matrix of the fault signal is constructed by the HHT band-pass filtering method, which is regarded as the two-dimensional matrix form of input data of CNN. Then the time-frequency energy matrix is input into the CNN, and the fault features are extracted autonomously through the convolution layer and pooling layer of the network, which is used to train the network to realize fault line selection and fault phase judgment. Finally, the results show that the method not only has a high accuracy of fault line selection but also can complete the fault line selection and fault phase judgment at the same time without adjusting any parameters, which realize the shared weights of the two non-independent problems. Meanwhile, under the influence of noise interference, compensation degree, network structure change, and other factors, the proposed method has good robustness. However, compared with the FCM, SVM, DNN, and DBN, CNN can still identify the faulty line and keep the optimal accuracy in the case of two-point grounding fault, and other methods have errors inline selection or low accuracy. Therefore, the experimental results also provide a new idea for fault line selection of distribution networks.Jingjing TianFang GengFeng ZhaoFengyang GaoXinqiang NiuTamkang University Pressarticlefault line selectiontime-frequency energy matrixconvolutional neural networkshared weightsEngineering (General). Civil engineering (General)TA1-2040Chemical engineeringTP155-156PhysicsQC1-999ENJournal of Applied Science and Engineering, Vol 25, Iss 1, Pp 195-205 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	fault line selection time-frequency energy matrix convolutional neural network shared weights Engineering (General). Civil engineering (General) TA1-2040 Chemical engineering TP155-156 Physics QC1-999
spellingShingle	fault line selection time-frequency energy matrix convolutional neural network shared weights Engineering (General). Civil engineering (General) TA1-2040 Chemical engineering TP155-156 Physics QC1-999 Jingjing Tian Fang Geng Feng Zhao Fengyang Gao Xinqiang Niu Application of Convolutional Neural Network in Fault Line Selection of Distribution Network
description	Aiming at the problem that the effect of the existing fault line selection methods is mainly determined by the fault features constructed by manual extraction, and the fault feature extraction process is complex and time-consuming, a new method based on convolutional neural network (CNN) is proposed. Firstly, the fault voltage and current signal data are collected, and the time-frequency energy matrix of the fault signal is constructed by the HHT band-pass filtering method, which is regarded as the two-dimensional matrix form of input data of CNN. Then the time-frequency energy matrix is input into the CNN, and the fault features are extracted autonomously through the convolution layer and pooling layer of the network, which is used to train the network to realize fault line selection and fault phase judgment. Finally, the results show that the method not only has a high accuracy of fault line selection but also can complete the fault line selection and fault phase judgment at the same time without adjusting any parameters, which realize the shared weights of the two non-independent problems. Meanwhile, under the influence of noise interference, compensation degree, network structure change, and other factors, the proposed method has good robustness. However, compared with the FCM, SVM, DNN, and DBN, CNN can still identify the faulty line and keep the optimal accuracy in the case of two-point grounding fault, and other methods have errors inline selection or low accuracy. Therefore, the experimental results also provide a new idea for fault line selection of distribution networks.
format	article
author	Jingjing Tian Fang Geng Feng Zhao Fengyang Gao Xinqiang Niu
author_facet	Jingjing Tian Fang Geng Feng Zhao Fengyang Gao Xinqiang Niu
author_sort	Jingjing Tian
title	Application of Convolutional Neural Network in Fault Line Selection of Distribution Network
title_short	Application of Convolutional Neural Network in Fault Line Selection of Distribution Network
title_full	Application of Convolutional Neural Network in Fault Line Selection of Distribution Network
title_fullStr	Application of Convolutional Neural Network in Fault Line Selection of Distribution Network
title_full_unstemmed	Application of Convolutional Neural Network in Fault Line Selection of Distribution Network
title_sort	application of convolutional neural network in fault line selection of distribution network
publisher	Tamkang University Press
publishDate	2021
url	https://doaj.org/article/f4f1c7caf231413f81a2527550918bbe
work_keys_str_mv	AT jingjingtian applicationofconvolutionalneuralnetworkinfaultlineselectionofdistributionnetwork AT fanggeng applicationofconvolutionalneuralnetworkinfaultlineselectionofdistributionnetwork AT fengzhao applicationofconvolutionalneuralnetworkinfaultlineselectionofdistributionnetwork AT fengyanggao applicationofconvolutionalneuralnetworkinfaultlineselectionofdistributionnetwork AT xinqiangniu applicationofconvolutionalneuralnetworkinfaultlineselectionofdistributionnetwork
_version_	1718416249409503232

Application of Convolutional Neural Network in Fault Line Selection of Distribution Network

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