基于小波和深度学习的配电网单相接地故障辨识

TM933; 随着配电网规模的不断扩大,发生单相接地故障后产生的危害也愈加严重,为避免故障进一步升级,必须迅速采取措施切除故障.配电网故障辨识有利于快速查明故障原因,进而采取相应措施切除故障.同时,故障辨识也是故障选线的前提.针对上述情况,文中介绍了一种利用小波分析提取故障特征量并用深度神经网络进行故障辨识的方法.结果 表明,该方法可对小电流接地系统各类单相接地故障进行辨识且辨识准确率高,而且辨识精度受噪声污染影响比传统人工神经网络小....

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Published in电测与仪表 Vol. 58; no. 4; pp. 115 - 120
Main Authors 李晓波, 陈义刚, 陈文斌, 高帅, 包从波
Format Journal Article
LanguageChinese
Published 中国矿业大学电气与动力工程学院,江苏徐州221008 15.04.2021
Subjects
单相接地
故障辨识
中压配电网
深度神经网络
Online AccessGet full text
ISSN1001-1390
DOI10.19753/j.issn1001-1390.2021.04.017

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Abstract TM933; 随着配电网规模的不断扩大,发生单相接地故障后产生的危害也愈加严重,为避免故障进一步升级,必须迅速采取措施切除故障.配电网故障辨识有利于快速查明故障原因,进而采取相应措施切除故障.同时,故障辨识也是故障选线的前提.针对上述情况,文中介绍了一种利用小波分析提取故障特征量并用深度神经网络进行故障辨识的方法.结果 表明,该方法可对小电流接地系统各类单相接地故障进行辨识且辨识准确率高,而且辨识精度受噪声污染影响比传统人工神经网络小.
AbstractList TM933; 随着配电网规模的不断扩大,发生单相接地故障后产生的危害也愈加严重,为避免故障进一步升级,必须迅速采取措施切除故障.配电网故障辨识有利于快速查明故障原因,进而采取相应措施切除故障.同时,故障辨识也是故障选线的前提.针对上述情况,文中介绍了一种利用小波分析提取故障特征量并用深度神经网络进行故障辨识的方法.结果 表明,该方法可对小电流接地系统各类单相接地故障进行辨识且辨识准确率高,而且辨识精度受噪声污染影响比传统人工神经网络小.
Author 包从波
陈义刚
李晓波
陈文斌
高帅
AuthorAffiliation 中国矿业大学电气与动力工程学院,江苏徐州221008
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Bao Congbo
Gao Shuai
Li Xiaobo
Chen Yigang
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故障辨识
中压配电网
深度神经网络
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