Yang Liu, Lisheng Li, Kai Chen, Linli Zhang, Shidong Zhang
{"title":"基于小波分析和支持向量机的10kv配电网永暂故障识别","authors":"Yang Liu, Lisheng Li, Kai Chen, Linli Zhang, Shidong Zhang","doi":"10.1109/AEEES51875.2021.9403018","DOIUrl":null,"url":null,"abstract":"Conventional automatic closing may reclose on a permanent fault and cause severe consequences without judging the fault type after a delay. It is proposed to use the fault recording data after the circuit breaker trips to identify the types of faults in real-time. If it is a transient fault, the switch will be closed; then the power supply will be restored. While it is identified to be a permanent fault, the switch will not be closed and wait for maintenance. In this paper, wavelet analysis is adopted to extract real-time features from the fault recording data, and then the support vector machine (SVM) model is used to identify permanent and transient faults, which can avoid blind automatic reclosing.","PeriodicalId":356667,"journal":{"name":"2021 3rd Asia Energy and Electrical Engineering Symposium (AEEES)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Permanent and Transient Fault Identification for 10 kV Distribution Network Using Wavelet Analysis and Support Vector Machine\",\"authors\":\"Yang Liu, Lisheng Li, Kai Chen, Linli Zhang, Shidong Zhang\",\"doi\":\"10.1109/AEEES51875.2021.9403018\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Conventional automatic closing may reclose on a permanent fault and cause severe consequences without judging the fault type after a delay. It is proposed to use the fault recording data after the circuit breaker trips to identify the types of faults in real-time. If it is a transient fault, the switch will be closed; then the power supply will be restored. While it is identified to be a permanent fault, the switch will not be closed and wait for maintenance. In this paper, wavelet analysis is adopted to extract real-time features from the fault recording data, and then the support vector machine (SVM) model is used to identify permanent and transient faults, which can avoid blind automatic reclosing.\",\"PeriodicalId\":356667,\"journal\":{\"name\":\"2021 3rd Asia Energy and Electrical Engineering Symposium (AEEES)\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-03-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 3rd Asia Energy and Electrical Engineering Symposium (AEEES)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AEEES51875.2021.9403018\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 3rd Asia Energy and Electrical Engineering Symposium (AEEES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AEEES51875.2021.9403018","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Permanent and Transient Fault Identification for 10 kV Distribution Network Using Wavelet Analysis and Support Vector Machine
Conventional automatic closing may reclose on a permanent fault and cause severe consequences without judging the fault type after a delay. It is proposed to use the fault recording data after the circuit breaker trips to identify the types of faults in real-time. If it is a transient fault, the switch will be closed; then the power supply will be restored. While it is identified to be a permanent fault, the switch will not be closed and wait for maintenance. In this paper, wavelet analysis is adopted to extract real-time features from the fault recording data, and then the support vector machine (SVM) model is used to identify permanent and transient faults, which can avoid blind automatic reclosing.