{"title":"基于小波变换技术的输电线路故障检测与诊断","authors":"Bilal Masood, Umar Saleem, M. Anjum, Usman Arshad","doi":"10.1109/AEECT.2017.8257776","DOIUrl":null,"url":null,"abstract":"The demand of electrical power energy has grown exponentially in recent times and to meet this demand electrical power system network needs more sophistication and consequently more complexity. Transmission lines, expanded over several kilometers, are the backbone of the electrical power system which acts as interconnection between power houses and electricity consumers. Transmission lines are mostly located in the open and therefore, environmental effects can result in fault occurrences. The ability to detect and diagnose the faults can help greatly in the protection of transmission line. This paper presents modern solution of fault detection and diagnosis of overhead transmission lines by implementing Discrete Wavelet Transform (DWT). Faults in transmission line of various categories have been created using MATLAB/Simulink. The current signals of each phase are obtained from sending end, and then decompose using DWT to obtain the details coefficients up to five levels. Furthermore, normalized values are calculated from the norm of detail coefficients. In order to detect and diagnose the faults on transmission lines normalized values of each phase are compared with threshold values of the system. The proposed approach has been successfully tested for various categories of faults at different operating conditions.","PeriodicalId":286127,"journal":{"name":"2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Faults detection and diagnosis of transmission lines using wavelet transformed based technique\",\"authors\":\"Bilal Masood, Umar Saleem, M. Anjum, Usman Arshad\",\"doi\":\"10.1109/AEECT.2017.8257776\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The demand of electrical power energy has grown exponentially in recent times and to meet this demand electrical power system network needs more sophistication and consequently more complexity. Transmission lines, expanded over several kilometers, are the backbone of the electrical power system which acts as interconnection between power houses and electricity consumers. Transmission lines are mostly located in the open and therefore, environmental effects can result in fault occurrences. The ability to detect and diagnose the faults can help greatly in the protection of transmission line. This paper presents modern solution of fault detection and diagnosis of overhead transmission lines by implementing Discrete Wavelet Transform (DWT). Faults in transmission line of various categories have been created using MATLAB/Simulink. The current signals of each phase are obtained from sending end, and then decompose using DWT to obtain the details coefficients up to five levels. Furthermore, normalized values are calculated from the norm of detail coefficients. In order to detect and diagnose the faults on transmission lines normalized values of each phase are compared with threshold values of the system. The proposed approach has been successfully tested for various categories of faults at different operating conditions.\",\"PeriodicalId\":286127,\"journal\":{\"name\":\"2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT)\",\"volume\":\"114 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AEECT.2017.8257776\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AEECT.2017.8257776","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Faults detection and diagnosis of transmission lines using wavelet transformed based technique
The demand of electrical power energy has grown exponentially in recent times and to meet this demand electrical power system network needs more sophistication and consequently more complexity. Transmission lines, expanded over several kilometers, are the backbone of the electrical power system which acts as interconnection between power houses and electricity consumers. Transmission lines are mostly located in the open and therefore, environmental effects can result in fault occurrences. The ability to detect and diagnose the faults can help greatly in the protection of transmission line. This paper presents modern solution of fault detection and diagnosis of overhead transmission lines by implementing Discrete Wavelet Transform (DWT). Faults in transmission line of various categories have been created using MATLAB/Simulink. The current signals of each phase are obtained from sending end, and then decompose using DWT to obtain the details coefficients up to five levels. Furthermore, normalized values are calculated from the norm of detail coefficients. In order to detect and diagnose the faults on transmission lines normalized values of each phase are compared with threshold values of the system. The proposed approach has been successfully tested for various categories of faults at different operating conditions.