{"title":"Analysis of high impedance faults using fractal techniques","authors":"Alexander Mamishev, B. Russell, C. Benner","doi":"10.1109/PICA.1995.515270","DOIUrl":null,"url":null,"abstract":"Phase currents and voltages in a power distribution system change with a certain degree of chaos when high impedance faults (HIFs) occur. This paper describes application of the concepts of fractal geometry to analyze chaotic properties of high impedance faults. Root-mean-square rather that instantaneous values of currents are used for characterization of temporal system behavior; this results in relatively short time-series available for analysis. An algorithm is presented for pattern recognition and detection of HIFs; it is based on techniques suited for analysis of relatively small data sets. Examples are given to illustrate the ability of this approach to discriminate between faults and other transients in a power system.","PeriodicalId":294493,"journal":{"name":"Proceedings of Power Industry Computer Applications Conference","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"131","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of Power Industry Computer Applications Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PICA.1995.515270","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 131
Abstract
Phase currents and voltages in a power distribution system change with a certain degree of chaos when high impedance faults (HIFs) occur. This paper describes application of the concepts of fractal geometry to analyze chaotic properties of high impedance faults. Root-mean-square rather that instantaneous values of currents are used for characterization of temporal system behavior; this results in relatively short time-series available for analysis. An algorithm is presented for pattern recognition and detection of HIFs; it is based on techniques suited for analysis of relatively small data sets. Examples are given to illustrate the ability of this approach to discriminate between faults and other transients in a power system.