{"title":"配电系统暂态现象检测与分类的无监督学习策略","authors":"D.L. Lubkeman, C.D. Fallon, A. Girgis","doi":"10.1109/ANN.1991.213506","DOIUrl":null,"url":null,"abstract":"A number of utilities are currently installing high-speed data acquisition equipment in their distribution substations. This equipment will make it possible to record the transient waveforms due to events such as low and high-impedance faults, capacitor switching, and load switching. The authors describe the potential of applying unsupervised learning strategies to the classification of the various events observed by a substation recorder. Several strategies are tested using simulation studies and the effectiveness of unsupervised learning is compared to current classification strategies as well as supervised learning.<<ETX>>","PeriodicalId":119713,"journal":{"name":"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1991-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Unsupervised learning strategies for the detection and classification of transient phenomena on electric power distribution systems\",\"authors\":\"D.L. Lubkeman, C.D. Fallon, A. Girgis\",\"doi\":\"10.1109/ANN.1991.213506\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A number of utilities are currently installing high-speed data acquisition equipment in their distribution substations. This equipment will make it possible to record the transient waveforms due to events such as low and high-impedance faults, capacitor switching, and load switching. The authors describe the potential of applying unsupervised learning strategies to the classification of the various events observed by a substation recorder. Several strategies are tested using simulation studies and the effectiveness of unsupervised learning is compared to current classification strategies as well as supervised learning.<<ETX>>\",\"PeriodicalId\":119713,\"journal\":{\"name\":\"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1991-07-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ANN.1991.213506\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ANN.1991.213506","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Unsupervised learning strategies for the detection and classification of transient phenomena on electric power distribution systems
A number of utilities are currently installing high-speed data acquisition equipment in their distribution substations. This equipment will make it possible to record the transient waveforms due to events such as low and high-impedance faults, capacitor switching, and load switching. The authors describe the potential of applying unsupervised learning strategies to the classification of the various events observed by a substation recorder. Several strategies are tested using simulation studies and the effectiveness of unsupervised learning is compared to current classification strategies as well as supervised learning.<>
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