{"title":"基于机器学习的专家系统在电压暂降自动识别与分析中的应用","authors":"D. Sabin, Colton Peltier","doi":"10.1109/ICHQP53011.2022.9808700","DOIUrl":null,"url":null,"abstract":"This paper summarizes a software algorithm used to automatically classify voltage sags using an expert systems and machine learning algorithms. In particular, the paper focuses on algorithms used to classify voltage sag events that were caused by downstream events (faults, inrush, or load start events) and voltage sags cause by upstream events.","PeriodicalId":249133,"journal":{"name":"2022 20th International Conference on Harmonics & Quality of Power (ICHQP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Utilization of an Expert System Enhanced with Machine Learning for Automatic Voltage Sag Identification and Analysis\",\"authors\":\"D. Sabin, Colton Peltier\",\"doi\":\"10.1109/ICHQP53011.2022.9808700\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper summarizes a software algorithm used to automatically classify voltage sags using an expert systems and machine learning algorithms. In particular, the paper focuses on algorithms used to classify voltage sag events that were caused by downstream events (faults, inrush, or load start events) and voltage sags cause by upstream events.\",\"PeriodicalId\":249133,\"journal\":{\"name\":\"2022 20th International Conference on Harmonics & Quality of Power (ICHQP)\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 20th International Conference on Harmonics & Quality of Power (ICHQP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICHQP53011.2022.9808700\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 20th International Conference on Harmonics & Quality of Power (ICHQP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICHQP53011.2022.9808700","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Utilization of an Expert System Enhanced with Machine Learning for Automatic Voltage Sag Identification and Analysis
This paper summarizes a software algorithm used to automatically classify voltage sags using an expert systems and machine learning algorithms. In particular, the paper focuses on algorithms used to classify voltage sag events that were caused by downstream events (faults, inrush, or load start events) and voltage sags cause by upstream events.