Kaustubh Bhatnagar, Subham S. Sahoo, F. Iov, F. Blaabjerg
{"title":"Physics Guided Data-Driven Characterization of Anomalies in Power Electronic Systems","authors":"Kaustubh Bhatnagar, Subham S. Sahoo, F. Iov, F. Blaabjerg","doi":"10.1109/eGRID52793.2021.9662148","DOIUrl":null,"url":null,"abstract":"The transition of conventional power system onto power electronics dominated grid (PEDG) has lead to amplified complexity in system-level control schemes to maintain reliability and operational stability. Considering the abundance of data in PEDG, machine learning (ML) schemes have emerged as a promising alternative. In this article, a physical guided data-driven approach using pattern recognition neural network (PRNN) is employed with semi-supervised learning. To distinguish between the faults and cyber-attacks without relying historical data scenarios. Finally, the results of proposed approach are discussed by utilizing ML tools.","PeriodicalId":198321,"journal":{"name":"2021 6th IEEE Workshop on the Electronic Grid (eGRID)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 6th IEEE Workshop on the Electronic Grid (eGRID)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/eGRID52793.2021.9662148","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
The transition of conventional power system onto power electronics dominated grid (PEDG) has lead to amplified complexity in system-level control schemes to maintain reliability and operational stability. Considering the abundance of data in PEDG, machine learning (ML) schemes have emerged as a promising alternative. In this article, a physical guided data-driven approach using pattern recognition neural network (PRNN) is employed with semi-supervised learning. To distinguish between the faults and cyber-attacks without relying historical data scenarios. Finally, the results of proposed approach are discussed by utilizing ML tools.