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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.
Original languageEnglish
Title of host publicationProceedings of the 2021 6th IEEE Workshop on the Electronic Grid (eGRID)
Number of pages6
PublisherIEEE Press
Publication dateNov 2021
Article number9662148
ISBN (Print)978-1-6654-4980-9
ISBN (Electronic)978-1-6654-4979-3
Publication statusPublished - Nov 2021
Event2021 6th IEEE Workshop on the Electronic Grid (eGRID) - New Orleans, LA, United States
Duration: 8 Nov 202110 Nov 2021


Conference2021 6th IEEE Workshop on the Electronic Grid (eGRID)
Country/TerritoryUnited States
CityNew Orleans, LA


  • Machine Learning
  • Power Electronics
  • Anomaly Detection
  • Physics-informed techniques
  • Cyber-Physical Systems
  • Cyber Attacks
  • Faults
  • Power Systems


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