Abstrakt

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.
OriginalsprogEngelsk
TitelProceedings of the 2021 6th IEEE Workshop on the Electronic Grid (eGRID)
Antal sider6
ForlagIEEE Press
Publikationsdatonov. 2021
Sider1-6
Artikelnummer9662148
ISBN (Trykt)978-1-6654-4980-9
ISBN (Elektronisk)978-1-6654-4979-3
DOI
StatusUdgivet - nov. 2021
Begivenhed2021 6th IEEE Workshop on the Electronic Grid (eGRID) - New Orleans, LA, USA
Varighed: 8 nov. 202110 nov. 2021

Konference

Konference2021 6th IEEE Workshop on the Electronic Grid (eGRID)
Land/OmrådeUSA
ByNew Orleans, LA
Periode08/11/202110/11/2021

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