Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
Title A Novel Fault Type Identification and Fault Restoration Visualization for Substation
Authors 이경민(Kyung-Min Lee) ; 홍재영(Jae-Young Hong) ; 강태원(Tae-Won Kang) ; 박철원(Chul-Won Park)
Page pp.1432-1439
ISSN 1975-8359
Keywords Artificial intelligence technology; Deep neural network; Expert system; Fault restoration; Fault type identification; Substation
Abstract Recently, the 4th industrial revolution is affecting industry and society as a whole. Accordingly, the need for autonomous intelligence of substation and power grids is being raised through artificial intelligence technology. This article, as part of the results of basic research on a fault restoration plan using intelligent techniques for digital substations, studied on a novel fault type identification and fault restoration visualization of the 154kV substation using the deep neural network and expert system. We constructed learning data through the operation status information such as CB and relay of transmission line, bus, transformer, and distribution line, which are components of the substation, and identified 15 fault types through deep neural networks. And the implemented system outputs a fault recovery procedure of the determined fault type through the expert system. Finally, we performed 4 fault types simulations to verify the performance of the fault restoration visualization