Title :
Fault identification in an AC-DC transmission system using neural networks
Author :
Kandil, N. ; Sood, Y.K. ; Khorasani, K. ; Patel, R.V.
Author_Institution :
Dept. of Electr. & Comput. Eng., Concordia Univ., Montreal, Que., Canada
Abstract :
The possibility of using neural networks to identify faults that may have occurred in an AC-DC power system is explored. Based on the ability of these networks to distinguish reliably between different types of fault, appropriate control measures can be taken to improve the dynamic performance of the AC-DC power system. Three different neural network architectures to distinguish between different types of fault on the AC-DC system are proposed, and a comparison between them is made
Keywords :
electrical faults; neural nets; power system analysis computing; power system interconnection; transmission networks; AC-DC transmission system; architectures; control measures; dynamic performance; fault identification; interconnection; neural networks; power system analysis computing; Control systems; Fault diagnosis; Neural networks; Power measurement; Power system control; Power system dynamics; Power system faults; Power system measurements; Power system reliability; Power systems;
Conference_Titel :
Power Industry Computer Application Conference, 1991. Conference Proceedings
Conference_Location :
Baltimore, MD
Print_ISBN :
0-87942-620-9
DOI :
10.1109/PICA.1991.160590