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In complex emergency situations, failed protection relays and circuit breakers (CBs) have to be identified in order to begin the restoration process of a power system. This paper proposes a novel neural-network approach to identify multiple failures of protection relays and/or CBs. The approach uses information received from protection systems in the form of alarms and is able to deal with incomplete and distorted data. All possible emergencies are simulated and analyzed separately for each section of a power system. Taking into consideration supervisory control and data-acquisition system malfunctions, the corrupted patterns are used to train neural networks. The preliminary classification of emergencies into two different classes is applied to improve the system's performance. The evaluation of results shows that the overall error rate does not exceed 5 %. The developed system was tested on a real power system.

Item Type:	Refereed Article
Keywords:	alarm systems, fault diagnosis, identification, neural networks, pattern recognition
Research Division:	Information and Computing Sciences
Research Group:	Machine learning
Research Field:	Neural networks
Objective Division:	Energy
Objective Group:	Energy storage, distribution and supply
Objective Field:	Energy systems and analysis
UTAS Author:	Negnevitsky, M (Professor Michael Negnevitsky)
ID Code:	32852
Year Published:	2005
Web of Science® Times Cited:	19
Deposited By:	Engineering
Deposited On:	2005-08-01
Last Modified:	2012-11-06
Downloads:	4 View Download Statistics