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Neural Networks Approach to Online Identification of Multiple Failures of Protection Systems
Citation
Negnevitsky, M and Pavlovsky, V, Neural Networks Approach to Online Identification of Multiple Failures of Protection Systems, IEEE Transactions on Power Delivery, 20, (2) pp. 588-594. ISSN 0885-8977 (2005) [Refereed Article]
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Copyright Statement
Copyright 2005 IEEE
DOI: doi:10.1109/TPWRD.2004.843451
Abstract
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 Details
Item Type: | Refereed Article |
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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: | 21 |
Deposited By: | Engineering |
Deposited On: | 2005-08-01 |
Last Modified: | 2012-11-06 |
Downloads: | 4 View Download Statistics |
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