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Partial discharge pattern classification using multilayer neural networks

Satish, L and Gururaj, BI (1993) Partial discharge pattern classification using multilayer neural networks. In: IEE Proceedings of Science, Measurement and Technology, 140 (4). pp. 323-330.


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Partial discharge measurement is an important means of assessing the condition and integrity of insulation systems in high voltage power apparatus. Commercially available partial discharge detectors display them as patterns by an elliptic time base. Over the years, experts have been interpreting and recognising the nature and cause of partial discharges by studying these patterns. A way to automate this process is reported by using the partial discharge patterns as input to a multilayer neural network with two hidden layers. The patterns are complex and can be further complicated by interference. Therefore the recognition process appropriately qualifies as a challenging neural network task. The simulation results, and those obtained when tested with actual patterns, indicate the suitability of neural nets for real world applications in this emerging domain. Some limitations of this method are also mentioned.

Item Type: Journal Article
Publication: IEE Proceedings of Science, Measurement and Technology
Publisher: IEE
Additional Information: Copyright of this article belongs to Institution of Electrical Engineers (IEE)
Keywords: Neural networks;Multilayer;Partial discharge;Recognition process
Department/Centre: Division of Electrical Sciences > High Voltage Engineering (merged with EE)
Date Deposited: 25 Aug 2008
Last Modified: 19 Sep 2010 04:20
URI: http://eprints.iisc.ac.in/id/eprint/3723

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