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Artificial neural networks and genetic algorithm for transformer winding/insulation faults

K.S.R., Rao and K.N., Nashruladin (2008) Artificial neural networks and genetic algorithm for transformer winding/insulation faults. In: 4th IASTED Asian Conference on Power and Energy Systems, AsiaPES 2008, 2 April 2008 through 4 April 2008, Langkawi.

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Abstract

This paper presents an application of Artificial Neural Network and Genetic Algorithm for transformer winding/insulation faults diagnosed using Dissolved Gas in Oil Analysis. A back propagation training method is applied in neural network to detect the faults without cellulose involvement. Genetic Algorithm is used to derive the optimal key gas ratios to enhance the accuracy of fault detection. The dissolved gas in oil analysis method is known to be an early fault detection method and enables to carry out diagnosis during online operation of the transformer. Besides, the condition of the transformer could be monitored continuously by time to time. The results are compared between the real and predicted faults to observe the accuracy rate of the system.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Backpropagation; Dissolution; Electric fault location; Fault detection; Gases; Genetic algorithms; Oil filled transformers; Accuracy rates; Artificial Neural Network; Detection methods; Dissolved gas analysis; Dissolved gas-in-oil analysis; Early faults; Gas ratios; On-line operations; Training methods; Transformer fault detection and diagnosis; Neural networks
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments / MOR / COE:Departments > Electrical & Electronic Engineering
ID Code:479
Deposited By: Assoc Prof Dr K. S. Rama Rao
Deposited On:09 Mar 2010 02:01
Last Modified:19 Jan 2017 08:26

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