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APPLICATION OF ANN AND GA FOR TRANSFORMER WINDING/ INSULATION FAULTS

NASHRULADIN, KHAIRUN NISA' (2007) APPLICATION OF ANN AND GA FOR TRANSFORMER WINDING/ INSULATION FAULTS. Universiti Teknologi PETRONAS. (Unpublished)

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Abstract

This report 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. While, heuristic method of Genetic Algorithm is used to locate the optimal values to enhance the accuracy of fault detection. The dissolved gas in oil analysis is chosen to diagnosis the transformer faults in this project as the method is known to be an early fault detection method and enables to carry out during online operation of the transformer. Besides, the condition of the transformer could be monitored continuously by time to time. The project outcome is analyzed using Neural Network and Genetic Algorithm MATLAB Toolbox. Comparison between the real fault and predicted fault is made as to observe the accuracy rate of the system. As transformer faults detection concentrated more in conventional method such the stability of the voltage and current of the transformer. Therefore, hopefully the transformer winding and insulation faults could be studied from new point ofview and method.

Item Type: Final Year Project
Academic Subject : Academic Department - Electrical And Electronics - Power Systems - Distribution - Power transformers
Subject: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Engineering > Electrical and Electronic
Depositing User: Users 2053 not found.
Date Deposited: 22 Oct 2013 14:38
Last Modified: 22 Oct 2013 14:38
URI: http://utpedia.utp.edu.my/id/eprint/9476

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