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Bushing Fault Detection and Diagnosis using Extension Neural Network

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2 Author(s)
Vilakazi, C.B. ; Sch. of Electr. & Inf. Eng., Univ. of the Witwatersrand, Johannesburg ; Marwala, T.

This paper proposes an extension neural network (ENN) based bushing fault detection and diagnosis. Experimentation is done using dissolve gas-in-oil analysis (DGA) data from bushings based on IEEEc57.104, IEC599 and IEEE production rates methods for oil impregnated paper (OIP) bushings. The optimal learning rate for ENN is selected using genetic algorithm (GA). The classification process is a two stage phase. The first stage is the detection which identifies if the bushing is faulty or normal while the second stage determines the nature of fault. A classification rate of 100% and an average of 99.89% obtained for the detection and diagnosis stage, respectively. It takes 1.98s and 2.02s to train the ENN for the detection and diagnosis stage, respectively

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Intelligent Engineering Systems, 2006. INES '06. Proceedings. International Conference on

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