Title :
Bushing Fault Detection and Diagnosis using Extension Neural Network
Author :
Vilakazi, Christina B. ; Marwala, Tshilidzi
Author_Institution :
Sch. of Electr. & Inf. Eng., Univ. of the Witwatersrand, Johannesburg
Abstract :
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
Keywords :
IEEE standards; bushings; condition monitoring; fault diagnosis; genetic algorithms; insulating oils; learning (artificial intelligence); neural nets; paper; pattern classification; power engineering computing; IEC599 rate method; IEEE production rate method; IEEEc57.104 rate method; bushing fault detection; classification process; dissolve gas-in-oil analysis data; extension neural network; fault diagnosis; genetic algorithm; oil impregnated paper bushing; optimal learning rate; Condition monitoring; Dissolved gas analysis; Fault detection; Fault diagnosis; Hydrogen; Insulators; Neural networks; Oil insulation; Petroleum; Power transformer insulation;
Conference_Titel :
Intelligent Engineering Systems, 2006. INES '06. Proceedings. International Conference on
Conference_Location :
London
Print_ISBN :
0-7803-9708-8
DOI :
10.1109/INES.2006.1689363
