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《Power System Technology》 2004-04
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POWER TRANSFORMER FAULT DIAGNOSIS BY IMPROVED HYBRID ALGORITHM BASED ON GENETIC ALGORITHM AND BACK PROPAGATION ALGORITHM

WANG Shao-fang,CAI Jin-ding,LIU Qing-zhen (Dept.of Electrical Engineering,Fuzhou University,Fuzhou 350002,Fujian Province,China)  
The hybrid algorithm which combines improved genetic algorithm (GA) with error back-propagation algorithm (BP) is used to train artificial neural network. The defects of conventional BP algorithm, i.e., easy to fall into local minimum, slow convergence speed of the weight value of learning network, and that of GA, i.e., the training speed is too slow when GA is used to train the neural network effectively improved by itself, are effectively improved by the hybrid algorithm. The application of the hybrid algorithm to power transformer fault diagnosis is simulated, the results show that the hybrid algorithm possesses faster convergence speed and higher calculation accuracy.
【CateGory Index】: TM41
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