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《High Voltage Engineering》 2007-08
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Fault Diagnosis of Oil-immersed Power Transformer by DGA-NN

LI Qing-quan,WANG Wei,WANG Xiao-long(School of Electrical Engineering,Shandong University,Jinan 250061,China)  
Artificial neural network(ANN) has become a promising approach compared with conventional ratio method owing to its self-learning and adaptation capability.Following this approach,incipient fault detection in power transformers using ANN can be reduced to an association process of inputs(patterns of gases concentration) and outputs(fault type).Back-propagation Neural Network(BPNN) is one of most widely used neural network model,but the slow convergence and susceptible to network expansion restrain its future application.Another problem of BPNN is the determination of the nodes of hidden layer.Probabilistic neural network(PNN) has a fast training process to adjust weights for network training without any iterative or recursive computations.The decision boundaries and the training set can be easily modified using new data as it becomes available.The two different diagnosis models were established by Neural Network Tools Box that is available in MATLAB.The performance of two models was discussed in this paper.It is concluded from the simulation results that the convergence rapidity of PNN is five times that of BPNN approximately.
【Fund】: 山东省中青年科学家科研奖励基金(2005BS010002)。~~
【CateGory Index】: TM41
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