发电机进相能力的RBF神经网络模型

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Abstract Generator leading phase operation is a kind of economic and effective measures of voltage regulation and power quality improvement. Due to the synchronous generator is a multivariable and strong coupling nonlinear system, it is difficult to obtain satisfactory results by traditional analysis method. This paper proposes a new method of modeling generator leading phase ability based on radial basis function (RBF) neural network. The model with active power and reactive power of generator for input, with generator voltage and power-angle for output, using a 600 MW generator leading phase test data training RBF neural network and testing network generalization ability, the choice of the base wide, the number of neurons in hidden layer in RBF network convergence precision influence are discussed. Research shows that this generator leading phase RBF model set up in the paper has the advantages of high speed and precision, and its performance is superior to the BP neural network model.

Key words： RBF neural network generator leading phase modeling

Received: 01 November 2010 Published: 19 March 2014

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TM311

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	Articles by authors
	Wang Chengliang
	Wang Honghua
	Xiang Changming
	Xu Gang

Cite this article:

Wang Chengliang,Wang Honghua,Xiang Changming等. Generator Leading Phase Ability Model Based on RBF Neural Network[J]. Transactions of China Electrotechnical Society, 2012, 27(1): 124-129.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2012/V27/I1/124

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