基于小波神经网络的风力发电机故障诊断

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Abstract As wind power generation is a complicated nonlinear time-varying system, it's hard to extract effective fault feature. A novel algorithm that combined the modified local discriminant basis (LDB)algorithm and SOM-BP network is proposed to fault diagnosis and isolation. Extracting primal fault feature by improved LDB algorithm, then map this incipient fault feature into a new feature space with high class separability via self-organizing feature map (SOM) nonlinearly transform, finally BP is used as the nonlinear classifier to implement fault diagnosis and isolation.

Key words： Wavelet packet local discriminant basis neural network fault diagnosis

Received: 23 October 2007 Published: 13 February 2014

PACS:

TM183

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	Articles by authors
	Zhuang Zhemin
	Yin Guohua
	Li Fenlan
	Jiang Zhongwei

Cite this article:

Zhuang Zhemin,Yin Guohua,Li Fenlan等. Fault Diagnosis of Wind Power Generation Based on Wavelet Neural Network[J]. Transactions of China Electrotechnical Society, 2009, 24(4): 224-229.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2009/V24/I4/224

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