基于混合神经网络的风电场风资源评估

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Abstract Wind energy resource assessment is a key step of wind farm planning and design. A hybrid neural network (HNN) based wind energy resource assessment method is proposed to improve the assessment accuracy, and the method allows the use of regional information. Firstly, the HNN based correlate model is developed according to the concurrent wind speeds of the reference weather stations and the candidate wind farm. In order to obtain wider learning capability and avoid being trapped in a local minimum, the different training algorithm and the adaptive particle swarm optimization (APSO) are used in the HNN. Then, the whole long-term wind speed and direction data are applied to the model, thus the long-term wind characteristics of the candidate wind farm are obtained. The wind energy resource assessment parameters are subsequently computed on the basis of the knowledge of these wind speeds. The simulation results show that the proposed method has relatively high accuracy.

Key words： Wind farm wind energy resource assessment hybrid neural network particle swarm optimization

Received: 30 June 2014 Published: 08 September 2015

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TM614

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	Wang Na
	Zhou Youqing
	Shao Xia

Cite this article:

Wang Na,Zhou Youqing,Shao Xia. Wind Energy Resource Assessment of Wind Farm Based on Hybrid Neural Network[J]. Transactions of China Electrotechnical Society, 2015, 30(14): 370-376.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2015/V30/I14/370

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