基于小波包分解和改进Elman神经网络的风电场风速和风电功率预测

doi:10.19595/j.cnki.1000-6753.tces.160727

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Abstract Accurate prediction of wind speed and wind power is of great significance to the operation and maintenance of wind farms,the optimal scheduling of turbines and the safe and stable operation of power grids.A new method for wind speed and wind power forecasting based on the wavelet packet decomposition theory and an improved Elman neural network was put forward,and the concrete application steps of the method was given.Wavelet packet decomposition theory is firstly adopted to decompose wind speed data into several wavelet spaces,and according to the correlation,the optimal decomposition tree is persisted and random data are rejected.Then a new PSO training algorithm with disturbance is proposed to improve the training speed of neural networks and deal with the drawback of easily falling into local optimal solution of PSO.Finally,Elman neural networks with different structures are established and used to find the laws of wind speed in different frequency bands,wind speed and wind power prediction results are hence received.The forecasting results based on the wind speed data of a wind farm in south China show that the proposed method has higher forecasting accuracy and is able to reflect the laws of wind speed and wind power correctly.

Key words： Wind power wind farm wind speed forecasting wavelet packet decomposition(WPD) Elman neural network(ENN)

Received: 22 May 2016 Published: 10 November 2017

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TM614

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	Ye Ruili
	Guo Zhizhong
	Liu Ruiye
	Liu Jiannan

Cite this article:

Ye Ruili,Guo Zhizhong,Liu Ruiye等. Wind Speed and Wind Power Forecasting Method Based on Wavelet Packet Decomposition and Improved Elman Neural Network[J]. Transactions of China Electrotechnical Society, 2017, 32(21): 103-111.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.160727 OR https://dgjsxb.ces-transaction.com/EN/Y2017/V32/I21/103

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