SHORT-TERM PREDICTION OF WIND POWER CONSIDERING LOCAL CONDITION FEATURES

Zhang Jiaan; Huang Chenxu; Li Zhijun

doi:10.19912/j.0254-0096.tynxb.2023-1183

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (12) : 220-227. DOI: 10.19912/j.0254-0096.tynxb.2023-1183

SHORT-TERM PREDICTION OF WIND POWER CONSIDERING LOCAL CONDITION FEATURES

Zhang Jiaan^1,2, Huang Chenxu², Li Zhijun^1,2

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Abstract

A short-term prediction method of wind power considering local condition features is proposed. Firstly, based on the Spearman correlation coefficient, the correlation between local condition factors and wind turbine power is analyzed. Wind speed, wind direction together with yaw angle are selected as key factors. Then, the distribution parameters of key factors are estimated separately with the generalized extreme value distribution, and an average fluctuation coefficient index is constructed to describe the parameter differences between each wind turbine. The wind turbines are clustered into several groups with the K-means++ algorithm. Finally, the key features of each wind turbine cluster are extracted with principal component analysis （PCA）. Based on Bidirectional gated recurrent units （BiGRU）, the power of the cluster is accurately predicted and accumulated. Taking the operation data of a wind farm in North China as an example, the effectiveness of this method is verified.

Key words

wind power / forecasting / cluster analysis / neural networks / feature extraction

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Zhang Jiaan, Huang Chenxu, Li Zhijun. SHORT-TERM PREDICTION OF WIND POWER CONSIDERING LOCAL CONDITION FEATURES[J]. Acta Energiae Solaris Sinica. 2024, 45(12): 220-227 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1183

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