Wind speed correction for wind turbine based on convolutional neural network

doi:10.3969/j.issn.1674-1951.2021.05.012

Abstract

Abstract:

The wind turbine nacelle anemometer is affected by the wake of turbines and disturbance of blade.International Electrotechnical Commission（IEC） indicated that nacelle transfer function cannot accurately describe the complex relationship between measured wind velocity and inflowing wind velocity.A nacelle wind speed correction model based on convolution neural network is proposed.The model adopting multi-layer convolution pooling can effectively filter the influence brought by turbine wake and blade disturbance,abstract feature variables,and improve the accuracy of the corrected wind speed.The engineering example shows that the fitting accuracy R² of the convolution neural network method is 0.844 7,and its mean absolute error （MAE） is only 0.071.The difference between the power calculated by this method and the one measured by anemometers is only 4.07%.All the deviation indexes are better than those made by IEC nacelle transfer function,which fully reflects the advantages of the wind speed correction model.

Key words: wind turbine, convolutional neural network, IEC nacelle transfer function, nacelle wind speed, inflowing wind velocity, free flow velocity, power curve, wind speed correction

CLC Number:

TK29

YANG Mingming. Wind speed correction for wind turbine based on convolutional neural network[J]. Huadian Technology, 2021, 43(5): 75-79.

Figures/Tables 12

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项目	数据
叶轮直径/m	104
轮毂高度/m	80
RIX	≤0.04
地形倾角	符合附录B
最大脊台阶/m	4

计算方法	回归平方和（ESS）	平均绝对误差（MAE）	拟合优度R²
IEC机舱传递函数法	1 266.54	0.098	0.669 2
卷积神经网络法	757.12	0.071	0.844 7

风电场模型	发电量/（kW⋅h）	误差率/%
测试功率曲线模型	4 939 085.471	-
卷积神经网络功率曲线模型	5 139 934.554	4.07
IEC机舱传递函数功率曲线模型	5 225 233.242	5.79