基于极限学习机的风电机组叶根载荷辨识建模

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振动与冲击 ›› 2018, Vol. 37 ›› Issue (4) : 257-262.

论文

秦斌易怀洋王欣

作者信息 +

Modeling of wind turbine blade root loads based on extreme learning machine

QIN Bin YI Huai-yang WANG Xin

Author information +

文章历史 +

摘要

针对风机系统载荷种类多、非线性、强耦合，根据传统的机理建模方法建立叶根载荷模型复杂、运算量大难以用于实时控制的问题，提出基于极限学习机（ELM）的叶根载荷神经网络预测模型。首先对影响叶根载荷的因素进行了分析，并结合主元分析方法确定模型的输入，利用美国可再生能源实验室（NREL）数据建立训练集和测试集，对5MW风机的叶根载荷进行辨识建模和预测，并与用支持向量机（SVM）建立的叶根载荷模型进行比较，结果表明ELM模型的训练速度快、预测精度高，验证了所提方法的可行性和有效性。

Abstract

In view of load variety, nonlinear and strong coupling of the wind turbine system, and complex mechanism and large amount of computation of traditional blade root load models, a neural network model based on extreme learning machine (ELM) was proposed to predict the blade root loads in this paper. Firstly, the factors that affect the blade root loads were analyzed, and the model inputs were determined by the principal component analysis. The data from National Renewable Energy Laboratory (NREL) was then used to set up training and test sets, and the blade root loads of a 5MW wind turbine system were modelled and predicted by the proposed method. Finally, the model performance was compared with that of the model established based on the support vector machine (SVM). The simulation results show that the ELM model has high training speed and high prediction accuracy, which verified its feasibility and effectiveness.

导出引用

秦斌易怀洋王欣. 基于极限学习机的风电机组叶根载荷辨识建模[J]. 振动与冲击, 2018, 37(4): 257-262

QIN Bin YI Huai-yang WANG Xin. Modeling of wind turbine blade root loads based on extreme learning machine[J]. Journal of Vibration and Shock, 2018, 37(4): 257-262

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