STUDY ON EFFECT AND PREDICTION OF DIFFERENT WIND CONDITION CHARACTERISTICS ON WIND TURBINE BLADE LOADS

Zhang Xiaotao; Ma Jianlong; Su Hongjie; Li Qiuyan; Zhao Mingbo; Gao Zhiying

doi:10.19912/j.0254-0096.tynxb.2024-1702

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (2) : 435-443. DOI: 10.19912/j.0254-0096.tynxb.2024-1702

STUDY ON EFFECT AND PREDICTION OF DIFFERENT WIND CONDITION CHARACTERISTICS ON WIND TURBINE BLADE LOADS

Zhang Xiaotao¹, Ma Jianlong, Su Hongjie¹, Li Qiuyan¹, Zhao Mingbo¹, Gao Zhiying

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Abstract

Based on the NREL 5 MW wind turbine model, the simulation calculations of wind turbine blade loads under different average wind speeds, turbulence intensities and wind shear indices are carried out, and analyzing the effects of the blade loads on the combined moments in the swing and oscillation direction of blade loads and swing oscillations under various wind conditions. At the same time, a back-propagation （BP） neural network is established, and a prediction model is used to predict the complex coupling relationship of “average wind speed, turbulence intensity, wind shear index-blade load” The accuracy and reliability of the BP prediction model are compared with those of radial basis function （RBF）neural network , long and short-term memory （LSTM） neural network and support vector machine （SVM）. The results show that the turbulence intensity has a small effect on the average blade load, and has a great effect on the blade ultimate load and equivalent fatigue load in the high wind speed range; the wind shear index has a more obvious effect on the average load and ultimate load in the high wind speed condition; and has the greatest effect on the equivalent fatigue load in the rated wind speed range. Meanwhile, the BP neural network load prediction model has better accuracy than that of RBF, SVM and LSTM models, which verifies the feasibility and accuracy of the prediction model .

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wind condition characteristics / wind turbine blades / load / neural network / prediction models

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Zhang Xiaotao, Ma Jianlong, Su Hongjie, Li Qiuyan, Zhao Mingbo, Gao Zhiying. STUDY ON EFFECT AND PREDICTION OF DIFFERENT WIND CONDITION CHARACTERISTICS ON WIND TURBINE BLADE LOADS[J]. Acta Energiae Solaris Sinica. 2026, 47(2): 435-443 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1702

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