RELIABILITY STUDY OF WIND TURBINE BLADE STRENGTH BY COMBINING FORWARD AND INVERSE PREDICTION OF NEURAL NETWORK

被引：0
|
作者：
Ju H. [1 ]
Wang X. [1 ,2 ]
Lu J. [1 ]
机构：
[1] Chongqing Key Laboratory of Manufacturing Equipment Mechanism Design and Control, Chongqing Technology and Business University, Chongqing
[2] National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2024年 / 45卷 / 01期
关键词：
blades; neural networks; optimized design; reliability; strength analysis; wind turbines;
D O I：
10.19912/j.0254-0096.tynxb.2022-1540
中图分类号：
学科分类号：
摘要：
Aiming at the problem that the strength limit state of wind turbine blade is difficult to be defined under the mutual influence of each basic random variable，a wind turbine blade strength reliability analysis method combining forward and reverse prediction of generalized regression neural network was proposed. The states of each random variable at the time of blade failure were estimated by the reverse prediction model of the neural network，and then used as reinforcement samples for the training of the forward prediction model after calibrated by the finite element analysis method. The neural network model constructed by the above method was compared with that constructed by more random samples. The results show that the number of learning samples of the former is reduced by 26%，and the mean square error and mean absolute percentage error of the test set are reduced by 48.19% and 58.24%，respectively. Therefore，the neural network model constructed by this method has better prediction performance in the blade failure boundary region. Finally the strength reliability of the blade was calculated using the model，which further verified the effectiveness of the method. © 2024 Science Press. All rights reserved.
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页码：291 / 298
页数：7
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