可再生能源2024，Vol.42Issue(10)：1332-1340,9.
CEEMDAN联合自适应小波阈值算法的GA-BP风电发电机故障预测
CEEMDAN joint adaptive wavelet thresholding algorithm for GA-BP wind turbine fault prediction
摘要
Abstract
Generator is an important core component in wind power system,in order to improve the stable and efficient operation of wind turbine,the fault prediction of wind turbine generator is necessary.Focusing on the problem of generator machine-side bearing temperature overrun fault prediction in wind power system,this paper takes into account that the collected fault characteristic signal is characterized by large noise,introduces CEEMDAN joint adaptive wavelet threshold denoising method to realize effective denoising of the signal,and at the same time establishes a fault prediction model by combining GA-BP neural network.By comparing the prediction indexes,error indexes and prediction effect graphs with BP neural network and GA-BP neural network,it is verified that the proposed algorithm can obtain better prediction effect.The error index and prediction effect are improved,and the accuracy of the prediction of generator failure of wind power system 15 days in advance reaches 92.98%.关键词
风电系统/发电机故障/故障预测/CEEMDAN/GA-BP神经网络Key words
wind energy system/generator failure/fault prediction/CEEMDAN/GA-BP neural network分类
能源科技引用本文复制引用
肖成,曹万鹏,褚越强,杨政琨,王佳兴..CEEMDAN联合自适应小波阈值算法的GA-BP风电发电机故障预测[J].可再生能源,2024,42(10):1332-1340,9.基金项目
河北省教育厅重点项目(ZD2022089) （ZD2022089）
北华航天工业学院博士基金项目(BKY-2023-03) （BKY-2023-03）
北华航天工业学院校重点项目(ZD-2022-03). （ZD-2022-03）
