电器与能效管理技术Issue(3)：1-6,6.DOI:10.16628/j.cnki.2095-8188.2024.03.001
基于CNN-LSTM的永磁同步风力发电机转子偏心早期故障诊断
Early Fault Diagnosis of Permanent Magnet Synchronous Wind Turbine Rotor Eccentricity Based on CNN-LSTM
摘要
Abstract
Research is conducted on the characteristics and diagnostic methods of early dynamic eccentricity and early static eccentricity faults in the rotor of permanent magnet synchronous wind turbines.The early dynamic eccentricity and early static eccentricity models for permanent magnet synchronous wind turbines are established using Ansys,and a fault diagnosis and classification method based on CNN-LSTM is proposed.By analyzing the three-phase current and Welch power spectrum data of the stator of a permanent magnet synchronous wind turbine generator,the generator's current status can be judged whether it is normal dynamic eccentricity trend or normal static eccentricity trend.Then,the different fault levels are classified using no-load electromotive force.Finally,the fault diagnosis and classification tasks in the neural network model are completed.The proposed method greatly reduces equipment maintenance costs and can accurately and quickly identify early rotor eccentricity faults.关键词
卷积神经网络/长短期记忆网络/故障诊断/特征提取Key words
convolutional neural network(CNN)/long short term memory(LSTM)network/fault diagnosis/feature extraction分类
信息技术与安全科学引用本文复制引用
谢彤彤,刘颖明,王晓东,高兴..基于CNN-LSTM的永磁同步风力发电机转子偏心早期故障诊断[J].电器与能效管理技术,2024,(3):1-6,6.基金项目
国家自然科学基金(52007124) （52007124）
辽宁省揭榜挂帅科技攻关专项(2021JH1/10400009) （2021JH1/10400009）
