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
谢彤彤 1刘颖明 1王晓东 1高兴1
作者信息
- 1. 沈阳工业大学电气工程学院,辽宁沈阳 110870
- 折叠
摘要
对永磁同步风力发电机转子早期动偏心和早期静偏心故障的特点和诊断方法进行研究,通过Ansys建立永磁同步风力发电机的早期动偏心和早期静偏心模型,提出一种基于CNN-LSTM的故障诊断和分类方法.通过对永磁同步风力发电机定子三相电流及其Welch功率谱数据的分析,判断是否为正常的动偏心趋势和静偏心趋势;然后通过空载电动势对不同故障程度进行分类.最后,在神经网络模型中完成故障诊断和分类任务.所提方法大大降低了设备维修成本,可准确快速地识别转子早期偏心故障.
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引用本文复制引用
基金项目
国家自然科学基金(52007124)
辽宁省"揭榜挂帅"科技攻关专项(2021JH1/10400009)
出版年
2024
国家科技期刊平台
NSTL
万方数据