基于注意力机制的永磁同步风电机组早期匝间短路故障诊断方法  

Fault Diagnosis Method of Early Inter-Turn Short Circuit of Permanent Magnet Synchronous Wind Turbine Based on Attention Mechanism

作　　者：周凯 王晓东[1] 王刚 刘光伟[1] 刘颖明[1] ZHOU Kai;WANG Xiaodong;WANG Gang;LIU Guangwei;LIU Yingming(School of Electrical Engineering,Shenyang University of Technology,Shenyang 110870,China;Shanxi Branch,Inner Mongolia Power Investment Energy Co.,Ltd.,Taiyuan 012111,China)

机构地区：[1]沈阳工业大学电气工程学院,辽宁沈阳110870 [2]内蒙古电投能源股份有限公司山西分公司,山西太原012111

出　　处：《电器与能效管理技术》2023年第6期1-8,共8页Electrical & Energy Management Technology

基　　金：国家自然科学基金项目(52007124);辽宁省揭榜挂帅科技攻关专项(2021JH1/10400009)。

摘　　要：针对永磁风力发电机早期匝间短路引起的定子电流、电压波动微弱,导致故障识别难度大且容易发生误判的问题,提出一种基于注意力机制的卷积神经网络和双向长短期记忆网络(CNN-BiLSTM)算法故障诊断方法。以发电机电流、电压的零序分量作为故障特征,利用CNN和BiLSTM故障诊断模型进行特征自提取,引入注意力机制评估不同时刻特征的权重,并以此为依据加大故障诊断时关键特征的权重。算例结果表明,所提方法能够实现永磁风力发电机早期匝间短路故障诊断,较传统方法可显著提高识别准确率,并降低模型训练所用时间。The stator current and voltage fluctuation caused by early inter-turn short circuit of permanent magnet wind turbine is weak,which makes fault identification difficult and prone to miscalculation.To solve this problem,the convolutional neural network and bidirectional long short-term memory network(CNN-BiLSTM)fault diagnosis method based on attention mechanism is proposed.The zero sequence components of generator current and voltage are taken as fault features,and the CNN and BiLSTM fault diagnosis models are used for feature self extraction.Attention mechanism is introduced to evaluate the weight of features at different time,and the weight of key features in fault diagnosis is strengthened based on this.The results of numerical examples show that the proposed method can realize the early inter-turn short circuit fault diagnosis of permanent magnet wind turbine generator,significantly improve the identification accuracy compared with traditional methods,and reduce the time spent in model training.

关 键 词：风力发电机 匝间短路故障 故障诊断 深度学习 注意力机制 

分 类 号：TM315[电气工程—电机]