基于CWT和CNN-BiLSTM的散绕同步电机定子绕组短路故障检测方法  

Detection Method of Stator Winding Short Circuit Fault of Synchronous Motor Based on CWT and CNN-BiLSTM

作　　者：于跃强 陈宇[2] 赵仲勇 宫小宇 唐超[1] YU Yueqiang;CHEN Yu;ZHAO Zhongyong;GONG Xiaoyu;TANG Chao(College of Engineering and Technology,Southwest University,Chongqing 400716,China;State Key Laboratory of Advanced Electromagnetic Technology,School of Electrical and Electronic Engineering,Huazhong University of Science and Technology,Wuhan 430074,China;Yibin Academy of Southwest University,Yibin 644000,China;Beibei Power Supply Branch of State Grid Chongqing Electric Power Company,Chongqing 400014,China)

机构地区：[1]西南大学工程技术学院,重庆400716 [2]华中科技大学电气与电子工程学院强电磁技术全国重点实验室,武汉430074 [3]西南大学宜宾研究院,宜宾644000 [4]国网重庆市电力公司北碚供电分公司,重庆400014

出　　处：《高电压技术》2024年第5期2166-2176,共11页High Voltage Engineering

基　　金：四川省科技计划(2023NSFSC0829);中央高校基本科研业务费项目(SWU-KT22027);国家自然科学基金(51807166)。

摘　　要：近年来,基于脉冲频率响应法(impulse frequency response analysis,IFRA)的神经网络模型已被证实能够有效检测定子绕组故障。然而,这些模型普遍具有鲁棒性不强、抗噪能力差等特点,究其原因是大多数的模型采用简单的神经网络架构且常规的IFRA普遍采用快速傅里叶变换(fast Fourier transform,FFT)对暂态信号进行时频变换,而FFT并不适合处理暂态突变的非平稳信号。文中以散绕结构的同步电机定子绕组为检测对象,采用连续小波变换(continual wavelet transform,CWT)代替FFT处理IFRA的暂态信号,并基于一维卷积神经网络(convolutional neural networks,CNN)和双向长短时记忆网络(bi-directional long short-term memory,BiLSTM)构建CNN-BiLSTM模型对采用CWT变换之后的信号进行故障检测。实验结果表明:采用CWT处理后的频域序列作为该模型的输入,相较于其它结构单一的模型,其平均准确率最优且高达99.01%。噪声对比实验表明:采用CWT变换后的数据能使故障诊断模型的鲁棒性及泛化性更强。In recent years,neural network models based on impulse frequency response analysis(IFRA)have been proven effective in detecting stator winding faults.However,these models are generally characterized by weak robustness and poor noise resistance.The reason is that most of the models adopt simple neural network architecture and conventional IFRA generally use fast Fourier transform(FFT)to perform time-frequency transformation on transient signals,while FFT is not suitable for processing transient abrupt non-stationary signals.In this paper,the stator winding of a loose wound synchronous machine is taken as the detection object and continuous wavelet transform(CWT)instead of FFT is used to process the transient signal of IFRA,and based on one-dimensional convolutional neural networks(CNN)and bi-directional long short term memory(BiLSTM)networks,a CNN-BiLSTM model is constructed to detect the fault of the data transformed by CWT.The experimental results show that,compared with other models with the transformed single,the CNN-BiLSTM model with CWT processed frequency domain sequence as input has the best average accuracy of 99.01%.The noise contrast experiment shows that data transformed by CWT can enable the fault diagnosis model to be more robust and generalized.

关 键 词：同步电机 定子绕组 脉冲频率响应法 小波变换 CNN-BiLSTM 

分 类 号：TM341[电气工程—电机] TP183[自动化与计算机技术—控制理论与控制工程]