基于改进SE-CNN的风电机组故障诊断方法研究  

Research on Wind Turbine Fault Diagnosis Method based on Improved SE-CNN

作　　者：辛鹏[1] 杨剀勋 文孝强 XIN Peng;YANG Kaixun;WEN Xiaoqiang(School of Information and Control Engineering,Jilin Institute of Chemical Technology,Jilin City 132022,China;Department of Automation,Northeast Electric Power University,Jilin City 132012,China)

机构地区：[1]吉林化工学院信息与控制工程学院,吉林吉林132022 [2]东北电力大学自动化工程学院,吉林吉林132012

出　　处：《吉林化工学院学报》2023年第1期34-40,共7页Journal of Jilin Institute of Chemical Technology

摘　　要：针对风电机组发生故障时难以有效地提取故障特征并精准地识别故障等问题,提出了一种基于改进SE-CNN的风电机组故障诊断方法。首先,基于数据采集与监视控制(SCADA)系统采集到的故障风机历史运行数据,使用滑动窗口进行数据扩充,其次使用改进后的压缩激励网络(SEnet)对样本数据的权重进行调整,然后引入全局最大池化层对卷积神经网络(CNN)进行改进,最后使用改进后的CNN学习数据中的故障特征进行故障诊断。实验结果表明,改进SE-CNN的故障诊断性能均优于RNN、PCA-DNN、BiLSTM方法,验证了所提方法在风电机组故障诊断上的有效性。Aiming at the problem that it is difficult to effectively extract fault features and accurately identify faults when wind turbines fault,In this paper,a wind turbine fault diagnosis method based on improved SE-CNN was proposed.Firstly,a sliding window was used to expand the historical operating data of the wind turbine in fault collected by the supervisory control and data acquisition(SCADA)system.Secondly,the improved squeezed excitation network(SEnet)was used to adjust the weights of the sample data,the global maximum pooling layer was used to improve the convolutional neural network(CNN),and finally the improved CNN was used to learn the fault features and perform fault diagnosis.The experimental results showed that the improved SE-CNN outperforms RNN,PCA-DNN,and BiLSTM methods in fault diagnosis,which verifies the effectiveness of the proposed method in wind turbine fault diagnosis.

关 键 词：故障诊断 深度学习 风电机组 SEnet CNN 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]