摘要
同步发电机运行过程中可能出现诸如转子偏心、匝间短路和静电荷等缺陷,危及电机的安全运行。在对不同缺陷下轴电压信号和机械振动信号非线性相关分析基础上,提出融合轴电压-振动特征和深度学习的同步发电机缺陷诊断方法。首先,搭建三相同步发电机缺陷物理模拟试验平台,获取不同工况和缺陷下轴电压信号和机械振动信号数据,采用核典型相关分析获取了轴电压信号和振动信号的相关系数;采用梅尔语谱进行轴电压和振动信号图谱预处理,采用并行双分支残差网络分别对轴电压和振动图谱的高维特征进行提取,并采用双线性池化算法对不同模态的高维特征进行融合,在此基础上构建了融合轴电压-振动特征的同步发电机缺陷分类模型。结果表明:轴电压信号和同步电机本体振动信号关联度在故障和正常情况下均超过0.9,所提出的轴电压-振动联合诊断模型在测试集上的准确度、漏报率和误报率等性能方面优于单一轴电压和单一振动诊断算法。本文工作旨在通过监测和分析发电机的运行状态,及时识别潜在故障,提高发电机的运行可靠性。
During the operation of synchronous generators,various defects such as rotor eccentricity,turn-to-turn short circuits,and static charges may occur,jeopardizing the safe operation of the motor.A method for diagnosing defects in synchronous generators by integrating shaft voltage-vibration features with deep learning was proposed,based on a nonlinear correlation analysis of shaft voltage signals and mechanical vibration signals under different defects.Firstly,a physical simulation test platform for defects in a three-phase synchronous generator was established to obtain data on shaft voltage and mechanical vibration signals under various operating conditions and defects.The kernel canonical correlation analysis(KCCA)nonlinear correlation analysis algorithm was used to obtain the correlation coefficients between shaft voltage signals and vibration signals.Mel spectrograms were employed for preprocessing the spectrograms of shaft voltage and vibration signals.A parallel double-branch residual neural network(ResNet)was utilized to extract high-dimensional features from both the shaft voltage and vibration spectrograms.Furthermore,a bilinear pooling algorithm was applied to fuse high-dimensional features from different modalities,leading to the construction of a classification model for defects in synchronous generators based on the integration of shaft voltage and vibration features.The results indicates that the correlation between shaft voltage signals and the vibration signals of the synchronous motor exceeded 0.9 in both faulty and normal conditions.The proposed shaft voltage-vibration joint diagnosis model outperforms single shaft voltage and single vibration diagnosis algorithms in terms of accuracy,missed detection rate,and false alarm rate on the test dataset.This work aims to enable timely identification of potential faults and improve the reliability of generator operation by monitoring and analyzing their operational state.
作者
张杭
关向雨
廖景雯
徐欣灵
陈晓坤
ZHANG Hang;GUAN Xiangyu;LIAO Jingwen;XU Xinling;CHEN Xiaokun(College of Electrical Engineering and Automation,Fuzhou University,Fuzhou 350108,China)
出处
《电机与控制学报》
北大核心
2025年第7期53-62,共10页
Electric Machines and Control
基金
福建省自然科学基金(2020J01509)。
关键词
轴电压
机械振动
相关分析
信息融合
故障诊断
并行双分支残差网络
shaft voltage
mechanical vibration
correlation analysis
information fusion
fault diagnosis
parallel dual-branch residual network
