基于振动信号的高压并联电抗器故障诊断方法与监测系统研制

通过研究高压并联电抗器表面振动信号的幅频与分布特性,结合斯皮尔曼相关性分析,寻找并验证能够表征高压并联电抗器机械故障状态的特征参数,将振动信号的分段离散功率谱、主成分系数等参数组成特征向量,综合K-临近算法、支持向量、神经网络等多种机器学习方法实现对高压并联电抗器机械故障的诊断。在此基础上研发了一套在线监测系统,具有信号采集分析、故障诊断预警、数据智能采样存储、特征观察分析等功能。经实验测试表明,该系统诊断准确、性能稳定、方便智能,具有一定的实用价值。

Fault Diagnosis Method and Monitoring System of High Voltage Parallel Reactor Based on Vibration Signal

By studying the amplitude-frequency and distribution characteristics of the surface vibration signal of the high-voltage shunt reactor, combined with the Spearman correlation analysis, the characteristic parameters that can characterize the mechanical fault state of the high-voltage shunt reactor are found and verified. The segmented discrete power spectrum of the vibration signal, the principal component coefficients and other parameters are used to form the feature vector, and the KNN,SVM, neural network and other machine learning methods are used to diagnose the mechanical fault of the high voltage shunt reactor. Then, based on this, an online monitoring system has been developed, which has functions such as signal acquisition and analysis, fault diagnosis and early warning, data intelligent sampling and storage, and feature observation and analysis. The experimental results show that the system has accurate diagnosis, stable performance, convenient and intelligent, and has certain practical value.