基于矢量空间状态优化的GIS机械故障检测方法  

Mechanical Fault Detection Method for GIS Based on Vector Space State Optimization

作　　者：赵宏梅 丛培杰 李晨涛 曲德宇 魏宏升 ZHAO Hongmei;CONG Peijie;LI Chentao;QU Deyu;WEI Hongsheng(Guangzhou Power Supply Bureau of Guangdong Power Grid Co.,Ltd.,Guangzhou 510620,China)

机构地区：[1]广东电网有限责任公司广州供电局,广州510620

出　　处：《高压电器》2024年第6期65-72,共8页High Voltage Apparatus

摘　　要：GIS机械故障产生的声音信号蕴含了大量设备运行状态信息,基于声纹识别技术信号分析方法是实现GIS带电检测和故障诊断的有效手段。文中提取了GIS不同运行工况下的LPCC和MFCC特征向量,F比计算结果表明MFCC是一种更具区分度的特征向量。在此基础上,利用了粒子群优化算法的全局搜索能力和进化规划算法的局部调节能力,以粒子群优化为主,引入进化规划算法的变异操作,形成了一种基于矢量空间状态优化的混合优化方法。采用文中提出的矢量量化模型对了110 kV GIS不同运行工况下声音信号进行检测和识别,实验结果表明文中方案得到的码书失真度的均值和方差更小,具有更好的优化性能和稳定性,且同一条件下的识别准确率约为90%~96%,优于经典的LBG迭代算法。The sound signal generated by GIS mechanical fault contains a large amount of equipment operation state information.The signal analysis method based on voiceprint recognition technology is an effective means to realize GIS live detection and fault diagnosis.In this paper,LPCC and MFCC feature vectors under different operating conditions of GIS are extracted.The calculation results of F ratio show that MFCC is a more differentiated feature vector.On this basis,using the global search ability of particle swarm optimization algorithm and the local adjustment ability of evolutionary programming algorithm,focusing on particle swarm optimization and introducing the mutation operation of evolutionary programming algorithm,a hybrid optimization method based on vector space state optimization is formed.The vector quantization model proposed in this paper is used to detect and recognize the sound signals under different operating conditions of 110 kV GIS.The experimental results show that the codebook distortion obtained by this scheme has smaller mean and variance,better optimization performance and stability,and the recognition accuracy under the same conditions is about 90%~96%,which is better than the classical LBG iterative algorithm.

关 键 词：机械故障 声纹识别 带电检测 粒子群优化 进化规划 矢量量化 失真度 全局搜索 

分 类 号：TM595[电气工程—电器]