联合高频脉冲与超声波信号的局放估计  

Partial discharge estimation of jointed high-frequency pulse and ultrasonic signal

作　　者：曾虎[1] 李川[1] 李英娜[1] 陈武奋[1] 高立慧[1] 

机构地区：[1]昆明理工大学信息工程与自动化学院,云南昆明650500

出　　处：《传感器与微系统》2016年第8期37-39,43,共4页Transducer and Microsystem Technologies

基　　金：昆明理工大学人才培养基金资助项目(KKSY201303044);中国博士后科学基金面上资助(一等资助)项目(2014M552552XB)

摘　　要：局部放电是引起绝缘劣化的重要因素。选取高频脉冲电流与超声波信号作为局部放电的监测参量,采用超声波和高频电流传感器网络系统对开关柜局部放电的变化情况进行在线监测,利用支持向量回归(SVR)与反向传播(BP)神经网络分别对开关柜局部放电量进行建模分析。实验表明:基于SVR的局放估计模型决定系数为0.867,基于BP神经网络的局放估计模型决定系数为0.375。SVR的局放估计值与实际值的曲线趋势基本一致,选用SVR模型对开关柜局放量进行估计。Partial discharge is an important factor to cause insulation aging. Ultrasonic signal and high-frequency pulse current are selected as partial discharge monitoring parameters,a system based on ultrasonic and highfrequency current sensor network systems are adopted to on-line monitor changes in switchgear partial discharge,and support vector regression（ SVR） and back propagation（ BP） neural networks is respectively used for modeling analysis on amount of switchgear partial discharge. Experimental result data show that coefficient of determination of partial discharge estimation model based on SVR is 0. 867; coefficient of determination of partial discharge estimation model based on BP neural networks is 0. 375. The value of partial discharge estimation based on SVR is consistent with actual value,so SVR model is selected to estimate magnitude of switchgear partial discharge.

关 键 词：超声波传感器 高频电流传感器 支持向量机 反向传播神经网络 局部放电 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]