基于LS-SVM的电力电子电路故障预测方法  

Research on fault prediction method of power electronic circuits based on least squares support vector machine

作　　者：姜媛媛[1,2] 王友仁[1] 崔江[1] 孙凤艳[1] 

机构地区：[1]南京航空航天大学自动化学院,江苏南京210016 [2]安徽理工大学电气与信息工程学院,安徽淮南232001

出　　处：《电机与控制学报》2011年第8期64-68,74,共6页Electric Machines and Control

基　　金：国家自然科学基金(60871009);航空科学基金(2009ZD52045);江苏省普通高校研究生科研创新计划项目(CXLX11-0183);南京航空航天大学基本科研业务费专项科研项目(NS2010063)

摘　　要：针对现有电力电子电路故障预测技术的不足,提出将电路特征性能参数和最小二乘支持向量机(least squares support vector machine,LS-SVM)预测算法结合,对电力电子电路进行故障预测。以Buck电路为例,选择电路输出电压作为监测信号,提取输出电压平均值及纹波值作为电路特征性能参数,并利用LS-SVM回归算法实现故障预测。实验结果表明,利用LS-SVM对电路输出平均电压与输出纹波电压的预测相对误差均低于2%,能够跟踪故障特征性能参数的变化趋势,有效实现电力电子电路故障预测。Aiming at the issue of fault prediction technique of power electronic circuits,a method based on characteristic parameter data and least squares support vector machine（LS-SVM） for the prediction of power electronic circuits was proposed.Taking the Buck converter circuit as an example,the fault prediction of power electronic circuits was achieved.Firstly,the output voltage was selected as monitoring signal,and then the average voltage and ripple voltage were extracted as characteristic parameters.Lastly LS-SVM algorithm was used to predict Buck converter circuit.The experimental results show that the LS-SVM algorithm is especially accurate in predicting the average voltage and ripple voltage with the relative error less than 2%.The new method can trace the characteristic parameters＇ trend and can be effectively applied in fault prediction of power electronic circuits.

关 键 词：电力电子电路 故障预测 特征性能参数 数据驱动 最小二乘支持向量机 

分 类 号：TP206[自动化与计算机技术—检测技术与自动化装置]