电力电子变流器故障诊断的智能方法综述
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摘要
电力电子变流器作为能源变换的核心之一,其故障诊断技术为能源安全可靠转换提供了有力保障。本文综述了当前电力电子变流器故障诊断常用的智能算法,其中包括故障树分析、人工神经网络、支持向量机、模糊集理论及信息融合方法等。首先简单阐述了这些智能方法的基本概念,然后基于电力电子故障诊断领域的研究现状,对各种智能方法的特点和存在的不足做了简单分析,最后结合当前电力电子电路故障诊断领域的难点,探讨该领域未来研究的新思路。
As one of the cores of energy conversion, the fault diagnosis technology of power electronic converters provide a strong guarantee for energy safety and reliable conversion. The intelligence methods which are applied widely for fault diagnosis of power electronic converters,including fault tree analysis, artificial neural network, support vector machine, fuzzy set theory and information fusion, etc, are reviewed in this paper. First of all, the basic concepts of these intelligent methods are briefly expounded. Then from the research status of power electronics fau lt diagnosis, the characteristics and shortcomings of various intelligent methods are briefly analyzed.Finally, combining with the difficulties in current fault diagnosis of power electronic circuits,explored new ideas for the future development in this field.
As one of the cores of energy conversion, the fault diagnosis technology of power electronic converters provide a strong guarantee for energy safety and reliable conversion. The intelligence methods which are applied widely for fault diagnosis of power electronic converters,including fault tree analysis, artificial neural network, support vector machine, fuzzy set theory and information fusion, etc, are reviewed in this paper. First of all, the basic concepts of these intelligent methods are briefly expounded. Then from the research status of power electronics fau lt diagnosis, the characteristics and shortcomings of various intelligent methods are briefly analyzed.Finally, combining with the difficulties in current fault diagnosis of power electronic circuits,explored new ideas for the future development in this field.
引文
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[18]曾志强,高济.基于向量集约简的精简支持向量机[J].软件学报,2007,18(11):2719-2727.
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[20]李猛,王友仁.电力电子电路软故障诊断方法对比分析[J].电子测量技术,2015,38(7):110-114.
[21]M Beibei S Y,Zhipu Z.Three level inverter fault diagnosis using EMD and support vector machine approach[C]//12th IEEE Conference on Industrial Electronics and Applications,2017:1595-1598.
[22]Chen W,Bazzi A M.Logic-based methods for intelligent fault diagnosis and recovery in power electronics[J].IEEE Transactions on Power Electronics,2017,32(7):5573-5589.
[23]崔江,王友仁.采用基于模糊推理的分类器融合方法诊断电力电子电路参数故障[J].中国电机工程学报,2009,29(18):54-59.
[24]Tamer K,Yevgen B,Chang L C.Fault diagnosis and on-line monitoring for grid-connected single-phase inverters[J].Electric Power Systems Research,2015,126:68-77.
[25]张成军,阴妍,鲍久圣,等.多源信息融合故障诊断方法研究进展[J].河北科技大学学报,2014(3):213-221.
[26]Wang M,Zhao J,Wu F,et al.Transistor open-circuit fault diagnosis of three phase voltage-source inverter fed induction motor based on information fusion[C]//12th IEEE Conference on Industrial Electronics and Applications,2017:1591-1594.
[27]Zhou L J,Zhang J,Zhao Y.Research on analog circuit fault diagnosis of MFCS based on BP neural network information fusion technology[J].International Con-ference on Mechatronic Sciences,Electric Engineering and Computer(MEC),2013:483-486.
[28]姜书燕.电力电子电路故障诊断与故障预测方法研究[D].长沙:湖南大学,2016.
[29]徐流建.基于键合图和BP神经网络的并网逆变器故障诊断研究[D].乌鲁木齐:新疆大学,2015.
[30]吴袆.电力电子电路故障特征参数提取与健康预报研究[D].南京:南京航空航天大学,2013.
[31]李明昆,宋丹妮.基于小波分析和随机森林算法的变流器电路故障诊断研究[J].电气技术,2016,17(6):36-40,46.
[32]Wang Tianzhen,Xu Hao,Han Jingang,et al.Cascaded H-bridge multilevel inverter system fault diagnosis using a PCA and multiclass relevance vector machine approach[J].IEEE Transactions on Power Electronics,2015,30(12):7006-7018.
[33]刘权.电力电子电路智能故障诊断技术研究[D].南京:南京航空航天大学,2007.
[2]李练兵,张秀云,王志华,等.故障树和BAM神经网络在光伏并网故障诊断中的应用[J].电工技术学报,2015,30(2):248-254.
[3]姚轩宇.基于神经网络的三电平逆变器故障诊断研究[D].芜湖:安徽工程大学,2015.
[4]Cherif B E,Bendiabdellah A.Detection of two-level inverter open-circuit fault using a combined DWT-NN approach[J].Journal of Control Science&Engineering,2018(3):1-11.
[5]陈丹江,叶银忠.基于多神经网络的三电平逆变器器件开路故障诊断方法[J].电工技术学报,2013,28(6):120-126.
[6]陈子龙,冀卓婷,郑重,等.基于传递函数和小波变换的变压器故障诊断研究[J].电气技术,2017,18(12):30-37.
[7]况璟,何怡刚,邓芳明,等.基于交叉小波变换和主元分析的电力电子电路故障特征提取[J].电测与仪表,2017,54(11):1-7.
[8]褚召伟,李春茂,何登,等.基于小波神经网络的风电变流器故障诊断系统[J].电气技术,2012,13(9):34-37.
[9]于生宝,何建龙,王睿家,等.基于小波分析和概率神经网络的电磁法三电平变换器故障诊断方法[J].电工技术报,2016,31(17):102-112.
[10]Dibyendu K,Sankhadip S.Neural network based cycloconverter fault detection using wavelet decomposition[C]//3rd International Conference on Advances in Electrical,Electronics,Information,Communication and Bio-Informatics(AEEICB17),2017.
[11]付丽君,王光兴,任慧轩.小波包与径向基神经网络的电力电子装置故障诊断[J].沈阳理工大学学报,2016,35(3):47-51.
[12]陈娜,李鹏,江剑,等.中高压IGBT开关特性的遗传神经网络预测[J].电工技术学报,2013,28(2):239-247,254.
[13]Manjunath T G,Kusagur A.Multilevel inverter fault diagnosis using optimised radial basis neural network-a novel performance enhancement[C]//2016Inter-national Conference on Electrical,Electronics,Communication,Computer and Optimization Techniques(ICEECCOT),2016:102-105.
[14]Manjunath T G,Kusagur A.Performance evaluation of modified genetic algorithm over genetic algorithm implementation on fault diagnosis of cascaded multilevel inverter[C]//International Conference on Condition Assessment Techniques in Electrical Systems,2015:51-56.
[15]L Fu Q Y,Ren H.Fault diagnosis of power electronic device based on wavelet and neural network[C]//Chinese Control and Decision Conference,2016:2946-2950.
[16]Fu Lijun,Qing Yang,Wang Guangxing,et al.Fault diagnosis of power electronic device based on wavelet and neural network[C]//28th Chinese Control and Decision Conference,2016:2946-2950.
[17]龙伯华,谭阳红,许慧,等.基于量子神经网络的电力电子电路故障诊断[J].电工技术学报,2009,24(10):170-175.
[18]曾志强,高济.基于向量集约简的精简支持向量机[J].软件学报,2007,18(11):2719-2727.
[19]Zhan Huaqun.Application of rough set and support vector machine in fault diagnosis of power electronic circuit[C]//The 2nd IEEE International Conference,2010:289-292.
[20]李猛,王友仁.电力电子电路软故障诊断方法对比分析[J].电子测量技术,2015,38(7):110-114.
[21]M Beibei S Y,Zhipu Z.Three level inverter fault diagnosis using EMD and support vector machine approach[C]//12th IEEE Conference on Industrial Electronics and Applications,2017:1595-1598.
[22]Chen W,Bazzi A M.Logic-based methods for intelligent fault diagnosis and recovery in power electronics[J].IEEE Transactions on Power Electronics,2017,32(7):5573-5589.
[23]崔江,王友仁.采用基于模糊推理的分类器融合方法诊断电力电子电路参数故障[J].中国电机工程学报,2009,29(18):54-59.
[24]Tamer K,Yevgen B,Chang L C.Fault diagnosis and on-line monitoring for grid-connected single-phase inverters[J].Electric Power Systems Research,2015,126:68-77.
[25]张成军,阴妍,鲍久圣,等.多源信息融合故障诊断方法研究进展[J].河北科技大学学报,2014(3):213-221.
[26]Wang M,Zhao J,Wu F,et al.Transistor open-circuit fault diagnosis of three phase voltage-source inverter fed induction motor based on information fusion[C]//12th IEEE Conference on Industrial Electronics and Applications,2017:1591-1594.
[27]Zhou L J,Zhang J,Zhao Y.Research on analog circuit fault diagnosis of MFCS based on BP neural network information fusion technology[J].International Con-ference on Mechatronic Sciences,Electric Engineering and Computer(MEC),2013:483-486.
[28]姜书燕.电力电子电路故障诊断与故障预测方法研究[D].长沙:湖南大学,2016.
[29]徐流建.基于键合图和BP神经网络的并网逆变器故障诊断研究[D].乌鲁木齐:新疆大学,2015.
[30]吴袆.电力电子电路故障特征参数提取与健康预报研究[D].南京:南京航空航天大学,2013.
[31]李明昆,宋丹妮.基于小波分析和随机森林算法的变流器电路故障诊断研究[J].电气技术,2016,17(6):36-40,46.
[32]Wang Tianzhen,Xu Hao,Han Jingang,et al.Cascaded H-bridge multilevel inverter system fault diagnosis using a PCA and multiclass relevance vector machine approach[J].IEEE Transactions on Power Electronics,2015,30(12):7006-7018.
[33]刘权.电力电子电路智能故障诊断技术研究[D].南京:南京航空航天大学,2007.