基于蝙蝠算法优化最小二乘双支持向量机的变压器故障诊断
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摘要
为了提高变压器的故障诊断精度,提出了一种基于蝙蝠算法(BA)优化最小二乘双支持向量机(LS-TSVM)的变压器故障诊断方法。该方法针对变压器故障诊断过程中的多分类问题,通过计算类间相异度矩阵自下而上构建哈夫曼树,结合LS-TSVM建立了多类分类故障诊断模型,然后采用蝙蝠算法对模型中LS-TSVM分类器的参数进行优化。利用该方法对变压器进行故障诊断,实例仿真结果表明:与粒子群优化支持向量机(PSO-SVM)方法相比,所提方法不仅训练时间显著缩短,而且故障诊断精度更高,对于高温过热、低能放电故障的诊断精度均明显高于PSO-SVM方法。仿真结果说明所提方法在变压器故障诊断中具有较高的优越性。
In order to improve the fault diagnosis accuracy of transformer, we propose a method which is based on least squares twin support vector machine(LS-TSVM) optimized by bat algorithm(BA). To solve the multi-classification problem of transformer fault diagnosis in this method, a Huffman tree is established from the bottom to up by calculating the inter class dissimilarity matrix, and a multi-classification fault diagnosis model is built based on the LS-TSVM, then a bat algorithm is used to optimize the parameters of the LS-TSVM classifier in the model. The simulation results of using this method to diagnose the fault of a transformer show that, compared with particle swarm optimization-support vector machine(PSO-SVM), the proposed method not only shortens the training time, but also achieves a higher accuracy. The diagnostic accuracy for high-temperature overheating and low-energy discharge faults is obviously higher than that of PSO-SVM. The simulation results show that the method has high superiority in transformer fault diagnosis.
In order to improve the fault diagnosis accuracy of transformer, we propose a method which is based on least squares twin support vector machine(LS-TSVM) optimized by bat algorithm(BA). To solve the multi-classification problem of transformer fault diagnosis in this method, a Huffman tree is established from the bottom to up by calculating the inter class dissimilarity matrix, and a multi-classification fault diagnosis model is built based on the LS-TSVM, then a bat algorithm is used to optimize the parameters of the LS-TSVM classifier in the model. The simulation results of using this method to diagnose the fault of a transformer show that, compared with particle swarm optimization-support vector machine(PSO-SVM), the proposed method not only shortens the training time, but also achieves a higher accuracy. The diagnostic accuracy for high-temperature overheating and low-energy discharge faults is obviously higher than that of PSO-SVM. The simulation results show that the method has high superiority in transformer fault diagnosis.
引文
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[13]周湶,王时征,廖瑞金,等.基于AdaBoost优化云理论的变压器故障诊断方法[J].高电压技术,2015,41(11):3804-3811.ZHOU Quan,WANG Shizheng,LIAO Ruijin,et al.Power transformer fault diagnosis method based on cloud model of AdaBoost algorithm[J].High Voltage Engineering,2015,41(11):3804-3811.
[14]JAYADEVA,KHEMCHANDANI R,CHANDRA S,et al.Twin support vector machines for pattern classification[J].IEEE Transaction on Pattern Analysis&Machine Intelligence,2007,29(5):905-910.
[15]NASIRI J A,CHARKARI N M,MOZAFARI K,et al.Energy-based model of least squares twin support vector machines for human action recognition[J].Signal Processing,2014,104(6):248-257.
[16]YE Q L,ZHAO C X,YE N.Least squares twin support vector machine classification via maximum one-class within class variance[J].Optimization Methods&Software,2012,27(1):53-69.
[17]HUFFMAN D A.A method for the construction of minimum-redundancy codes[J].Resonance,2006,11(2):91-99.
[18]WANG D Y,XIAO Y.Huffman tree optimization particle swarm optimization[J].Second International Conference on Intelligent Systems Design&Engineering Application,2012,4:286-288.
[19]YANG X S.A new metaheuristic bat-inspired algorithm[J].Computer Knowledge&Technology,2010,284:65-74.
[20]费胜巍,苗玉彬,刘成良,等.基于粒子群优化支持向量机的变压器故障诊断[J].高电压技术,2009,35(3):509-513.FEI Shengwei,MIAO Yubin,LIU Chengliang,et al.Fault diagnosis of transformer based on particle swarm optimization-based support vector machine[J].High Voltage Engineering,2009,35(3):509-513.
[2]张珂斐,郭江,聂德鑫,等.基于化学反应优化神经网络与融合DGA算法的油浸式变压器模型研究[J].高电压技术,2016,42(4):1275-1281.ZHANG Kefei,GUO Jiang,NIE Dexin,et al.Diagnosis model for transformer fault based on CRO-BP neural network and fusion DGAmethod[J].High Voltage Engineering,2016,42(4):1275-1281.
[3]施恂山,马宏忠,张琳,等.PSO改进RBPNN在变压器故障诊断中的应用[J].电力系统保护与控制,2016,44(17):39-44.SHI Xunshan,MA Hongzhong,ZHANG Lin,et al.Application of RBPNN improved by PSO in fault diagnosis of transformers[J].Power System Protection and Control,2016,44(17):39-44.
[4]YANG Z Q.Power transformer fault diagnosis based on the SOMnetwork combined with the BP neural network[J].Revista Tecnica de la Facultad de Ingenieria Universidad del Zulia,2016,39(3):319-326.
[5]吴坤,康建设,池阔.基于改进多分类算法和相关向量机的电力变压器故障诊断方法[J].高电压技术,2016,42(9):3011-3017.WU Kun,KANG Jianshe,CHI Kuo.Fault diagnosis method of power transformers using improved multi-class classification algorithm and relevance vector machine[J].High Voltage Engineering,2016,42(9):3011-3017.
[6]朱永利,尹金良.组合核相关向量机在电力变压器故障诊断中的应用研究[J].中国电机工程学报,2013,22(22):68-74.ZHU Yongli,YIN Jinliang.Study on application of multi-kernel learning relevance vector machines in fault diagnosis of power transformers[J].Proceedings of the CSEE,2013,22(22):68-74.
[7]LI H Z,CHEN S X,QIAN T,et al.Power transformer fault classification by combining genetic reduction with optimized multilayer support vector machine[J].Smart Grid Technologies-Asia,2015:1-5.
[8]LI J Z,ZHANG Q G,WANG K,et al.Optimal dissolved gas ratios selected by genetic algorithm for power transformer fault diagnosis based on support vector machine[J].IEEE Transactions on Dielectrics and Electrical Insulation,2016,23(2):1198-1206..
[9]白翠粉,高文胜,金雷,等.基于3层贝叶斯网络的变压器综合故障诊断[J].高电压技术,2013,39(2):330-335.BAI Cuifen,GAO Wensheng,JIN Lei,et al.Integrated diagnosis of transformer faults based on three-layer Bayesian network[J].High Voltage Engineering,2013,39(2):330-335.
[10]赵文清.基于选择性贝叶斯分类器的变压器故障诊断[J].电力自动化设备,2011,31(2):44-47.ZHAO Wenqing.Transformer fault diagnosis based on selective Bayes classifier[J].Electric Power Automation Equipment,2011,31(2):44-47.
[11]刘凯,彭维捷,杨学君.特征优化和模糊理论在变压器故障诊断中的应用[J].电力系统保护与控制,2016,44(15):54-60.LIU Kai,PENG Weijie,YANG Xuejun.Method of fault diagnosis for power transformer based on optimizing characteristics and the fuzzy theory[J].Power System Protection and Control,2016,44(15):54-60.
[12]杜文霞,句希源,吕锋.基于模糊聚类算法的变压器故障诊断研究[J].变压器,2009,46(8):65-69.DU Wenxia,JU Xiyuan,LüFeng.Research on transformer fault diagnosis based on fuzzy clustering algorithm[J].Transformer,2009,46(8):65-69.
[13]周湶,王时征,廖瑞金,等.基于AdaBoost优化云理论的变压器故障诊断方法[J].高电压技术,2015,41(11):3804-3811.ZHOU Quan,WANG Shizheng,LIAO Ruijin,et al.Power transformer fault diagnosis method based on cloud model of AdaBoost algorithm[J].High Voltage Engineering,2015,41(11):3804-3811.
[14]JAYADEVA,KHEMCHANDANI R,CHANDRA S,et al.Twin support vector machines for pattern classification[J].IEEE Transaction on Pattern Analysis&Machine Intelligence,2007,29(5):905-910.
[15]NASIRI J A,CHARKARI N M,MOZAFARI K,et al.Energy-based model of least squares twin support vector machines for human action recognition[J].Signal Processing,2014,104(6):248-257.
[16]YE Q L,ZHAO C X,YE N.Least squares twin support vector machine classification via maximum one-class within class variance[J].Optimization Methods&Software,2012,27(1):53-69.
[17]HUFFMAN D A.A method for the construction of minimum-redundancy codes[J].Resonance,2006,11(2):91-99.
[18]WANG D Y,XIAO Y.Huffman tree optimization particle swarm optimization[J].Second International Conference on Intelligent Systems Design&Engineering Application,2012,4:286-288.
[19]YANG X S.A new metaheuristic bat-inspired algorithm[J].Computer Knowledge&Technology,2010,284:65-74.
[20]费胜巍,苗玉彬,刘成良,等.基于粒子群优化支持向量机的变压器故障诊断[J].高电压技术,2009,35(3):509-513.FEI Shengwei,MIAO Yubin,LIU Chengliang,et al.Fault diagnosis of transformer based on particle swarm optimization-based support vector machine[J].High Voltage Engineering,2009,35(3):509-513.