基于ACS-SA文化基因算法的BP神经网络变压器故障诊断
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摘要
针对BP神经网络在变压器故障诊断上存在的不足,提出基于ACS-SA文化基因算法的BP神经网络变压器故障诊断方法。在实际系统中,针对缺乏准确的变量参数估计,将边界变异策略和自适应步长策略引入标准布谷鸟算法中;提出一种在改进的布谷鸟算法中结合局部搜索策略的文化基因算法;建立BP神经网络变压器故障诊断模型,并用文化基因布谷鸟算法优化BP神经网络的权值和阈值。仿真实验及对比研究结果表明,该算法能准确有效地识别变压器的故障类型,较其他算法(CS-BP神经网络算法和POS-BP神经网络算法)有更高的准确率,为变压器故障诊断提供一种新思路。
Aiming at the deficiency of BP neural network in the fault diagnosis of transformer,to tackle this prob-lem,a BP neural network transformer fault diagnosis method based on ACS-SA culture genetic algorithm is pro-posed. In the practical system,in view of the lack of accurate variable parameter estimation,the boundary mutationstrategy and adaptive step strategy are introduced to the standard cuckoo algorithm.Puts forward a cultural genetic al-gorithm combining improved cuckoo algorithm and local search strategy. Establish the fault diagnosis model of trans-former based on BP neural network,and cultural gene cuckoo algorithm is used to optimize the weights and thresh-olds of BP neural network. The final findings show that,this method is accurate and effective in identifying the faulttype of the transformer,and compare with other algorithms(CS-BP neural network algorithm and POS-BP neural net-work algorithm),the method has a higher accuracy,as well as provides a new way of thinking for transformer fault di-agnosis.
Aiming at the deficiency of BP neural network in the fault diagnosis of transformer,to tackle this prob-lem,a BP neural network transformer fault diagnosis method based on ACS-SA culture genetic algorithm is pro-posed. In the practical system,in view of the lack of accurate variable parameter estimation,the boundary mutationstrategy and adaptive step strategy are introduced to the standard cuckoo algorithm.Puts forward a cultural genetic al-gorithm combining improved cuckoo algorithm and local search strategy. Establish the fault diagnosis model of trans-former based on BP neural network,and cultural gene cuckoo algorithm is used to optimize the weights and thresh-olds of BP neural network. The final findings show that,this method is accurate and effective in identifying the faulttype of the transformer,and compare with other algorithms(CS-BP neural network algorithm and POS-BP neural net-work algorithm),the method has a higher accuracy,as well as provides a new way of thinking for transformer fault di-agnosis.
引文
[1]郑含博,王伟,李晓纲,等.基于多分类最小二乘支持向量机和改进粒子群优化算法的电力变压器故障诊断方法[J].高电压技术,2014,40(11):3424-3429.ZHENG Hanbo,WANG Wei,LI Xiaogang,et al.Fault diagnosis method of power transformers using multi-class LSSVM and improved PSO[J].High Voltage Engineering,2014,40(11):3424-3429.
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[13]张燕.基于改进的BP神经网络的变压器故障诊断研究[D].昆明:昆明理工大学,2012.ZHANG Yan.Research on transformer fault diagnosis based on improved kernel PCA and BP neural network[D].Kunming:Kunming University of Science and Technology,2012.
[14]胡青,杜林,杨丽君,等.Kernel PCA与BP神经网络相结合的变压器故障诊断[J].计算机应用研究,2010,27(2):580-581.HU Qing,DU Lin,YANG Lijun,et al.Transformer fault diagnosis based on Kernel PCA and BPNN[J].Application Research of Computers,2010,27(2):580-581.
[15]王艳春.基于BP神经网络的变压器绕组故障诊断分析[D].保定:河北农业大学,2014.WANG Yanchun.Fault diagnosis and analysis of transformer winding based on BP neural netwark[D].Baoding:Agricultural University of Hebei,2014.
[16]王晓霞,王涛.基于BP神经网络的变压器故障诊断[J].华东电力,2008,36(2):112-116.WANG Xiaoxia,WANG Tao.Transformer fault diagnosis based on BP networks[J].East China Electric Power,2008,36(2):112-116.
[17]张锐,韩超,李晓娜.基于IPSO-BP神经网络的变压器故障诊断方法[J].计算机系统应用,2013,22(4):125-128.ZHANG Rui,HAN Chao,LI Xiaona.Transformer fault diagnosis method based on IPSO-BP neural network[J].Computer Systems&Applications,2013,22(4):125-128.
[18]杜文霞,吕锋,句希源.基于BP神经网络的电力变压器故障诊断[J].变压器,2007,44(3):45-47.DU Wenxia,LYU Feng,GOU Xiyuan.Fault diagnosis of power transformer based on BP neural network[J].Transformer,2007,44(3):45-47.
[19]段侯峰.基于遗传算法优化BP神经网络的变压器故障诊断[D].北京:北京交通大学,2008.DUAN Houfeng.Transformer fault diagnosis based on genetic algorithm to optimize BP neural network[D].Beijing:Beijing Jiaotong University,2008.
[20]殷跃.基于BP神经网络的电力变压器故障诊断的研究[D].长春:吉林大学,2007.YIN Yue.Research on fault diagnosis of power transformers based on BP neural network[D].Changchun:Jilin University,2007.
[2]杨志超,张成龙,吴奕,等.基于粗糙集和RBF神经网络的变压器故障诊断方法研究[J].电测与仪表,2014(21):34-39.YANG Zhichao,ZHANG Chenglong,WU Yi,et al.Research on rough sets and RBF neural network based transformer fault diagnosis method[J].Electrical Measurement&Instrumentation,2014(21):34-39.
[3]郑巧燕,莫愿斌,刘付永,等.一种小规模多种群布谷鸟算法[J].计算机应用与软件,2014(10):278-280.ZHENG Qiaoyan,MO Yuanbin,LIU Fuyong,et al.A SMALL-SCALE and multi-population cuckoo search[J].Computer Applications and Software,2014(10):278-280.
[4]薛浩然,张珂珩,李斌,等.基于布谷鸟算法和支持向量机的变压器故障诊断[J].电力系统保护与控制,2015(8):8-13.XUE Haoran,ZHANG Kehang,LI Bin,et al.Fault diagnosis of transformer based on the cuckoo search and support vector machine[J].Power System Protection and Control,2015(8):8-13.
[5]兰少峰,刘升.布谷鸟搜索算法研究综述[J].计算机工程与设计,2015(4):1063-1067.LAN Shaofeng,LIU Sheng.Overview of research on Cuckoo search algorithm[J].Computer Engineering and Design,2015(4):1063-1067.
[6]赵旭彤,沈倩,许家响,等.基于IEC三比值法与融合权重法的变压器可拓关联故障诊断[J].电网与清洁能源,2013,29(8):18-22.ZHAO Xutong,SHEN Qian,XU Jiaxiang,et al.Fault diagnosis for power transformer based on IEC three-ratio and extension association with combined weights[J].Advances of Power System&Hydroelectric Engineering,2013,29(8):18-22.
[7]刘瑞瑞.基于遗传模拟退火算法的三维离线装箱优化问题研究[D].吉林:吉林大学,2014.LIU Ruirui.Research on optimization of 3D off-line packing based on genetic simulated annealing algorithm[D].Jilin:Jilin University,2014.
[8]赵朋.变压器油中特征气体监测技术及应用的研究[D].保定:华北电力大学,2013.ZHAO Peng.Study on the monitoring technology and application of characteristic gas in transformer oil[D].Baoding:North China Electric Power University,2013.
[9]禹建丽,卞帅.基于BP神经网络的变压器故障诊断模型[J].系统仿真学报,2014,26(6):1343-1349.YU Jianli,BIAN Shuai.Fault diagnosis model of transformer based on BP neural network[J].Journal of System Simulation,2014,26(6):1343-1349.
[10]王重云.基于神经网络的变压器故障诊断技术研究[D].黑龙江:东北石油大学,2013.WANG Chongyun.Research on transformer fault diagnosis technology based on neural network[D].Helongjiang:Northeast Petroleum University,2013.
[11]李志斌,陈成优,李启本,等.基于BP神经网络的变压器故障诊断[J].化工自动化及仪表,2013,40(4):494-496.LI Zhibin,CHEN Chengyou,LI Qiben,et al.Fault diagnosis of power transformer based on BP neural network[J].Control and Instruments in Chemical Industry,2013,40(4):494-496.
[12]李霖.基于BP神经网络的变压器故障诊断方法研究[D].长沙:长沙理工大学,2013.LI Lin.Research on fault diagnosis of transformer based on BP neural network[D].Changsha:Changsha University of Science and Technology,2013.
[13]张燕.基于改进的BP神经网络的变压器故障诊断研究[D].昆明:昆明理工大学,2012.ZHANG Yan.Research on transformer fault diagnosis based on improved kernel PCA and BP neural network[D].Kunming:Kunming University of Science and Technology,2012.
[14]胡青,杜林,杨丽君,等.Kernel PCA与BP神经网络相结合的变压器故障诊断[J].计算机应用研究,2010,27(2):580-581.HU Qing,DU Lin,YANG Lijun,et al.Transformer fault diagnosis based on Kernel PCA and BPNN[J].Application Research of Computers,2010,27(2):580-581.
[15]王艳春.基于BP神经网络的变压器绕组故障诊断分析[D].保定:河北农业大学,2014.WANG Yanchun.Fault diagnosis and analysis of transformer winding based on BP neural netwark[D].Baoding:Agricultural University of Hebei,2014.
[16]王晓霞,王涛.基于BP神经网络的变压器故障诊断[J].华东电力,2008,36(2):112-116.WANG Xiaoxia,WANG Tao.Transformer fault diagnosis based on BP networks[J].East China Electric Power,2008,36(2):112-116.
[17]张锐,韩超,李晓娜.基于IPSO-BP神经网络的变压器故障诊断方法[J].计算机系统应用,2013,22(4):125-128.ZHANG Rui,HAN Chao,LI Xiaona.Transformer fault diagnosis method based on IPSO-BP neural network[J].Computer Systems&Applications,2013,22(4):125-128.
[18]杜文霞,吕锋,句希源.基于BP神经网络的电力变压器故障诊断[J].变压器,2007,44(3):45-47.DU Wenxia,LYU Feng,GOU Xiyuan.Fault diagnosis of power transformer based on BP neural network[J].Transformer,2007,44(3):45-47.
[19]段侯峰.基于遗传算法优化BP神经网络的变压器故障诊断[D].北京:北京交通大学,2008.DUAN Houfeng.Transformer fault diagnosis based on genetic algorithm to optimize BP neural network[D].Beijing:Beijing Jiaotong University,2008.
[20]殷跃.基于BP神经网络的电力变压器故障诊断的研究[D].长春:吉林大学,2007.YIN Yue.Research on fault diagnosis of power transformers based on BP neural network[D].Changchun:Jilin University,2007.