变压器绝缘故障类型的改进型RBF神经网络识别算法
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摘要
为精确诊断电力变压器内部潜在绝缘故障类型,通过对变压器内部油过热和油纸绝缘中局部放电等8种潜在绝缘故障发生时所产生的气体成分分析,提出了一种以人工免疫网络与粒子群算法改进径向基函数RBF(radial basis function)神经网络的变压器故障诊断算法。重点介绍了基于RBF神经网络的变压器故障诊断模型的构成原理、基于人工免疫网络算法的故障模型隐层中心确定方法以及基于粒子群算法的网络模型权重寻优方法,并进行了仿真实验。实验结果表明:该算法能有效地识别其绝缘故障类型,且识别精度可达90%以上。
To accurately diagnose the internal latent fault types of a power transformer, a novel radial basis function(RBF) neural network algorithm is proposed by analyzing the gas production under eight latent internal insulation fault types, such as oil overheating and partial discharging in oil paper insulation. This algorithm is improved by artificial immune network algorithm and particle swarm optimization algorithm. This paper focuses on the composition principle of transformer fault diagnosis model based on RBF neural network, the method for determining the center of hidden layer in the fault model based on artificial immune network algorithm, and the method of network weight optimization based on particle swarm optimization algorithm. Simulation experiments are carried out, showing that the proposed algorithm can effectively identify the insulation fault types at an accuracy of higher than 90%.
To accurately diagnose the internal latent fault types of a power transformer, a novel radial basis function(RBF) neural network algorithm is proposed by analyzing the gas production under eight latent internal insulation fault types, such as oil overheating and partial discharging in oil paper insulation. This algorithm is improved by artificial immune network algorithm and particle swarm optimization algorithm. This paper focuses on the composition principle of transformer fault diagnosis model based on RBF neural network, the method for determining the center of hidden layer in the fault model based on artificial immune network algorithm, and the method of network weight optimization based on particle swarm optimization algorithm. Simulation experiments are carried out, showing that the proposed algorithm can effectively identify the insulation fault types at an accuracy of higher than 90%.
引文
[1]郑蕊蕊,赵继印,赵婷婷,等.基于遗传支持向量机和灰色人工免疫算法的电力变压器故障诊断[J].中国电机工程学报,2011,31(7):57-63.Zheng Ruirui,Zhao Jiyin,Zhao Tingting,et al.Power transformer fault diagnosis based on genetic support vector machine and gray artificial immune algorithm[J].Proceeding of the CSEE,2011,31(7):57-63(in Chinese).
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[3]吴晓辉,刘炯,梁永春,等.支持向量机在电力变压器故障诊断中的应用[J].西安交通大学学报,2007,41(6):722-726.Wu Xiaohui,Liu Jiong,Liang Yongchun,et al.Application of support vector machine in transformer fault diagnosis[J].Journal of Xi’an Jiaotong University,2007,41(6):722-726(in Chinese).
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[6]宋志杰,王健.模糊聚类和LM算法改进BP神经网络的变压器故障诊断[J].高压电器,2013,49(5):54-59.Song Zhijie,Wang Jian.Transformer fault diagnosis based on BP neural network optimized by fuzzy clustering and LM algorithm[J].High Voltage Apparatus,2013,49(5):54-59(in Chinese).
[7]任静,黄家栋.基于免疫RBF神经网络的变压器故障诊断[J].电力系统保护与控制,2010,38(11):6-9.Ren Jing,Huang Jiadong.Transformer fault diagnosis Based on immune RBF neural network[J].Power System Protection and Control,2010,38(11):6-9(in Chinese).
[8]陈江波,文习山,蓝磊,等.基于新径向基函数网络的变压器故障诊断法[J].高电压技术,2007,33(3):140-143.Chen Jiangbo,Wen Xishan,Lan Lei,et al.Fault diagnosis of power transformer by novel radial basis function neural network approach[J].High Voltage Engineering,2007,33(3):140-143(in Chinese).
[9]毛向德,王庆贤,董唯光,等.小波包神经网络与数据降维的移相全桥变换器的故障诊断[J].电源学报,2014,12(4):68-75.Mao Xiangde,Wang Qingxian,Dong Weiguang,et al.Phase-shift full bridge converter fault diagnosis based on wavelet packet and neural network and data dimensionality[J].Journal of Power Supply,2014,12(4):68-75(in Chinese).
[10]周爱华,张彼德,张厚宣.基于人工免疫分类算法的电力变压器故障诊断[J].高电压技术,207,33(8):77-80.Zhou Aihua,Zhang Bide,Zhang Houxuan.Power transformer fault diagnosis by using the artificial immune classification algorithm[J].High Voltage Engineering,2007,33(8):77-80(in Chinese).
[11]董明,孟源源,徐长响,等.基于支持向量机及油中溶解气体分析的大型电力变压器故障诊断模型研究[J].中国电机工程学报2003,23(7):88-92.Dong Ming,Meng Yuanyuan,Xu Changxiang,et al.Fault diagnosis model for power transformer based on support vector machine and dissolved gas analysis[J].Proceeding of the CSEE,2003,23(7):88-92(in Chinese).
[12]韩富春,高文君,廉建鑫,等.基于免疫优化多分类SVM的变压器故障诊断新方法[J].电力系统保护与控制,2012,40(2):106-110.Han Fuchun,Gao Wenjun,Lian Jianxin,et al.A novel approach based on multi-class support vector machine of immune for transformer fault diagnosis[J].Power System Protection and Control,2012,40(2):106-110(in Chinese).
[13]付强,陈特放,朱佼佼.采用自组织RBF网络算法的变压器故障诊断[J].高电压技术,2012,38(6):1368-1375.Fu Qiang,Chen Tefang,Zhu Jiaojiao.Transformer fault diagnosis using self-adaptive RBF neural network algorithm[J].High Voltage Engineering,2012,38(6):1368-1375(in Chinese).
[14]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,2014,51(21):34-39.Yang Zhichao,Zhang Chenlong,Wu Yi,et al.Research on sets and RBF neural network based transformer fault diagnosis method[J].Electrical Measurement&Instrumentation,2014,51(21):34-39(in Chinese).
[15]李晴,何怡刚,包伟.免疫蚂蚁算法优化的RBF网络用于模拟电路故障诊断[J].仪器仪表学报,2010,31(6):1255-1261.Li Qing,He Yigang,Bao Wei.Immune-ant algorithm based RBFNN for fault diagnosis of analog circuits[J].Chinese Journal of Science Instrument,2010,31(6):1255-1261(in Chinese).
[16]梁永春,李彦明.改进型组合RBF神经网络的变压器故障诊断[J].高电压技术,2005,31(9):31-33.Liang Yongchun,Li Yanming.Application of modified combinatorial radial basis function neural network in fault diagnosis of power transformer[J].High Voltage Engineering,2005,31(9):31-33(in Chinese).
[17]Marwah G,Boggess L.Artificial immune systems for classification:some issues[C].Proceeding of the 1st International Conference on Artificial Immune Systems,University of Kent,2002.
[18]熊浩,孙才新,陈伟根,等.电力变压器故障诊断的人工免疫网络分类算法[J].电力系统自动化,2006,30(6):57-60.Xiong Hao,Sun Caixin,Chen Weigen,et al.Artificial immune network classification algorithm for fault diagnosis of power transformers[J].Automation of Electric Power System,2006,30(6):57-60(in Chinese).
[19]Watkins A,Timms J,Boggess L.Artificial immune recognition systems(AIRS):an immune-inspired supervised learning algorithm[J].Genetic Programming and Evolvable Machines,2004,5(3):291-317.
[20]赵安新,汤晓君,王尔珍,等.变压器油溶解气体的FTIR定量分析[J].光谱学与光谱分析,2013,33(9):2407-2410.Zhao Anxin,Tang Xiaojun Wang Erzhen,et al.Quantitative analysis of transformer oil dissolved gases using FTIR[J].Spectroscopy and Spectral Analysis,2013,33(9):2407-2410(in Chinese).
[21]王福忠,邵淑敏,董鹏飞.变压器油中气体组分含量在线监测与故障诊断[J].河南理工大学学报:自然科学版,2015,34(3):379-383.Wang Fuzhong,Shao Shumin,Dong Pengfei.The on-line monitoring and fault diagnosis of dissolved gas constituent content in transformer oil[J].Journal of Henan Polytechnic University:Natural Science,2015,34(3):379-383(in Chinese).
[22]王华国,孙玉坤,王博,等.改进的PSO-FNN在发酵软测量中的应用[J].自动化仪表,2016,37(3):62-64.Wang Guohua,Sun Yunkun,Wang Bo,et al.Application of the Improved PSO-FNN in fermentation soft sensing[J].Process Automation Instrumentation,2016,37(3):62-64(in Chinese).
[23]王晓霞,王涛.基于粒子群优化神经网络的变压器故障诊断[J].高电压技术,2008,34(11):2362-2367.Wang Xiaoxia,Wang Tao.Power transformer fault diagnosis based on neural network evolved by particle swarm optimization[J].High Voltage Engineering,2008,34(11):2362-2367(in Chinese).
[2]刘景艳,王福忠,杨占山.基于RBF神经网络和自适应遗传算法的变压器故障诊断[J].武汉大学学报(工学版),2016,49(1):88-93.Liu Jingyan,Wang Fuzhong,Yang Zhanshan.Transformer fault diagnosis based on RBF neural network and adaptive genetic algorithm[J].Engineering Journal of Wuhan University,2016,49(1):88-93(in Chinese).
[3]吴晓辉,刘炯,梁永春,等.支持向量机在电力变压器故障诊断中的应用[J].西安交通大学学报,2007,41(6):722-726.Wu Xiaohui,Liu Jiong,Liang Yongchun,et al.Application of support vector machine in transformer fault diagnosis[J].Journal of Xi’an Jiaotong University,2007,41(6):722-726(in Chinese).
[4]王雪梅,李文申,严璋.BP网络在电力变压器故障诊断中的应用[J].高电压技术,2005,31(7):12-14.Wang Xuemei,Li Wenshen,Yan Zhang.Application study of BP network used in the fault diagnosis of power transformer[J].High Voltage Engineering,2005,31(7):12-14(in Chinese).
[5]项文强,张华,王姮,等.基于L-M算法的BP网络在变压器故障诊断中的应用[J].电力系统保护与控制,2011,39(8):100-103.Xiang Wenqiang,Zhang Hua,Wang Yuan,et al.Application of BP neural network with L-M algorithm in power transformer fault diagnosis[J].Power System Protection and Control,2011,39(8):100-103(in Chinese).
[6]宋志杰,王健.模糊聚类和LM算法改进BP神经网络的变压器故障诊断[J].高压电器,2013,49(5):54-59.Song Zhijie,Wang Jian.Transformer fault diagnosis based on BP neural network optimized by fuzzy clustering and LM algorithm[J].High Voltage Apparatus,2013,49(5):54-59(in Chinese).
[7]任静,黄家栋.基于免疫RBF神经网络的变压器故障诊断[J].电力系统保护与控制,2010,38(11):6-9.Ren Jing,Huang Jiadong.Transformer fault diagnosis Based on immune RBF neural network[J].Power System Protection and Control,2010,38(11):6-9(in Chinese).
[8]陈江波,文习山,蓝磊,等.基于新径向基函数网络的变压器故障诊断法[J].高电压技术,2007,33(3):140-143.Chen Jiangbo,Wen Xishan,Lan Lei,et al.Fault diagnosis of power transformer by novel radial basis function neural network approach[J].High Voltage Engineering,2007,33(3):140-143(in Chinese).
[9]毛向德,王庆贤,董唯光,等.小波包神经网络与数据降维的移相全桥变换器的故障诊断[J].电源学报,2014,12(4):68-75.Mao Xiangde,Wang Qingxian,Dong Weiguang,et al.Phase-shift full bridge converter fault diagnosis based on wavelet packet and neural network and data dimensionality[J].Journal of Power Supply,2014,12(4):68-75(in Chinese).
[10]周爱华,张彼德,张厚宣.基于人工免疫分类算法的电力变压器故障诊断[J].高电压技术,207,33(8):77-80.Zhou Aihua,Zhang Bide,Zhang Houxuan.Power transformer fault diagnosis by using the artificial immune classification algorithm[J].High Voltage Engineering,2007,33(8):77-80(in Chinese).
[11]董明,孟源源,徐长响,等.基于支持向量机及油中溶解气体分析的大型电力变压器故障诊断模型研究[J].中国电机工程学报2003,23(7):88-92.Dong Ming,Meng Yuanyuan,Xu Changxiang,et al.Fault diagnosis model for power transformer based on support vector machine and dissolved gas analysis[J].Proceeding of the CSEE,2003,23(7):88-92(in Chinese).
[12]韩富春,高文君,廉建鑫,等.基于免疫优化多分类SVM的变压器故障诊断新方法[J].电力系统保护与控制,2012,40(2):106-110.Han Fuchun,Gao Wenjun,Lian Jianxin,et al.A novel approach based on multi-class support vector machine of immune for transformer fault diagnosis[J].Power System Protection and Control,2012,40(2):106-110(in Chinese).
[13]付强,陈特放,朱佼佼.采用自组织RBF网络算法的变压器故障诊断[J].高电压技术,2012,38(6):1368-1375.Fu Qiang,Chen Tefang,Zhu Jiaojiao.Transformer fault diagnosis using self-adaptive RBF neural network algorithm[J].High Voltage Engineering,2012,38(6):1368-1375(in Chinese).
[14]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,2014,51(21):34-39.Yang Zhichao,Zhang Chenlong,Wu Yi,et al.Research on sets and RBF neural network based transformer fault diagnosis method[J].Electrical Measurement&Instrumentation,2014,51(21):34-39(in Chinese).
[15]李晴,何怡刚,包伟.免疫蚂蚁算法优化的RBF网络用于模拟电路故障诊断[J].仪器仪表学报,2010,31(6):1255-1261.Li Qing,He Yigang,Bao Wei.Immune-ant algorithm based RBFNN for fault diagnosis of analog circuits[J].Chinese Journal of Science Instrument,2010,31(6):1255-1261(in Chinese).
[16]梁永春,李彦明.改进型组合RBF神经网络的变压器故障诊断[J].高电压技术,2005,31(9):31-33.Liang Yongchun,Li Yanming.Application of modified combinatorial radial basis function neural network in fault diagnosis of power transformer[J].High Voltage Engineering,2005,31(9):31-33(in Chinese).
[17]Marwah G,Boggess L.Artificial immune systems for classification:some issues[C].Proceeding of the 1st International Conference on Artificial Immune Systems,University of Kent,2002.
[18]熊浩,孙才新,陈伟根,等.电力变压器故障诊断的人工免疫网络分类算法[J].电力系统自动化,2006,30(6):57-60.Xiong Hao,Sun Caixin,Chen Weigen,et al.Artificial immune network classification algorithm for fault diagnosis of power transformers[J].Automation of Electric Power System,2006,30(6):57-60(in Chinese).
[19]Watkins A,Timms J,Boggess L.Artificial immune recognition systems(AIRS):an immune-inspired supervised learning algorithm[J].Genetic Programming and Evolvable Machines,2004,5(3):291-317.
[20]赵安新,汤晓君,王尔珍,等.变压器油溶解气体的FTIR定量分析[J].光谱学与光谱分析,2013,33(9):2407-2410.Zhao Anxin,Tang Xiaojun Wang Erzhen,et al.Quantitative analysis of transformer oil dissolved gases using FTIR[J].Spectroscopy and Spectral Analysis,2013,33(9):2407-2410(in Chinese).
[21]王福忠,邵淑敏,董鹏飞.变压器油中气体组分含量在线监测与故障诊断[J].河南理工大学学报:自然科学版,2015,34(3):379-383.Wang Fuzhong,Shao Shumin,Dong Pengfei.The on-line monitoring and fault diagnosis of dissolved gas constituent content in transformer oil[J].Journal of Henan Polytechnic University:Natural Science,2015,34(3):379-383(in Chinese).
[22]王华国,孙玉坤,王博,等.改进的PSO-FNN在发酵软测量中的应用[J].自动化仪表,2016,37(3):62-64.Wang Guohua,Sun Yunkun,Wang Bo,et al.Application of the Improved PSO-FNN in fermentation soft sensing[J].Process Automation Instrumentation,2016,37(3):62-64(in Chinese).
[23]王晓霞,王涛.基于粒子群优化神经网络的变压器故障诊断[J].高电压技术,2008,34(11):2362-2367.Wang Xiaoxia,Wang Tao.Power transformer fault diagnosis based on neural network evolved by particle swarm optimization[J].High Voltage Engineering,2008,34(11):2362-2367(in Chinese).