改进人工鱼群算法优化小波神经网络的变压器故障诊断
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摘要
针对油浸式变压器故障类型的复杂难辨,结合油中气体分析法,提出一种基于改进人工鱼群算法优化小波神经网络的故障诊断模型。基于经典三层小波神经网络,采用粒子化的人工鱼群算法对小波神经网络输入和输出层的权值、小波神经元的伸缩和平移系数进行修正,通过引入动态反向学习策略实时优化人工鱼分布,迭代后半程采用基于柯西分布的自适应人工鱼视野范围提高算法精度。结果表明,该改进鱼群算法优化的小波神经网络相比标准粒子群算法优化小波神经网络和标准鱼群算法优化小波神经网络,诊断速度更快,准确率更高。
Combining dissolved gas analysis,a wavelet neural network diagnosis model based on the improved artificial fish-swarm algorithm was proposed to accommodate complicated fault types of the transformer. Based on the standardized three-layer wavelet neural network structure,the input and output weighting coefficients,the stretching and translation coefficients of wavelet elements were modified by the particle artificial fish-swarm algorithm. A dynamic opposition-based learning strategy was introduced to optimize the distribution of artificial fish,and the adaptive vision of artificial fish based on Cauchy distribution was used to improve the accuracy in the latter half-progress. Experimental results showed that the proposed method applied to the transformer diagnosis well for its faster and more accurate search results than those obtained by wavelet neural network diagnosis optimized by standard particle swarm algorithm and wavelet neural network diagnosis optimized by standard artificial fish-swarm algorithm.
Combining dissolved gas analysis,a wavelet neural network diagnosis model based on the improved artificial fish-swarm algorithm was proposed to accommodate complicated fault types of the transformer. Based on the standardized three-layer wavelet neural network structure,the input and output weighting coefficients,the stretching and translation coefficients of wavelet elements were modified by the particle artificial fish-swarm algorithm. A dynamic opposition-based learning strategy was introduced to optimize the distribution of artificial fish,and the adaptive vision of artificial fish based on Cauchy distribution was used to improve the accuracy in the latter half-progress. Experimental results showed that the proposed method applied to the transformer diagnosis well for its faster and more accurate search results than those obtained by wavelet neural network diagnosis optimized by standard particle swarm algorithm and wavelet neural network diagnosis optimized by standard artificial fish-swarm algorithm.
引文
[1]汪可,李金忠,张书琦,等.变压器故障诊断用油中溶解气体新特征参量[J].中国电机工程学报,2016,36(23):6570-6578,6625.WANG K,LI J Z,ZHANG S Q,et al. New features derived from dissolved gas analysis for fault diagnosis of power transformers[J]. Proceedings of the CSEE,2016,36(23):6570-6578,6625.
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[8]苑津莎,张利伟,李中,等.基于互补免疫算法的变压器故障诊断[J].电工技术学报,2015,30(24):67-75.YUAN J S,ZHANG L W,LI Z,et al. Fault diagnosis of transformers based on complementary immune algorithm[J]. Transactions of China Electrotechnical Society,2015,30(24):67-75.
[9] WANG H,WU Z J,RAHNAMAYAN S,et al. Enhancing particle swarm optimization using generalized opposition-based learning[J]. Information Sciences,2011,3(16):4699-4714.
[10]杨少兵,吴命利.基于广义柯西分布的系统侧谐波阻抗估计[J].中国电机工程学报,2014,34(7):1159-1166.YANG S B,WU M L. An assessment method of power system harmonic impedance based on generalized Cauchy distribution[J]. Proceedings of the CSEE,2014,34(7):1159-1166.
[11]电力行业电力变压器标准化技术委员会. DL/T722—2014,变压器油中溶解气体分析和判断导则[S].北京:中国电力出版社,2015.TECHNICAL COMMITTEE FOR STANDARDIZATION OF POWER TRANSFORMERS IN ELECTRIC POWER INDUSTRY. DL/T 722—2014,Guide to the analysis and the diagnosis of gases dissolved in transformer oil[S]. Beijing:China Electric Power Press,2015.
[12]袁兴起,杨双艳.变压器油温监控系统研究与实现[J].河南理工大学学报(自然科学版),2014,33(5):626-629.YUAN X Q,YANG S Y. Study and implementation on temperature monitoring system of transformer[J]. Journal of Henan Polytechnic University(Natural Science),2014,33(5):626-629.
[13]尹金良.基于相关向量机的油浸式电力变压器故障诊断方法研究[D].保定:华北电力大学,2013.YIN J L. Study on oil-immersed power transformer fault diagnosis based on relevance vector machine[D].Baoding:North China Electric Power University,2013.
[14]李建坡.基于油中溶解气体分析的电力变压器故障诊断技术的研究[D].长春:吉林大学,2008.LI J P. Study on power transformer fault diagnosistechnology based on dissolved gases analysis[D]. Changchun:Jilin University,2008.
[15]李俭.大型电力变压器以油中溶解气体为特征量的内部故障诊断模型研究[D].重庆:重庆大学,2001.LI J. Study on the models of fault diagnosis aboutpower transformer based on dissolved gases analysis[D].Chongqing:Chongqing University,2001.
[2] UARDADO J L,NAREDO J L,MORENO P,et al. A comparative study of neural network efficiency in powertransformers diagnosisusing dissolved gas analysis[J]. IEEE Trans on Power Delivery,2001,16(4):643-647.
[3]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 F Z,SHAO S M,DONG P F. The on-line monitoring and fault diagnosis of dissolved gas constituent contentin transformer oil[J]. Journal of Henan Polytechnic University(Natural Science),2015,34(3):379-383.
[4]谢红侠,史丽萍,惠正运.改进免疫聚类算法的变压器故障诊断研究[J].电测与仪表,2012,49(3):15-18,32.XIE H X,Shi L P,HUI Z Y. Research on grey immune clustering algorithm for transformer fault diagnosis[J].Electrical Measurement&Instrumentation,2012,49(3):15-18,32.
[5]吴冰,魏建,刘艳昌,等.粗糙集神经网络在图像分割中的应用[J].河南理工大学学报(自然科学版),2007,26(5):550-554.WU B,WEI J,LIU Y C,et al. Rough sets neural network used image segmentation[J]. Journal of Henan Polytechnic University(Natural Science),2007,26(5):550-554.
[6]李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式:鱼群算法[J].系统工程理论与实践,2002(12):32-38.LI X L,SHAO Z J,QIAN J X. An optimizing method based on autonomous animats fish-warm algorithm[J].System Engineering Theory and Practice,2002(12):32-38.
[7]王新,徐娟.基于小波包神经网络的级联式变频器故障诊断仿真研究[J].河南理工大学学报(自然科学版),2012,31(6):706-711.WANG X,XU J. Fault diagnosis simulation study of cascaded based on wavelet packet neuralconverter[J].Journal of Henan Polytechnic University(Natural Science),2012,31(6):706-711.
[8]苑津莎,张利伟,李中,等.基于互补免疫算法的变压器故障诊断[J].电工技术学报,2015,30(24):67-75.YUAN J S,ZHANG L W,LI Z,et al. Fault diagnosis of transformers based on complementary immune algorithm[J]. Transactions of China Electrotechnical Society,2015,30(24):67-75.
[9] WANG H,WU Z J,RAHNAMAYAN S,et al. Enhancing particle swarm optimization using generalized opposition-based learning[J]. Information Sciences,2011,3(16):4699-4714.
[10]杨少兵,吴命利.基于广义柯西分布的系统侧谐波阻抗估计[J].中国电机工程学报,2014,34(7):1159-1166.YANG S B,WU M L. An assessment method of power system harmonic impedance based on generalized Cauchy distribution[J]. Proceedings of the CSEE,2014,34(7):1159-1166.
[11]电力行业电力变压器标准化技术委员会. DL/T722—2014,变压器油中溶解气体分析和判断导则[S].北京:中国电力出版社,2015.TECHNICAL COMMITTEE FOR STANDARDIZATION OF POWER TRANSFORMERS IN ELECTRIC POWER INDUSTRY. DL/T 722—2014,Guide to the analysis and the diagnosis of gases dissolved in transformer oil[S]. Beijing:China Electric Power Press,2015.
[12]袁兴起,杨双艳.变压器油温监控系统研究与实现[J].河南理工大学学报(自然科学版),2014,33(5):626-629.YUAN X Q,YANG S Y. Study and implementation on temperature monitoring system of transformer[J]. Journal of Henan Polytechnic University(Natural Science),2014,33(5):626-629.
[13]尹金良.基于相关向量机的油浸式电力变压器故障诊断方法研究[D].保定:华北电力大学,2013.YIN J L. Study on oil-immersed power transformer fault diagnosis based on relevance vector machine[D].Baoding:North China Electric Power University,2013.
[14]李建坡.基于油中溶解气体分析的电力变压器故障诊断技术的研究[D].长春:吉林大学,2008.LI J P. Study on power transformer fault diagnosistechnology based on dissolved gases analysis[D]. Changchun:Jilin University,2008.
[15]李俭.大型电力变压器以油中溶解气体为特征量的内部故障诊断模型研究[D].重庆:重庆大学,2001.LI J. Study on the models of fault diagnosis aboutpower transformer based on dissolved gases analysis[D].Chongqing:Chongqing University,2001.