固体绝缘开关柜局部放电模式识别优化算法
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摘要
电力设备内部绝缘缺陷引起的局部放电,含有可用于绝缘状态评估的特征信息。可以有效识别不同局部放电的类型。现有基于传统BP神经网络或SVDD模式识别方法在函数参数选择自主性很强,但由于不同放电类型的特征量在分布上是重叠、非线性的,BP神经网络容易陷入局部最优,识别率不高,SVDD算法在自由金属微粒缺陷识别效果不好。文中对SVDD算法提出了改进,在AP聚类与GPAM-PSO优化算法基础上提出一种用于固体绝缘开关柜局部放电模式识别的SA-SVDD算法。以解决传统模式识别算法在参数选择、训练方法上的不足,通过训练不同放电类型下的分类器,以达到准确识别不同放电类型。仿真结果显示该方法能自主识别不同PD类型,识别率、收敛速度较传统方法有较大提高,以便电力人员准确判断局部放电类型并制定相对应的抢修方案。
The partial discharge caused by insulation defects inside the electric power equipment contains the characteristic information which can be used for the evaluation of insulation state. The characteristic information can effectively identify the types of different partial discharges. The existing BP neural network or SVDD pattern recognition method is very independent in the parameter selection. However,the characteristic quantities of different discharge types are overlapped and non-linear in the distribution,the BP neural network is easy to fall into the local optimum and the recognition rate is not high,SVDD in free metal particles defect recognition effect is not good. Based on the algorithm of AP clustering and GPAM-PSO,a SA-SVDD algorithm for local discharge pattern recognition of solid insulated switchgear is proposed. To solve the shortcomings of the traditional pattern recognition algorithm in the parameter selection and training methods,by training the classifier under different discharge types to achieve accurate identification of different discharge types. The simulation results show that the method can identify different PD types,and the recognition rate and convergence rate are greatly improved compared with the traditional methods so that the electric power personnel can judge the partial discharge type accurately and form the corresponding repair scheme.
The partial discharge caused by insulation defects inside the electric power equipment contains the characteristic information which can be used for the evaluation of insulation state. The characteristic information can effectively identify the types of different partial discharges. The existing BP neural network or SVDD pattern recognition method is very independent in the parameter selection. However,the characteristic quantities of different discharge types are overlapped and non-linear in the distribution,the BP neural network is easy to fall into the local optimum and the recognition rate is not high,SVDD in free metal particles defect recognition effect is not good. Based on the algorithm of AP clustering and GPAM-PSO,a SA-SVDD algorithm for local discharge pattern recognition of solid insulated switchgear is proposed. To solve the shortcomings of the traditional pattern recognition algorithm in the parameter selection and training methods,by training the classifier under different discharge types to achieve accurate identification of different discharge types. The simulation results show that the method can identify different PD types,and the recognition rate and convergence rate are greatly improved compared with the traditional methods so that the electric power personnel can judge the partial discharge type accurately and form the corresponding repair scheme.
引文
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[19]颜晓娟,龚仁喜,张千锋.优化遗传算法寻优的SVM在短期风速预测中的应用[J].电力系统保护与控制,2016,44(9):38-42.YAN Xiaojuan,GONG Renxi,ZHANG Qianfeng.Application of optimization SVM based on improved genetic algorithm in short-term wind speed prediction[J].Power System Protection and Control,2016,44(9):38-42.
[20]陶新民,张冬雪,郝思媛,等.基于谱聚类欠取样的不均衡数据SVM分类算法[J].控制与决策,2012,27(12):1761-1768.TAO Xinmin,ZHANG Dongxue,HAO Siyuan,et al.SVMclassifier for unbalanced data based on spectrum clusterbased under-sampling approaches[J].Control and Decision,2012,27(12):1761-1768.
[2]任先文,薛雷,宋阳,等.基于分形特征的最小二乘支持向量机局部放电模式识别[J].电力系统保护与控制,2011,39(14):143-147.REN Xianwen,XUE Lei,SONG Yang,et al.The pattern recognition of partial discharge based on fractal characteristics using LS-SVM[J].Power System Protection and Control,2011,39(14):143-147.
[3]程昊翔,王坚.基于快速聚类分析的支持向量数据描述算法[J].控制与决策,2016,31(3):551-554.CHENG Haoxiang,WANG Jian.Support vector data description based on fast clustering analysis[J].Control and Decision,2016,31(3):551-554.
[4]孙文柱,曲建岭,袁涛,等.基于改进SVDD的飞参数据新异检测方法[J].仪器仪表学报,2014,35(4):932-939.SUN Wenzhu,QU Jianling,YUAN Tao,et al.Flight data novelty detection method based on improved SVDD[J].Chinese Journal of Scientific Instrument,2014,35(4):932-939.
[5]孙才新,许高峰,唐炬,等.以盒维数和信息维数为识别特征量的GIS局部放电模式识别方法[J].中国电机工程学报,2005,25(3):100-104.SUN Caixin,XU Gaofeng,TANG Ju,et al.PD pattern recognition method using box dimension and information dimension as discriminating features in GIS[J].Proceedings of the CSEE,2005,25(3):100-104.
[6]胡文堂,高胜友,余绍峰,等.统计参数在变压器局部放电模式识别中的应用[J].高电压技术,2009,35(2):277-281.HU Wentang,GAO Shengyou,YU Shaofeng,et al.Application of statistic parameters in recognition of partial discharge in transformers[J].High Voltage Engineering,2009,35(2):277-281.
[7]周沙,景亮.基于矩特征与概率神经网络的局部放电模式识别[J].电力系统保护与控制,2016,44(3):98-102.ZHOU Sha,JING Liang.Pattern recognition of partial discharge based on moment features and probabilistic neural network[J].Power System Protection and Control,2016,44(3):98-102.
[8]唐炬,林俊亦,卓然,等.基于支持向量数据描述的局部放电类型识别[J].高电压技术,2013,39(5):1046-1053.TANG Ju,LIN Junyi,ZHUO Ran,et al.Partial discharge type recognition based on support vector data description[J].High Voltage Engineering,2013,39(5):1046-1053.
[9]李辉,丁世飞.基于AP二次聚类的神经网络集成算法研究[J].计算机科学,2015,42(2):224-227.LI Hui,DING Shifei.AP twice clustering based neural network ensemble algorithm[J].Computer Science,2015,42(2):224-227.
[10]朱红,丁世飞,许新征.基于改进属性约简的细粒度并行AP聚类算法[J].计算机研究与发展,2012,49(12):2638-2644.ZHU Hong,DING Shifei,XU Xinzheng.An AP clustering algorithm of fine-rain parallelism based on improved attribute reduction[J].Journal of Computer Research and Development,2012,49(12):2638-2644.
[11]刘艳红,薛安荣,史习云.K-means聚类与SVDD结合的新的分类算法[J].计算机应用研究,2010,27(3):883-886.LIU Yanhong,XUE Anrong,SHI Xiyun.New classifiaction algorithm K-means clustering combined with SVDD[J].Application Research of Computers,2010,27(3):883-886.
[12]胡文军,王士同.SVDD的快速实时决策方法[J].自动化学报,2011,37(9):1085-1094.HU Wenjun,WANG Shitong.Fast real-time decision decision approach of support vector data description[J].Acta Automatica Sinica,2011,37(9):1085-1094.
[13]ARAKI S,YAMAGUCHI Y,SHIMADA H,et al.Unknown attack detection by multistage one-class SVM focusing on com,unication interval[C]//21st International Conference on Neural Information Processing(ICNIP).[Sl.]:[s.n.],2014:325-332.
[14]杨志超,范立新,杨成顺,等.基于GK模糊聚类和LS-SVC的GIS局部放电类型识别[J].电力系统保护与控制,2014,42(20):38-45.YANG Zhichao,FAN Lixin,YANG Chengshun,et al.Identification of partial discharge in gas insulated switchgears based on GK fuzzy clustering&LS-SVM[J].Power System Protection and Control,2014,42(20):38-45.
[15]李善寿,张兴,张鸿恺,等.基于功率闭环控制与PSO算法的全局MPPT方法[J].中国电机工程学报,2014,34(28):4809-4816.LI Shanshou,ZHANG Xing,ZHANG Hongkai,et al.Global MPPT method based on power closed-loop control and PSOalgorithm[J].Proceedings of the CSEE,2014,34(28):4809-4816.
[16]张栋梁,谢业华,刘娟,等.基于遗传算法的有源电力滤波器滑模控制[J].电力系统保护与控制,2016,44(5):69-74.ZHANG Dongliang,XIE Yehua,LIU Juan,et al.Sliding mode control of active power filters based on genetic algorithm[J].Power System Protection and Control,2016,44(5):69-74.
[17]叶林,陈政,赵永宁,等.基于遗传算法-模糊径向基神经网络的光伏发电功率预测模型[J].电力系统自动化,2015,39(16):16-22.YE Lin,CHEN Zheng,ZHAO Yongning,et al.Photovoltaic power forecasting model based on genetic algorithm and fuzzy radial basis function neural network[J].Automation of Electric Power Systems,2015,39(16):16-22.
[18]赵莉,候兴哲,胡君,等.基于改进K-means算法的海量智能用电数据分析[J].电网技术,2014,38(10):2715-2720.ZHAO Li,HOU Xingzhe,HU Jun,et al.Improved K-means algorithm based analysis on massive data of intelligent power utilization[J].Power System Technology,2014,38(10):2715-2720.
[19]颜晓娟,龚仁喜,张千锋.优化遗传算法寻优的SVM在短期风速预测中的应用[J].电力系统保护与控制,2016,44(9):38-42.YAN Xiaojuan,GONG Renxi,ZHANG Qianfeng.Application of optimization SVM based on improved genetic algorithm in short-term wind speed prediction[J].Power System Protection and Control,2016,44(9):38-42.
[20]陶新民,张冬雪,郝思媛,等.基于谱聚类欠取样的不均衡数据SVM分类算法[J].控制与决策,2012,27(12):1761-1768.TAO Xinmin,ZHANG Dongxue,HAO Siyuan,et al.SVMclassifier for unbalanced data based on spectrum clusterbased under-sampling approaches[J].Control and Decision,2012,27(12):1761-1768.