基于样本熵和蜻蜓算法优化SVM的电能质量扰动识别和诊断研究
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摘要
为了避免支持向量机预测结果易受惩罚因子和核函数参数参数选择的影响,提出一种DA算法优化SVM的电能质量扰动诊断和识别模型,实现电能质量扰动最优化诊断和识别。首先运用EMD将电能质量扰动信号进行分解,之后计算各尺度下的IMF分量的样本熵,并将其作为电能质量扰动信号的特征向量,建立SVM的电能质量扰动信号的识别模型。实验结果表明,与GA_SVM、PSO_SVM和DE_SVM相比,本文提出的算法DA_SVM可以有效提高电能质量扰动识别的准确率,收敛速度快,为电能质量扰动诊断和识别提供新的方法和途径。
For preventing the prediction result of the support vector machine(SVM for short)from the punishment factor and parameter selection of kernel function parameter optimal,a kind of algorithm is proposed to optimize power quality disturbance diagnosis and identification of SVM so to the most optimal diagnosis and identification of the power quality.Firstly,power quality disturbance signal is decomposed by EMD method,then the sample entropy of different scale signal of EMD decomposition is calculated and is considered as a signal of power quality disturbance characteristic vector and the identification model based on SVM for the power quality disturbance signal is established.It is shown by the experimental result that compared with GA_SVM,PSO_SVM and DE_SVM,the algorithm DA_SVM proposed in this paper can effectively improve the accuracy of the power quality disturbance identification,has fast convergence rate and provide new methods and means for the power quality disturbance diagnosis and identification.
For preventing the prediction result of the support vector machine(SVM for short)from the punishment factor and parameter selection of kernel function parameter optimal,a kind of algorithm is proposed to optimize power quality disturbance diagnosis and identification of SVM so to the most optimal diagnosis and identification of the power quality.Firstly,power quality disturbance signal is decomposed by EMD method,then the sample entropy of different scale signal of EMD decomposition is calculated and is considered as a signal of power quality disturbance characteristic vector and the identification model based on SVM for the power quality disturbance signal is established.It is shown by the experimental result that compared with GA_SVM,PSO_SVM and DE_SVM,the algorithm DA_SVM proposed in this paper can effectively improve the accuracy of the power quality disturbance identification,has fast convergence rate and provide new methods and means for the power quality disturbance diagnosis and identification.
引文
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[4]王云静,李燕,曲正伟,等.基于PSO-MP算法和RBF神经网络的电能质量扰动识别[J].电测与仪表,2016,53(13):54-58.WANG Yunjing,LI Yan,QU Zhengwei,et al. The identification of power quality disturbance based on PSO-MP algorithm and RBF neural network[J]. Electrical Measurement&Instrumentation,2016,53(13):54-58.
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[10]黄敏敏,颜文俊.基于遗传算法优化神经网络的光伏发电预测[J].机电工程,2015,32(11):1509-1512.HUANG Minmin,YAN Wenjun. Photovoltaic power forecast based on neural network optimized by genetic algorithm[J]. Mechanical&Electrical Engineering Magazine,2015,32(11):1509-1512.
[11]杨耿煌,温渤婴.基于量子行为粒子群优化-人工神经网络的电能质量扰动识别[J].中国电机工程学报,2008,28(10):123-129.YANG Genghuang,WEN Boying. Identification of power quality disturbance based on QPSO-ANN[J]. Proceedings of the CSEE,2008,28(10):123-129.
[12]李燕楠,包广清.基于小波包和支持向量机的电能质量扰动识别[J].电力科学与工程,2012,28(3):21-26.LI Yannan,BAO Guangqing. Power quality disturbance signals identification based on wavelet packet and SVM[J]. Electric Power Science and Engineering,2012,28(3):21-26.
[13]韩刚,张建文,褚鑫,等.多特征组合及优化SVM的电能质量扰动识别[J].电力系统及其自动化学报,2015,27(8):71-76.HAN Gang,ZHANG Jianwen,CHU Xin,et al. Power quality disturbance classification based on multi-features combination and optimizing parameters of SVM[J]. Proceedings of the CSU-EPSA,2015,27(8):71-76.
[14]韩富春,孙碣,令狐进军,等.基于改进PSO-WNN的电能质量扰动分类研究[J].电气自动化,2011,33(5):59-60.HAN Fuchun,SUN Jie,LINGHU Jinjun,et al. Power qual-ity disturbances classification based on improved PSO-WNN[J]. Electrical Automation,2011,33(5):59-60.
[15]张卫辉,黄南天,杨金成,等.基于广义S变换和DE-ELM的电能质量扰动信号分类[J].电测与仪表,2016,53(20):50-55.ZHANG Weihui,HUANG Natian,YANG Jincheng,et al.Power quality disturbances classification based on general-ized S-transform and differential evolution extreme learningmachine[J]. Electrical Measurement&Instrumentation,2016,53(20):50-55.
[2]任子晖,刘昊岳,徐进霞.基于小波变换和改进Prony方法的电能质量扰动分析[J].电力系统保护与控制,2016,44(9):122-128.REN Zihui LIU Haoyue XU Jinxia Power quality disturbance analysis based on wavelettransform and improved Prony method[J]. Power System Protection and Control,2016,44(9):122-128.
[3]徐志超,杨玲君,李晓明.基于聚类改进S变换与直接支持向量机的电能质量扰动识别[J].电力自动化设备,2015,35(7):50-58.XU Zhichao,YANG Lingjun,LI Xiaoming. Power quality disturbance identification based on clustering-modified Stransform and direct support vector machine[J]. Electric Power Automation Equipment,2015,35(7):50-58.
[4]王云静,李燕,曲正伟,等.基于PSO-MP算法和RBF神经网络的电能质量扰动识别[J].电测与仪表,2016,53(13):54-58.WANG Yunjing,LI Yan,QU Zhengwei,et al. The identification of power quality disturbance based on PSO-MP algorithm and RBF neural network[J]. Electrical Measurement&Instrumentation,2016,53(13):54-58.
[5] FLANDRIN P,RILLING G,GONCALVES P. Empirical mode decomposition as a filter bank[J]. Signal Processing Letters,IEEE,2004,11(2):112-114.
[6] MIRJALILI S. Dragonfly algorithm:a new meta-heuristic optimization technique for solving single-objective,discrete,and multi-objective problems[J]. Neural Computing&Applications,2016,27(4):1053-1073.
[7]何尚平,罗小青,许仙明,等.基于样本熵的电网传输电能质量扰动信号分析[J].电网与清洁能源,2016,32(11):98-102.HE Shangping,LUO Xiaoqing,XU Xianming,et al. Analysis of the power quantity disturbance signal in the transmission grid based on the sample entropy method[J]. Advances of Power System&Hydroelectric Engineering,2016,32(11):98-102.
[8]王瑜,苑津莎,尚海昆,等.组合核支持向量机在放电模式识别中的优化策略[J].电工技术学报,2015,30(2):229-236.WANG Yu,YUAN Jinsha,SHANG Haikun,et al. Optimization strategy research on combined-kernel support vector machine for partial discharge pattern recognition[J]. Transactions of China Electrotechnical Society,2015,30(2):229-236.
[9]徐志超,杨玲君,李晓明.基于聚类改进S变换与直接支持向量机的电能质量扰动识别[J].电力自动化设备,2015,35(7):50-58.XU Zhichao,YANG Lingjun,LI Xiaoming. Power quality disturbance identification based on clustering-modified Stransform and direct support vector machine[J]. Electric Power Automation Equipment,2015,35(7):50-58.
[10]黄敏敏,颜文俊.基于遗传算法优化神经网络的光伏发电预测[J].机电工程,2015,32(11):1509-1512.HUANG Minmin,YAN Wenjun. Photovoltaic power forecast based on neural network optimized by genetic algorithm[J]. Mechanical&Electrical Engineering Magazine,2015,32(11):1509-1512.
[11]杨耿煌,温渤婴.基于量子行为粒子群优化-人工神经网络的电能质量扰动识别[J].中国电机工程学报,2008,28(10):123-129.YANG Genghuang,WEN Boying. Identification of power quality disturbance based on QPSO-ANN[J]. Proceedings of the CSEE,2008,28(10):123-129.
[12]李燕楠,包广清.基于小波包和支持向量机的电能质量扰动识别[J].电力科学与工程,2012,28(3):21-26.LI Yannan,BAO Guangqing. Power quality disturbance signals identification based on wavelet packet and SVM[J]. Electric Power Science and Engineering,2012,28(3):21-26.
[13]韩刚,张建文,褚鑫,等.多特征组合及优化SVM的电能质量扰动识别[J].电力系统及其自动化学报,2015,27(8):71-76.HAN Gang,ZHANG Jianwen,CHU Xin,et al. Power quality disturbance classification based on multi-features combination and optimizing parameters of SVM[J]. Proceedings of the CSU-EPSA,2015,27(8):71-76.
[14]韩富春,孙碣,令狐进军,等.基于改进PSO-WNN的电能质量扰动分类研究[J].电气自动化,2011,33(5):59-60.HAN Fuchun,SUN Jie,LINGHU Jinjun,et al. Power qual-ity disturbances classification based on improved PSO-WNN[J]. Electrical Automation,2011,33(5):59-60.
[15]张卫辉,黄南天,杨金成,等.基于广义S变换和DE-ELM的电能质量扰动信号分类[J].电测与仪表,2016,53(20):50-55.ZHANG Weihui,HUANG Natian,YANG Jincheng,et al.Power quality disturbances classification based on general-ized S-transform and differential evolution extreme learningmachine[J]. Electrical Measurement&Instrumentation,2016,53(20):50-55.