基于广义S变换和PSO-ELM的电能质量扰动信号识别
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摘要
电能质量扰动信号识别是电能质量扰动参数分析、扰动源定位和综合治理的前提。针对S变换在电能质量扰动信号分析中特征表现能力不足,以及极限学习机随机设置输入权值和隐藏层阈值造成识别准确率低的问题,提出一种基于广义S变换(generalized S-transform,GST)和粒子群(particle swarm optimization,PSO)优化极限学习机(extreme learning machine,ELM)的电能质量扰动信号识别新方法。首先,将粗调、微调和精调因子引入S变换的高斯窗函数中,并根据扰动信号的频率特点调整各因子值,从而获得更具针对性的时-频分辨率,以增强特征表现能力。其次,利用PSO的寻优能力,获取最大适应度时对应的输入权值和隐藏层阈值,提升ELM的识别准确率。最后,根据GST时-频模矩阵生成特征集,对PSO-ELM进行训练并测试其识别能力。对比实验表明,相较于S变换和ELM方法,本文提出方法识别准确率更高、抗噪性更强,能够满足工业环境下的电能质量扰动信号识别需要。
The disturbance signal recognition of power quality is the premise of distribution parameter analysis,localization of disturbance source and comprehensive control of power quality. As for such issue as insufficient performance ability of S transform in the analysis of power quality disturbance signal and low recognition precision due to random set input weight of extreme learning machine and threshold of hidden layer,a kind of new recognition method of power quality disturbance signal based on S transform(generalized S-transform,GST)and particle swarm optimization(PSO)extreme learning machine(ELM).Firstly,such three adjustment factors as rough,fine and accurate adjustment are introduced to the Gauss window function of S transform,to adjust each factor value as per the frequency feature of the disturbance signal,so to obtain specific time-frequency resolution and increase feature. Secondly,the corresponding values of input weights and hidden layer threshold at maximum fitness are obtained by the optimizing capacity of PSO and improve the recognition accuracy of ELM. Finally,PSO-ELM is trained and its recognition ability is tested in accordance with the feature set of GST time-frequency module matrix. It is shown by the comparison experiment that the recognition method proposed in this paper,compared to the S transform and ELM method,has high recognition accuracy and strong noise-resistance performance and can meet the need for the recognition of power quality disturbance signal at the industrial environment.
The disturbance signal recognition of power quality is the premise of distribution parameter analysis,localization of disturbance source and comprehensive control of power quality. As for such issue as insufficient performance ability of S transform in the analysis of power quality disturbance signal and low recognition precision due to random set input weight of extreme learning machine and threshold of hidden layer,a kind of new recognition method of power quality disturbance signal based on S transform(generalized S-transform,GST)and particle swarm optimization(PSO)extreme learning machine(ELM).Firstly,such three adjustment factors as rough,fine and accurate adjustment are introduced to the Gauss window function of S transform,to adjust each factor value as per the frequency feature of the disturbance signal,so to obtain specific time-frequency resolution and increase feature. Secondly,the corresponding values of input weights and hidden layer threshold at maximum fitness are obtained by the optimizing capacity of PSO and improve the recognition accuracy of ELM. Finally,PSO-ELM is trained and its recognition ability is tested in accordance with the feature set of GST time-frequency module matrix. It is shown by the comparison experiment that the recognition method proposed in this paper,compared to the S transform and ELM method,has high recognition accuracy and strong noise-resistance performance and can meet the need for the recognition of power quality disturbance signal at the industrial environment.
引文
[1]BORGES F,FERNANDES R,SILVA I,et al.Feature extraction and power quality disturbances classification using smart meters[J].IEEE Transactions on Industrial Informatics,2016,12(2):824-833.
[2]王金浩,齐月文,徐龙,等.10 k V电能质量扰动综合发生装置的研制[J].电力电容器与无功补偿,2015,36(2):66-72.WANG Jinhao,QI Yuewen,XU Long,et al.Development of10 k V power quality disturbance generator[J].Power Capacitor&Reactive Power Compensation,2015,36(2):66-72.
[3]VALTIERRA-RODRIGUEZ M,DE JESUS ROMERO-TRONCOSO R A,GARCIA-PEREZ A.Detection and classification of single and combined power quality disturbances using neural networks[J].IEEE Transactions on Industrial Electronics,2014,61(5):2473-2482.
[4]JURADO F,SAENZ J R.Comparison between discrete STFTand wavelets for the analysis of power quality events[J].Electric Power Systems Research,2002,62(3):183-190.
[5]吴兆刚,李唐兵,姚建刚,等.基于小波和改进神经树的电能质量扰动分类[J].电力系统保护与控制,2014(24):86-92.WU Zhaogang,LI Tangbing,YAO Jiangang,et al.Power quality disturbance classification based on a wavelet and improved neural tree[J].Power System Protection and Control,2014(24):86-92.
[6]LATRAN,MOHAMMAD BARGHI,TEKE,AHMET.A novel wavelet transform based voltage sag/swell detection algorithm[J].International journal of electrical power and energy systems,2015,71(Oct.):131-139.
[7]RAY P K,KISHOR N,MOHANTY S R.Islanding and power quality disturbance detection in grid-connected hybrid power system using wavelet and S-transform[J].IEEE Transactions on Smart Grid,2012,3(3):1082-1094.
[8]黄南天,徐殿国,刘晓胜.基于S变换与SVM的电能质量复合扰动识别[J].电工技术学报,2011(10):23-30.HUANG Natian,XU Dianguo,LIU Xiaosheng.Identification of power quality complex disturbances based on S-transform and SVM[J].Transactions of China Electrotechnical Society,2011(10):23-30.
[9]唐瑞,李晓辉,曹英丽,等.用户光伏发电电能质量检测与分析[J].电网与清洁能源,2016(5):25-28.
[10]张华赢,姚森敬,耿介雯,等.广域分布式电能质量监测系统数据整合技术研究[J].电器与能效管理技术,2016(17):62-66.ZHANG Huaying,YAO Senjing,GENG Jiewen,et al.Research of data integration technique for wide-area distributed power quality monitoring system[J].Electrical&Energy Management Technology,2016(17):62-66.
[11]周娟娟,李虹.改进双曲S变换与动态测度结合在电能质量检测中的应用[J].陕西电力,2015,43(1):33-38.ZHOU Juanjuan,LI Hong.Application of combination of dynamics measure and improved hyperbolic S-transform in power quality detection[J].Shaanxi Electric Power,2015,43(1):33-38.
[12]MOHANTY S R,RAY P K,KISHOR N,et al.Classification of disturbances in hybrid DG system using modular PNNand SVM[J].International Journal of Electrical Power&Energy Systems,2013,44(1):764-777.
[13]DE YONG D,BHOWMIK S,MAGNAGO F.An effective power quality classifier using wavelet transform and support vector machines[J].Expert Systems with Applications,2015,42(15/16):6075-6081.
[14]ERISTI H,DEMIR Y.Automatic classification of power quality events and disturbances using wavelet transform and support vector machines[J].IET Generation Transmission&Distribution,2012,6(10):968-976.
[15]RAY P K,MOHANTY S R,KISHOR N,et al.Optimal feature and decision tree-based classification of power quality disturbances in distributed generation systems[J].IEEETransactions on Sustainable Energy,2014,5(1):200-208.
[16]ERISTI H,YLIDIRIM O,ERISTI B,et al.Automatic recognition system of underlying causes of power quality disturbances based on S-transform and extreme learning machine[J].International Journal of Electrical Power&Energy Systems,2014,61(6):553-562.
[17]STOCKWELL R G,MANSINHA L,LOWE R P.Localization of the complex spectrum:the S-transform[J].IEEE Transactions on Signal Processing,1996,44(4):998-1001.
[18]唐贵基,王晓龙.自适应最大相关峭度解卷积方法及其在轴承早期故障诊断中的应用[J].中国电机工程学报,2015,35(6):1436-1444.TANG Guiji,WANG Xiaolong.Adaptive maximum correlated kurtosis deconvolution method and its application on incipient fault diagnosis of bearing[J].Proceedings of the CSEE,2015,35(6):1436-1444.
[2]王金浩,齐月文,徐龙,等.10 k V电能质量扰动综合发生装置的研制[J].电力电容器与无功补偿,2015,36(2):66-72.WANG Jinhao,QI Yuewen,XU Long,et al.Development of10 k V power quality disturbance generator[J].Power Capacitor&Reactive Power Compensation,2015,36(2):66-72.
[3]VALTIERRA-RODRIGUEZ M,DE JESUS ROMERO-TRONCOSO R A,GARCIA-PEREZ A.Detection and classification of single and combined power quality disturbances using neural networks[J].IEEE Transactions on Industrial Electronics,2014,61(5):2473-2482.
[4]JURADO F,SAENZ J R.Comparison between discrete STFTand wavelets for the analysis of power quality events[J].Electric Power Systems Research,2002,62(3):183-190.
[5]吴兆刚,李唐兵,姚建刚,等.基于小波和改进神经树的电能质量扰动分类[J].电力系统保护与控制,2014(24):86-92.WU Zhaogang,LI Tangbing,YAO Jiangang,et al.Power quality disturbance classification based on a wavelet and improved neural tree[J].Power System Protection and Control,2014(24):86-92.
[6]LATRAN,MOHAMMAD BARGHI,TEKE,AHMET.A novel wavelet transform based voltage sag/swell detection algorithm[J].International journal of electrical power and energy systems,2015,71(Oct.):131-139.
[7]RAY P K,KISHOR N,MOHANTY S R.Islanding and power quality disturbance detection in grid-connected hybrid power system using wavelet and S-transform[J].IEEE Transactions on Smart Grid,2012,3(3):1082-1094.
[8]黄南天,徐殿国,刘晓胜.基于S变换与SVM的电能质量复合扰动识别[J].电工技术学报,2011(10):23-30.HUANG Natian,XU Dianguo,LIU Xiaosheng.Identification of power quality complex disturbances based on S-transform and SVM[J].Transactions of China Electrotechnical Society,2011(10):23-30.
[9]唐瑞,李晓辉,曹英丽,等.用户光伏发电电能质量检测与分析[J].电网与清洁能源,2016(5):25-28.
[10]张华赢,姚森敬,耿介雯,等.广域分布式电能质量监测系统数据整合技术研究[J].电器与能效管理技术,2016(17):62-66.ZHANG Huaying,YAO Senjing,GENG Jiewen,et al.Research of data integration technique for wide-area distributed power quality monitoring system[J].Electrical&Energy Management Technology,2016(17):62-66.
[11]周娟娟,李虹.改进双曲S变换与动态测度结合在电能质量检测中的应用[J].陕西电力,2015,43(1):33-38.ZHOU Juanjuan,LI Hong.Application of combination of dynamics measure and improved hyperbolic S-transform in power quality detection[J].Shaanxi Electric Power,2015,43(1):33-38.
[12]MOHANTY S R,RAY P K,KISHOR N,et al.Classification of disturbances in hybrid DG system using modular PNNand SVM[J].International Journal of Electrical Power&Energy Systems,2013,44(1):764-777.
[13]DE YONG D,BHOWMIK S,MAGNAGO F.An effective power quality classifier using wavelet transform and support vector machines[J].Expert Systems with Applications,2015,42(15/16):6075-6081.
[14]ERISTI H,DEMIR Y.Automatic classification of power quality events and disturbances using wavelet transform and support vector machines[J].IET Generation Transmission&Distribution,2012,6(10):968-976.
[15]RAY P K,MOHANTY S R,KISHOR N,et al.Optimal feature and decision tree-based classification of power quality disturbances in distributed generation systems[J].IEEETransactions on Sustainable Energy,2014,5(1):200-208.
[16]ERISTI H,YLIDIRIM O,ERISTI B,et al.Automatic recognition system of underlying causes of power quality disturbances based on S-transform and extreme learning machine[J].International Journal of Electrical Power&Energy Systems,2014,61(6):553-562.
[17]STOCKWELL R G,MANSINHA L,LOWE R P.Localization of the complex spectrum:the S-transform[J].IEEE Transactions on Signal Processing,1996,44(4):998-1001.
[18]唐贵基,王晓龙.自适应最大相关峭度解卷积方法及其在轴承早期故障诊断中的应用[J].中国电机工程学报,2015,35(6):1436-1444.TANG Guiji,WANG Xiaolong.Adaptive maximum correlated kurtosis deconvolution method and its application on incipient fault diagnosis of bearing[J].Proceedings of the CSEE,2015,35(6):1436-1444.
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