基于分段改进S变换的复合电能质量扰动识别
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摘要
针对存在多种单一电能质量扰动的复合扰动分类识别问题,提出了一种基于分段改进S变换和RBF神经网络相结合的复合电能质量扰动识别新方法。首先对离散S变换进行了分段改进,将时域分辨率和频域分辨率进行分段处理,通过分析改进S变换得到的模时频矩阵,绘制了能够反映扰动信号不同突变参数的特性曲线。其次利用统计方法优化计算提取了10种用于模式识别的特征量,并用局部逼近的RBF神经网络设计了分类器对提取的特征样本进行训练和分类,最后在不同噪声环境下对5种单一扰动及谐波+电压暂降、电压暂降+闪变等6类复合电能质量扰动的分类识别进行了仿真验证。仿真结果表明,该方案时频处理、分类能力和学习速度等方面均优于普通改进S变换+全局逼近网络的方法,且鲁棒性强,能准确识别多种单一扰动及两种扰动同时存在的复合电能质量扰动。
Aiming at the classification and recognition problem of composite power quality disturbances, a composite power quality disturbance recognition algorithm based on piecewise-modified S transform and RBF neural network is proposed. Firstly, the S transform is modified by segmenting the time resolution and the frequency resolution. By analyzing the obtained mode time-frequency matrix, the characteristic curve that can reflect different mutation parameters of the disturbance signal is drawn. Secondly, 10 types of characteristic parameters for pattern recognition are extracted by using statistical methods and optimization. The RBF neural network classifier is designed to classify the extracted feature samples by training and classification. Finally, six types of composite power quality disturbance classification including five single disturbances and harmonic and voltage sag, voltage sag and flicker, etc. are simulated under different noise environment. The simulation results show that the proposed scheme is superior to S transform and global approximation networks in terms of time-frequency processing ability, classification ability and learning speed, and is robust and can accurately identify multiple kinds of single disturbances and two kinds of disturbances simultaneously composite power quality disturbance.
Aiming at the classification and recognition problem of composite power quality disturbances, a composite power quality disturbance recognition algorithm based on piecewise-modified S transform and RBF neural network is proposed. Firstly, the S transform is modified by segmenting the time resolution and the frequency resolution. By analyzing the obtained mode time-frequency matrix, the characteristic curve that can reflect different mutation parameters of the disturbance signal is drawn. Secondly, 10 types of characteristic parameters for pattern recognition are extracted by using statistical methods and optimization. The RBF neural network classifier is designed to classify the extracted feature samples by training and classification. Finally, six types of composite power quality disturbance classification including five single disturbances and harmonic and voltage sag, voltage sag and flicker, etc. are simulated under different noise environment. The simulation results show that the proposed scheme is superior to S transform and global approximation networks in terms of time-frequency processing ability, classification ability and learning speed, and is robust and can accurately identify multiple kinds of single disturbances and two kinds of disturbances simultaneously composite power quality disturbance.
引文
[1]唐求,王耀南,郭斯羽,等.基于S变换与PNN的电能质量多扰动检测[J].仪器仪表学报,2009,30(8):1668-1673.TANG Qiu,WANG Yaonan,GUO Siyu,et al.Power quality disturbance detection based on S-transform and PNN[J].Chinese Journal of Scientific Instrument,2009,30(8):1668-1673.
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[6]曹玲芝,李振杰.基于dq变换与小波多分辨率分析的电力系统暂态复合扰动信号检测方法[J].电力系统保护与控制,2016,44(21):138-143.CAO Lingzhi,LI Zhenjie.Detection of transient complex disturbance signal in power system based on dq transform and wavelet-multiresolution analysis[J].Power System Protection and Control,2016,44(21):138-143.
[7]郑志宇,蔡翀,张昭丞,等.基于小波域相子的电压暂降特征提取与成因辨识[J].电力系统保护与控制,2018,46(1):16-22.ZHENG Zhiyu,CAI Chong,ZHANG Zhaocheng,et al.Wavelet-based phasor to detect and identify the voltage sag characteristics[J].Power System Protection and Control,2018,46(1):16-22.
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[9]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,2011,26(10):23-30.HUANG Nantian,XU Dianguo,LIU Xiaosheng.Identification of power quality complex disturbances based on S-transform and SVM[J].Transactions of China Electrotechnical Society,2011,26(10):23-30.
[10]李立,易吉良,朱建林.采用改进不完全S变换估计电能质量扰动参数[J].电工技术学报,2011,26(6):187-193.LI Li,YI Jiliang,ZHU Jianling.Parameter estimation of power quality disturbances using modified incomplete S-transform[J].Transactions of China Electrotechnical Society,2011,26(6):187-193.
[11]刘志刚,张巧革,张杨.电能质量复合扰动分类的研究进展[J].电力系统保护与控制,2013,41(13):146-153.LIU Zhigang,ZHANG Qiaoge,ZHANG Yang.Review of power quality mixed disturbances identification[J].Power System Protection and Control,2013,41(13):146-153.
[12]覃星福,龚仁喜.基于广义S变换与PSO-PNN的电能质量扰动识别[J].电力系统保护与控制,2016,44(15):10-17.QIN Xingfu,GONG Renxi.Power quality disturbances classification based on generalized S-transform and PSO-PNN[J].Power System Protection and Control,2016,44(15):10-17.
[13]瞿合祚,刘恒,李晓明,等.基于多标签随机森林的电能质量复合扰动分类方法[J].电力系统保护与控制,2017,45(11):1-7.QU Hezuo,LIU Heng,LI Xiaoming,et al.Recognition of multiple power quality disturbances using multi-label random forest[J].Power System Protection and Control,2017,45(11):1-7.
[14]何巨龙,王根平,刘丹,等.基于提升小波和改进BP神经网络的配电网系统电能质量扰动定位与识别[J].电力系统保护与控制,2017,45(10):69-76.HE Julong,WANG Genping,LIU Dan,et al.Localization and identification of power quality disturbance in distribution network system based on lifting wavelet and improved BP neural network[J].Power System Protection and Control,2017,45(10):69-76.
[15]陈华丰,杨志刚,曾涛.基于S变换和规则基的复合电能质量扰动识别[J].电测与仪表,2015,52(12):122-128.CHEN Huafeng,YANG Zhigang,ZENG Tao.Multiple power quality disturbances recognition using S-transform and rule based classification technique[J].Electrical Measurement&Instrumentation,2015,52(12):122-128.
[16]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.
[17]何智龙,苏娟,覃芳.S变换在电能质量扰动中的分析[J].电测与仪表,2015,52(22):25-30.HE Zhilong,SU Juan,QIN Fang.Analysis of power quality disturbances using S-transform[J].Electrical Measurement&Instrumentation,2015,52(22):25-30.
[18]郭俊文,李开成.基于改进S变换和复合特征量的多级支持向量机的电能质量扰动分类[J].电测与仪表,2014,51(8):19-25.GUO Junwen,LI Kaicheng.Automatic classification of power quality events based on MST and multi-lay SVMs of multiple features[J].Electrical Measurement&Instrumentation,2014,51(8):19-25.
[19]BAGHAEE H R,MIRSALIM M,GHAREHPETIAN GB.Power calculation using RBF neural networks to improve power sharing of hierarchical control scheme in multi-DER microgrids[J].IEEE Journal of Emerging&Selected Topics in Power Electronics,2016,4(4):1217-1225.
[20]刘永,张立毅.BP和RBF神经网络的实现及其性能比较[J].电子测量技术,2007,30(4):77-80.LIU Yong,ZHANG Liyi.Implementation of BP and RBFneural network and their performance comparison[J].Electronic Measurement Technology,2007,30(4):77-80.
[21]马斌,张丽艳.一种基于径向基神经网络的短期风电功率直接预测方法[J].电力系统保护与控制,2015,43(19):78-82.MA Bin,ZHANG Liyan.Short-term wind power direct forecasting based on RBF neural network[J].Power System Protection and Control,2015,43(19):78-82.
[22]任子晖,王琦.基于优化DDAGSVM多类分类策略的电能质量扰动识别[J].电力系统保护与控制,2018,46(5):82-88.REN Zihui,WANG Qi.Power quality disturbance recognition based on improved DDAGSVM multi-class classification strategy[J].Power System Protection and Control,2018,46(5):82-88.
[2]占勇,程浩忠.电能质量复合扰动分类识别[J].电力自动化设备,2009,29(3):93-97.ZHAN Yong,CHENG Haozhong.Classification of power quality complex disturbances[J].Electric Power Automation Equipment,2009,29(3):93-97.
[3]沈跃,张瀚文,刘国海,等.基于判别字典学习的电能质量扰动识别方法[J].仪器仪表学报,2015,36(10):2167-2173.SHEN Yue,ZHANG Hanwen,LIU Guohai,et al.Power quality disturbance identification method based on discriminative dictionary learning[J].Chinese Journal of Scientific Instrument,2015,36(10):2167-2173.
[4]徐健,张语勍,李彦斌,等.短时傅里叶变换和S变换用于检测电压暂降的对比研究[J].电力系统保护与控制,2014,42(16):44-48.XU Jian,ZHANG Yuqing,LI Yanbin,et al.Comparative study of STFT and S transform on detecting voltage sag[J].Power System Protection and Control,2014,42(16):44-48.
[5]SENROY N,SURYANARAYANAN S,RIBEIRO P F.An improved Hilbert-Huang method for analysis of time-varying waveforms in power quality[J].IEEETransactions on Power Systems,2007,22(4):1843-1850.
[6]曹玲芝,李振杰.基于dq变换与小波多分辨率分析的电力系统暂态复合扰动信号检测方法[J].电力系统保护与控制,2016,44(21):138-143.CAO Lingzhi,LI Zhenjie.Detection of transient complex disturbance signal in power system based on dq transform and wavelet-multiresolution analysis[J].Power System Protection and Control,2016,44(21):138-143.
[7]郑志宇,蔡翀,张昭丞,等.基于小波域相子的电压暂降特征提取与成因辨识[J].电力系统保护与控制,2018,46(1):16-22.ZHENG Zhiyu,CAI Chong,ZHANG Zhaocheng,et al.Wavelet-based phasor to detect and identify the voltage sag characteristics[J].Power System Protection and Control,2018,46(1):16-22.
[8]管春,何丰,周冬生,等.电能质量复合扰动分类方法研究[J].重庆邮电大学学报(自然科学版),2010,22(5):618-623.GUAN Chun,HE Feng,ZHOU Dongsheng,et al.Classification method for the multiple power quality disturbances[J].Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition),2010,22(5):618-623.
[9]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,2011,26(10):23-30.HUANG Nantian,XU Dianguo,LIU Xiaosheng.Identification of power quality complex disturbances based on S-transform and SVM[J].Transactions of China Electrotechnical Society,2011,26(10):23-30.
[10]李立,易吉良,朱建林.采用改进不完全S变换估计电能质量扰动参数[J].电工技术学报,2011,26(6):187-193.LI Li,YI Jiliang,ZHU Jianling.Parameter estimation of power quality disturbances using modified incomplete S-transform[J].Transactions of China Electrotechnical Society,2011,26(6):187-193.
[11]刘志刚,张巧革,张杨.电能质量复合扰动分类的研究进展[J].电力系统保护与控制,2013,41(13):146-153.LIU Zhigang,ZHANG Qiaoge,ZHANG Yang.Review of power quality mixed disturbances identification[J].Power System Protection and Control,2013,41(13):146-153.
[12]覃星福,龚仁喜.基于广义S变换与PSO-PNN的电能质量扰动识别[J].电力系统保护与控制,2016,44(15):10-17.QIN Xingfu,GONG Renxi.Power quality disturbances classification based on generalized S-transform and PSO-PNN[J].Power System Protection and Control,2016,44(15):10-17.
[13]瞿合祚,刘恒,李晓明,等.基于多标签随机森林的电能质量复合扰动分类方法[J].电力系统保护与控制,2017,45(11):1-7.QU Hezuo,LIU Heng,LI Xiaoming,et al.Recognition of multiple power quality disturbances using multi-label random forest[J].Power System Protection and Control,2017,45(11):1-7.
[14]何巨龙,王根平,刘丹,等.基于提升小波和改进BP神经网络的配电网系统电能质量扰动定位与识别[J].电力系统保护与控制,2017,45(10):69-76.HE Julong,WANG Genping,LIU Dan,et al.Localization and identification of power quality disturbance in distribution network system based on lifting wavelet and improved BP neural network[J].Power System Protection and Control,2017,45(10):69-76.
[15]陈华丰,杨志刚,曾涛.基于S变换和规则基的复合电能质量扰动识别[J].电测与仪表,2015,52(12):122-128.CHEN Huafeng,YANG Zhigang,ZENG Tao.Multiple power quality disturbances recognition using S-transform and rule based classification technique[J].Electrical Measurement&Instrumentation,2015,52(12):122-128.
[16]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.
[17]何智龙,苏娟,覃芳.S变换在电能质量扰动中的分析[J].电测与仪表,2015,52(22):25-30.HE Zhilong,SU Juan,QIN Fang.Analysis of power quality disturbances using S-transform[J].Electrical Measurement&Instrumentation,2015,52(22):25-30.
[18]郭俊文,李开成.基于改进S变换和复合特征量的多级支持向量机的电能质量扰动分类[J].电测与仪表,2014,51(8):19-25.GUO Junwen,LI Kaicheng.Automatic classification of power quality events based on MST and multi-lay SVMs of multiple features[J].Electrical Measurement&Instrumentation,2014,51(8):19-25.
[19]BAGHAEE H R,MIRSALIM M,GHAREHPETIAN GB.Power calculation using RBF neural networks to improve power sharing of hierarchical control scheme in multi-DER microgrids[J].IEEE Journal of Emerging&Selected Topics in Power Electronics,2016,4(4):1217-1225.
[20]刘永,张立毅.BP和RBF神经网络的实现及其性能比较[J].电子测量技术,2007,30(4):77-80.LIU Yong,ZHANG Liyi.Implementation of BP and RBFneural network and their performance comparison[J].Electronic Measurement Technology,2007,30(4):77-80.
[21]马斌,张丽艳.一种基于径向基神经网络的短期风电功率直接预测方法[J].电力系统保护与控制,2015,43(19):78-82.MA Bin,ZHANG Liyan.Short-term wind power direct forecasting based on RBF neural network[J].Power System Protection and Control,2015,43(19):78-82.
[22]任子晖,王琦.基于优化DDAGSVM多类分类策略的电能质量扰动识别[J].电力系统保护与控制,2018,46(5):82-88.REN Zihui,WANG Qi.Power quality disturbance recognition based on improved DDAGSVM multi-class classification strategy[J].Power System Protection and Control,2018,46(5):82-88.