基于DILCD和SDEO的电能质量扰动检测新方法
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摘要
针对局部特征尺度分解(LCD)出现的模态混叠现象,提出了基于微积分局部特征尺度分解(DILCD)和对称差分能量算子(SDEO)的电能质量扰动信号分析方法。首先选取电力系统中的暂降、暂升,谐波、间谐波,衰减振荡等单一及其复合扰动信号分别进行DILCD分解可得到若干个内禀尺度分量(ISC);然后利用SDEO解调ISC分量求取瞬时频率和瞬时幅值,定位扰动发生的起止时刻。仿真结果表明,该方法能有效检测分析非平稳电能质量扰动信号,获得的瞬时幅值波动、端部效应小,频率检测精度高,时间定位准确。
For mode mixing of local characteristic-scale decomposition,a new method of differential and integral local characteristic-scale decomposition and symmetrical difference energy operator was proposed to detect the signal of power quality disturbances.Voltage sag,voltage swells,harmonics,inter-harmonic and damped oscillation and other single and composite disturbance signals were decomposed by DILCD to obtain several intrinsic scale components,and then ISC was demodulated by SDEO to obtain instantaneous frequency and amplitude and locate the starting and ending time of the disturbance occurs.The simulation results showed that the instantaneous amplitude obtained of fluctuations and end effect were better,frequency detection and time positioning were accurate.It can detect non-stationary power quality disturbance signals.
For mode mixing of local characteristic-scale decomposition,a new method of differential and integral local characteristic-scale decomposition and symmetrical difference energy operator was proposed to detect the signal of power quality disturbances.Voltage sag,voltage swells,harmonics,inter-harmonic and damped oscillation and other single and composite disturbance signals were decomposed by DILCD to obtain several intrinsic scale components,and then ISC was demodulated by SDEO to obtain instantaneous frequency and amplitude and locate the starting and ending time of the disturbance occurs.The simulation results showed that the instantaneous amplitude obtained of fluctuations and end effect were better,frequency detection and time positioning were accurate.It can detect non-stationary power quality disturbance signals.
引文
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[9]宋海军,黄传金,刘宏超,等(Song Haijun,Huang Chuanjin,Liu Hongchao,et al).基于改进LMD的电能质量扰动检测新方法(A new power quality disturbance detection method based on improved LMD)[J].中国电机工程学报(Proceedings of the CSEE),2014,34(10):1700-1708.
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[11]黄文清,戴瑜兴(Huang Wenqing,Dai Yuxing).基于Teager能量算子的电能质量扰动实时检测方法(Real-time detection power quality of disturbances based on Teager energy operator)[J].电工技术学报(Transactions of China Electrotechnical Society),2007,6(20):154-158.
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[13]王冰,李洪儒,许葆华(Wang Bing,Li Hongru,Xu Baohua).基于LMD和平滑Teager能量算子解调的电机滚动轴承故障特征提取(Fault feature extraction of motor rolling bearing based on LMD and smoothed Teager energy operator demodulating)[J].机械传动(Journal of Mechanical Transmission),2012,36(9):89-92.
[14]杨宇,潘海洋,程军圣(Yang Yu,Pan Haiyang,Cheng Junsheng).基于LCD降噪和VPMCD的滚动轴承故障诊断方法(A rolling bearing fault diagnosis method based on LCD de-noising and VPMCD)[J].中国机械工程(China Mechanical Engineering),2013,24(24):3338-3344.
[15]吕晨杰,王斌,唐涛(LüChenjie,Wang Bin,Tang Tao).采用局部特征尺度分解的跳频信号参数盲估计算法(Blind parameter estimation of frequency hopping signal using local characteristic-scale decomposition)[J].信号处理(Journal of Signal Processing),2015,31(3):308-313.
[16]钟先友,赵春华,陈保家,等(Zhong Xianyou,Zhao Chunhua,Chen Baojia,et al).基于改进的本征时间尺度分解和基本尺度熵的齿轮故障诊断方法(Gear fault diagnosis method based on IITD and base-scale entropy)[J].中南大学学报(自然科学版)(Journal of Central South University(Science and Technology)),2015,46(3):870-877.
[17]程军圣,郑近德,杨宇(Cheng Junsheng,Zheng Jinde,Yang Yu).一种新的非平稳信号分析方法——局部特征尺度分解法(A nonstationary signal analysis approach-the local characteristic-scale decompositionmethod)[J].振动工程学报(Journal of Vibration Engineering),2012,25(2):215-220.
[18]沈国际,陶利民,陈仲生(Shen Guoji,Tao Limin,Chen Zhongsheng).多频信号经验模态分解的理论研究及应用(Theoretical research on empirical mode decomposition of multi-frequency signal and its application)[J].振动工程学报(Journal of Vibration Engineering),2005,18(1):91-94.
[19]郑近德,程军圣,杨宇(Zheng Jinde,Cheng Junsheng,Yang Yu).部分集成局部特征尺度分解:一种新的基于噪声辅助数据分析方法(Partly ensemble local characteristic-scale decomposition:a new noise assisted data analysis method)[J].电子学报(Acta Electronica Sinica),2013,5(5):1030-1035.
[20] Mark G Frei,Ivan Osorio. Intrinsic time-scale decomposition:time-frequency-energy analysis and real-time filtering of non-stationary signals[J]. Proceedings:Mathematical, Physical and Engineering Sciences,2007,463(2078):321-342.
[2]徐永海,赵燕(Xu Yonghai,Zhao Yan).基于短时傅里叶变换的电能质量扰动识别与采用奇异值分解的扰动时间定位(Identification of power quality disturbance based on short-term Fourier transform and disturbance time orientation by singular value decomposition)[J].电网技术(Power System Technology),2011,35(8):174-180.
[3]杨进,李庚银,周明,等(Yang Jin,Li Gengyin,Zhou Ming,et al).广义内插小波在电能质量扰动信号分析中应用(Application of generalized interpolating wavelet in power quality disturbance signal analysis)[J].电力自动化设备(Electric Power Automation Equipment),2007,27(1):21-25.
[4]张志禹,满蔚仕,郗垒,等(Zhang Zhiyu,Man Weishi,Xi Lei,et al).快速S变换在电能质量分析中的应用(Application of fast S-transform in power quality analysis)[J].电网技术(Power System Technology),2013,37(5):1285-1290.
[5]黄南天,徐殿国,刘晓胜(Huang Nantian,Xu Dianguo,Liu Xiaosheng).基于S变换与SVM的电能质量复合扰动识别(Identification of power quality complex disturbances based on S-transform and SVM)[J].电工技术学报(Transactions of China Electrotechnical Society),2011,26(10):23-30.
[6]张杨,刘志刚(Zhang Yang,Liu Zhigang). EEMD在电能质量扰动检测中的应用(Application of EEMD in power quality disturbance detection)[J].电力自动化设备(Electric Power Automation Equipment),2011,31(12):86-91.
[7]于浩明,黄纯,江亚群,等(Yu Haoming,Huang Chun,Jiang Yaqun,et al).采用快速原子分解的电能质量扰动信号参数辨识(Parameter identification of power quality disturbance signal using fast atomic decomposition method)[J].电网技术(Power System Technology),2014,38(8):2237-2243.
[8]王宁,李林川,贾清泉,等(Wang Ning,Li Linchuan,Jia Qingquan,et al).应用原子分解的电能质量扰动信号分类方法(Classification power quality disturbance signals using atomic decomposition method)[J].中国电机工程学报(Proceedings of the CSEE),2011,31(4):51-58.
[9]宋海军,黄传金,刘宏超,等(Song Haijun,Huang Chuanjin,Liu Hongchao,et al).基于改进LMD的电能质量扰动检测新方法(A new power quality disturbance detection method based on improved LMD)[J].中国电机工程学报(Proceedings of the CSEE),2014,34(10):1700-1708.
[10]杜培伟,黄纯,王美,等(Du Peiwei,Huang Chun,Wang Mei,et al).基于改进LMD的谐波扰动检测新方法(New method of harmonic disturbance detection based on improved LMD)[J].电力系统及其自动化学报(Proceedings of the CSU-EPSA),2016,28(8):74-78.
[11]黄文清,戴瑜兴(Huang Wenqing,Dai Yuxing).基于Teager能量算子的电能质量扰动实时检测方法(Real-time detection power quality of disturbances based on Teager energy operator)[J].电工技术学报(Transactions of China Electrotechnical Society),2007,6(20):154-158.
[12]李辉,郑海起,杨绍普(Li Hui,Zheng Haiqi,Yang Shaopu).基于EMD和Teager能量算子的轴承故障诊断研究(Bearing fault diagnosis based on EMD and Teager Kaiser Energy Operator)[J].振动与冲击(Journal of Vibration and Shock),2008,27(10):15-17,22,188.
[13]王冰,李洪儒,许葆华(Wang Bing,Li Hongru,Xu Baohua).基于LMD和平滑Teager能量算子解调的电机滚动轴承故障特征提取(Fault feature extraction of motor rolling bearing based on LMD and smoothed Teager energy operator demodulating)[J].机械传动(Journal of Mechanical Transmission),2012,36(9):89-92.
[14]杨宇,潘海洋,程军圣(Yang Yu,Pan Haiyang,Cheng Junsheng).基于LCD降噪和VPMCD的滚动轴承故障诊断方法(A rolling bearing fault diagnosis method based on LCD de-noising and VPMCD)[J].中国机械工程(China Mechanical Engineering),2013,24(24):3338-3344.
[15]吕晨杰,王斌,唐涛(LüChenjie,Wang Bin,Tang Tao).采用局部特征尺度分解的跳频信号参数盲估计算法(Blind parameter estimation of frequency hopping signal using local characteristic-scale decomposition)[J].信号处理(Journal of Signal Processing),2015,31(3):308-313.
[16]钟先友,赵春华,陈保家,等(Zhong Xianyou,Zhao Chunhua,Chen Baojia,et al).基于改进的本征时间尺度分解和基本尺度熵的齿轮故障诊断方法(Gear fault diagnosis method based on IITD and base-scale entropy)[J].中南大学学报(自然科学版)(Journal of Central South University(Science and Technology)),2015,46(3):870-877.
[17]程军圣,郑近德,杨宇(Cheng Junsheng,Zheng Jinde,Yang Yu).一种新的非平稳信号分析方法——局部特征尺度分解法(A nonstationary signal analysis approach-the local characteristic-scale decompositionmethod)[J].振动工程学报(Journal of Vibration Engineering),2012,25(2):215-220.
[18]沈国际,陶利民,陈仲生(Shen Guoji,Tao Limin,Chen Zhongsheng).多频信号经验模态分解的理论研究及应用(Theoretical research on empirical mode decomposition of multi-frequency signal and its application)[J].振动工程学报(Journal of Vibration Engineering),2005,18(1):91-94.
[19]郑近德,程军圣,杨宇(Zheng Jinde,Cheng Junsheng,Yang Yu).部分集成局部特征尺度分解:一种新的基于噪声辅助数据分析方法(Partly ensemble local characteristic-scale decomposition:a new noise assisted data analysis method)[J].电子学报(Acta Electronica Sinica),2013,5(5):1030-1035.
[20] Mark G Frei,Ivan Osorio. Intrinsic time-scale decomposition:time-frequency-energy analysis and real-time filtering of non-stationary signals[J]. Proceedings:Mathematical, Physical and Engineering Sciences,2007,463(2078):321-342.