基于自适应互补LMD方法的谐波检测与分析
|
摘要
针对电力系统信号非线性、非稳态的特征,自适应分解方法具有高效简便的处理优势。但此类方法在加噪参数选择时存在需要人为经验性确定的缺点,为此,提出一种自适应互补LMD(ACLMD)方法。该方法通过对加噪辅助分解方法噪声准则的研究,引入间距标准差(STD)与信噪比(SNR)两个参数作为分解性能评价指标,自适应确定最优加噪幅值与集总分解次数,且加噪形式采用正负成对的形式,抑制了分解过程中的残余噪声。并对标准LMD方法特征提取步骤进行改进,将分解所得的乘积函数进行Hilbert变换提取瞬时特征参数,消除了阶跃分量干扰下纯调频函数提取范围的限制。最后将改进的方法运用在谐波检测中,通过仿真实验验证所提方法既可以有效提取谐波的特征参数,也可以准确定位扰动的时间,且具有一定的抗噪性能。
Aiming at the non-linear and non-stationary characteristics of power system signals,the adaptive decomposition method has efficient and simple advantages on processing signals. However,there is a shortcoming that the selection of additive noise parameters in such methods are required to be obtained artificially. In order to counter the above-mentioned shortage,an adaptively complementary LMD( ACLMD) method was proposed in this paper. The added-noise principle of noise assisted decomposition method is analyzed in the proposed method,and the two parameters( standard deviation and signal-to-noise) are taken as an evaluation index of adding noise to adaptively determine the optimal amplitude of additive white-noise and number of ensemble trials. And the additive noises are added in the form of positive and negative pairs to the targeted signal,which inhibits residual noise in the decomposition process. And then,the feature extraction steps in standard LMD method are modified,the decomposed product functions are extracted the instantaneous characteristic parameters by Hilbert transformation,which eliminates the limitation of the extraction range of pure frequency modulation function under step component interference. Finally,the modified method is utilized in the harmonic detection,and the simulation experiments verify that this method is not only able to effectively extract harmonics characteristic parameters,but also accurately locate disturbances time,and has a certain anti-noise performance as well.
Aiming at the non-linear and non-stationary characteristics of power system signals,the adaptive decomposition method has efficient and simple advantages on processing signals. However,there is a shortcoming that the selection of additive noise parameters in such methods are required to be obtained artificially. In order to counter the above-mentioned shortage,an adaptively complementary LMD( ACLMD) method was proposed in this paper. The added-noise principle of noise assisted decomposition method is analyzed in the proposed method,and the two parameters( standard deviation and signal-to-noise) are taken as an evaluation index of adding noise to adaptively determine the optimal amplitude of additive white-noise and number of ensemble trials. And the additive noises are added in the form of positive and negative pairs to the targeted signal,which inhibits residual noise in the decomposition process. And then,the feature extraction steps in standard LMD method are modified,the decomposed product functions are extracted the instantaneous characteristic parameters by Hilbert transformation,which eliminates the limitation of the extraction range of pure frequency modulation function under step component interference. Finally,the modified method is utilized in the harmonic detection,and the simulation experiments verify that this method is not only able to effectively extract harmonics characteristic parameters,but also accurately locate disturbances time,and has a certain anti-noise performance as well.
引文
[1]Shafie S M.A review on paddy residue based power generation:Energy,environment and economic perspective[J].Renewable and Sustainable Energy Reviews,2016,59:1089-1100.
[2]付学谦,陈皓勇,刘国特,等.分布式电源电能质量综合评估方法[J].中国电机工程学报,2014,34(25):4270-4276.
[3]聂程,王跃,雷万钧,等.新能源并网输电电缆谐波谐振分析及抑制方法[J].电力系统自动化,2017,41(17):128-133.
[4]付克勤,游文钧,杨子康,等.电力系统谐波源及谐波检测方法研究[J].电测与仪表,2014,51(12):81-86.
[5]崔浩,舒朝君,陈言.基于瞬时无功功率理论的谐波检测和抑制的模型参考自适应控制系统[J].电测与仪表,2017,54(4):6-10.Cui Hao,Shu Chaojun,Chen Yan,et al.Model reference self-adaptive control system of harmonic detection and suppression based on instantaneous reactive power theory[J].Electrical Measurement&Instrumentation,2017,54(4):6-10.
[6]赵庆生,王宇,郭贺宏,等.扩展Prony算法在电力系统非整次谐波检测中的应用研究[J].电测与仪表,2016,53(7):57-60.Zhao Qingsheng,Wang Yu,Guo Hehong,et al.Study on the application of extended Prony algorithm in non-integral harmonics detection of power system[J].Electrical Measurement&Instrumentation,2016,53(7):57-60.
[7]郑曙光,刘观起.基于广义双曲S变换的快速谐波检测算法[J].电测与仪表,2015,52(9):30-35.Zheng Shuguang,Liu Guanqi.Fast harmonic detection based on generalized HS-transformElectrical[J].Electrical Measurement&Instrumentation,2015,52(9):30-35.
[8]李天云,程思勇,杨梅.基于希尔伯特-黄变换的电力系统谐波分析[J].中国电机工程学报,2008,28(4):109-113.Li Tianyun,Cheng Siyong,Yang Mei.Power System Harmonic Analysis Based on Hilbert-Huang Transform[J].Proceedings of the CSEE,2008,28(4):109-113.
[9]宋海军,黄传金,刘宏超,等.基于改进LMD的电能质量扰动检测新方法[J].中国电机工程学报,2014,34(10):1700-1708.Song Haijun,Huang Chuanjin,Liu Hongchao,et al.A New Power Quality Disturbance Detection Method Based on the Improved LMD[J].Proceedings of the CSEE,2014,34(10):1700-1708.
[10]Zhaohua Wu,Norden E.Huang.Ensemble empirical mode decomposition:a noise-assisted data analysis method[J].Advances in Adaptive Data Analysis,2011,1(1):1-41.
[11]Smith J S.The local mean decomposition and its application to EEGperceptiondata[J].Journal of the Royal Society Interface,2005,2(5):443.
[12]YEH J R,SHIEH J S,HUANG N E.Complementary ensemble empirical mode decomposition:a novel noise enhanced data analysis method[J].Advances in Adaptive Data Analysis,2010,2(2):135-156.
[13]孔德同,刘庆超,雷亚国,等.一种改进的EEMD方法及其应用研究[J].振动工程学报,2015,28(6):1 015-1021.
[14]Wang Y,He Z,Zi Y.A comparative study on the local mean decomposition and empirical mode decomposition and their applications to rotating machinery health diagnosis[J].Journal of Vibration and Acoustics,2010,132(2):021010.
[15]王学伟,王琳,苗桂君,等.暂态和短时电能质量扰动信号压缩采样与重构方法[J].电网技术,2012,36(3):191-196.
[2]付学谦,陈皓勇,刘国特,等.分布式电源电能质量综合评估方法[J].中国电机工程学报,2014,34(25):4270-4276.
[3]聂程,王跃,雷万钧,等.新能源并网输电电缆谐波谐振分析及抑制方法[J].电力系统自动化,2017,41(17):128-133.
[4]付克勤,游文钧,杨子康,等.电力系统谐波源及谐波检测方法研究[J].电测与仪表,2014,51(12):81-86.
[5]崔浩,舒朝君,陈言.基于瞬时无功功率理论的谐波检测和抑制的模型参考自适应控制系统[J].电测与仪表,2017,54(4):6-10.Cui Hao,Shu Chaojun,Chen Yan,et al.Model reference self-adaptive control system of harmonic detection and suppression based on instantaneous reactive power theory[J].Electrical Measurement&Instrumentation,2017,54(4):6-10.
[6]赵庆生,王宇,郭贺宏,等.扩展Prony算法在电力系统非整次谐波检测中的应用研究[J].电测与仪表,2016,53(7):57-60.Zhao Qingsheng,Wang Yu,Guo Hehong,et al.Study on the application of extended Prony algorithm in non-integral harmonics detection of power system[J].Electrical Measurement&Instrumentation,2016,53(7):57-60.
[7]郑曙光,刘观起.基于广义双曲S变换的快速谐波检测算法[J].电测与仪表,2015,52(9):30-35.Zheng Shuguang,Liu Guanqi.Fast harmonic detection based on generalized HS-transformElectrical[J].Electrical Measurement&Instrumentation,2015,52(9):30-35.
[8]李天云,程思勇,杨梅.基于希尔伯特-黄变换的电力系统谐波分析[J].中国电机工程学报,2008,28(4):109-113.Li Tianyun,Cheng Siyong,Yang Mei.Power System Harmonic Analysis Based on Hilbert-Huang Transform[J].Proceedings of the CSEE,2008,28(4):109-113.
[9]宋海军,黄传金,刘宏超,等.基于改进LMD的电能质量扰动检测新方法[J].中国电机工程学报,2014,34(10):1700-1708.Song Haijun,Huang Chuanjin,Liu Hongchao,et al.A New Power Quality Disturbance Detection Method Based on the Improved LMD[J].Proceedings of the CSEE,2014,34(10):1700-1708.
[10]Zhaohua Wu,Norden E.Huang.Ensemble empirical mode decomposition:a noise-assisted data analysis method[J].Advances in Adaptive Data Analysis,2011,1(1):1-41.
[11]Smith J S.The local mean decomposition and its application to EEGperceptiondata[J].Journal of the Royal Society Interface,2005,2(5):443.
[12]YEH J R,SHIEH J S,HUANG N E.Complementary ensemble empirical mode decomposition:a novel noise enhanced data analysis method[J].Advances in Adaptive Data Analysis,2010,2(2):135-156.
[13]孔德同,刘庆超,雷亚国,等.一种改进的EEMD方法及其应用研究[J].振动工程学报,2015,28(6):1 015-1021.
[14]Wang Y,He Z,Zi Y.A comparative study on the local mean decomposition and empirical mode decomposition and their applications to rotating machinery health diagnosis[J].Journal of Vibration and Acoustics,2010,132(2):021010.
[15]王学伟,王琳,苗桂君,等.暂态和短时电能质量扰动信号压缩采样与重构方法[J].电网技术,2012,36(3):191-196.