基于多通道快速傅里叶小波变换的电力系统主导振荡模式及模态协同辨识方法研究
|
摘要
针对连续小波变换在主导振荡模式辨识中存在效率低的不足,提出一种快速傅里叶小波变换(CWTFT)方法以提高小波变换效率;针对单通道小波辨识的结果受振荡模式可观性影响的缺陷,提出一种多通道CWTFT,实现多通道量测信息的时频域分解,进而获得对应的小波系数矩阵;在此基础上,借助小波尺度相对能量甄别出与主导振荡模式强相关的关键小波尺度,以其为基准重构小波系数矩阵;对重构的小波系数矩阵进行奇异值分解,利用重构小波系数矩阵的第一左、右奇异特征向量辨识系统主导振荡模式及振荡模态。将所提方法应用到16机68节点测试系统和南方电网的广域实测数据中,结果验证了该方法的准确性和有效性。
Aiming at the shortage of low efficiency of continuous wavelet transform in dominant oscillation mode identification,a CWTFT(fast Fourier Transform based Continuous Wavelet Transform) method is proposed to improve the efficiency. Since the results of single-channel wavelet identification are influenced by the observability of oscillation mode,a multi-channel CWTFT is proposed to realize time-frequency domain decomposition of measured data from multi-channel and obtain the corresponding wavelet coefficient matrix,on this basis,the relative wavelet scale energy is used to identify the critical wavelet scales strongly related to the dominant oscillation modes,with which the wavelet coefficient matrix is reconstructed. The matrix is carried out with SVD(Singular Value Decomposition),and the left and right singular eigenvectors of first singular value of the matrix are adopted to identify the dominant oscillation modes and mode shapes. The proposed method is applied in the measured data of a 16-machine 68-bus test system and China Southern Power Grid,and the results verify the correctness and effectiveness of the method.
Aiming at the shortage of low efficiency of continuous wavelet transform in dominant oscillation mode identification,a CWTFT(fast Fourier Transform based Continuous Wavelet Transform) method is proposed to improve the efficiency. Since the results of single-channel wavelet identification are influenced by the observability of oscillation mode,a multi-channel CWTFT is proposed to realize time-frequency domain decomposition of measured data from multi-channel and obtain the corresponding wavelet coefficient matrix,on this basis,the relative wavelet scale energy is used to identify the critical wavelet scales strongly related to the dominant oscillation modes,with which the wavelet coefficient matrix is reconstructed. The matrix is carried out with SVD(Singular Value Decomposition),and the left and right singular eigenvectors of first singular value of the matrix are adopted to identify the dominant oscillation modes and mode shapes. The proposed method is applied in the measured data of a 16-machine 68-bus test system and China Southern Power Grid,and the results verify the correctness and effectiveness of the method.
引文
[1] 姜涛.基于广域量测信息的电力大系统安全性分析与协调控制[D].天津:天津大学,2015.JIANG Tao.Wide area security assessment and control for bulk power system with WAMS data[D].Tianjin:Tianjin University,2015.
[2] 秦超,曾沅,苏寅生,等.基于安全域的大规模风电并网系统低频振荡稳定分析[J].电力自动化设备,2017,37(5):100-106.QIN Chao,ZENG Yuan,SU Yinsheng,et al.Stability analysis of low-frequency oscillation of large-scale wind power grid-connected system based on security domain[J].Electric Power Automation Equipment,2017,37(5):100-106.
[3] ROGERS G.Power system oscillation[M].Norwell,MA,USA:Kluwer,2000:3-4.
[4] 马静,彭明法,王彤,等.故障系统低频振荡特征值分析方法[J].电力自动化设备,2014,34(4):13-19.MA Jing,PENG Mingfa,WANG Tong,et al.Analysis methods for low-frequency oscillation eigenvalue of faulty system[J].Electric Power Automation Equipment,2014,34(4):13-19.
[5] 张俊峰,杨婷,陈珉,等.基于Prony滑动平均窗算法的电力系统低频振荡特征分析[J].电力自动化设备,2018,38(10):178-183.ZHANG Junfeng,YANG Ting,CHEN Min,et al.Analysis of low-frequency oscillation characteristics of power system based on Prony sliding average window algorithm[J].Electric Power Automation Equipment,2018,38(10):178-183.
[6] HAUER J F,DEMEURE C J,SCHARF L L.Initial results in Prony analysis of power system response signals[J].IEEE Transactions on Power Systems,1990,5(1):80-89.
[7] PENG J C H,NAIR N K C.Enhancing Kalman filter for tracking ringdown electromechanical oscillations[J].IEEE Transactions on Power Systems,2012,27(2):1042-1050.
[8] 高洁,李群湛,汪佳,等.基于模糊聚类的NExT-ERA低频振荡类噪声辨识[J].电力系统保护与控制,2016,44(22):40-49.GAO Jie,LI Qunzhan,WANG Jia,et al.Modal parameter identification of low frequency oscillation through NExT-ERA based on fuzzy clustering[J].Power System Protection and Control,2016,44(22):40-49.
[9] 韩润,滕予非,谢剑,等.基于改进STD法的电力系统低频振荡辨识[J].电力自动化设备,2019,39(3):58-63.HAN Run,TENG Yufei,XIE Jian,et al.Power system low-frequency oscillation identification based on improved STD method[J].Electric Power Automation Equipment,2019,39(3):58-63.
[10] JIANG T,MU Y F,JIA H J,et al.A novel dominant mode estimation method for analyzing inter-area oscillation in China Southern Power Grid[J].IEEE Transactions on Smart Grid,2016,7(5):2549-2560.
[11] ZHOU N,PIERRE J W,HAUER J F.Initial results in power system identification from injected probing signals using a subspace method[J].IEEE Transactions on Power Systems,2006,21(C-3):1296-1302.
[12] 喻敏,王斌,陈绪轩,等.同步挤压小波变换在电力系统低频振荡模态参数提取中的应用[J].电工技术学报,2017,32(6):14-20.YU Min,WANG Bin,CHEN Xuxuan,et al.Application of synchro-squeezed wavelet transform for extraction of the oscillatory parame-ters of low frequency oscillation in power systems[J].Transactions of China Electrotechnical Society,2017,32(6):14-20.
[13] 李国庆,王丹,姜涛,等.基于递归连续小波变换的电力系统振荡模式辨识[J].电力自动化设备,2016,36(9):8-16.LI Guoqing,WANG Dan,JIANG Tao,et al.Power system oscillation mode identification based on recursive continuous wavelet transform[J].Electric Power Automation Equipment,2016,36(9):8-16.
[14] TORRENCE C,COMPO G P.A practical guide to wavelet analysis[J].Bull Amer Meteorol Soc,1998,79(1):61-78.
[15] 章国稳,汤宝平,唐光武.基于数据缩减的分频段小波模态参数快速识别[J].振动工程学报,2012,25(1):49-54.ZHANG Guowen,TANG Baoping,TANG Guangwu.Sub-frequency band rapid modal identification using wavelet based on data reduction[J].Journal of Vibration Engineering,2012,25(1):49-54.
[2] 秦超,曾沅,苏寅生,等.基于安全域的大规模风电并网系统低频振荡稳定分析[J].电力自动化设备,2017,37(5):100-106.QIN Chao,ZENG Yuan,SU Yinsheng,et al.Stability analysis of low-frequency oscillation of large-scale wind power grid-connected system based on security domain[J].Electric Power Automation Equipment,2017,37(5):100-106.
[3] ROGERS G.Power system oscillation[M].Norwell,MA,USA:Kluwer,2000:3-4.
[4] 马静,彭明法,王彤,等.故障系统低频振荡特征值分析方法[J].电力自动化设备,2014,34(4):13-19.MA Jing,PENG Mingfa,WANG Tong,et al.Analysis methods for low-frequency oscillation eigenvalue of faulty system[J].Electric Power Automation Equipment,2014,34(4):13-19.
[5] 张俊峰,杨婷,陈珉,等.基于Prony滑动平均窗算法的电力系统低频振荡特征分析[J].电力自动化设备,2018,38(10):178-183.ZHANG Junfeng,YANG Ting,CHEN Min,et al.Analysis of low-frequency oscillation characteristics of power system based on Prony sliding average window algorithm[J].Electric Power Automation Equipment,2018,38(10):178-183.
[6] HAUER J F,DEMEURE C J,SCHARF L L.Initial results in Prony analysis of power system response signals[J].IEEE Transactions on Power Systems,1990,5(1):80-89.
[7] PENG J C H,NAIR N K C.Enhancing Kalman filter for tracking ringdown electromechanical oscillations[J].IEEE Transactions on Power Systems,2012,27(2):1042-1050.
[8] 高洁,李群湛,汪佳,等.基于模糊聚类的NExT-ERA低频振荡类噪声辨识[J].电力系统保护与控制,2016,44(22):40-49.GAO Jie,LI Qunzhan,WANG Jia,et al.Modal parameter identification of low frequency oscillation through NExT-ERA based on fuzzy clustering[J].Power System Protection and Control,2016,44(22):40-49.
[9] 韩润,滕予非,谢剑,等.基于改进STD法的电力系统低频振荡辨识[J].电力自动化设备,2019,39(3):58-63.HAN Run,TENG Yufei,XIE Jian,et al.Power system low-frequency oscillation identification based on improved STD method[J].Electric Power Automation Equipment,2019,39(3):58-63.
[10] JIANG T,MU Y F,JIA H J,et al.A novel dominant mode estimation method for analyzing inter-area oscillation in China Southern Power Grid[J].IEEE Transactions on Smart Grid,2016,7(5):2549-2560.
[11] ZHOU N,PIERRE J W,HAUER J F.Initial results in power system identification from injected probing signals using a subspace method[J].IEEE Transactions on Power Systems,2006,21(C-3):1296-1302.
[12] 喻敏,王斌,陈绪轩,等.同步挤压小波变换在电力系统低频振荡模态参数提取中的应用[J].电工技术学报,2017,32(6):14-20.YU Min,WANG Bin,CHEN Xuxuan,et al.Application of synchro-squeezed wavelet transform for extraction of the oscillatory parame-ters of low frequency oscillation in power systems[J].Transactions of China Electrotechnical Society,2017,32(6):14-20.
[13] 李国庆,王丹,姜涛,等.基于递归连续小波变换的电力系统振荡模式辨识[J].电力自动化设备,2016,36(9):8-16.LI Guoqing,WANG Dan,JIANG Tao,et al.Power system oscillation mode identification based on recursive continuous wavelet transform[J].Electric Power Automation Equipment,2016,36(9):8-16.
[14] TORRENCE C,COMPO G P.A practical guide to wavelet analysis[J].Bull Amer Meteorol Soc,1998,79(1):61-78.
[15] 章国稳,汤宝平,唐光武.基于数据缩减的分频段小波模态参数快速识别[J].振动工程学报,2012,25(1):49-54.ZHANG Guowen,TANG Baoping,TANG Guangwu.Sub-frequency band rapid modal identification using wavelet based on data reduction[J].Journal of Vibration Engineering,2012,25(1):49-54.