基于多尺度线调频基稀疏信号分解的电力系统非平稳振荡信号特征提取

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摘要

随着电力电子装置在源-网-荷侧的大规模应用,电力系统功率振荡表现出较强的非平稳特性和模式耦合性。本文提出一种基于多尺度线调频基稀疏信号分解的电力系统非平稳振荡信号特征提取方法。多尺度线调频基稀疏信号分解方法能够在动态的时间支撑区内对信号进行投影分解,逐次获得能量最大的信号分量,具有良好的时频聚集性,特别适用于非平稳振荡信号的分解。电力系统的功率振荡信号本质上是一种多模态时变振动系统响应信号。首先通过多尺度线调频基稀疏信号分解方法得到多个单模态振动响应信号,然后根据单模态振动响应特性采用最小二乘法进行振荡特征的提取。仿真算例和实例证明了该方法在电力系统非平稳振荡信号特征提取中的有效性和适应性。

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	孙正龙
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	杨德友
	刘铖

关键词 ：多尺度线调频基, 稀疏信号分解, 非平稳信号, 模态混叠

Abstract：

Along with the large-scale integration of power electronic equipment in power system, the oscillation of power system shows the features of non-stationary and mode coupling obviously. Based on multi-scale chirplet sparse signal decomposition (MCSSD), a method for extracting modes from non-stationary oscillation signals is proposed in this paper. The MCSSD method can decompose a signal into different dynamic time regions, and generate the signal component with the largest energy in each step with excellent aggregation of time-frequency. It is quite suitable for decomposing non-stationary signals. The power oscillation signal in power system can be considered as the response of multiple-modal system. The single modal responses are firstly obtained by MCSSD, and then the oscillation characteristic parameters are extracted by least square method. Cases study has proved the validity and adaptability of the proposed method in extracting modes from non-stationary oscillation signals in power system.

Key words： Multi-scale chirplet sparse signal decomposition non-stationary signal mode coupling

收稿日期: 2016-07-21 出版日期: 2017-03-29

PACS:

TM712

基金资助:

国家重点研发计划（2016YFB0900100）和国家自然科学基金（51377017）资助项目

通讯作者: 孙正龙男,1988年生,博士研究生,讲师,研究方向为电力系统稳定和运行分析。E-mail: nedusunzl@mail.nedu.edu.cn

作者简介: 蔡国伟男,1968年生,教授,博士生导师,研究方向为电力系统运行分析方面教学和科研工作。E-mail: caigw@mail.nedu.edu.cn

引用本文:

孙正龙, 蔡国伟, 王雨薇, 杨德友, 刘铖. 基于多尺度线调频基稀疏信号分解的电力系统非平稳振荡信号特征提取[J]. 电工技术学报, 2017, 32(6): 31-40. Sun Zhenglong, Cai Guowei, Wang Yuwei, Yang Deyou, Liu Cheng. Extracting Modes from Non-Stationary Oscillation Signals for Power System Based on Multi-Scale Chirplet Sparse Signal Decomposition. Transactions of China Electrotechnical Society, 2017, 32(6): 31-40.

链接本文:

https://dgjsxb.ces-transaction.com/CN/Y2017/V32/I6/31

[1] 余晓丹, 徐宪东, 陈硕翼, 等. 综合能源系统与能源互联网简述[J]. 电工技术学报, 2016, 31(1): 1-13.
Yu Xiaodan, Xu Xiandong, Chen Shuoyi, et al. A brief review to integrated energy system and energy internet[J]. Transactions of China Electrotechnical Society, 2016, 31(1): 1-13.
[2] 陈恩泽, 刘涤尘, 廖清芬, 等. 多重扰动下的跨区电网低频振荡研究[J]. 电工技术学报, 2014, 29(2): 290-296.
Chen Enze, Liu Dichen, Liao Qingfen, et al. Research on low frequency oscillation of interconnected power grid based on multiple disturbances[J]. Transactions of China Electrotechnical Society, 2014, 29(2): 290-296.
[3] 宋墩文, 杨学涛, 丁巧林, 等. 大规模互联电网低频振荡分析与控制方法综述[J]. 电网技术, 2011, 35(10): 22-28.
Song Dunwen, Yang Xuetao, Ding Qiaolin, et al. A survey on analysis on low frequency oscillation in large-scale interconnected power grid and its control measures[J]. Power System Technology, 2011, 35(10): 22-28.
[4] 戚军, 江全元, 曹一家. 采用时滞广域量测信号的区间低频振荡阻尼控制器设计[J]. 电工技术学报, 2009, 24(6): 154-159.
Qi Jun, Jiang Quanyuan, Cao Yijia. Design of damping controller for inter-area low frequency oscillation using time delay wide area measure- ment[J]. Transactions of China Electrotechnical Society, 2009, 24(6): 154-159.
[5] Hauer J F. Application of Prony analysis to the determination of modal content and equivalent models for measured power system response[J]. IEEE Transactions on Power Systems, 1991, 6(3): 1062- 1068.
[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] 李安娜, 吴熙, 蒋平, 等. 基于形态滤波和Prony算法的低频振荡模式辨识的研究[J]. 电力系统保护与控制, 2015, 43(3): 137-142.
Li Anna, Wu Xi, Jiang Ping, et al. Research on identifying low frequency oscillation modes based on morphological filtering theory and Prony algo- rithm[J]. Power System Protection and Control, 2015, 43(3): 137-142.
[8] 竺炜, 唐颖杰, 周有庆, 等. 基于改进Prony算法的电力系统低频振荡模式识别[J]. 电网技术, 2009, 33(5): 44-47, 53.
Zhu Wei, Tang Yingjie, Zhou Youqing, et al. Identi- fication of power system low frequency oscillation mode based on improved Prony algorithm[J]. Power System Technology, 2009, 33(5): 44-47, 53.
[9] 张静, 徐政, 王峰, 等. TLS-ESPRIT算法在低频振荡分析中的应用[J]. 电力系统自动化, 2007, 31(20): 84-88.
Zhang Jing, Xu Zheng, Wang Feng, et al. TLS- ESPRIT based method for low frequency oscillation analysis in power system[J]. Automation of Electric Power Systems, 2007, 31(20): 84-88.
[10] Tripathy P, Srivastava S C, Singh S N. A modified TLS-ESPRIT-based method for low-frequency mode identification in power systems utilizing syn- chrophasor measurements[J]. IEEE Transactions on Power Systems, 2011, 26(2): 719-727.
[11] Wies R W, Pierre J W, Trundnowski D J. Use of ARMA block processing for estimating stationary low-frequency electromechanical modes of power systems[J]. IEEE Transactions on Power Systems, 2003, 18(1): 167-173.
[12] 陈刚, 段晓, 张继红, 等. 基于ARMA模型的低频振荡模式在线辨识技术研究[J]. 电网技术, 2010, 34(11): 48-54.
Chen Gang, Duan Xiao, Zhang Jihong, et al. A new approach for online identification of low frequency oscillation modes based on auto-regressive moving- average model[J]. Power System Technology, 2010, 34(11): 48-54.
[13] 李天云, 高磊, 赵妍. 基于HHT的电力系统低频振荡分析[J]. 中国电机工程学报, 2006, 26(14): 24-30.
Li Tianyun, Gao Lei, Zhao Yan. Analysis of low frequency oscillations using HHT method[J]. Pro- ceedings of the CSEE, 2006, 26(14): 24-30.
[14] 周小龙, 姜振海, 马风雷. 基于改进HHT的微弱故障信号特征提取方法[J]. 东北电力大学学报, 2016, 36(5): 52-56.
Zhou Xiaolong, Jiang Zhenhai, Ma Fenglei. Feature extraction method for weak fault signal based on improved HHT[J]. Proceedings of the CSEE, 2016, 36(5): 52-56.
[15] 杨德昌, C. Rehtanz, 李勇, 等. 基于改进希尔伯特-黄变换算法的电力系统低频振荡分析[J]. 中国电机工程学报, 2011, 31(10): 102-108.
Yang Dechang, Rehtanz C, Li Yong, et al. Researching on low frequency oscillation in power system based on improved HHT algorithm[J]. Proceedings of the CSEE, 2011, 31(10): 102-108.
[16] 胡昊明, 郑伟, 徐伟, 等. Prony和HHT算法在低频振荡在线辨识中的适用性比较[J]. 电力系统保护与控制, 2013, 41(14): 33-40.
Hu Haoming, Zheng Wei, Xu Wei, et al. Comparison of the applicability of Prony and HHT algorithms for on-line identification of low-frequency oscillation[J]. Power System Protection and Control, 2013, 41(14): 33-40.
[17] 赵妍, 李志民, 李天云. 低频振荡模态参数辨识的共振稀疏分解SSI分析方法[J]. 电工技术学报, 2016, 31(2): 136-144.
Zhao Yan, Li Zhimin, Li Tianyun. Low frequency oscillation modal parameter identification using resonance-based sparse signal decomposition and SSI method[J]. Transactions of China Electrotechnical Society, 2016, 31(2): 136-144.
[18] 李勋, 龚庆武, 贾晶晶, 等. 基于原子稀疏分解的低频振荡模态参数辨识方法[J]. 电工技术学报, 2012, 27(9): 124-133.
Li Xun, Gong Qingwu, Jia Jingjing, et al. Atomic sparse decomposition based identification method for low-frequency oscillation modal parameters[J]. Transactions of China Electrotechnical Society, 2012, 27(9): 124-133.
[19] Plett M I. Transient detection with cross wavelet transforms and wavelet coherence[J]. IEEE Transa- ctions on Signal Processing, 2007, 55(5): 1605-1611.
[20] 邓集祥, 欧小高, 姚天亮. 基于小波能量系数的主导低频振荡模式检测[J]. 电工技术学报, 2009, 24(8): 141-146.
Deng Jixiang, Ou Xiaogao, Yao Tianliang. Detection of the dominant inertial modes based on wavelet energy coefficient[J]. Transactions of China Electro- technical Society, 2009, 24(8): 141-146.
[21] 赵妍, 李志民, 李天云. 电力系统低频振荡监测的Duffing振子可停振动系统法[J]. 电工技术学报, 2015, 30(20): 159-167.
Zhao Yan, Li Zhimin, Li Tianyun. Duffing oscillator order stopping oscillation system method for moni- toring of low-frequency oscillation in power system[J]. Transactions of China Electrotechnical Society, 2015, 30(20): 159-167.
[22] 蔡国伟, 张涛, 孙秋鹏. 模糊聚类分析在低频振荡主导模式辨识中的应用[J]. 电网技术, 2008, 32(11): 30-33.
Cai Guowei, Zhang Tao, Sun Qiupeng. Application of fuzzy clustering analysis in identification of low- frequency oscillations dominant mode[J]. Power System Technology, 2008, 32(11): 30-33.
[23] 彭富强, 余德介, 刘坚. 一种基于多尺度线调频基的稀疏信号分解方法[J]. 振动工程学报, 2010, 23(3): 333-338.
Peng Fuqiang, Yu Dejie, Liu Jian. Sparse signal decomposition method based on multi-scale chirplet[J]. Journal of Vibration Engineering, 2010, 23(3): 333- 338.
[24] 彭富强, 余德介, 罗思洁,等. 基于多尺度线调频基稀疏信号分解的齿轮故障诊断[J]. 中国机械工程, 2009, 20(14): 1726-1730.
Peng Fuqiang, Yu Dejie, Luo Sijie, et al. Sparse signal decomposition method based on multi-scale chirplet and its application to gear fault diagnosis[J]. China Mechanical Engineering, 2009, 20(14): 1726- 1730.
[25] 彭富强, 余德介, 罗思洁,等. 基于多尺度线调频基稀疏信号分解的轴承故障诊断[J]. 机械工程学报, 2010, 46(7): 88-95.
Peng Fuqiang, Yu Dejie, Luo Sijie, et al. Sparse signal decomposition method based on multi-scale chirplet and its application to bearing fault diag- nosis[J]. Journal of Mechanical Engineering, 2010, 46(7): 88-95.
[26] 任凌志, 余德介, 彭富强. 基于多尺度线调频基稀疏信号分解的广义解调方法及其在滚动轴承故障诊断中的应用[J]. 中国电机工程学报, 2010, 30(11): 102-108.
Ren Lingzhi, Yu Dejie, Peng Fuqiang. Generalized demodulation method based on multi-scale chirplet and sparse signal decomposition and its application to roller bearing fault diagnosis[J]. Proceedings of the CSEE, 2010, 30(11): 102-108.
[27] D'Apuzzo M, D'Arco M. A time-domain approach for the analysis of nonstationary signals in power systems[J]. IEEE Transactions on Instrumentation and Measurement, 2008, 57(9): 1969-1977.
[28] Graham Rogers. Power system oscillations[M]. Dordrecht: Kluwer Academic Publishers, 2000.