基于广义形态滤波与改进矩阵束的电力系统低频振荡模态辨识

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (556 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Since more and more serious low-frequency oscillation phenomena have happened in interconnected power grids, a high-accuracy low-frequency oscillation identification method is proposed to overcome the shortages of the existing methods. The method is based on the opening and closing operations of generalized morphology to design an improved generalized morphological filter, which can effectively eliminate the noise and retain the original features of signals. An advanced matrix pencil algorithm was proposed to identify parameters from low frequency oscillation signals. A standardized singular entropy technique was utilized to solve the key problem of order determination. By this way the estimating value of the order can be very close to the real value in the power system, which enhances identification accuracy. Simulations verified the proposed low-frequency oscillation identification method.

Key words： Low frequency oscillation generalized morphology matrix pencil singular entropy mode identification

Received: 27 May 2016 Published: 29 March 2017

PACS:

TM712

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Jin Tao
	Liu Dui

Cite this article:

Jin Tao,Liu Dui. Power System Low Frequency Oscillation Identification Based on the Generalized Morphological Method and Improved Matrix Pencil Algorithm[J]. Transactions of China Electrotechnical Society, 2017, 32(6): 3-13.

URL:

https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2017/V32/I6/3

[1] 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.
[2] 董恒锋, 徐政, 黄弘扬, 等. 大电源特高压集中外送系统低频振荡的网侧控制技术[J]. 高电压技术, 2016, 42(29): 571-580.
Dong Hengfeng, Xu Zheng, Huang Hongyang, et al. Grid side damping control technologies of low frequency oscillation of ultra high voltage trans- mission systems for large-scale energy bases[J]. High Voltage Engineering, 2016, 42(29): 571-580.
[3] 李从善, 刘天琪, 刘利兵, 等. 直流多落点系统自抗扰附加阻尼控制[J]. 电工技术学报, 2015, 30(7): 10-17.
Li Congshan, Liu Tianqi, Liu Libing, et a1. A auto- disturbance rejection controller of multi-HVDC[J]. Transactions of China Electrotechnical Society, 2015, 30(7): 10-17.
[4] Ma Jin, Zhang Pu, Fu Hongjun, et al. Application of phasor measurement unit on locating disturbance source for low-frequency oscillation[J]. IEEE Transa- ctions on Smart Grid, 2010, 1(3): 340-346.
[5] 顾丽鸿, 周孝信, 严剑秋, 等. 特高压联网区域实时小干扰稳定分析策略[J]. 中国电机工程学报, 2010, 30(13): 1-7.
Gu Lihong, Zhou Xiaoxin, Yan Jianqiu, et al. Real time small signal stability analysis of power systems interconnected by ultra high voltage transmission line [J]. Proceedings of the CSEE, 2010, 30(13): 1-7.
[6] 张程, 金涛. 基于ISPM和SDM-Prony算法的电力系统低频振荡模式辨识[J]. 电网技术, 2016, 40(4): 1209-1216.
Zhang Cheng, Jin Tao. Identification of low frequency oscillations in power systems using an improved smoothness priors method and second- derivative method-Prony[J]. Power System Tech- nology, 2016, 40(4): 1209-1216.
[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): 137-141.
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): 137-141.
[8] Darshana P W, Udaya D A, Krish N. Identification of dominant low-frequency modes in ring-down oscillations using multiple Prony models[J]. IET Generation Transmission & Distribibution, 2015, 9(15): 2206-2214.
[9] Seyyed A H, Nima A, Mohammad H V. A fourier based wavelet approach using Heisenberg’s uncertainty principle and shannon’sentropy criterion to monitor power system small signal oscillations[J]. IEEE Transactions on Power Systems, 2015, 30(6): 3314-3325.
[10] Jonsson M, Begovic M, Daalder J. A new method suitable for real-time generator coherency deter- mination[J]. IEEE Transactions on Power Systems, 2004, 19(3): 1473-1482.
[11] 唐西胜, 孙玉树, 齐智平. 基于HHT的风电功率波动及其对电力系统低频振荡的影响分析[J]. 电网技术, 2015, 39(8): 2115-2121.
Tang Xisheng, Sun Yushu, Qi Zhiping. Analysis of wind power fluctuation characteristics and its impact on power system low frequency oscillation based on HHT[J]. Power System Technology, 2015, 39(8): 2115-2121.
[12] 马燕峰, 赵书强. 用改进的Hilbert-Huang变换辨识电力系统低频振荡[J]. 高电压技术, 2012, 38(6): 1492-1499.
Ma Yanfeng, Zhao Shuqiang. Identification of low frequency oscillations in power system based on improved Hilbert-Huang transform[J]. High Voltage Engineering, 2012, 38(6): 1492-1499.
[13] 栾某德, 刘涤尘, 廖清芬, 等. 基于改进小波系数奇异值分解和小波去噪的低频振荡时变模式辨识[J]. 电网技术, 2012, 36(6): 141-147.
Luan Moude, Liu Dichen, Liao Qingfen, et a1. A method to identify time-varying mode of low frequency oscillation by continuous wavelet trans- form based on raising singular value decomposition of wavelet coefficient and wavelet denoising[J]. Power System Technology, 2012, 36(6): 141-147.
[14] José L Rueda, Carlos A Juárez, István Erlich. Wavelet-based analysis of power system low- frequency electromechanical oscillations[J]. IEEE Transactions on Power Systems, 2011, 26(3): 1733- 1743.
[15] 胡楠, 李兴源, 李宽, 等. 基于四阶混合平均累计量的矩阵束算法在低频振荡在线辨识中的应用[J]. 中国电机工程学报, 2013, 33(7): 53-60.
Hu Nan, Li Xingyuan, Li Kuan, et al. A modified matrix pencil method based on fourth-order mixed mean cumulant for low frequency oscillation mode on-line identification[J]. Proceedings of the CSEE, 2013, 33(7): 53-60.
[16] 王臻, 李承, 张舜钦, 等. 基于改进矩阵束滤波与检测的异步电动机故障诊断新方法[J]. 电工技术学报, 2015, 30(12): 213-219.
Wang Zhen, Li Cheng, Zhang Shunqin, et al. A new filter and detection approach of fault in asynchronous motor based on improved matrix pencil[J]. Transa- ctions of China Electrotechnical Society, 2015, 30(12): 213-219.
[17] 黄南天, 张卫辉, 蔡国伟, 等. 采用改进多分辨率快速S变换的电能质量扰动识别[J]. 电网技术, 2015, 39(5): 1412-1418.
Huang Nantian, Zhang Weihui, Cai Guowei, et al. Power quality disturbances classification with improved multiresolution fast S-transform[J]. Power System Technology, 2015, 39(5): 1412-1418.
[18] 董飞飞, 刘涤尘, 涂炼, 等. 基于MM-ARMA算法的次同步振荡模态参数辨识[J]. 高电压技术, 2013, 39(5): 1252-1257.
Dong Feifei, Liu Dichen, Tu Lian, et al. Subsvnchronous oscillation modal parameter identi- fication based on MM-ARMA algorithm[J]. High Voltage Engineering, 2013, 39(5): 1252-1257.
[19] 党杰, 李勇, 徐友平, 等. 基于WAMS和奇异熵矩阵束方法的电网低频振荡仿真分析[J]. 电力系统自动化, 2010, 34(15): 14-18.
Dang Jie, Li Yong, Xu Youping, et al. Power grid low frequency oscillation simulation analysis based on WAMS and singular entropy matrix pencil method[J]. Automation of Electric Power Systems, 2010, 34(15): 14-18.
[20] 曾正, 赵荣祥, 杨欢. 基于奇异熵TLS-ESPRIT算法的微电网小信号稳定性分析[J]. 电力自动化设备, 2012, 32(5): 7-12.
Zeng Zheng, Zhao Rongxiang, Yang Huan. Small signal stability analysis based on singular entropy and TLS-ESPRIT algorithm for microgrid[J]. Electric Power Automation Equipment, 2012, 32(5): 7-12.
[21] 蔡国伟, 杨德友, 张俊丰, 等. 基于实测信号的电力系统低频振荡模态辨识[J]. 电网技术, 2011, 35(1): 59-65.
Cai Guowei, Yang Deyou, Zhang Junfeng, et al. Mode identification of power system low-frequency oscillation based on measured signal[J]. Power System Technology, 2011, 35(1): 59-65.
[22] 马燕峰, 刘伟东, 赵书强. 基于包络线拟合的低频振荡性质在线判别[J]. 电力系统自动化, 2014, 38(23): 46-53.
Ma Yanfeng, Liu Weidong, Zhao Shuqiang. On-line identification of low-frequency oscillation properties based on envelope fitting[J]. Automation of Electric Power Systems, 2014, 38(23): 46-53.