Power System Low Frequency Oscillation Modal Identification Based on the FastICA Technique and TLS-ESPRIT Algorithm

被引:0
|
作者
Zhang C. [1 ,2 ]
Liu J. [1 ]
Kuang Y. [1 ]
Qiu B. [1 ]
机构
[1] School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou
[2] Fujian Colleges and Universities Engineering Research Center of Smart Grid Simulation & Analysis and Integrated Control, Fuzhou
来源
基金
中国国家自然科学基金;
关键词
Fast independent component analysis; Low frequency oscillation; Mode identification; Noise interference; Power system; TLS-ESPRIT algorithm;
D O I
10.13336/j.1003-6520.hve.20201744
中图分类号
学科分类号
摘要
The noise interference and accuracy of low-frequency oscillation parameter identification in the power system are discussed, and a new method for extracting the modal parameters of low frequency oscillation is put forward. The FastICA (Fast Independent Component Analysis) is combined with the total least squares-estimation of signal parameters via rotational invariance technique (TLS-ESPRIT). Firstly, the FastICA technology is employed to pre-process the low-frequency oscillation wide-area measurement signal of power system containing noise so as to achieve noise reduction effect.Then, the TLS-ESPRIT algorithm is employed to estimate and identify the filtered signal to obtain each modal parameter. Finally, the validity and feasibility of FastICA-TLS-ESPRIT method are verified by simulation of ideal signal and EPRI-36 machine system and grid measure signal, and it is that this method not only can be adopted to effectively suppress noise and accurately identify low-frequency oscillation parameters, but also has certain advantages in anti-interference and extraction accuracy compared with traditional identification methods. © 2021, High Voltage Engineering Editorial Department of CEPRI. All right reserved.
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收藏
页码:2214 / 2222
页数:8
相关论文
共 21 条
  • [1] DANG Jie, SHI Mengxuan, LIANG Chen, Et al., LFO damping method and mechanism analysis based on energy storage system, High Voltage Engineering, 45, 12, pp. 4029-4037, (2019)
  • [2] JIANG Tao, LIU Fangzheng, CHEN Houhe, Et al., Cooperated identification method of dominant oscillation modes and mode shapes for power system base on multi-channel fast Fourier transform based continuous wavelet transform, Electric Power Automation Equipment, 39, 7, pp. 125-132, (2019)
  • [3] YU Min, WANG Bin, CHEN Xuxuan, Et al., Application of synchrosqueezed wavelet for extraction of the oscillatory parameters of low frequency oscillation in power systems, Transactions of China Electrotechnical Society, 32, 6, pp. 14-20, (2017)
  • [4] RUEDA J L, JUAREZ C A, ERLICH I., Wavelet-based analysis of power system low-frequency electromechanical oscillations, IEEE Transactions on Power Systems, 26, 3, pp. 1733-1743, (2011)
  • [5] ZHAO Feng, WU Mengdi, Application of EEMD-RobustICA andProny algorithm in modes identification of power system low frequency oscillation, Acta Energiae Solaris Sinica, 40, 10, pp. 2919-2929, (2019)
  • [6] ZHANG Cheng, JIN Tao, Identification of power system low frequency oscillations with ISPM and SDM-Prony, Power System Technology, 40, 4, pp. 1209-1216, (2016)
  • [7] WANG Yuhong, DONG Rui, Improved low frequency oscillation analysis based on multi-signal power system, Control Engineering of China, 26, 7, pp. 1335-1340, (2019)
  • [8] GE Weichun, YIN Xiangxiang, GE Yanfeng, Et al., Estimating low frequency oscillation mode in power system using multivariate empirical mode decomposition and Hilbert-Huang transform, Power System Potection and Control, 48, 6, pp. 124-135, (2020)
  • [9] CHEN Jian, LIU Siyi, JIN Tao, Analysis of low-frequency oscillation based on SURE wavelet threshold de-noising and MCEEMD-HHT method, High Voltage Engineering, 46, 1, pp. 151-160, (2020)
  • [10] LONG Jianting, WANG Qingfeng, LIAO Fangfan, Et al., Identification of power system low frequency oscillation mode based on fourth-order mixed mean cumulant and TLS-ESPRIT algorithm, Smart Power, 47, 11, pp. 67-72, (2019)