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

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摘要针对当前互联电力系统中越来越严重的低频振荡现象,提出一种高精度低频振荡模式辨识方法来克服现有方法的一些不足。该方法基于广义形态开、闭运算设计了新型广义形态滤波器,可以有效地去除噪声,较好地保留信号的原有特征;低频振荡信号通过该滤波器滤波后再使用改进矩阵束算法进行模式辨识,可以获得高精度的各个模式参数。对于辨识算法的关键定阶问题,采用归一化奇异熵定阶方法,该方法能在系统拟合精度指标相差不大的情况下使模态阶数的估计值更加接近真实值,提高了辨识的准确性。通过仿真算例、测试系统及电网实际案例验证了本文提出的方法的有效性和可行性,为电力系统阻尼控制和电网的稳定运行提供了有效依据。

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	金涛
	刘对

关键词 ：低频振荡, 广义形态学, 矩阵束, 奇异熵, 模态辨识

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

收稿日期: 2016-05-27 出版日期: 2017-03-29

PACS:

TM712

基金资助:欧盟FP7国际科技合作基金（909880）,国家自然科学基金（61304260）和福建省杰出青年科学基金（2012J06012）资助项目

通讯作者: 金涛男,1976年生,博士,研究员,博士生导师,研究方向为在线测量理论与方法、电力系统广域监测和智能电网等。

作者简介: 刘对男,1990年生,硕士研究生,研究方向为电力系统广域监测信号处理与低频振荡分析。E-mail: 396640292@qq.com

引用本文:

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

链接本文:

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

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