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

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。

Power System Low Frequency Oscillation Identification Based on the Generalized Morphological Method and Improved Matrix Pencil Algorithm

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.