基于改进小波阈值去噪和RCRSV-MP算法的电力系统低频振荡模态辨识

针对广域测量系统低频振荡辨识中存在噪声干扰和定阶不准确的问题,提出了基于改进小波阈值去噪和奇异值相对变化率(RCRSV)定阶的矩阵束(MP)算法相结合的方法对电力系统低频振荡模态进行辨识。在小波去噪基础上对阈值进行改进,使得阈值随分解层数的增加而发生改变,能够有效地抑制低频振荡信号的噪声;然后将去噪后的信号用RCRSV-MP算法进行辨识,从而获取低频振荡各个模态参数。根据RCRSV定阶具有自适应性,无需人为设定阈值。通过仿真算例、测试系统及电网实际案例的结果显示,所提方法相比于其他方法具有抗噪性能好、拟合精度高等优点,具有较强的实用性,能够实现在线辨识。

Power system low-frequency oscillation mode identification base on improved wavelet threshold de-noising and RCRSV-MP algorithm

Aiming at the noise interference and incorrect order in the low-frequency oscillation identification of wide-area measurement system, a method based on the improved wavelet threshold de-noising and RCRSV-MP(Relative Change Rate Singular Value ordered Matrix Pencil) algorithm is proposed to identify the low-frequency oscillation modes of power system. Based on the wavelet de-noising, the improved threshold varies with the increase of decomposition levels to effectively suppress the noise of low-frequency oscillation signals. The de-noised signals are then identified by the RCRSV-MP algorithm to obtain the parameters of each oscillation mode. The order set by the RCRSV is adaptive and no manual threshold is needed. Research results of simulation example, test system and actual grid case show that, compared to other methods, the proposed method has better anti-noise performance, higher fitting accuracy and stronger practicability in the on-line identification.