基于四阶混合平均累积量与TLS-ESPRIT的电力系统低频振荡研究

Identification of Power System Low Frequency Oscillation Mode Based on Fourth-order Mixed Mean Cumulant and TLS-ESPRIT Algorithm

实际现场中WAMS采集到的信号易受周围环境的影响,导致信号往往会有高斯色噪声的干扰,给模态辨识精度带来很大影响。针对以上问题,提出了基于四阶混合平均累积量和总体最小二乘-空间旋转不变技术(TLS-ESPRIT)相结合的方法来对电力系统低频振荡模态进行辨识。通过四阶混合平均累积量的方法对信号进行预处理,去除高斯色噪声的干扰,然后通过TLS-ESPRIT辨识方法对预处理后的信号进行辨识。通过仿真结果比较,该方法相比于其他方法具有抗噪性能好,辨识结果精度高的优点。

The signals collected by WAMS in a actual scene are susceptible to the surrounding environment,resulting in the interference of signal with colored Gaussian noise,and greatly affecting the accuracy of mode identification.In the light of the problems,the paper proposes a method to identify the low-frequency oscillation mode of power system by combining fourth-order mixed mean cumulant with total least square-estimation of signal parameters via rotational invariance technique(TLS-ESPRIT).The signal is preprocessed by the fourth-order mixed average cumulant method to remove the Gaussian noise interference,and then the signal after preprocessing is identified by the TLS-ESPRIT algorithm.The simulation results show that the method has the advantages of good anti-noise performance and high accuracy of the identification comparing to other methods.