基于改进变分模态分解的低频振荡模式辨别

针对现有信号处理方法无法有效解决电力系统低频振荡信号中的非线性及混叠问题的现状,将一种变分模态分解(VMD)方法引入到低频振荡的模式辨识中,并利用样本熵与快速傅里叶变换(FFT)对VMD无法自适应分解的情况进行了改进。原始信号由改进变分模态分解(IVMD)方法分解为若干模态分量,然后利用Teager-Kaiser能量算子(TKEO)对各分量分别拟合即可获得幅值、频率和阻尼等参数。在构造的测试信号下,令提出方法与VMD、经验模态分解(EMD)、总体最小二乘旋转矢量不变技术(TLS-ESPRIT)和Prony等方法进行模式参数辨识性能对比,结果表明,IVMD方法有效克服了EMD、TLS-ESPRIT和Prony在处理模态混叠、含噪声序列和非平稳信号等方面的不足。最后,通过对IEEE 4机2区域系统和新英格兰39节点系统仿真信号的辨识,验证了该方法在提取电力系统低频振荡模式参数中的有效性。

An identification method for power system low-frequency oscillation based on parameter optimized variational mode decomposition

A Variational Mode Decomposition (VMD) method is introduced into the pattern recognition of low frequency oscillation of power system in this paper. Firstly, FFT (Fast Fourier Transform) is used to determine the number of VMD decomposition layers, and the approximate fitting formula of the best equilibrium parameter a is obtained by a large number of tests. The original signal is decomposed into several mode components by IVMD method. Then, Teager-Kaiser energy operator(TKEO) is applied on the fitting of each component to get the amplitude, frequency and damping of it. By the constructed test signal, the method of this paper is compared with VMD, Empirical Mode Decomposition (EMD), Total Least Squares-Estimation of Signal Parameters via Rotational Invariance Techniques (TLS-ESPRIT), and Prony on the performance of mode parameter identification. Results show that the IVMD method effectively overcomes the shortcomings of EMD, TLS-ESPRIT and Prony in dealing with mode mixing, noise sequence and non-stationary signals. Finally, the feasibility of the method of this paper in extracting the low frequency oscillation mode parameters of power system is verified by the simulation signal identification of the IEEE two-area four-generator power system and the New England 39-bus system.#$NLKeywords: low-frequency oscillation; fast Fourier transform; sample entropy; variational mode decomposition; identification