基于SSEWT与经验包络解调技术的低频、超低频混合振荡信号参数辨识

Parameter Identification of Low-frequency and Ultra-low-frequency Mixed Oscillation Signals Based on Second-order Synchroextraction Wavelet Transform and Empirical Envelope Demodulation

异步联网后,水电机组渗透率高的系统中陆续会出现低频与超低频混合的振荡现象,给电网的安全及稳定运行带来很大危害。针对混合振荡信号特征参数辨识不精准的问题,提出采用二阶同步提取小波变换(second-order synchroextracting wavelet transform,SSEWT)的方法分解混合振荡信号,并与经验包络(empirical envelope,EE)解调技术相结合,进行振荡信号实时模态参数辨识。SSEWT将现有的同步提取变换扩展到小波变换,并引入二阶瞬时频率估计量,对信号进行分解;然后结合EE解调技术对分解得到的每组振荡分量进行参数辨识。最后通过自合成信号仿真、10机39节点系统仿真和实测信号仿真,证明所提方法的可行性与准确性。通过与经验模态分解(empirical mode decomposition,EMD)算法和Fourier同步压缩变换(Fourier synchrosqueezed transform,FSST)算法进行仿真对比,表明所提方法能够有效抑制噪声与端点效应,准确地辨识出混合振荡信号的特征参数,在处理精度与运算时间上较传统方法有一定优势。

After asynchronous interconnection,the mixed oscillation of low frequency and ultra-low frequency will appear in the system with high permeability of hydropower units,which will do great harm to the safe and stable operation of power grid.To solve the problem of inaccurate identification of characteristic parameters of mixed oscillation signals,a second-order synchronous extracting wavelet transform(SSEWT)method is proposed,which is combined with empirical envelope(EE)demodulation technology to identify real-time modal parameters of oscillation signals.The SSEWT extends the existing synchronous extraction transform to wavelet transform,and introduces the second-order instantaneous frequency estimator to decompose the signal.Then,combined with EE demodulation technology,the parameters of each group of oscillation components are identified.Finally,the feasibility and accuracy of the proposed method are proved by self-synthesis signal simulation,10-machine 39-bus system simulation,and measured signal simulation.Compared with empirical mode decomposition(EMD)algorithm and Fourier synchrosqueezed transform transform(FSST)algorithm,the simulation results show that the proposed method can be adopted to effectively suppress noise and endpoint effect,and accurately identify the characteristic parameters of mixed oscillation signals.