基于改进小波变换方法的电力系统低频振荡参数辨识

Parameter identification of low frequency oscillation based on improved wavelet transform in power system

基于多点量测数据的低频振荡模态参数辨识方法具有辨识精度高,覆盖模态信息全的特点,但是该方法存在数据量增大,计算时间冗长的问题。针对上述问题,将基于数据缩减技术的改进小波变换参数识别方法应用于电力系统低频振荡参数辨识中。该方法通过对发电机出口有功功率信号的正功率谱密度矩阵进行奇异值分解,有效识别系统的模态阶数。利用奇异值分解将待辨识信号的协方差信号进行数据缩减,充分保留信号的信息量,从而在保证计算合理及精度的前提有效地减少待辨识的数据量,进而利用连续Morlet小波变换识别电力系统低频振荡参数。通过对4机2区域系统和EPRI-36节点系统进行算例对分分析,结果表明改进的小波变换方法能够有在准确提取电力系统低频振荡模态参数的前提下,有效减少计算所用数据量,提高计算效率。

The approach of modes parameter identification of low frequency oscillation based on multi-measurement data, has the advantages of high precision and reliability. However, the huge amounts of data and long calculation time have limited the application of the approach. In this paper, a robust online approach based on improved wavelet transform is proposed to extract the parameters of dominant oscillation mode from wide-area measurements in power system. The singular value decomposition （SVD） is used to analyze the positive power spectrum matrix of generator e- lectromagnetism power to determine the orders of osciUation modes. To reduce the covariance signals, the SYD is used to diminish the amount of data which is involved in the extraction. Finally, the modal parameters are extracted from each mode of reduced signals using the improved wavelet transform in the specified frequency ranges. Through the study of 4-generator 2-area and EPRI -36 test systems, it is verified that the proposed improved wavelet transform could extract the accurate oscillation parameters with reduced computation data size to improve calculation efficiency.