基于改进小波变换方法的电力系统低频振荡参数辨识

发布时间：2018-04-02 00:07

本文选题：低频振荡　切入点：模态辨识　出处：《电测与仪表》2016年22期

【摘要】：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。
[Abstract]:Multi point measurement based data of the low frequency oscillation modal parameter identification method has high identification accuracy, coverage characteristics of modal information, but the method has increased the amount of data, the lengthy computing time. In view of the above problems, the improved data reduction technique based on wavelet transform parameter identification method is applied to parameter identification of power system low frequency oscillation in this method the spectral density matrix by singular value decomposition is power on the generator active power signal, the mode order effective recognition system. By using the singular value decomposition of the covariance identification difference signals for data reduction, full reservation information signal, thus reasonable calculation accuracy in the premise and guarantee the effective to reduce the amount of data to be identified, and the use of low frequency oscillation parameters of continuous wavelet transform Morlet identification of power system. Based on the 4 machine 2 area system and EPRI-36 The example analysis of the node system shows that the improved wavelet transform method can effectively reduce the amount of data and improve the computation efficiency under the premise of accurately extracting the modal parameters of the power system low frequency oscillation.

【作者单位】：东北电力大学电气工程学院;国网吉林省电力有限公司吉林供电公司;
【分类号】：TM712

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