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Online Identification of Low Frequency Oscillation in Power System Based on WAMS

Author: HuChun
Tutor: JiaoYanJun
School: North China Electric Power University
Course: Proceedings of the
Keywords: Improved EEMD Adaptive filtering Power system Denoising Improved Prony method Low-frequency oscillation Normalized singular value
CLC: TN713
Type: Master's thesis
Year: 2011
Downloads: 118
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Abstract


Research online oscillation modal parameter identification algorithm is an important theoretical basis for online monitoring, as well as wide-area damping control power system low frequency oscillation . Online analysis of low frequency oscillation method , Prony (Prony) method has unique advantages . Traditional Prony method is high on the requirements of the input signal is very sensitive to noise data , on the basis of the analysis of the basic principles of Prony algorithm for traditional Prony method noise - sensitive defects , study the following : ( 1) the analysis and summarizes the advantages and disadvantages of existing filtering algorithms based on digital filtering algorithm based on improved overall average empirical mode decomposition (ensemble empirical mode decomposition, EEMD) method . The algorithm for empirical mode decomposition (empirical mode decomposition, EMD) filtering methods lack the noisy signal smoothing using median filter , and then the EEMD signal processing method of decomposition . The improved method combines smoothing processing was filtered impulse noise and EEMD methods to filter out random noise, and high - frequency noise continuously advantages exclude presence mode mixing . No fixed basis functions of the method is an adaptive analysis method , very suitable for processing non-linear , non- stationary signals . ( 2 ) by means of digital simulation research to improve filtering performance of the algorithm and wavelet filtering method compared with the simulation results show that : this method can effectively suppress various noises in the sampling signal of the power system , better than wavelet filtering method filter characteristics , and avoid difficult wavelet filtering method is difficult to select the wavelet basis . (3) propose a method of filtering based on improved overall average empirical mode decomposition method and Prony method combining low frequency oscillation analysis . The method first for adaptive filtering using the improved EEMD low frequency oscillation signal analysis on the filtered signal , and then Prony method . Simulation results show that : in larger noise environment , the method can still accurately identify the modal parameters of the low frequency oscillation .

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CLC: > Industrial Technology > Radio electronics, telecommunications technology > Basic electronic circuits > Filtering techniques,the filter
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