Multi Channel Identification Method For Dominant Oscillation Parameters In Power System Based On Fast Fourier Transform And Singular Value Decomposition

With the rapid increase of demand for electricity and the improvement of construction planning,the construction of power grid in China is developing rapidly in the direction of large scale and close interconnection.The characteristics of large scale and wide interconnection make the power grid system work at a critical state nowadays,and any anomaly at any point in the network will bring large or maybe small impact on the whole network.Some of the effects will gradually disappear through the network itself,and some may gradually expand and develop,even affecting the safe and stable operation of the power grid.Low frequency oscillation is one of the ubiquitous fluctuations that may cause irreparable consequences to the power grid.Therefore,it is of great practical significance to identify the parameters of the oscillation mode of the low frequency oscillation,which is used as the adjustment of the operation adjustment of the following power grid.Dominant oscillation mode,mode shape and modal participation factor estimations are three important issues in power system small signal stability analysis(SSSA),but most of the measurement-based mode estimation methods focus on the dominant oscillation mode estimation,few of methods explore to estimate the mode shapes and modal participation factors from the measurements.In this paper,a multi-channel fast Fourier transform based continuous wavelet transform(CWTFT)is developed to systematically estimate the dominant oscillation modes,modes shapes and modal participation factors from the measurements.The CWTFT is first used to replace the CWT to enhance the computational efficiency of WT,and then a multi-channel CWTFT is developed to suppress the influences of measurement noise and oscillation mode observability on the estimated results of the single-channel based CWT and CWTFT.In aid of the developed multi-channel CWTFT,the wavelet coefficient matrixes with same scales are obtained and the critical scales associated with the dominate oscillation modes are detected by the relative wavelet energy.For the wavelet coefficient matrixes with critical scales,singular value decomposition(SVD)is applied,and the right and left singular eigenvectors of first singular value are then used to estimate the dominant oscillation modes,mode shapes and modal participation factors.The proposed approach is applied and evaluated with the simulation data from the 16-generator 68-bus test system and China Southern Power Grid(CSG)as well as the field measurements from Phasor Measurement Units(PMUs)of CSG.Results confirm that the proposed approach is accurate and efficient to estimate dominant oscillation modes,mode shapes and modal participation factors from synchrophasor measurements.