Holistic data-driven framework for estimating electromechanical dynamic patterns from synchrophasor measurements in bulk power grids

Abstract

The electromechanical dynamic patterns of the power system, which normally refer to electromechanical oscillation dominant modes, mode shapes, participation factors and coherent groups, are important for the study of power system dynamic behaviours. Different from the existing measurement-based methods which mainly estimate one or two facts of dominant modes, mode shapes or coherent groups, a holistic data-driven estimation approach is developed to estimate all the four electromechanical dynamic patterns from the measurements systematically and estimate the participation factors from measurements. In the developed approach, the multichannel continuous wavelet transform is firstly employed to dominant modes and mode shapes estimation. Besides, the estimated mode shapes are used to calculate the left eigenvalue vectors of the dominant modes. With the estimated mode shapes and left eigenvalue vectors, the participation factors are solved and coherent groups are separated. Finally, the proposed data-driven approach is evaluated by the 16-machine, 68-bus test system and China Southern Power Grid. The results validate that the proposed data-driven approach can accurately estimate all the four electromechanical dynamic patterns from synchrophasor measurements in a single way.