Power System Electromechanical Mode Estimation using Lower-Order Recursive Subspace Method

Abstract

This paper presents an improved model for the oscillatory mode estimation of the power system using ambient data. The measured data-based recursive stochastic subspace method is integrated with the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to facilitate mode estimation in parallel with a reduced model order of the recursive subspace method. The CEEMDAN is used to decompose the input synchrophasor into intrinsic mode functions (IMF) groups which serve as input vectors to the parallel engines for mode estimation. The resulting mode estimator has a lower model order which reduces the computation cost, a major drawback of the subspace methods. The modified small-order recursive stochastic subspace algorithm is validated to estimate the ambient modes using the simulated data from a reduced-order model of the Western Electricity Coordinating Council (WECC) system.