PROJECT TITLE :

Tracking Electromechanical Oscillations: An Enhanced Maximum-Likelihood Based Approach

ABSTRACT:

Lightly damped electromechanical oscillations are major operating considerations if did not be detected at an early stage. This paper improved the prevailing extended complicated Kalman filter (ECKF) technique of tracking electromechanical oscillations using synchrophasor measurements. The proposed algorithm adopted a distributed design for estimating oscillatory parameters from local substations. The novelty lies in handling most likelihood (ML) to enhance the convergence property in tracking multiple modes using an expectation maximization (EM) approach. This was achieved by encapsulating the augmented Lagrangian (AL) in the maximization step of the EM algorithm, which used a completely unique ECKF-primarily based smoother (ECKS). Performance evaluations were conducted using IEEE sixty eight-bus system and recorded synchrophasor measurements collected from the New Zealand grid. Random noise variance check cases were generated to look at the performance of the proposed algorithm. To make sure the robustness to random noisy conditions, the algorithm was tested based mostly on exhaustive Monte Carlo simulations. Comparisons were made with the prevailing Prony analysis (PA), Kalman filter (KF), and distributed EM-based FB-KLPF.