Tracking Electromechanical Oscillations: An Enhanced Maximum-Likelihood Based Approach

Lightly damped electromechanical oscillations are major operating concerns if failed to be detected at an early stage. This paper improved the existing extended complex Kalman filter (ECKF) technique of tracking electromechanical oscillations using synchrophasor measurements. The proposed algorithm adopted a distributed architecture for estimating oscillatory parameters from local substations. The novelty lies in handling maximum 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 utilized a novel ECKF-based smoother (ECKS). Performance evaluations were conducted using IEEE 68-bus system and recorded synchrophasor measurements collected from the New Zealand grid. Random noise variance test cases were generated to examine the performance of the proposed algorithm. To ensure the robustness to random noisy conditions, the algorithm was tested based on exhaustive Monte Carlo simulations. Comparisons were made with the existing Prony analysis (PA), Kalman filter (KF), and distributed EM-based FB-KLPF.