Fast Tracking of Power Quality Disturbance Signals Using an Optimized Unscented Filter

This paper presents a hybrid approach for tracking the amplitude, phase, frequency, and harmonic content of power quality disturbance signals occurring in power networks using an unscented Kalman filter (UKF) and swarm intelligence. The UKF is a novel extension of the well-known extended Kalman filter (EKF) using an unscented transformation to overcome the difficulties of linearization and derivative calculations of signals with a low signal-to-noise ratio (SNR). Further, the model and measurement error covariance matrices Q and R, along with the UKF parameters, are selected using a modified particle swarm optimization (PSO) algorithm for accurate tracking of signal parameters. To circumvent the problem of premature convergence and local minima in conventional PSO, a dynamically varying inertia weight based on the variance of the population fitness is used. This results in a better local and global searching ability of the particles, which improves the convergence of the velocity, and in a better accuracy of the UKF parameters. Various simulation results for nonstationary sinusoidal signals occurring in power networks with varying amplitudes, phases, and harmonic contents corrupted with noise having a low SNR reveal significant improvements in noise rejection and speed of convergence and accuracy.