A Hybrid GA-Adaptive Particle Swarm Optimization Based Tuning of Unscented Kalman Filter for Harmonic Estimation

摘要

This paper proposes Hybrid Genetic Algorithm (GA)-Adaptive Particle Swarm Optimization (APSO) aided Unscented Kalman Filter (UKF) to estimate the harmonic components present in power system voltage/current waveforms. The initial choice of the process and measurement error covariance matrices Q and R (called tuning of the filter) plays a vital role in removal of noise. Hence, hybrid GA-APSO algorithm is used to estimate the error covariance matrices by minimizing the Root Mean Square Error(RMSE) of the UKF. Simulation results are presented to demonstrate the estimation accuracy is significantly improved in comparison with that of conventional UKF.