Volterra LMS/F Based Control Algorithm for UPQC With Multi-Objective Optimized PI Controller Gains

Abstract

This paper describes Volterra expansions filter with Least Mean Square (LMS) and Least Mean Fourth (LMF) control algorithm on Unified Power Quality Controller (UPQC). The adaptive models based on the LMS provide a simplified structure to approximate the necessary parameters for a particular PQ interruption. However, these algorithms are ineffective in the case of time-varying perturbations. Besides that, LMF is a comparatively better solution for multiple PQ disruptions with high computational complexity than LMS. So the LMS/Fourth (LMS/F) algorithm is a mixture of LMS and LMF to improve the error convergence quality of a given filter. The Volterra expansions filter has the advantageous characteristic of estimating fundamental components at any PQ conditions without any delay and less computational calculation. Furthermore, an optimization approach named Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to calculate Proportional integral (PI) controller gains in the Volterra LMS/F control algorithms. The PI controller gains of dc link voltage control loop and ac terminal voltage control loop are optimized in a coordinated manner using the multi-objective NSGA-II. The applied algorithms deliver Pareto’s set of optimal global solutions. This complete algorithm is implemented through MATLAB in four-wire UPQC for mitigation of power quality problems.