LMS Adaptive FIR Filter-Predictive Controller Based On D-Q Control Theory for Grid-Connected Solar PV System

Abstract

A Least Mean Square (LMS) adaptive Finite Impulse Response (FIR) filter-predictive controller is presented for the control of a grid-connected solar PV energy system. The objective of the work is to reduce total harmonic distortion (THD) in the grid and load current. It follows the systematic development and analysis of the implementation of an adaptive LMS control based on the direct-quadrature control theory. The analysis is done for the proposed adaptive control technique for grid-connected solar photovoltaic systems under the variation of solar irradiation and load. In this proposed work, the reference current is estimated by developing the LMS control technique. The proposed system is modeled in d-q frame form and simulated on the MATLAB Simulink platform. Developing an LMS control algorithm using the p-q theory reduces computational hard work. The reduction in load current harmonics is improved. The simulation results are used to discuss the convergence rate, harmonics reduction, and advantages of the adaptive LMS algorithm d-q control theory.