Investigation of Kalman Filter-KF-Application on a Quasi-Newtonian - QN - Algorithm for Photovoltaic Maximum Power Point Detection - MPPT

The objective of this work is to investigate the application of Kalman Filters to the estimation of parameters of a Quasi-Newtonian (QN) algorithm in the photovoltaic analysis to improve the Maximum Power Point Tracking (MPPT). Recent work in PV-MPPT [1], demonstrate the applicability of double Kalman Filter to PV-MPPT. In this work, we implement a simulation worksheet of a Kalmar Filter (KF) to identify some improvement on the QN algorithm. The QN algorithm is a powerful method of optimization of convex curves, which is the case of the power curve of a solar module when applied to controlled conditions of temperature and weather. But, there is a distortion of the curve that is made by random processes such temperature changes and shadowing. The objective is to apply the KF to estimate the noise provoked by those processes to obtain a rapid convergence of the algorithm and to mitigate the oscillation around the Maximum Power Point (MPP). The basic idea behind this strategy is to analyze the viability of this method by an implementation and simulation of the convergence time and the oscillation around the MPP using MATLAB.