Maximum Power Point Tracking for a PV System using Tuned Support Vector Regression by Particle Swarm Optimization

Abstract

The Photovoltaic (PV) system is always operated at the maximum power point (MPP) condition irrespective of the fluctuations in PV voltage. The maximum power point tracking (MPPT) employed in PV system is not effective during the presence of current ripple as normal tracking becomes increasingly complex during fluctuation in solar radiation or due to change in MPP condition. This paper proposes a high-efficiency power point tracking algorithm to minimize the current ripple and power oscillation around the maximum power point.  The developed algorithm is based on particle swarm optimization-Support vector regression (PSO-SVR) technique. The proposed algorithm is implemented to select and tune the Support Vector Regression (SVR) parameters for predicting the irradiance level as well as to determine the PV voltage corresponding of maximum power point. From the experimental results, the efficiency of maximum power point tracking is found to be 99.8%. The proposed algorithm is also found to ensure the stability of MPPT even during the rapid fluctuation of solar radiation from 100% to 50% and vice-versa.