MPPT Controller Design Using TLBO Algorithm for Photovoltaic Systems Under Partial Shading Conditions

Abstract

In full illumination conditions, there is a distinct maximum power point (MPP) in the power-voltage curve of a photovoltaic system, which can be found using a conventional algorithm, such as the perturb and observe (P&O) method. However, the irradiation reduces due to the presence of shading conditions on the PV panels. Hence, multiple local MPPs (LMPP) may arise while there is only one global MPP (GMPP). An evolutionary algorithm is required for accurate tracking of the true GMPP. In this paper, an MPP Tracking (MPPT) controller design using Teaching Learning Based Optimization (TLBO) for photovoltaic systems under partial shading conditions is presented. Also, the MPPT controller design based on the Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and P&O algorithms are presented for the competence investigation of the TLBO algorithm. The proposed system is simulated considering the shading effect, then the feasibility of the presented controller is investigated.