Hybrid QPSO assisted Incremental Conductance MPPT Algorithm under Extreme Partial Shading Conditions

Photovoltaic (PV) technology has seen rapid growth over the last decade owing to its declining cost and minimal pollution. However, one major drawback of the PV systems is the non-linear characteristics of its output power under partial shading conditions (PSC). Varying weather parameters such as temperature and solar irradiance result in a multi-peak maximum power point tracking (MPPT) problem. To resolve this, many conventional and metaheuristic optimization MPPT algorithms have been proposed, some of which come with problems like slow tracking speed, increased oscillation, and no guarantee of convergence at the global maximum power point (GMPP). This paper presents a hybrid quantum particle swarm optimization assisted variable incremental conductance (QPSOVIC) algorithm which efficiently tracks maximum power (Pmax) under varying weather conditions. This metaheuristic algorithm overcomes the conventional tracking algorithm’s inability to track the GMPP and bypasses premature convergence of the traditional PSO algorithm. MATLAB/Simulink has been used for modeling and demonstration of the proposed algorithm.