Power optimization in partially shaded PV arrays using the hybrid PSO-CS metaheuristic algorithm

Abstract

This thesis presents a contribution to the study and optimization of the energy generated by photovoltaic (PV) arrays subjected to partial shading (PS) applying meta-heuristic algorithms to track the global maximum power point (GMPP). Under PSC, the P-V and I-V output characteristics of a photovoltaic array are multimodal. Conventional MPPT techniques are mostly local searches and thus cannot guarantee global optimality resulting in significant power losses. Additionally, metaheuristic algorithms, might converge to a local maximum power point (LMPP) rather than the global maximum (GMPP) under certain complex shading scenarios or when their parameters are not adjusted precisely to mitigate the PS effect. In this context, this thesis presents an intelligent hybrid approach combining two Swarm Intelligence metaheuristic algorithms namely, Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA). This hybrid approach enhances the strengths of each algorithm while minimizing their drawbacks when used separately, which improves significantly the PV system output. The proposed hybrid meta-heuristic approach overcomes the shortcomings of conventional techniques and many similar Swarm Intelligence (SI) approaches; it comes with improved performance as well as reduced convergence time and remarkable robustness to weather changes due to the reset function integrated within the proposed MPPT system. The proposed hybrid MPPT controller has been validated using series and series-parallel PV configurations of different dimensions under various shading scenarios. The obtained results were very satisfactory in terms of efficiency, stability and convergence time. We can conclude that the proposed hybrid MPPT controller contributes significantly in optimizing the power output of PV systems affected by PS or rapid change of weather circumstances.