Smart photovoltaic module with embedded power electronic systems for optimizing solar electricity generation

Abstract

The performance of photovoltaic (PV) modules is highly affected by the changing climatic conditions mainly the partial shading condition (PSC), where the generated power may be reduced dramatically. Intelligent maximum power point tracking (MPPT) techniques are highly required to track the global MPP in the multi-peak power curve and mitigate PSC effects. Thus, the integration of an MPPT controller with the PV system is vital to improve its performance and efficiency and optimize the harvested power. Various MPPT techniques have been presented in the literature, however, metaheuristics-based MPPTs are given great concern due to their powerful performance. This thesis is concerned with the power optimization of the PV module under PSC, and this is using MPPT controllers based on metaheuristic optimization algorithms. In our work, three approaches have been presented; the first approach is the hybridization of both the genetic algorithm GA and the particle swarm optimization PSO with the conventional perturb and observe P&O. Both P&O-based GA and P&O-based PSO has been evaluated and compared. The second approach presents metaheuristic-based MPPTs; the monarch butterfly optimization (MBO) and the novel squirrel search algorithm SSA that has been designed for the first time as MPPT controllers. In the third approach, an improved version of the SSA called ISSA has been developed to overcome the limitations of the SSA in terms of the tracking time and power oscillations. All the presented MPPT controllers have been implemented within a PV system in the MATLAB/SIMULINK environment. They have been tested for various PSCs and compared with some common MPPT methods. The proposed ISSA-based MPPT followed by MBO proved their superiority in terms of efficiency and the reduced tracking time. The ISSA has been experimentally validated; the obtained experimental results demonstrated its capability in optimizing the power of the PV system under all the PSCs selected with an average efficiency of 99.48% and an average tracking time of 0.66s