Control of stand-alone PV system with global maximum power point identification

Abstract

As the world faces the depletion of fossil fuels and the adverse environmental impacts of their use, renewable energy sources have become crucial for sustainable development. Solar energy, one of the most abundant renewable resources, is harnessed using photovoltaic (PV) systems that convert sunlight into electrical energy. Despite their potential, PV systems are plagued by low efficiency and dependency on various factors such as solar irradiance, temperature, electrical load, and ambient conditions. One of the major challenges in PV systems is partial shading, which occurs when only a portion of the PV array is obstructed from sunlight. This shading can drastically reduce the overall power output and create multiple local maximum power points (LMPs) on the power curve, complicating the optimization process. In PV systems with partial shading, multiple LMPs and one global maximum power point (GMPP) exist. Hence, the identification of global maximum power point GMPP is needed, which is the main topic of this thesis. The project's method is applied and simulated using MATLAB and Simulink on a stand-alone photovoltaic system powered by an MPPT controller. The suggested method (Enhanced Adaptive P&O) produced outstanding results in differentiating between uniform irradiance and partial shading occurrences under a variety of insolation levels and complex shading scenarios. A comparative study based on convergence time, and efficiency is conducted along with other well-known techniques: Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). The obtained results demonstrated that the EA-P&O is either excellent or competitive with respect to tracking efficiency, convergence speed and eliminate the oscillation problem.