Development of new maximum power tracking techniques for stand-alone PV system under nonuniform irradiance conditions

Abstract

The overwhelming need to decarbonize the energy sector to peter out climate changes, and catch up with the increasing demand of energy, have paved the way to an immense deployment of renewables around the globe. Solar systems are used to convert sunlight that hits their panels into electrical energy via the photovoltaic effect. However, photovoltaics have a very low efficiency, and the generated power depends almost entirely on the amount of collected solar irradiance, temperature, the electrical load and the ambient circumstances that surrounds them. Since it is not possible to have a fixed stream of solar radiation or temperature, it is crucial to come up with effective means to tackle these problems. In this regard, Maximum power trackers are integrated with PV systems to cope with the dynamically fluctuating operating conditions, and keep the generated power as high as possible. This thesis focuses on maximum power point tracking (MPPT) in PV systems using soft computing techniques. Equilibrium Optimizer, Seagull Optimization and Slime Mould Algorithm are three novel metaheuristic techniques proposed in this project. Matlab and Simulink are used to simulate a standalone PV system driven by an MPPT controller and assess the three stated optimizers. The recommended techniques demonstrated outstanding results, under distinct insolation levels and complex shading conditions. To confirm their effectiveness, a comparative study on the basis of robustness, convergence time and efficiency, is carried out along with other well-known techniques: Particle Swarm Optimization (PSO), Whale Optimization (WOA), Grey wolf Optimization (GWO), Wind Driven Optimization (WDO) and the Grasshopper Optimization algorithm (GOA). Obtained results revealed that the proposed algorithms are either superlative or competitive in terms of both convergence speed and tracking efficiency.