A modified hybrid ALO–PSO-based maximum power point tracking for photovoltaic systems

Renewable energy sources are promising for electricity generation because they do not pollute the environment, require low maintenance and are easy to install. However, the characteristics of solar photovoltaic (PV) cells are not linear and depend on weather and environmental conditions. To maximize the power output of a PV system, it is essential to use a maximum power point tracking (MPPT) controller that can quickly and accurately adjust the system to changes in solar radiation and other environmental factors. This thesis proposes a hybrid MPPT algorithm combining antlion optimization (ALO) and particle swarm optimization (PSO) for a PV system under realistic solar radiation conditions. The algorithm calculates an appropriate duty cycle for a DC-DC converter based on the total number of solar panels and demand load. The proposed algorithm achieves high performance with over 99.97% efficiency, low ripple of 0.25%, zero oscillation and fast response time of 0.013s to achieve MPP. Compared to previous works, this algorithm offers improved responses, efficiency, reliability, complexity and cost performance. To verify the robustness of the system, the PV system was subjected to EN50530 European standard testing using Matlab (R 2021a) Simulink. The system was tested under four conditions: standard test condition (STC), irradiation variation, temperature variation, and simultaneous temperature and irradiation variations. The results show that the proposed hybrid ALO-PSO MPPT algorithm offers an efficient and reliable method to maximize power output in PV systems under varying environmental conditions.