PARTICLE SWARM OPTIMIZATION MAXIMUM POWER POINT TRACKING FOR PARTIALLY SHADED SOLAR PV

Renewable energy sources are becoming increasingly popular to address the problem of global warming by reducing the amount of carbon in the atmosphere. Solar energy is one of the RES that is widely used for generating electricity through a photovoltaic generating system (PGS). Partial shading phenomenon occurs when the solar irradiation or ambient temperature received by the PV modules varies. The maximum power point tracking (MPPT) is crucial to extract the maximum power from PV modules under partial shading circumstances (PSC). However, several MPPT techniques are less effective under PSC due to the multiple peaks on the P-V curve of PGS in terms of the robustness, complexity, and efficiency. This study proposes a particle swarm optimization (PSO) algorithm based on MPPT for the PGS to operate under PSC. The objectives of this thesis are to synthesis, simulate and evaluate the robustness of PSO algorithm for MPPT under PSC. The energy conversion system and PSO algorithm were simulated in MATLAB/Simulink. The simulation results demonstrate the viability of the developed PSO method because the PSO-based MPPT controller can maximize power from the solar panel under a solar irradiation variation. Through the simulations, the proposed PSOMPPT algorithm can provide high tracking accuracy and low tracking speed of global maximum power point