Adapting Photovoltaic Perturbation Voltage with Particle Swarm Optimization

This work presents a particle swarm optimization (PSO) based maximum power point tracking (MPPT) system for maximizing the output power of a photovoltaic (PV) system and minimizing the output power fluctuation. Under partially shaded condition (PSC), the P-V characteristics will exhibit multiple peaks. The conventional MPPT approaches, such as perturb and observe (P&O) algorithm will tend to track the local maximum power point (LMPP) instead of the global maximum power point (GMPP). The fix perturbation size of the P&O algorithm will lead to power losses as the output power fluctuates at steady state. Thus, PSO based MPPT algorithm is proposed to track the GMPP and minimize the fluctuation in output power. The proposed algorithm will determine the optimum perturbation size for MPPT controller based on the information of change in voltage and change in power. The simulation results show that the optimized MPPT is able to track the GMPP under PSC accurately, fast and with zero oscillation in comparison with conventional MPPT. Parameter’s tuning is important for PSO based MPPT to achieve good transient response, particularly the inertia weight. In conclusion, the PSO based MPPT can ensure optimal and stable power generation from the PV system.