Random Inertia Weight PSO Based MPPT for Solar PV under Partial Shaded Condition

Since the power-voltage characteristic curve of a photovoltaic (PV) arrays has multiple peaks under partially shading conditions (PSC), the conventional maximum power point tracking (MPPT) control methods fail to work. In this paper, a PSO algorithm based on random inertia weights is proposed to achieve global maximum power tracking. By improving the inertia weight coefficient of the traditional PSO algorithm and optimizing the search order of the particles, the population size and the number of iterations are decreased, thus finding the MPP (maximum power point) in a shorter time to ensure accurate tracking of the maximum power. By using the same parameters, its tracking performance is compared with traditional perturb and observe (P&O) method and particle swarm optimization (PSO) method, and the existed PSO algorithm is compared with the improved PSO to verify the correctness of the algorithm. The concordance of simulation results prove the advantage of the proposed MPPT method to ensure rapidity and stability of the output PV power.