Modified Particle Swarm Optimization Based MPPT with Adaptive Inertia Weight

In this paper we study Maximum Power Point Tracking of PV system under partial shading condition using PSO with a new approach of adaptive weight. The proposed approach has a rapid convergence and high efficiency over the simple PSO and the conventional P&O algorithm. The exploitation and exploration phase are maintained by choosing the right values for c1 and c2 and by integrating a random inertia weight with exponential decrease over iterations, this leads to high speed convergence and maintains a zero steady state oscillation compared to P&O. Also, we used a statistical characteristics (Variance and Mean) to detect the step change in the irradiance. The simulation was carried out on MATLAB Simulink using two diode model of MSX60 solar power system under uniform and partial shading condition with step change in irradiance. To see the effectiveness of the method we compared it with the conventional P&O. The results shows that our approach is more effective in finding the Global peak and in avoiding the local peak stagnation.