Particle Swarm Optimization Based Maximum Power Point Tracking

Solar energy is revolutionizing the field of clean energy production because of its immense energy yield at economical costs. Fast growing field of renewable energy systems is facing a lot of challenges in power production and maintenance. This study focuses on extraction of the maximum power from the Photovoltaic (PV) System using maximum power point tracking (MPPT) controller. The need for tracking of the maximum power point is huge, since it increases the efficiency even during the rapid change in the environmental conditions. Maximum power point (MPP) is tracked by various conventional algorithms such as Incremental Conductance algorithm and Perturb and Observe method. These algorithms are well adequate in tracking the maximum power, but it suffers a lot of power losses at the steady state. So, it is necessary to device a new algorithm that is more beneficial in tracking the MPP. This work discusses about the technique called Particle Swarm Optimisation (PSO); a nature inspired algorithm like the Genetic Algorithm. The study revolves around the various key outcomes that makes the PSO differ from the conventional MPPT algorithms. From the analysis PSO Algorithm efficiently tracks the MPP than the conventional tracking techniques.