J. Jeffine Bene

Abstract: Solar Power is the potential alternative to other sources of energy due to its pure and readily available nature. Solar power wattage is not always wonted or constant. It is due to its unaccustomed multi-peak power voltage characteristics exhibited by a variable swing in environmental conditions and shading effects. Swarm Intelligence has been playing a vital role in optimization problem owing to its intelligibility and directness. This article presents an Inertia Modified Particle Swarm Optimization (IM-PSO) algorithm for photovoltaic power extraction. The proposed method helps to keep track of the maximum power point in lesser effort thereby ensuring proper maximum power. The algorithm is modeled and simulated in MATLAB/SIMULINK environment. The results prove it especial to other traditional techniques.

Abstract: Multiple Local Maximums (LM) and one Global Maximum (GM) can be present on the Power-Voltage (P-V) output curve during partial shading conditions of a Photovoltaic system. Particle Swarm Optimization (PSO) can consistently find the GM 100% of the time when the swarm size is large enough but the time to find the GM also increases as the swarm size increases - thus reducing the speed of tracking. In this work, we present the performance results of a modified PSO algorithm with dynamically reduced boundary (PSO-RB) for the search space of the particles. Our results indicate that the global maxima search time can be significantly improved while maintaining the accuracy by implementing the PSO-RB algorithm. A direct comparison between the original PSO algorithm and the new PSO-RB algorithm showed that PSO-RB can find the GM point 54.3% (over 2 times) faster than the conventional PSO.