MPPT of Solar PV Panels using Chaos PSO Algorithm under Varying Atmospheric Conditions

This paper proposes a novel application of a chaos particle swarm optimization (PSO) algorithm for tracking a maximum power point (MPP) of a solar photovoltaic (PV) panel under varying atmospheric conditions. Solar PV cells have a nonlinear V-I characteristic with a distinct MPP which depends on environmental factors such as temperature and irradiation. In order to continuously harvest maximum power from the solar PV panel, it always has to be operated at its MPP. The proposed chaos PSO algorithm is one of the standard PSO algorithm variants. A chaos PSO algorithm with a logistic map has been used for initializing random values of MPPs, as well as the inertia weight in the velocity update equation of the standard PSO algorithm. This creates the best balance for the inertia weight during the evolution process of the standard PSO algorithm which results in the best convergence capability and search performance. Additionally, the algorithm has also been improved with regards to the diversity in the solution space through two independent chaotic random sequences. The obtained simulation results are compared with MPPs achieved using other algorithms such as the standard PSO, and Perturbation and Observation (P&O) algorithms. The results show that the chaos PSO algorithm is better than the standard PSO and P&O algorithms for tracking MPPs of solar PV panels. Keywords— Solar photovoltaic panels, Maximum power point tracking, and Particle swarm optimization algorithm.