A hybrid global maximum power point tracking method based on butterfly particle swarm optimization and perturb and observe algorithms for a photovoltaic system under partially shaded conditions

摘要

Background In this paper, a new hybrid global maximum power point tracking (GMPPT) technique is proposed for faster and accurate tracking of global maximum power point (GMPP) without premature convergence. It is a combination of modified particle swarm optimization (PSO) and perturb and observe (P&O) methods. The proposed GMPPT technique, adaptive butterfly PSO (ABF-PSO) uses butterfly swarm intelligence for modifying the conventional PSO algorithm with parameter tuning to avoid premature convergence. Aims Hybrid global maximum power point tracking (GMPPT) technique is proposed for faster and accurate tracking of global maximum power point (GMPP) without premature convergence. Further, a new reinitialization of particles for any irradiance change is proposed to get faster tracking. Materials & Methods In the proposed hybrid GMPPT technique, first GP region is easily identified with adaptive sensitivity parameter of the ABF-PSO algorithm and in the region identified, GMPP tracking is continued with P&O algorithm with variable length perturbations to avoid the unnecessary exploration of search space even after reaching global peak (GP) region. Results The combined effect of adaptive parameters, global region identification with adaptive sensitivity, proposed reinitialization method, and steady-state tracking with variable step P&O results in fast and accurate tracking of GMPP with low power oscillations during GP region identification stage and steady-state. Discussion Boost DC-DC converter is used as an MPPT controller to test the performance of the proposed algorithm under different irradiance patterns by using MATLAB/Simulink model and hardware prototype developed. Conclusion The combined effect of adaptive parameters, global region identification with adaptive sensitivity, proposed reinitialization method, and steady-state tracking with variable step P&O results in fast and accurate tracking of GMPP with low power oscillations during GP region identification stage and steady-state.