A new hybrid swarm intelligence-based maximum power point tracking technique for solar photovoltaic systems under varying irradiations

Abstract

Partial shading condition (PSC) adversely affects the maximum power production from the solar array. To overcome this issue, this study has proposed a new hybrid swarm intelligence-based maximum power point tracking (MPPT) algorithm namely marine predator algorithm-particle swarm optimization (MPA-PSO). The novel MPA-PSO is implemented on a recently proposed 4 × 4 permutation combination-based solar topology (PCR). The proposed MPA-PSO improves the efficacy of MPA by updating the velocity in three successive steps; low-velocity phase (v = 0.1), unit velocity phase (v = 1), and high-velocity phase (v ≧10) respectively. Later the global searching and local searching ability is confirmed by PSO. Thus, the novel proposed MPA-PSO improves the optimization efficiency of MPA and updates the position of the PSO algorithm with the MPA algorithm leading to an effective handling of exploration and exploitation phases. The novel MPA-PSO overcomes the challenges of long convergence time by decreasing the swarm size. Further, the challenges like capture of global power in the local peaks and slow change of shading patterns are overcome by the novel hybrid MPA-PSO. The performance of the proposed MPA-PSO is compared with MPA, PSO, and influential flower pollination algorithm (IFPA) under various realistic shading patterns. The specific improvements of the novel MPA-PSO include the reduction in convergence time by 9.08 % to 15.16 % and an increment in average power by 67.29 W against MPA, PSO, and IFPA respectively. Thus, the novel hybrid MPA-PSO discloses greater flexibility and versatility over other considered algorithms in this study.