Detection and Identification of Global Maximum Power Point Operation in Solar PV Applications Using a Hybrid ELPSO-Px0026;O Tracking Technique

Nonhomogeneous irradiation conditions due to environmental changes introduce multiple peaks in nonlinear characteristics. Hence, to operate photovoltaic at the global power point, numerous algorithms have been proposed in the literature. However, due to the insufficient exploitation of control variables, all the maximum power point tracking (MPPT) methods presented in the literature fail to guarantee global maximum power point (GMPP) operation. In this paper, a new detection technology to identify GMPP zones using hybrid enhanced leader particle swarm optimization (ELPSO) assisted by a conventional perturb and observe (Px0026;O) algorithm is proposed. With inherent mutations, ELPSO applied to MPPT excels in exploring global regions at initial stages to determine the global best leader, whereas Px0026;O is reverted back soon after global solution space is detected. The transition from ELPSO to Px0026;O is mathematically verified and allowed only when ELPSO finds the global optimal zone. Adapting this hybrid strategy, the proposed method has produced interesting results under partial shaded conditions. For further validation, the results of the proposed hybrid ELPSO-Px0026;O are compared with the conventional ELPSO and the hybrid PSO-Px0026;O methods. The experimental results along with energy evaluations confirmed the superiority of the ELPSO-Px0026;O method in obtaining the maximum available power under all shaded conditions.