Under partially shaded conditions (PSC), obtaining the global maximum on the multi-peak power-voltage (P-V) curve by particle swarm optimisation (PSO) method requires proper tuning of parameters to avoid premature convergence problems. In this study, a new fast tracking global maximum power point tracking (GMPPT) technique for a photovoltaic (PV) string under PSC is proposed. The proposed GMPPT technique has the advantages of two PSO techniques: leader PSO (LPSO) and adaptive velocity PSO (AVPSO). The proposed enhanced leader adaptive velocity PSO (ELAVPSO) GMPPT technique overcomes the limitations of conventional PSO such as premature convergence and difficulty in parameter tuning. Furthermore, a new shading detection scheme is proposed, which accurately finds the type of shading from P-V curve scanning. With the proposed scheme, ELAVPSO GMPPT method is used only to find the global maximum power point during PSC and perturb & observe algorithm is used for maximum power point tracking during uniform irradiance. Under PSC, the limits of the search space of the proposed GMPPT technique can be identified at the time of curve scanning. The proposed algorithm is simulated and experimentally validated by using a boost direct current-direct current (DC-DC) converter prototype.

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 steadystate 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.

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