An innovative grey wolf optimizer with Nelder–mead search method based MPPT technique for fast convergence under partial shading conditions

Abstract

Partial shading condition (PSC) is an unavoidable problem faced by photovoltaic arrays, where multiple peaks appear on the power–voltage (P–V) curve. The conventional techniques are unable to track the global power under PSCs, and thus, various optimization techniques aim to reduce the impact of PSCs. This paper proposes a hybrid Grey wolf optimization with Nelder–mead (GWO-NM) algorithm for tracking the maximum power point. The GWO algorithm assures achieving the GMPP through search space exploration. In contrast, NM is a direct search technique that aids all particles in rapidly converging on the GMPP. Consequently, the proposed hybrid method has the ability to track actual GMPP with high efficiency, providing an enhanced convergence rate and fast response time, and is highly capable of operating under varying irradiance. The proposed algorithm avoids unnecessary exploration of particles and also effectively reduces steady-state oscillations. The proposed algorithm is experimentally validated using a DSP controller, showing an average tracking time of 0.54 s with a steady-state efficiency of 99.91% under different shading conditions. Finally, the effectiveness of the proposed GWO-NM algorithm is compared to recent meta-heuristic algorithms in terms of tracking power, speed, and efficiency.