A novel hybrid grey wolf optimized fuzzy logic control based photovoltaic water pumping system

This paper describes the novel scientific contribution of a modified hybrid grey wolf optimization-fuzzy logic controller (GWO-FLC) based on maximum power point tracking (MPPT) for a photovoltaic (PV) system. The modified GWO eliminates the lower rank (δ) and lowest rank (Ψ) wolves, which provides a rapid search method to track global maximum point (GMPP) in a lesser period. The hybrid GWO-FLC based MPPT technique provides tracking of GMPP with zero oscillations and high PV power tracking under rapid weather fluctuations. The GWO technique acquires GMPP and provides soft tuning of fuzzy parameters to achieve high PV power tracking with zero oscillations around GMPP. The experimental responses confirm changing environmental situations. The dSPACE real-time platform has been employed to validate the proposed hybrid MPPT controller design, which provides optimal PV power tracking with accurate efficiency, fast convergence velocity, and less computational period. The switched reluctance motor (SRM) driven PV water pump is proposed in this research work. The proposed PV water pumping has low cost and no voltage, and current sensors are employed to generate a gating pattern of an SRM based mid-point converter, which reduces the losses.