Grey wolf optimization-recurrent neural network based maximum power point tracking for photovoltaic application

Abstract

To increase the photovoltaic (PV) power-generation conversion, maximum power point tracking (MPPT) is the primary concern. This works explains about the grey wolf optimization (GWO-RNN)-based hybrid MPPT method to get quick and maximum photovoltaic (PV) power with zero oscillation tracking. The GWO-RNN based MPPT method doesn't need additional sensor for measuring irradiance and temperature variables. The NLT is used for the multi-level inverter (MLI) control strategy to achieve less harmonics distraction and less switching losses with better voltage and current profile. This employed methodology brings remarkable aspects in the PV boosting potential extraction. A GWO-RNN controlled LUO converter is a zero-output harmonic agreement impedance matching interface that is MPPT is performed by placing the PV modules between the load regulator power circuit and the load regulator power circuit. To actualize the proposed hybrid GWO-RNN model for the PV system, perturb and observe, RNN, ant colony optimization, and artificial bee colony MPPT techniques are employed. The MATLAB interfaced dSPACE interface is used to finish the hands-on validation of the intended grid-integrated PV system. The obtained results eloquently support the appropriate design of higher-performance control algorithms.