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

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