Implementation and Validation of an AI-Based MPPT Framework for Standalone PV Systems with Hybrid Energy Storage

Abstract

Traditional Maximum Power Point Tracking (MPPT) algorithms such as Perturb and Observe and Incremental Conductance have several disadvantages such as slow convergence rate, continuous oscillations at steady state, low tracking accuracy and complexities under partial shade conditions. In this research an artificial neural network based MPPT algorithm is proposed for a stand-alone PV system with a battery-supercapacitor hybrid energy storage system (HESS). It is equipped with a sophisticated Energy Management System (EMS), hybrid storage integration, advanced MPPT and real-time monitoring to optimise the use of renewable energy and improve system stability. The MMPT attributes are studied such as MPPT efficiency, tracking errors, AI prediction accuracy, energy conversion efficiency, energy storage utilisation, and converter power loss.  It can be shown from the figure that AI-MPPT, HESS efficiency, intelligent EMS and independent PV system have a high statistical correlation with temporal performance. Additional analyses are provided here that further corroborate the results of the model reported above and imply that regulating regulatory variables may explain a considerable amount of the variation in overall PV system efficiency. Finally, the suggested AI-MPPT-HESS framework provides a feasible integrated infrastructure for photovoltaic systems aiming at great improvement in efficiency, flexibility, dependability and resilience of stand-alone PV systems. Future studies should result in physical validation of hardware in dual irradiance, shadowed regions, fluctuating loads and battery energy storage.