Maximizing photovoltaic efficiency using dual core boost converter and artificial intelligence-based MPPT

The growing adoption of Photovoltaic (PV) systems for renewable energy requires advanced techniques to manage their efficiency under fluctuating environmental conditions. Henceforth, this work proposes an advanced PV energy conversion system incorporating a Dual Core Active Boost Converter and an Artificial Rabbit Optimized (ARO) Radial Basis Function Neural Network (RBFNN) Maximum Power Point Tracking (MPPT) algorithm. The system is designed to maximize energy extraction from solar module and distribute a DC output for various applications. Dual Core Active Boost Converter employs a high-step-up design with active clamping to diminish switching losses and raises power conversion efficiency. The Artificial Rabbit Optimized RBFNN MPPT algorithm dynamically tracks PV system's MPP under changing circumstances such as irradiance and temperature, ensuring optimal energy harvesting. The validation of proposed model is done over MATLAB, which evaluates the performance of converter and MPPT algorithm. The outcomes attained demonstrate faster convergence to MPP with 99.76%, improved energy efficiency (93%) and greater stability under dynamic operating conditions with a response time of 0.0125s. This advanced PV energy conversion system showcases its effectiveness in increasing solar power conversion and highlights its potential for practical deployment in renewable energy technologies.