PV Power Optimization with Dual Winding Step-Up Converter and Woodpecker Mating Algorithm Based Cascaded ANN MPPT

The main objectives of this study are to enhance the power extraction of photovoltaic (PV) systems under rapidly changing ecological circumstances using Maximum Power Point Tracking (MPPT) and to improve the power quality of grid connected renewable energy systems (RESs) via developing an intelligent high-gain energy conversion architecture. These objectives are achieved by designing a PV energy conversion system using an extended dual winding ( ) step-up converter and an intelligent optimized MPPT method for efficient integration with the utility grid connected systems. The step up converter is employed for enhancing high voltage gain from low-output PV Module. The Woodpecker Mating Algorithm (WMA) based optimization optimally tune the parameters of cascaded artificial neural network (CANN) MPPT, which improved the convergence speed and accurate tracking under dynamic weather condition. A three-phase voltage source inverter (3 VSI) is employed for converting DC to AC power, enabling effective integration with utility grids. The most important results are that the proposed system achieves exceptional tracking accuracy, a shortened convergence time, better dynamic stability during load and grid disturbances, increased voltage boosting performance, and a highest power conversion efficiency of 94.84%, verified by simulation studies carried out on the MATLAB and Simulink platform. The significance of the obtained results lies in offering a stable, scalable, and intelligent control solution for modern PV grid connected systems, increasing the overall system efficiency, lowering energy losses, enhancing the operational reliability of future sustainable power networks, and facilitating the evolution of next-generation smart energy infrastructures.