Artificial Neural Network Based Grid-Integrated Photovoltaic/Wind Hybrid Power Generation to Improve PCC Voltage and Power Factor

Abstract

In response to growing energy demands and the imperative for sustainability, this paper introduces a three-phase Solar-Wind hybrid system for efficient grid integration. Building upon previous research in solar and wind power, our study addresses the intermittent nature of these sources by proposing a synergistic approach. The integration of solar and wind technologies aims to enhance overall system reliability and contribute to a more robust and environmentally friendly energy grid. By combining solar photovoltaic and wind power, the system optimizes performance at the grid connection. The integration uses MPPT techniques to enhance power output in varying weather. An ANN controller is developed for precise power point tracking of the photovoltaic array. This controller maintains stable grid voltage and unity power factor using Vector Control in a multilevel inverter. Simulation in MATLAB/SIMULINK validates the system's ability to optimize power utilization and stabilize the grid under changing conditions. In summary, the paper proposes a method to enhance Solar-Wind hybrid performance using MPPT and ANN control for accurate voltage regulation, resulting in significant improvements.