Intelligent Control Strategies for Enhancing Power Quality in Hybrid Renewable Energy System for Agricultural Applications

This paper presents a study on intelligent control strategies aimed at improving power quality in hybrid renewable energy systems (HRES) for agricultural applications. The increasing adoption of renewable energy sources, such as solar and wind, in agricultural settings necessitates effective control techniques to ensure reliable and stable power supply. The proposed intelligent control strategies leverage advanced algorithms to optimize the operation of various system components, including renewable energy sources, energy storage systems, and loads. The strategies encompass energy management systems (EMS) that monitor and coordinate the operation of different energy sources and storage systems, predictive control algorithms that utilize real-time data and predictive models to anticipate power generation and demand, smart grid integration techniques for seamless interaction between the hybrid system and the grid, and fault detection and diagnostic algorithms for identifying and mitigating issues that may affect power quality. The effectiveness of these intelligent control strategies is assessed through simulation studies and experimental validations in representative agricultural environments. The results demonstrate improved power quality, enhanced energy utilization, and increased system reliability, highlighting the potential of intelligent control strategies for optimizing hybrid renewable energy systems in agricultural applications.