Iot and AI-Based MPPT Techniques for Hybrid Solar and Fuel Cell

Abstract

Rising electricity demands necessitate advancements in energy generation efficiency and reliability, prompting increased interest in hybrid systems integrating solar and fuel cell renewable sources. This study proposes the MPPTs (maximum power point tracking) approach utilizing Internet of Things (IoT) for actual-time data collections from diverse sensors in a hybrid system. Sensors measuring solar irradiance, temperature, fuel cell performance, and energy storage levels feed data into AI algorithms, dynamically optimizing operational settings for enhanced energy extraction. The AI algorithms adapt to environmental changes, component deterioration, and energy consumption fluctuations, resulting in a continuous efficiency improvement. The proposed technique demonstrates superior tracking accuracy, boosting energy conversion efficiency from 88% to 94%, with a reduced system response time from 4.5 to 2.25 min. Performance metrics validate the effectiveness, suggesting potential for advanced control strategies in future renewable energy systems. Further research avenues include scalability, robustness, and cost-effectiveness for broader real-world applications.