Solar inverters are critical components in photovoltaic (PV) systems, directly influencing energy conversion efficiency and system reliability. Traditional maintenance approaches often rely on reactive or scheduled checks, leading to costly downtimes and inefficiencies. This paper proposes a novel AI-powered condition monitoring framework that leverages embedded edge devices to perform real-time diagnostics on solar inverters. By utilizing machine learning algorithms deployed at the edge, the system enables decentralized decision-making, reduces latency, and minimizes cloud dependency. The results demonstrate significant improvements in fault detection accuracy, system responsiveness, and energy yield optimization, making the approach suitable for scalable smart grid applications.
Fozlur Rayhan (Thu,) studied this question.