A Review of Partial Shading Fault Detection in Solar Power Systems: Toward an Internet of Energy‐Based Framework

Partial shading (PS) is one of the most critical issues that severely limits the efficiency and reliability of photovoltaic (PV) systems. Traditional detection methods, especially in large‐scale applications, lack real‐time adaptability and accuracy. This review comprehensively examines current approaches to PS fault detection, classifying algorithm‐based, circuit‐based, hybrid, and machine learning‐assisted methods, and critically comparing their strengths and limitations. It also evaluates the development of smart monitoring systems based on the Internet of Things (IoT) and the Internet of Energy (IoE), highlighting the opportunities these technologies offer for scalable and real‐time fault management. The review identifies key challenges in the literature—low‐latency communication, interoperability, security, lack of standards, and insufficient field data—and highlights future research directions. The main argument of this article is that PS fault detection will not only improve the performance of PV systems but also play a decisive role in the construction of smart and resilient energy networks. We predict that interdisciplinary research and IoE‐based architecture will determine the pace and direction of developments in this field in the next decade.