Advancements and Challenges: A Comprehensive Review of Machine Learning and IoT-enabled Approaches for Fault Detection and Mitigation in Solar Photovoltaic Systems

Abstract

This extensive analysis explores the dynamic convergence of machine learning (ML) and the Internet of Things (IoT) within the domain of solar photovoltaic (PV) systems. It reveals a progression of developments, obstacles, and prospective goals. By examining the historical progression of solar PV technology, the text emphasises the critical significance of ML and the IoT in transforming approaches to fault detection and mitigation. The paper clarifies how ML techniques improve accuracy and flexibility, going from rule-based systems to the modern fusion of predictive analytics and adaptive control. The paper emphasises the critical significance of integrating ML and IoT methodologies, showcasing their capacity to develop robust, self-educating solar PV systems. Security, scalability, and interoperability challenges are analysed, with an emphasis on the necessity for resilient solutions that guarantee the dependability of the system. A review of prospective developments emphasises the following: enhancing the precision of ML algorithms, incorporating edge computing to enable real-time responsiveness, and guaranteeing the comprehensibility of artificial intelligence models. It is suggested that blockchain technology could be potentially integrated into interconnected systems to protect them. This investigation ultimately functions as a reference point for scholars and professionals, envisioning a forthcoming era in which intelligent PV systems, enabled by the integration of ML and IoT technologies, make a substantial contribution to the efficacy and sustainability of renewable energy.