A multi-stage review framework for AI-driven predictive maintenance and fault diagnosis in photovoltaic systems

The photovoltaic (PV) sector encounters challenges such as high initial costs, reliance on weather, susceptibility to faults, irregularities in the grid, and degradation of components. Predictive maintenance (PdM) aims to proactively identify issues, thereby enhancing reliability and efficiency but may lack specific fault details without additional diagnostics efforts. This research presents an advanced PdM and fault diagnosis framework that integrates fault pattern analysis, severity assessments, and critical fault predictions. It aims to improve the functionality of PV systems, minimize downtime, and enhance reliability by identifying and analysing specific fault patterns. Consequently, our article provides a critical review of current Artificial Intelligence (AI) methodologies for PdM and fault diagnosis in PV systems. Moreover, this study highlights the significance of data standardisation and offers recommendations on how PdM, when combined with fault diagnosis, can utilize various data sources to anticipate faults in advance, assess their severity, and optimise system performance and maintenance activities. To the best of the authors’ knowledge, no such review study exists.