Machine Learning Based Predictive Maintenance for Improved Reliability and Efficiency of Solar Farms

Abstract

The increasing deployment of solar photovoltaic (PV) systems has intensified the need for intelligent approaches that ensure reliable operation, minimize performance losses, and improve economic viability. Conventional maintenance strategies in solar farms are largely reactive or scheduled, often leading to delayed fault detection, reducing energy yield, and increased operational costs. This study presents a machine learning-based predictive maintenance and optimization framework aimed at enhancing the operational efficiency of a solar photovoltaic power plant, using Azari Farm as a case study. Operational data such as PV array voltage, current, irradiance, temperature, and power output are analyzed to identify performance degradation patterns and detect potential system faults before failure occurs. Machine learning algorithms are applied to monitor system behavior, predict anomalies, and optimize maintenance scheduling. The results demonstrate that integrating machine learning with predictive maintenance strategies significantly improves system reliability, reduces downtime, and enhances overall solar farm efficiency. The study highlights the potential of intelligent monitoring and optimization techniques to improve energy generation performance and sustainability in large scale solar photovoltaic installations.