AI-Driven Predictive Maintenance in Renewable Energy Systems

Abstract

Renewable energy systems (RES), i.e., wind turbines, solar photovoltaic arrays, and hydroelectric plants are fast being integrated as a result of global energy transition objectives. Nevertheless, it is extremely difficult to ensure the reliability and efficiency of such systems due to their distributed character, the harsh conditions under which they operate, and the complicated mechanical and electrical assemblies. Staffed or reactive maintenance is a classic approach to maintenance, which tends to lead to the high operational cost and unplanned downtime. Predictive maintenance (PdM) is an innovative solution proposed by artificial intelligence (AI) as an application of machine learning algorithms and sensor data and real-time analytics to predict the failure of equipment before it happens. The paper addresses the AI-based predictive maintenance models in renewable energy systems, addressing anomaly detection, prediction of faults, and the maintenance schedule optimization. The methodology used in the research is a literature review that is carried out systematically, modeling with data on past operations, and the performance of predictive algorithms in the context of accuracy, reliability, and cost-effectiveness. The results have shown that predictive maintenance using AI can provide a substantial decrease in the occurrence of unforeseen failures, an increase in energy production, and optimization of maintenance resources, as well as identify issues associated with data quality, model generalization, and the connection with existing energy management systems. The study contains practical implications on the energy operators and policy makers that can be used in ensuring that the measures of intelligent maintenance that can be undertaken to make the renewable energy systems more sustainable and efficient are implemented.