Machine Learning for Predictive Maintenance in Solar Farms

Abstract

Predictive maintenance (PdM) has emerged as a critical strategy for enhancing the reliability and performance of solar farms by leveraging machine learning (ML) techniques to forecast equipment failures and optimize maintenance schedules. This paper presents a comprehensive review and analysis of the application of ML for predictive maintenance in solar farms. The abstract begins with an introduction to the concept of predictive maintenance and its significance in the context of solar energy infrastructure. It emphasizes the role of machine learning techniques in enabling proactive maintenance strategies, which can minimize downtime, reduce maintenance costs, and maximize energy production efficiency. The abstract highlights the key components of predictive maintenance systems, including data acquisition, feature engineering, model training, and deployment. It discusses the challenges associated with data collection and preprocessing in solar farm environments, such as the heterogeneity of sensor data, data quality issues, and the need for real-time monitoring capabilities. The abstract outlines the machine learning algorithms commonly used for predictive maintenance in solar farms, including supervised learning techniques such as logistic regression, decision trees, and support vector machines, as well as unsupervised learning approaches such as clustering and anomaly detection. It discusses the importance of model interpretability and scalability in real-world deployment scenarios.