Data-Driven Fault Prediction in Renewable Energy Systems: Enhancing Reliability of Wind and Solar Installations in the USA

Abstract

The growing reliance on renewable energy sources, particularly wind and solar power, highlights the critical need for intelligent fault prediction systems to ensure operational reliability and minimize downtime. This research presents a comprehensive, data-driven machine learning framework designed for fault detection and predictive maintenance in renewable energy systems across the United States. We begin by integrating sensor, environmental, and operational data collected from wind turbines and photovoltaic (PV) systems to create a unified analytical foundation. Through robust feature engineering, we extract domain-specific indicators such as power conversion efficiency, inverter performance metrics, temperature anomalies, and temporal patterns (hourly, daily, and seasonal). Time series decomposition and statistical aggregations are utilized to identify deviations from normal operating behavior. We explore both traditional and deep learning models for supervised classification, including Random Forest, XGBoost, and Long Short-Term Memory (LSTM) networks. Additionally, we train unsupervised models, such as Autoencoders, to reconstruct normal sequences and flag abnormal behaviors based on high reconstruction errors. We evaluate the models using metrics including accuracy, precision, recall, F1-score, and ROC-AUC, with CNN-LSTM hybrids demonstrating the best performance in detecting early-stage faults across various system types. To address class imbalance, we apply SMOTE and other resampling techniques. Visual analysis using Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) confirms effective separation between faulty and healthy system states in reduced feature spaces. Finally, we propose a fault risk index that aggregates model outputs, anomaly scores, and temporal deviation metrics to enable real-time prioritization of at-risk components. Our framework shows strong potential for proactive fault management, promoting a more resilient and cost-effective operation of solar and wind energy infrastructures.