ML-Based Fault Prediction in Wind Turbine Monitoring Systems

Abstract

Wind energy has emerged as one of the most sustainable alternatives to fossil fuels. However, the reliability and operational efficiency of wind turbines remain critical due to mechanical failures and environmental uncertainties. Predictive maintenance using Machine Learning (ML) has gained traction in recent years to proactively identify faults before catastrophic failures occur. This manuscript explores the development and implementation of ML models for fault prediction in wind turbine monitoring systems. It discusses the collection and preprocessing of operational data, selection and training of predictive models, and their evaluation using statistical analysis. Key algorithms like Random Forest, Support Vector Machines (SVM), and Artificial Neural Networks (ANN) are compared based on their fault detection capabilities.

A case study utilizing SCADA (Supervisory Control and Data Acquisition) data demonstrates the effectiveness of these models. Statistical tools such as confusion matrix, precision, recall, F1-score, and ROC-AUC are employed to evaluate model performance. The study also outlines the practical implications of ML integration in real-time monitoring systems and discusses the challenges such as data imbalance, false positives, and system scalability. The manuscript concludes with a review of the potential scope of ML applications and the limitations that must be addressed for successful deployment in wind farm operations.