Machine Learning-Driven Predictive Maintenance Framework for Anomaly Detection and Prognostics in Wind Farm Operations

This research paper presents a comprehensive Machine Learning-Driven Predictive Maintenance Framework designed for Anomaly Detection and Prognostics in Wind Farm Operations. The framework aims to address the critical challenges of minimizing downtime, optimizing maintenance schedules, and extending the lifespan of wind turbine components through proactive maintenance strategies. An extensive dataset comprising operational parameters and maintenance records from a 50-turbine wind farm over a 24-month period was utilized for model development and evaluation. Anomaly detection performance was evaluated using three machine learning algorithms: Isolation Forest, One-Class SVM, and Autoencoder. Results demonstrated that the Autoencoder algorithm outperformed the others, achieving precision, recall, and F1-score values of 0.94, 0.90, and 0.92 respectively. This highlights its effectiveness in accurately identifying anomalies in wind turbine operations.Furthermore, a Long Short-Term Memory (LSTM) neural network-based prognostic model was developed to predict the remaining useful life (RUL) of critical components such as bearings and gearboxes. The prognostic model achieved a mean absolute error (MAE) of 12.3 hours and an R2 score of 0.85, indicating its capability to provide reliable estimates of component degradation and anticipate impending failures. Overall, the results demonstrate the viability and practicality of the proposed predictive maintenance framework in enhancing the reliability, efficiency, and cost-effectiveness of wind farm operations. By leveraging machine learning algorithms and historical sensor data, the framework enables proactive maintenance interventions, ultimately contributing to the sustainability and efficiency of renewable energy production.