Health Monitoring and Predictive Maintenance of Wind Turbines Using Generative Artificial Intelligence

This paper introduces an innovative method for the health monitoring and predictive maintenance of wind turbines, leveraging Generative Adversarial Networks (GANs). The study employs a comprehensive dataset spanning five years, collected from a North Sea wind farm comprising 50 turbines equipped with extensive sensor networks. The dataset encompasses diverse operational parameters, including vibration, temperature, wind speed, direction, and power output. Rigorous preprocessing steps were implemented to ensure data integrity, addressing issues such as missing values, outliers, and noise reduction. The research methodology involves developing and training a GAN, consisting of a Generator and a Discriminator, to generate synthetic data that mimics normal operational conditions. Anomaly detection is achieved by comparing real-time data with synthetic data based on reconstruction error, employing a threshold-based approach to identify anomalies. For predictive maintenance, a time-to-failure (TTF) model is constructed using the Cox Proportional Hazards model, integrating detected anomalies and operational parameters. The results demonstrate that the GAN effectively learns normal operational patterns, as evidenced by the convergence of Generator and Discriminator losses over training epochs. The anomaly detection system achieves an F1 score of 0.81, indicating high accuracy. The predictive maintenance model exhibits a Concordance Index of 0.82, reflecting robust predictive performance. This study highlights the potential of generative artificial intelligence in enhancing the reliability and efficiency of wind energy systems through proactive maintenance strategies.