Abstract
The increasing deployment of wind turbines, both onshore and offshore, demands advanced maintenance strategies to reduce downtime and operational costs. Digital twin technology offers a promising solution by creating high-fidelity virtual replicas of physical assets that integrate real-time sensor data, physics-based models, and machine learning algorithms. This paper presents a comprehensive framework for digital twin-enabled predictive maintenance of wind turbines, combining multi-physics simulation, data-driven anomaly detection, and remaining useful life estimation. The methodology involves constructing a digital twin that fuses supervisory control and data acquisition (SCADA) data with computational fluid dynamics and structural models. Real-time data streams update the twin, enabling continuous condition monitoring and fault prognosis. A case study using data from a 5 MW offshore wind turbine demonstrates the framework's effectiveness. Results show that the digital twin accurately predicts gearbox temperature trends with a mean absolute error of 1.2°C, and detects incipient faults up to 48 hours before traditional threshold-based alarms. The predictive maintenance model reduces unplanned maintenance events by 35% compared to a reactive strategy. The findings highlight the potential of digital twins to transform wind turbine maintenance from reactive to predictive, enhancing reliability and reducing levelized cost of energy.
Keywords
digital twin, predictive maintenance, wind turbine, condition monitoring, machine learning, remaining useful life, SCADA data, fault detection