VR-Enabled Digital Twin Framework for Real-Time Wind Turbine Fault Detection and Sustainability Monitoring

The importance of wind turbine reliability for the performance and cost-effectiveness of wind power systems cannot be overstated, and advanced wind turbine condition monitoring is crucial to the solution. Providing effective condition monitoring for wind turbines is still difficult, as there is not a lot of insight in real time, nor is there any integrated sustainability assessment. While some solutions can be offered by digital twins, most existing solutions are not immersive, and do not merge performance information with environmental information. In this paper, an integrated VR-based Digital Twin framework for real-time simulation and condition monitoring as well as deterministic fault-risk assessment of wind turbines is presented. The model is developed in MATLAB using historical UK wind data and includes a reduced-order physics-based turbine model, aerodynamic behaviour, rotor dynamics, thermal modelling, controlled fault injection, and a healthy-reference/fault-exposed dual-turbine configuration. The temperature and vibration results are aggregated into a Fault-Risk Score (FRS) using predefined warning and fault thresholds, while a Health Index (HI), energy production and consequence metrics related to CO₂ are complementary indicators of turbine condition and performance. The results indicate that there is a significant difference between the healthy turbine and the fault-exposed turbine, where the fault-exposed turbine has a higher health-index degradation, lower energy production, and higher fault-risk score. By the end of the simulation period, the turbine's health index was reduced from >80% to 38% under fault conditions, leading to a 5% penalty in energy generation and a 5.1% elevation in the environmental CO2 impact metric. Additionally, the implementation using MATLAB-Unity showed real-time telemetry transmission and immersive visualisation at 72-90 FPS from the communication response time of less than 50 milliseconds. The system therefore provides an integrated platform for physics-based condition monitoring, controlled fault-risk assessment, sustainability consequence evaluation, and immersive operator visualisation, while offering a basis for future individual components and hardware-oriented validation.