Development of a Physics-Based Digital Twin Framework for a 3 MW Class Wind Turbine

The increasing size and complexity of wind turbines have intensified the need for reliable real-time condition monitoring and health assessment. However, conventional numerical models often involve high computational demand, limiting their applicability for real-time digital twin implementation. This paper proposes a physics-based digital twin framework for the real-time health monitoring of a 3 MW class wind turbine. A physics-based numerical model was developed using Modelica 4.0.0 to simulate the electrical and mechanical behaviors of the wind turbine based on supervisory control and data acquisition (SCADA) inputs. Data preprocessing and wind speed calibration strategies were applied to reconcile nacelle-measured SCADA data with the turbine design specifications. Furthermore, reduced-order models (ROMs) were integrated with the physics-based numerical model to predict the thermal states of the generator and gearbox. Key operational parameters were selected through correlation analysis to enable accurate temperature prediction. Validation results demonstrate that the proposed digital twin accurately reproduces the dynamic behavior of the wind turbine, with the ROM-based temperature predictions showing agreement with SCADA measurements. The overall framework achieves a computation time within one second, indicating its suitability for real-time diagnostic and predictive maintenance applications.