<p>Digital twin technology is an effective means for industry 4.0 to move toward dynamic monitoring and flexible control. Utilizing digital twin technology to track the operational status of wind turbine generator system (WTGS) throughout its lifecycle is crucial for achieving real-time analysis, reliability estimation, predictive maintenance, and optimized design in the future. This approach will help enhance the service life and operational stability of WTGS and facilitates the transformation of wind farm from traditional management methods to flexible digital. However, the application of digital twin technology in wind industry has been limited due to issues such as massive heterogeneous data and modeling accuracy requirements. This paper proposes a new twin model modeling method that addresses these issues by combining data analytics with mechanistic modeling techniques. The proposed method constructs a digital twin model of wind power equipment based on its operational principles, considering multiple aspects including geometry, physics, behavior, and rules. It drives the model using the operating conditions of actual wind turbines in physical space and calibrates the model in real-time using limited measurable data generated during the operation of the actual wind turbine. Consequently, this achieves real-time dynamic monitoring of the operational status of wind power equipment. This work contributes to the digital transformation of wind farms by predicting the real-time operational status of WTGS using the limited data collected during wind turbine operations, thereby guiding the maintenance of wind farms. Additionally, it provides a new solution for digital twin technology in large-scale equipment. A case study on a 2 MW WTGS is used to validate the feasibility of this method in industrial scenarios.</p>