A multi-scale digital twin framework integrating real-time control and long-term prognostics for wind energy infrastructure
Wind turbine life-cycle management involves disparate timescales, including real-time control for operational stability and long-term prognostics for structural integrity. Current digital twin applications typically treat these aspects as isolated domains. This study proposes and validates a unified, multi-scale digital twin framework tailored for the support structure, integrating subsystems for high-frequency operational control and low-frequency health prognostics. The proposed framework is validated through a case study on the NREL 5-MW reference wind turbine. The real-time control module, employing a Model Predictive Control (MPC) strategy for a magnetorheological (MR) damper, achieved a 37.3% reduction in the root mean square (RMS) tower top-displacement compared with the uncontrolled structure under turbulent wind conditions. Subsequently, the long-term prognostics module predicted a baseline remaining useful life (RUL) of 6.7 years at the tower base for the uncontrolled structure under an aggressive corrosion-fatigue scenario. The integrated framework demonstrated that the implementation of real-time control extends the predicted RUL to 19.8 years, representing a 198% improvement in service life. Furthermore, probabilistic assessment quantified the sensitivity of RUL to environmental and loading uncertainties, enabling risk-informed maintenance planning. The synergistic value of the proposed architecture lies in its bi-directional data exchange: prognostic data maintains control model fidelity, while operational data refines RUL predictions. By validating these capabilities on a representative case, the framework provides an effective solution for optimizing performance and ensuring the structural integrity of wind turbine support systems throughout their life-cycle. • Proposes a unified multi-scale digital twin for wind turbines. • Integrates real-time control with magnetorheological dampers. • Reduces tower displacement by over thirty-seven percent. • Predicts remaining life under corrosion and fatigue damage. • Enables two-way synergy between control and health prognosis.
Authors
- Junlin Heng (ORCID: https://orcid.org/0000-0002-2562-611X)
- Charalampos Baniotopoulos (ORCID: https://orcid.org/0000-0003-4740-8479)
- Ahmed Elgammal (ORCID: https://orcid.org/0000-0002-4144-667X)
- Yasmin Ali (ORCID: https://orcid.org/0000-0001-7413-0885)
- Kaoshan Dai
Institutions
- Leibniz University Hannover (DE)
- Sichuan University (CN)
- University of Birmingham (GB)
- Delta University for Science and Technology (EG)
Publication Details
- Journal
- Journal of Energy Infrastructure
- Published
- 2026-05-01
- DOI
- https://doi.org/10.1016/j.jei.2026.100003
- Primary Topic
- Machine Fault Diagnosis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- China Scholarship Council
- National University's Basic Research Foundation of China
- Sichuan Province Science and Technology Support Program
- National Key Research and Development Program of China
- Fundamental Research Funds for the Central Universities