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.

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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

Controls
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article

A multi-scale digital twin framework integrating real-time control and long-term prognostics for wind energy infrastructure

Junlin Heng, Charalampos Baniotopoulos, Ahmed Elgammal, Yasmin Ali et al.
Journal of Energy Infrastructure
Machine Fault Diagnosis Techniques
article

A multi-scale digital twin framework integrating real-time control and long-term prognostics for wind energy infrastructure

Junlin Heng, Charalampos Baniotopoulos, Ahmed Elgammal, Yasmin Ali, Kaoshan Dai
article en

Abstract

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.

Journal of Energy Infrastructure
Leibniz University Hannover (DE), Sichuan University (CN), University of Birmingham (GB), Delta University for Science and Technology (EG)
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
Affordable and clean energy
Openalex Percentile: Top 7%
Machine Fault Diagnosis Techniques
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