Fatigue Assessment and Lifetime Extension of Wind Turbines

Wind turbines operate in highly variable environments that impose cyclic aerodynamic, hydrodynamic and structural loads. Over time, these fluctuating stresses lead to fatigue damage, which is the principal design driver for blades, towers and foundations. Fatigue assessment quantifies the accumulation of damage under operational loading and predicts remaining service life through models that relate stress cycles to material degradation. Lifetime extension strategies aim to prolong safe operation beyond the original design period, thereby maximising return on investment and reducing environmental impact. This requires a combination of advanced monitoring, data-driven modelling, structural health assessment and targeted retrofits. Key components include remote sensing of loads via supervisory control and data acquisition systems, physics-based and machine learning approaches to damage estimation, and probabilistic frameworks to manage uncertainties in load spectra and material response. By integrating real-world measurements with digital representations of turbine behaviour, operators can identify critical hotspots, adapt maintenance schedules and assess the feasibility of blade repairs, tower stiffening or foundation reinforcement. The global significance of this work lies in its contribution to energy security, cost reduction and circular economy objectives as the fleet of ageing turbines approaches end-of-design life.

Research from Nature Portfolio

No recent Nature Portfolio content available.

Fatigue Assessment and Lifetime Extension of Wind Turbines publication trend

The graph below shows the total number of articles in fatigue assessment and lifetime extension of wind turbines across all publications each year (not limited to Nature Index journals).

Technical terms

Damage equivalent load (DEL): A representation of variable cyclic loading reduced to a constant amplitude load that causes equivalent fatigue damage over a specified period.

Digital twin: A real-time virtual model of a turbine that integrates operational data with physics-based simulations to estimate unmeasured states and loads.

Remaining useful life (RUL): The projected time period or number of load cycles for which a component can continue to operate safely before failure.

Physics-informed machine learning: A modelling approach that embeds governing equations or damage laws into data-driven algorithms to improve prediction accuracy and physical consistency.

References

1. Long-term fatigue estimation on offshore wind turbines interface loads through loss function physics-guided learning of neural networks. Renewable Energy (2023).
2. A digital twin solution for floating offshore wind turbines validated using a full-scale prototype. Wind Energy Science (2024).
3. Extending the Lifetime of Offshore Wind Turbines: Challenges and Opportunities. Energies (2024).