Project Details
Description
The purpose of the LERCat project (Leading Edge Categorisation) is to enable the repair of leading edge roughness (LER) on wind turbine blades at the right time. Achieving this will result in an annual gain of 1 billion DKK for the combined turbine fleet of the project partners. LER is the largest cost-driver in servicing offshore wind farms, causing annual expenses of anywhere between 17-70 million DKK for a typically sized offshore wind park. Considering the increasing dependence of Danish green energy supply on offshore wind, this directly translates into higher electricity prices.
With existing solutions no modern turbine will avoid LER during its 25-year lifetime, yet little is known about the precise performance loss (both aerodynamic and acoustic) it incurs. LERCat sets out to close the link between observed LER (images/scans collected at least once a year) and performance losses by establishing an open, industry-wide standard for LER loss categorisation. It will thus serve as a universal tool for deciding on the most cost-effective time of blade repair, allowing optimally balancing losses and maintenance costs. This will shift the status-quo away from repairing blades at fixed intervals or subjective rules of thumb.
To formulate the LER categorisation multiple images and laser surface scans from operating turbine blades will be collected and statistically analysed to device digital twins of real-world LER. Their impact on performance is then evaluated by numerical simulations using high-fidelity computational fluid dynamics and wind tunnel measurements. Those results are then correlated back to the observed LER and categorised in distinct loss levels.
The ambition of establishing the LER categorisation as common terminology in the global wind turbine industry is supported by the strong LERCat consortium that consists of world-leading wind turbine manufacturers (Vestas, LM, SGRE, Suzlon) and the well-reputed independent service provider PowerCurve and the associated Sounding Board formed by wind farm developers/owners (Ørsted, Vattenfall, RWE, EuropeanEnergy) and independent service providers (SkySpecs, RobeRobotics, Alerion Technologies and Wind Power LAB).
With existing solutions no modern turbine will avoid LER during its 25-year lifetime, yet little is known about the precise performance loss (both aerodynamic and acoustic) it incurs. LERCat sets out to close the link between observed LER (images/scans collected at least once a year) and performance losses by establishing an open, industry-wide standard for LER loss categorisation. It will thus serve as a universal tool for deciding on the most cost-effective time of blade repair, allowing optimally balancing losses and maintenance costs. This will shift the status-quo away from repairing blades at fixed intervals or subjective rules of thumb.
To formulate the LER categorisation multiple images and laser surface scans from operating turbine blades will be collected and statistically analysed to device digital twins of real-world LER. Their impact on performance is then evaluated by numerical simulations using high-fidelity computational fluid dynamics and wind tunnel measurements. Those results are then correlated back to the observed LER and categorised in distinct loss levels.
The ambition of establishing the LER categorisation as common terminology in the global wind turbine industry is supported by the strong LERCat consortium that consists of world-leading wind turbine manufacturers (Vestas, LM, SGRE, Suzlon) and the well-reputed independent service provider PowerCurve and the associated Sounding Board formed by wind farm developers/owners (Ørsted, Vattenfall, RWE, EuropeanEnergy) and independent service providers (SkySpecs, RobeRobotics, Alerion Technologies and Wind Power LAB).
| Short title | LERCat |
|---|---|
| Acronym | LERCat |
| Status | Finished |
| Effective start/end date | 01/03/2021 → 29/02/2024 |
Collaborative partners
- Technical University of Denmark (lead)
- LM Wind Power
- Vestas Wind Systems AS
- Siemens Gamesa Renewable Energy
- Suzlon Energy A/S
Funding
- Energiteknologisk Udviklings- og Demonstrationsprogram
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
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SDG 7 Affordable and Clean Energy
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SDG 8 Decent Work and Economic Growth
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SDG 9 Industry, Innovation, and Infrastructure
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SDG 13 Climate Action
Keywords
- Leading edge roughness
- CFD
Fingerprint
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Research output
- 1 Paper
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The impact of leading edge damage and repair on sectional aerodynamic performance
Forsting, A. M., Olsen, A. S., Sørensen, N. N. & Bak, C., 2023. 12 p.Research output: Contribution to conference › Paper › Research › peer-review