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UPscaling WIND turbines using digital twins technologies. Repowering design, validation and monitoring.

Project description

Machine learning to upscale existing onshore wind structures

By 2025, 38 GW of existing onshore wind capacity will reach the end of its operational life. Since developing new wind farms typically takes around 13 years, upgrading existing structures is the fastest way to boost generation capacity. Supported by the Marie Skłodowska-Curie Actions programme, the UPWIND project aims to accelerate the scale-up of onshore wind turbine capacity to help meet sustainable energy goals and address the climate crisis. The project will develop a machine learning-assisted framework to upgrade current wind infrastructure, replacing outdated turbines with more efficient models, increasing the height and load-bearing capacity of tubular steel towers, and extending their lifespan. This approach will reduce the need for costly tower replacements.

Objective

Clean energy production needs to expand in order for Europe to achieve its net-zero emission goals of 2050. UPWIND aims at the “massive” and “rapid” upscaling of the current onshore wind turbine stock in order to address the highly challenging sustainable energy targets and mitigate climate crisis. 38GW of existing onshore wind structures will reach their end-of-life in Europe by 2025 and new wind farms typically take about 13 years from concept to operation. Upscaling existing structures is the only reliable strategy to immediately increase wind energy installations as it can double generation capacity (in MW) of existing onshore wind farms within a very short period.

UPWIND aims to develop an innovative machine learning-assisted upscaling framework for existing onshore wind structures to be converted to new-age powerful ones. This will be achieved by replacing old turbines with more efficient and powerful new-age turbines, increasing the height of the existing onshore tubular steel wind turbine towers, enhancing the load-bearing capacity of the towers and by extending their operational life. The research output of UPWIND will enable decision-making for innovative repowering strategies focusing on upscaling the existing support towers to bear the new loads (increased self-weight, fatigue vibrations) caused by heightening and new turbines installation, by selecting suitable strengthening schemes. New upscaling methods and strengthening schemes will be implemented and a new digitalized decision-making framework for repowering old wind turbine towers will be developed.

UPWIND will introduce a gamechanger strategy that will directly increase energy production from wind structures, taking advantage of the already used best onshore locations and existing permissions, avoiding non-economic and non-environmental friendly total tower replacements, thus significantly contributing to a fully circular net-zero future for the european energy sector.

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Topic(s)

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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

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(opens in new window) HORIZON-MSCA-2024-PF-01

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Coordinator

ARISTOTELIO PANEPISTIMIO THESSALONIKIS
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 181 423,68
Address
KEDEA BUILDING, TRITIS SEPTEMVRIOU, ARISTOTLE UNIVERSITY CAMPUS
546 36 THESSALONIKI
Greece

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Region
Βόρεια Ελλάδα Κεντρική Μακεδονία Θεσσαλονίκη
Activity type
Higher or Secondary Education Establishments
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Total cost

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