Español English Contact

Funding programme and call:
Call: H2020-LC-SC3-2018-2019-2020 /
Topic: LC-SC3-RES-14-2019: Optimising
manufacturing and system operation /
Deadline Id: H2020-LC-SC3-2019-RES-

Start Date:

End Date:

36 months

Total budget:
4,709,368.75 €

Budget for PLOCAN:
252,500.00 €

Total funding:
4,709,368.75 €

Funding for PLOCAN:
252,500.00 €

Asociación Centro Tecnológico CEIT-IK4

Ceit-IK4 (ES) - Delft Dynamics B.V. (NL) -
SWC (AT) - TU Delft (NL) - SINTEF

Web Project:

O&M tools integrating accurate structural health in offshore energy

Operation & Maintenance (O&M) costs are the main cost driver in offshore energy due to the difficult accessibility to the WTs, but also due to the environmental conditions. O&M costs can account for up to 30% of the levelised cost of energy (LCOE) and sensing & monitoring systems could help attain the expected fall to 70 EUR/MWh by 2030.
The highest criticality (in €/kWh) in offshore wind is caused by structural failure, that mainly occurs due to corrosion processes non-adequately neither predicted nor monitored. For that reason, it is crucial to implement new monitoring, diagnosis, prognosis and control tools into the offshore wind farms (WFs) to enable Wind Farm Operators (WFOs) to take predictive smart O&M decisions fully considering structural components real and future status.
WATEREYE aims to develop an integral solution that will allow to WFOs a 4% reduction of OPEX, accurately predicting the need for future maintenance strategy and increasing the offshore wind annual energy production. To this end, WATEREYE will:
1/ develop a monitoring system capable of remotely estimating the corrosion level in exact WT locations (tower, splash-zone, tower-platform junction) as a supporting tool for predictive maintenance to considerably reduce the O&M costs and reduce the risk for operation failures; New Ultrasound corrosion sensors (ad-hoc, low-cost, high accuracy, fast-response, non- invasive) will be developed, as well as high efficient and robust wireless communications specifically conceived for offshore WTs hard communicating environment. Besides, a novel drone-based mobile platform to move one mobile sensor inside the WT tower will be developed.
2/ develop enhanced prediction models by analysing the acquired data in novel ways (semantic models);
3/ develop WT & WF control algorithms with accurate consideration of the structural health, giving operators freedom to choose the best balance between energy production, protective control, and predictive maintenance.