Filipe Magalhães
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Recent Grants
Grant: Close
Bridge Engineering Information System
Open Date: 2020-10-01
Close Date: 2022-09-30
Grant: Close
Smart-OPS - Smart Monitoring System for large equipment for bridge construction
Open Date: 2018-09-01
Close Date:
Grant: Close
SAFESUSPENSE - SAFety control and managEment of longspan SUSPENSion bridgEs
Open Date: 2018-01-01
Close Date:
Grant: Close
DESDEMONA - DEtection of Steel DEfects by Enhanced MONitoring and Automated procedure for self-inspection and maintenance
Open Date: 2018-01-01
Close Date:
Grant: Close
WindFarmSHM - Development, validation and optimization of Structural Health Monitoring strategies for onshore and offshore Wind Farms
Open Date: 2017-09-01
Close Date:
Articles (17)
Year:
2024
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2024
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2024
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2024
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2024
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2024
To develop reliable numerical models and better interpret monitoring campaigns experimental data of wind turbines, knowing the structure operation conditions, in particular the rotor angular velocity and blades’ pitch angle, is of paramount importance, but often not known due to confidentiality restrictions, or known with low time resolution (typically 10 min average values). In this work, it is shown analytically that blades accelerations measurements contain valuable information that allow for a better characterisation of the effective rotor shaft tilt and blades cone angle for different operating conditions. It is also shown that these measurements can be used to reconstruct the time history of the rotor angular velocity and blades’ pitch angle. After presented in an analytical framework, the methodology is validated with experimental data of two full scale wind turbines. The successful reconstruction of the rotor operating conditions shows that the method presented can be used to provide further insight into the dynamics of the structure that aids monitoring data analysis and provides an alternative method to monitor the SCADA systems themselves. The paper combines quite unique experimental data collected at two operating rotors with original data processing strategies that provide very valuable information to researchers and wind turbine operators.
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2023
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2023
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2023
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2023
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2022
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2022
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2022
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2022
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2021
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2021
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2021
Collaborators (3)
David Pardo
Professor
Universidad del País Vasco (UPV/EHU)
Álvaro Cunha
University of Porto
João Pacheco
Head of Data Science
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