Filipe Magalhães

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University of Porto
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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)

SPAIN
View details

Álvaro Cunha

University of Porto

PORTUGAL
View details

João Pacheco

Head of Data Science

-

PORTUGAL
View details
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