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Vibration-Based Monitoring of Wind Turbines: Influence of Layout and Noise of Sensors

Author

Listed:
  • João Pacheco

    (Construct-ViBest, Faculty of Engineering (FEUP), University of Porto, 4200-465 Porto, Portugal)

  • Gustavo Oliveira

    (Construct-ViBest, Faculty of Engineering (FEUP), University of Porto, 4200-465 Porto, Portugal)

  • Filipe Magalhães

    (Construct-ViBest, Faculty of Engineering (FEUP), University of Porto, 4200-465 Porto, Portugal)

  • Carlos Moutinho

    (Construct-ViBest, Faculty of Engineering (FEUP), University of Porto, 4200-465 Porto, Portugal)

  • Álvaro Cunha

    (Construct-ViBest, Faculty of Engineering (FEUP), University of Porto, 4200-465 Porto, Portugal)

Abstract

The reduction in operating and maintenance costs of wind farms is a fundamental element to guarantee the competitiveness and growth of the wind market. Wind turbines are highly dynamic structures prone to wear during their lifetime. Therefore, dynamic monitoring systems represent an excellent option to continuously evaluate their structural conditions. These systems allow early detection of damages, permit a proactive response, minimising downtime, and maximising productivity. In this context, the present paper describes the main results obtained with alternative instrumentation strategies tested in a 2.0 MW onshore wind turbine to reduce the costs of the monitoring equipment and at the same time ensure an adequate accuracy in structural condition evaluation. The data processing strategy encompasses the use of operational modal analysis combined with algorithms that deal with the particularities of operation of the wind turbines to continuously track the main vibration modes. After this automated online identification, the influence of the environmental and operating conditions on the tracked natural frequencies is mitigated, making the detection of abnormal variations of the natural frequencies possible, which might flag the appearance of damage. A database of continuously collected acceleration time series during one year is adopted to test the efficiency of alternative monitoring system layouts in detecting simulated damage scenarios. The tested alternative monitoring layouts present a varying number of sensors, alternative distributions in the wind turbine tower, and different sensor noise levels.

Suggested Citation

  • João Pacheco & Gustavo Oliveira & Filipe Magalhães & Carlos Moutinho & Álvaro Cunha, 2021. "Vibration-Based Monitoring of Wind Turbines: Influence of Layout and Noise of Sensors," Energies, MDPI, vol. 14(2), pages 1-19, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:441-:d:480911
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    References listed on IDEAS

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    1. Pierre Tchakoua & René Wamkeue & Mohand Ouhrouche & Fouad Slaoui-Hasnaoui & Tommy Andy Tameghe & Gabriel Ekemb, 2014. "Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges," Energies, MDPI, vol. 7(4), pages 1-36, April.
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    Cited by:

    1. David Pérez Granados & Mauricio Alberto Ortega Ruiz & Joel Moreira Acosta & Sergio Arturo Gama Lara & Roberto Adrián González Domínguez & Pedro Jacinto Páramo Kañetas, 2023. "A Wind Turbine Vibration Monitoring System for Predictive Maintenance Based on Machine Learning Methods Developed under Safely Controlled Laboratory Conditions," Energies, MDPI, vol. 16(5), pages 1-17, February.
    2. Altaf Hussain Rajpar & Imran Ali & Ahmad E. Eladwi & Mohamed Bashir Ali Bashir, 2021. "Recent Development in the Design of Wind Deflectors for Vertical Axis Wind Turbine: A Review," Energies, MDPI, vol. 14(16), pages 1-23, August.
    3. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2022. "In-situ condition monitoring of wind turbine blades: A critical and systematic review of techniques, challenges, and futures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    4. Nathali Rolon Dreher & Gustavo Chaves Storti & Tiago Henrique Machado, 2023. "Vibration Signal Evaluation Based on K-Means Clustering as a Pre-Stage of Operational Modal Analysis for Structural Health Monitoring of Rotating Machines," Energies, MDPI, vol. 16(23), pages 1-14, November.

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