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Fault Detection Algorithm for Wind Turbines’ Pitch Actuator Systems

Author

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  • Gisela Pujol-Vazquez

    (Department of Mathematics, Universitat Politècnica de Catalunya (UPC), 08222 Terrassa, Spain
    These authors contributed equally to this work.)

  • Leonardo Acho

    (Department of Mathematics, Universitat Politècnica de Catalunya (UPC), 08222 Terrassa, Spain
    These authors contributed equally to this work.)

  • José Gibergans-Báguena

    (Department of Mathematics, Universitat Politècnica de Catalunya (UPC), 08222 Terrassa, Spain
    These authors contributed equally to this work.)

Abstract

A fault detection innovation to wind turbines’ pitch actuators is an important subject to guarantee the efficiency wind energy conversion and long lifetime operation of these rotatory machines. Therefore, a recent and effective fault detection algorithm is conceived to detect faults on wind turbine pitch actuators. This approach is based on the interval observer framework theory that has proved to be an efficient tool to measure dynamic uncertainties in dynamical systems. It is evident that almost any fault in any actuator may affect its historical-time behavior. Hence, and properly conceptualized, a fault detection system can be successfully designed based on interval observer dynamics. This is precisely our main contribution. Additionally, we realize a numerical analysis to evaluate the performance of our approach by using a dynamic model of a pitch actuator device with faults. The numerical experiments support our main contribution.

Suggested Citation

  • Gisela Pujol-Vazquez & Leonardo Acho & José Gibergans-Báguena, 2020. "Fault Detection Algorithm for Wind Turbines’ Pitch Actuator Systems," Energies, MDPI, vol. 13(11), pages 1-14, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2861-:d:367146
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    References listed on IDEAS

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    Cited by:

    1. Jersson X. Leon-Medina & Francesc Pozo, 2023. "Moving towards Preventive Maintenance in Wind Turbine Structural Control and Health Monitoring," Energies, MDPI, vol. 16(6), pages 1-4, March.
    2. Annalisa Santolamazza & Daniele Dadi & Vito Introna, 2021. "A Data-Mining Approach for Wind Turbine Fault Detection Based on SCADA Data Analysis Using Artificial Neural Networks," Energies, MDPI, vol. 14(7), pages 1-25, March.
    3. Ali Fayazi & Hossein Ghayoumi Zadeh & Hossein Ahmadian & Mahdi Ghane & Omid Rahmani Seryasat, 2024. "Pitch Actuator Fault-Tolerant Control of Wind Turbines via an L 1 Adaptive Sliding Mode Control ( SMC ) Scheme," Energies, MDPI, vol. 17(16), pages 1-20, August.
    4. José Gibergans-Báguena & Pablo Buenestado & Gisela Pujol-Vázquez & Leonardo Acho, 2022. "A Proportional Digital Controller to Monitor Load Variation in Wind Turbine Systems," Energies, MDPI, vol. 15(2), pages 1-27, January.

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