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A Proportional Digital Controller to Monitor Load Variation in Wind Turbine Systems

Author

Listed:
  • José Gibergans-Báguena

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

  • Pablo Buenestado

    (Department of Mathematics, EEBE-Universitat Politècnica de Catalunya (UPC), 08019 Barcelona, Spain
    These authors contributed equally to this work.)

  • Gisela Pujol-Vázquez

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

  • Leonardo Acho

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

Abstract

Monitoring the variation of the loading blades is fundamental due to its importance in the behavior of the wind turbine system. Blade performance can be affected by different loads that alter energy conversion efficiency and cause potential safety hazards. An example of this is icing on the blades. Therefore, the main objective of this work is to propose a proportional digital controller capable of detecting load variations in wind turbine blades together with a fault detection method. An experimental platform is then built to experimentally validate the main contribution of the article. This platform employs an automotive throttle device as a blade system emulator of a wind turbine pitch system. In addition, a statistical fault detection algorithm is established based on the point change methodology. Experimental data support our approach.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:2:p:568-:d:724215
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    References listed on IDEAS

    as
    1. Njiri, Jackson G. & Beganovic, Nejra & Do, Manh H. & Söffker, Dirk, 2019. "Consideration of lifetime and fatigue load in wind turbine control," Renewable Energy, Elsevier, vol. 131(C), pages 818-828.
    2. Yang, Bin & Sun, Dongbai, 2013. "Testing, inspecting and monitoring technologies for wind turbine blades: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 515-526.
    3. Dong, Xinghui & Gao, Di & Li, Jia & Jincao, Zhang & Zheng, Kai, 2020. "Blades icing identification model of wind turbines based on SCADA data," Renewable Energy, Elsevier, vol. 162(C), pages 575-586.
    4. 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.
    5. Habibi, Hamed & Howard, Ian & Simani, Silvio, 2019. "Reliability improvement of wind turbine power generation using model-based fault detection and fault tolerant control: A review," Renewable Energy, Elsevier, vol. 135(C), pages 877-896.
    6. Yolanda Vidal & Christian Tutivén & José Rodellar & Leonardo Acho, 2015. "Fault Diagnosis and Fault-Tolerant Control of Wind Turbines via a Discrete Time Controller with a Disturbance Compensator," Energies, MDPI, vol. 8(5), pages 1-17, May.
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