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Fault-Tolerant Control of a Wind Turbine Generator Based on Fuzzy Logic and Using Ensemble Learning

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  • Jordi Cusidó

    (SMARTIVE, Ps. Almogàvers 44, 08204 Sabadell, Spain
    Unitat Transversal de Gestió de l’Àmbit de Camins (UTGAC), Universitat Politécnica de Catalunya (UPC), 08034 Barcelona, Spain)

  • Arnau López

    (Unitat Transversal de Gestió de l’Àmbit de Camins (UTGAC), Universitat Politécnica de Catalunya (UPC), 08034 Barcelona, Spain)

  • Mattia Beretta

    (SMARTIVE, Ps. Almogàvers 44, 08204 Sabadell, Spain
    Unitat Transversal de Gestió de l’Àmbit de Camins (UTGAC), Universitat Politécnica de Catalunya (UPC), 08034 Barcelona, Spain)

Abstract

Wind energy is a form of renewable energy with the highest installed capacity. However, it is necessary to reduce the operation and maintenance costs and extend the lifetime of wind turbines to make wind energy more competitive. This paper presents a power-derating-based Fault-Tolerant Control (FTC) model in 2 MW three-bladed wind turbines implemented using the National Renewable Energy Laboratory’s (NREL) Fatigue, Aerodynamics, Structures, and Turbulence (FAST) wind turbine simulator. This control strategy is potentially supported by the health status of the gearbox, which was predicted by means of algorithms and quantified in an indicator denominated as a merge developed by SMARTIVE , a pioneering of in this idea. Fuzzy logic was employed in order to decide whether to down-regulate the output power or not, and to which level to adjust to the needs of the turbines. Simulation results demonstrated that a reduction in the power output resulted in a safer operation, since the stresses withstood by the blades and tower significantly decreased. Moreover, the results supported empirically that a diminution in the generator torque and speed was acheived, resulting in a drop in the gearbox bearing and oil temperatures. By implementing this power-derating FTC, the downtime due to failure stops could be controlled, and thus the power production noticeably grew. It has been estimated that more than 325,000 tons of CO 2 could be avoided yearly if implemented globally.

Suggested Citation

  • Jordi Cusidó & Arnau López & Mattia Beretta, 2021. "Fault-Tolerant Control of a Wind Turbine Generator Based on Fuzzy Logic and Using Ensemble Learning," Energies, MDPI, vol. 14(16), pages 1-20, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:5167-:d:618770
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    References listed on IDEAS

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    1. Arash E. Samani & Jeroen D. M. De Kooning & Nezmin Kayedpour & Narender Singh & Lieven Vandevelde, 2020. "The Impact of Pitch-To-Stall and Pitch-To-Feather Control on the Structural Loads and the Pitch Mechanism of a Wind Turbine," Energies, MDPI, vol. 13(17), pages 1-21, September.
    2. Kuichao Ma & Mohsen Soltani & Amin Hajizadeh & Jiangsheng Zhu & Zhe Chen, 2021. "Wind Farm Power Optimization and Fault Ride-Through under Inter-Turn Short-Circuit Fault," Energies, MDPI, vol. 14(11), pages 1-16, May.
    3. Li, Jianshen & Wang, Shuangxin, 2021. "Dual multivariable model-free adaptive individual pitch control for load reduction in wind turbines with actuator faults," Renewable Energy, Elsevier, vol. 174(C), pages 293-304.
    4. 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.
    5. Alejandro Blanco-M. & Karina Gibert & Pere Marti-Puig & Jordi Cusidó & Jordi Solé-Casals, 2018. "Identifying Health Status of Wind Turbines by Using Self Organizing Maps and Interpretation-Oriented Post-Processing Tools," Energies, MDPI, vol. 11(4), pages 1-21, March.
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    1. Zemali, Zakaria & Cherroun, Lakhmissi & Hadroug, Nadji & Hafaifa, Ahmed & Iratni, Abdelhamid & Alshammari, Obaid S. & Colak, Ilhami, 2023. "Robust intelligent fault diagnosis strategy using Kalman observers and neuro-fuzzy systems for a wind turbine benchmark," Renewable Energy, Elsevier, vol. 205(C), pages 873-898.

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