IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i16p5167-d618770.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/16/5167/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/16/5167/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jorge Maldonado-Correa & Sergio Martín-Martínez & Estefanía Artigao & Emilio Gómez-Lázaro, 2020. "Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review," Energies, MDPI, vol. 13(12), pages 1-21, June.
    2. Camila Correa-Jullian & Sergio Cofre-Martel & Gabriel San Martin & Enrique Lopez Droguett & Gustavo de Novaes Pires Leite & Alexandre Costa, 2022. "Exploring Quantum Machine Learning and Feature Reduction Techniques for Wind Turbine Pitch Fault Detection," Energies, MDPI, vol. 15(8), pages 1-29, April.
    3. Kumarasamy Palanimuthu & Ganesh Mayilsamy & Ameerkhan Abdul Basheer & Seong-Ryong Lee & Dongran Song & Young Hoon Joo, 2022. "A Review of Recent Aerodynamic Power Extraction Challenges in Coordinated Pitch, Yaw, and Torque Control of Large-Scale Wind Turbine Systems," Energies, MDPI, vol. 15(21), pages 1-27, November.
    4. Bofeng Xu & Yue Yuan & Haoming Liu & Peng Jiang & Ziqi Gao & Xiang Shen & Xin Cai, 2020. "A Pitch Angle Controller Based on Novel Fuzzy-PI Control for Wind Turbine Load Reduction," Energies, MDPI, vol. 13(22), pages 1-16, November.
    5. Mingzhu Tang & Wei Chen & Qi Zhao & Huawei Wu & Wen Long & Bin Huang & Lida Liao & Kang Zhang, 2019. "Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data," Energies, MDPI, vol. 12(17), pages 1-15, September.
    6. Brooks, Sam & Mahmood, Minhal & Roy, Rajkumar & Manolesos, Marinos & Salonitis, Konstantinos, 2023. "Self-reconfiguration simulations of turbines to reduce uneven farm degradation," Renewable Energy, Elsevier, vol. 206(C), pages 1301-1314.
    7. Kehinde A. Adeyeye & Nelson Ijumba & Jonathan S. Colton, 2021. "Multi-Parameter Optimization of Efficiency, Capital Cost and Mass of Ferris Wheel Turbine for Low Wind Speed Regions," Energies, MDPI, vol. 14(19), pages 1-18, September.
    8. Mohamed Benbouzid & Tarek Berghout & Nur Sarma & Siniša Djurović & Yueqi Wu & Xiandong Ma, 2021. "Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review," Energies, MDPI, vol. 14(18), pages 1-33, September.
    9. Liu, Yanhua & Patton, Ron J. & Shi, Shuo, 2023. "Actuator fault tolerant offshore wind turbine load mitigation control," Renewable Energy, Elsevier, vol. 205(C), pages 432-446.
    10. Francesco Papi & Alberto Nocentini & Giovanni Ferrara & Alessandro Bianchini, 2021. "On the Use of Modern Engineering Codes for Designing a Small Wind Turbine: An Annotated Case Study," Energies, MDPI, vol. 14(4), pages 1-23, February.
    11. Narender Singh & Dibakor Boruah & Jeroen D. M. De Kooning & Wim De Waele & Lieven Vandevelde, 2023. "Impact Assessment of Dynamic Loading Induced by the Provision of Frequency Containment Reserve on the Main Bearing Lifetime of a Wind Turbine," Energies, MDPI, vol. 16(6), pages 1-14, March.
    12. Mojtaba Ahmadieh Khanesar & Jingyi Lu & Thomas Smith & David Branson, 2021. "Electrical Load Prediction Using Interval Type-2 Atanassov Intuitionist Fuzzy System: Gravitational Search Algorithm Tuning Approach," Energies, MDPI, vol. 14(12), pages 1-18, June.
    13. Xia, Tangbin & Dong, Yifan & Pan, Ershun & Zheng, Meimei & Wang, Hao & Xi, Lifeng, 2021. "Fleet-level opportunistic maintenance for large-scale wind farms integrating real-time prognostic updating," Renewable Energy, Elsevier, vol. 163(C), pages 1444-1454.
    14. Shi Liu & Yi Yang & Chengyuan Wang & Yuangang Tu & Zhenqing Liu, 2021. "Proposal of a Novel Mooring System Using Three-Bifurcated Mooring Lines for Spar-Type Off-Shore Wind Turbines," Energies, MDPI, vol. 14(24), pages 1-33, December.
    15. Hamed Habibi & Hamed Rahimi Nohooji & Ian Howard & Silvio Simani, 2019. "Fault-Tolerant Neuro Adaptive Constrained Control of Wind Turbines for Power Regulation with Uncertain Wind Speed Variation," Energies, MDPI, vol. 12(24), pages 1-33, December.
    16. Do, M. Hung & Söffker, Dirk, 2021. "State-of-the-art in integrated prognostics and health management control for utility-scale wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    17. Kong, Yun & Qin, Zhaoye & Wang, Tianyang & Han, Qinkai & Chu, Fulei, 2021. "An enhanced sparse representation-based intelligent recognition method for planet bearing fault diagnosis in wind turbines," Renewable Energy, Elsevier, vol. 173(C), pages 987-1004.
    18. Mazare, Mahmood & Taghizadeh, Mostafa & Ghaf-Ghanbari, Pegah, 2021. "Fault tolerant control of wind turbines with simultaneous actuator and sensor faults using adaptive time delay control," Renewable Energy, Elsevier, vol. 174(C), pages 86-101.
    19. Pere Marti-Puig & Alejandro Blanco-M & Juan José Cárdenas & Jordi Cusidó & Jordi Solé-Casals, 2019. "Feature Selection Algorithms for Wind Turbine Failure Prediction," Energies, MDPI, vol. 12(3), pages 1-18, January.
    20. Shrabani Sahu & Sasmita Behera, 2022. "A review on modern control applications in wind energy conversion system," Energy & Environment, , vol. 33(2), pages 223-262, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:5167-:d:618770. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.