IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v130y2019icp256-267.html
   My bibliography  Save this article

Finite time estimation of actuator faults, states, and aerodynamic load of a realistic wind turbine

Author

Listed:
  • Rahnavard, Mostafa
  • Ayati, Moosa
  • Hairi Yazdi, Mohammad Reza
  • Mousavi, Mohammad

Abstract

This paper provides finite time estimation of wind turbine actuator faults and unknown aerodynamic load. Furthermore, finite-time state estimation of drivetrain, generator, and pitch subsystems are addressed in the contrary of asymptotic state/fault estimation in previous works. A realistic wind turbine model, incorporating the aero-elastic FAST simulator, is considered as the simulation example. Generally, aerodynamic load is not measurable in real applications due to instrument limitations, then, it is considered as an unknown input in this study. A novel terminal sliding mode observer is introduced for finite-time estimation of generator/convertor states, faults, and unknown aerodynamic load. Pitch actuator hydraulic pressure drop is modelled as an additive fault, by introducing a fault indicator. Then, two cascaded sliding mode observers are exploited for each pitch subsystem, to provide finite time state and fault reconstructions. Sufficient number of design parameters helps to achieve desired accuracy and convergence time. Finally, simulation results authenticate finite time estimation of wind turbine states and simultaneous actuator faults.

Suggested Citation

  • Rahnavard, Mostafa & Ayati, Moosa & Hairi Yazdi, Mohammad Reza & Mousavi, Mohammad, 2019. "Finite time estimation of actuator faults, states, and aerodynamic load of a realistic wind turbine," Renewable Energy, Elsevier, vol. 130(C), pages 256-267.
    • Handle: RePEc:eee:renene:v:130:y:2019:i:c:p:256-267
    DOI: 10.1016/j.renene.2018.06.053
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148118306980
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2018.06.053?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    2. Shi, Fengming & Patton, Ron, 2015. "An active fault tolerant control approach to an offshore wind turbine model," Renewable Energy, Elsevier, vol. 75(C), pages 788-798.
    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. Xin Wang & Seyed Mehdi Abtahi & Mahmood Chahari & Tianyu Zhao, 2022. "An Adaptive Neuro-Fuzzy Model for Attitude Estimation and Control of a 3 DOF System," Mathematics, MDPI, vol. 10(6), pages 1-16, March.

    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. 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. Chen, Xuejun & Yang, Yongming & Cui, Zhixin & Shen, Jun, 2019. "Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy," Energy, Elsevier, vol. 174(C), pages 1100-1109.
    3. Arkaitz Rabanal & Alain Ulazia & Gabriel Ibarra-Berastegi & Jon Sáenz & Unai Elosegui, 2018. "MIDAS: A Benchmarking Multi-Criteria Method for the Identification of Defective Anemometers in Wind Farms," Energies, MDPI, vol. 12(1), pages 1-19, December.
    4. Przemyslaw Baranski & Piotr Pietrzak, 2016. "Computational Effective Fault Detection by Means of Signature Functions," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-20, March.
    5. Hur, S. & Recalde-Camacho, L. & Leithead, W.E., 2017. "Detection and compensation of anomalous conditions in a wind turbine," Energy, Elsevier, vol. 124(C), pages 74-86.
    6. Azizi, Askar & Nourisola, Hamid & Shoja-Majidabad, Sajjad, 2019. "Fault tolerant control of wind turbines with an adaptive output feedback sliding mode controller," Renewable Energy, Elsevier, vol. 135(C), pages 55-65.
    7. 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.
    8. Moynihan, Bridget & Moaveni, Babak & Liberatore, Sauro & Hines, Eric, 2022. "Estimation of blade forces in wind turbines using blade root strain measurements with OpenFAST verification," Renewable Energy, Elsevier, vol. 184(C), pages 662-676.
    9. Pierre Tchakoua & René Wamkeue & Mohand Ouhrouche & Tommy Andy Tameghe & Gabriel Ekemb, 2015. "A New Approach for Modeling Darrieus-Type Vertical Axis Wind Turbine Rotors Using Electrical Equivalent Circuit Analogy: Basis of Theoretical Formulations and Model Development," Energies, MDPI, vol. 8(10), pages 1-34, September.
    10. Pu Shi & Wenxian Yang & Meiping Sheng & Minqing Wang, 2017. "An Enhanced Empirical Wavelet Transform for Features Extraction from Wind Turbine Condition Monitoring Signals," Energies, MDPI, vol. 10(7), pages 1-13, July.
    11. Odofin, Sarah & Bentley, Edward & Aikhuele, Daniel, 2018. "Robust fault estimation for wind turbine energy via hybrid systems," Renewable Energy, Elsevier, vol. 120(C), pages 289-299.
    12. Liang, Jinping & Zhang, Ke & Al-Durra, Ahmed & Zhou, Daming, 2020. "A novel fault diagnostic method in power converters for wind power generation system," Applied Energy, Elsevier, vol. 266(C).
    13. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
    14. Jijian Lian & Ou Cai & Xiaofeng Dong & Qi Jiang & Yue Zhao, 2019. "Health Monitoring and Safety Evaluation of the Offshore Wind Turbine Structure: A Review and Discussion of Future Development," Sustainability, MDPI, vol. 11(2), pages 1-29, January.
    15. Luis M. Abadie & Nestor Goicoechea, 2021. "Old Wind Farm Life Extension vs. Full Repowering: A Review of Economic Issues and a Stochastic Application for Spain," Energies, MDPI, vol. 14(12), pages 1-24, June.
    16. Papini, Guglielmo & Faedo, Nicolás & Mattiazzo, Giuliana, 2024. "Fault diagnosis and fault-tolerant control in wave energy: A perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    17. Wei Teng & Xiaolong Zhang & Yibing Liu & Andrew Kusiak & Zhiyong Ma, 2016. "Prognosis of the Remaining Useful Life of Bearings in a Wind Turbine Gearbox," Energies, MDPI, vol. 10(1), pages 1-16, December.
    18. Kevin Leahy & Colm Gallagher & Peter O’Donovan & Ken Bruton & Dominic T. J. O’Sullivan, 2018. "A Robust Prescriptive Framework and Performance Metric for Diagnosing and Predicting Wind Turbine Faults Based on SCADA and Alarms Data with Case Study," Energies, MDPI, vol. 11(7), pages 1-21, July.
    19. 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.
    20. Xin Wu & Hong Wang & Guoqian Jiang & Ping Xie & Xiaoli Li, 2019. "Monitoring Wind Turbine Gearbox with Echo State Network Modeling and Dynamic Threshold Using SCADA Vibration Data," Energies, MDPI, vol. 12(6), pages 1-19, 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:eee:renene:v:130:y:2019:i:c:p:256-267. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    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.