IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i5p1305-d507480.html
   My bibliography  Save this article

Fault Detection and Diagnosis Methods for Fluid Power Pitch System Components—A Review

Author

Listed:
  • Magnus F. Asmussen

    (Hydratech Industries, 9000 Silkeborg, Denmark
    Department of Energy Technology, Aalborg University, 9220 Aalborg East, Denmark)

  • Jesper Liniger

    (Department of Energy Technology, Aalborg University, 6700 Esbjerg, Denmark)

  • Henrik C. Pedersen

    (Department of Energy Technology, Aalborg University, 9220 Aalborg East, Denmark)

Abstract

Wind turbines have become a significant part of the global power production and are still increasing in capacity. Pitch systems are an important part of modern wind turbines where they are used to apply aerodynamic braking for power regulation and emergency shutdowns. Studies have shown that the pitch system is responsible for up to 20% of the total down time of a wind turbine. Reducing the down time is an important factor for decreasing the total cost of energy of wind energy in order to make wind energy more competitive. Due to this, attention has come to condition monitoring and fault detection of such systems as an attempt to increase the reliability and availability, hereby the reducing the turbine downtime. Some methods for fault detection and condition monitoring of fluid power systems do exists, though not many are used in today’s pitch systems. This paper gives an overview of fault detection and condition monitoring methods of fluid power systems similar to fluid power pitch systems in wind turbines and discuss their applicability in relation to pitch systems. The purpose is to give an overview of which methods that exist and to find areas where new methods need to be developed or existing need to be modified. The paper goes through the most important components of a pitch system and discuss the existing methods related to each type of component. Furthermore, it is considered if existing methods can be used for fluid power pitch systems for wind turbine.

Suggested Citation

  • Magnus F. Asmussen & Jesper Liniger & Henrik C. Pedersen, 2021. "Fault Detection and Diagnosis Methods for Fluid Power Pitch System Components—A Review," Energies, MDPI, vol. 14(5), pages 1-15, February.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1305-:d:507480
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/5/1305/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/5/1305/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jesper Liniger & Nariman Sepehri & Mohsen Soltani & Henrik C. Pedersen, 2017. "Signal-Based Gas Leakage Detection for Fluid Power Accumulators in Wind Turbines," Energies, MDPI, vol. 10(3), pages 1-18, March.
    2. Hameed, Z. & Hong, Y.S. & Cho, Y.M. & Ahn, S.H. & Song, C.K., 2009. "Condition monitoring and fault detection of wind turbines and related algorithms: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(1), pages 1-39, January.
    3. García Márquez, Fausto Pedro & Tobias, Andrew Mark & Pinar Pérez, Jesús María & Papaelias, Mayorkinos, 2012. "Condition monitoring of wind turbines: Techniques and methods," Renewable Energy, Elsevier, vol. 46(C), pages 169-178.
    Full references (including those not matched with items on IDEAS)

    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. Beganovic, Nejra & Söffker, Dirk, 2016. "Structural health management utilization for lifetime prognosis and advanced control strategy deployment of wind turbines: An overview and outlook concerning actual methods, tools, and obtained result," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 68-83.
    2. 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.
    3. Jin, Xin & Ju, Wenbin & Zhang, Zhaolong & Guo, Lianxin & Yang, Xiangang, 2016. "System safety analysis of large wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1293-1307.
    4. Ruiz de la Hermosa González-Carrato, Raúl & García Márquez, Fausto Pedro & Dimlaye, Vichaar, 2015. "Maintenance management of wind turbines structures via MFCs and wavelet transforms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 472-482.
    5. 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.
    6. Enevoldsen, Peter, 2016. "Onshore wind energy in Northern European forests: Reviewing the risks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1251-1262.
    7. Oh, Ki-Yong & Park, Joon-Young & Lee, Jun-Shin & Lee, JaeKyung, 2015. "Implementation of a torque and a collective pitch controller in a wind turbine simulator to characterize the dynamics at three control regions," Renewable Energy, Elsevier, vol. 79(C), pages 150-160.
    8. Rodríguez-López, Miguel A. & López-González, Luis M. & López-Ochoa, Luis M. & Las-Heras-Casas, Jesús, 2016. "Development of indicators for the detection of equipment malfunctions and degradation estimation based on digital signals (alarms and events) from operation SCADA," Renewable Energy, Elsevier, vol. 99(C), pages 224-236.
    9. Faiz, Jawad & Moosavi, S.M.M., 2016. "Eccentricity fault detection – From induction machines to DFIG—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 169-179.
    10. Kong, Yun & Wang, Tianyang & Feng, Zhipeng & Chu, Fulei, 2020. "Discriminative dictionary learning based sparse representation classification for intelligent fault identification of planet bearings in wind turbine," Renewable Energy, Elsevier, vol. 152(C), pages 754-769.
    11. Artigao, Estefania & Martín-Martínez, Sergio & Honrubia-Escribano, Andrés & Gómez-Lázaro, Emilio, 2018. "Wind turbine reliability: A comprehensive review towards effective condition monitoring development," Applied Energy, Elsevier, vol. 228(C), pages 1569-1583.
    12. Kong, Yun & Wang, Tianyang & Chu, Fulei, 2019. "Meshing frequency modulation assisted empirical wavelet transform for fault diagnosis of wind turbine planetary ring gear," Renewable Energy, Elsevier, vol. 132(C), pages 1373-1388.
    13. 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.
    14. Kandukuri, Surya Teja & Klausen, Andreas & Karimi, Hamid Reza & Robbersmyr, Kjell Gunnar, 2016. "A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 697-708.
    15. Dao, Phong B., 2022. "Condition monitoring and fault diagnosis of wind turbines based on structural break detection in SCADA data," Renewable Energy, Elsevier, vol. 185(C), pages 641-654.
    16. Bakir, I. & Yildirim, M. & Ursavas, E., 2021. "An integrated optimization framework for multi-component predictive analytics in wind farm operations & maintenance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    17. Colak, Ilhami & Fulli, Gianluca & Bayhan, Sertac & Chondrogiannis, Stamatios & Demirbas, Sevki, 2015. "Critical aspects of wind energy systems in smart grid applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 155-171.
    18. Papatheou, Evangelos & Dervilis, Nikolaos & Maguire, Andrew E. & Campos, Carles & Antoniadou, Ifigeneia & Worden, Keith, 2017. "Performance monitoring of a wind turbine using extreme function theory," Renewable Energy, Elsevier, vol. 113(C), pages 1490-1502.
    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. Zengyi Zhang & Zhenru Shu, 2024. "Unmanned Aerial Vehicle (UAV)-Assisted Damage Detection of Wind Turbine Blades: A Review," Energies, MDPI, vol. 17(15), pages 1-31, July.

    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:5:p:1305-:d:507480. 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.