IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i14p5286-d1190872.html
   My bibliography  Save this article

Wind Turbine Gearbox Diagnosis Based on Stator Current

Author

Listed:
  • Idris Issaadi

    (LAS Laboratory, Department of Electrical Engineering, Faculty of Technology, Ferhat Abbas University Setif-1, Campus El Bez, Setif 19137, Algeria)

  • Kamel Eddine Hemsas

    (LAS Laboratory, Department of Electrical Engineering, Faculty of Technology, Ferhat Abbas University Setif-1, Campus El Bez, Setif 19137, Algeria)

  • Abdenour Soualhi

    (Laspi, University of Jean Monnet, 20 Avenue de Paris, 42300 Roanne, France)

Abstract

Early detection of faults in wind energy systems can reduce downtime, operating, and maintenance costs while increasing productivity. This paper proposes a method based on the analysis of generator stator current signals to detect faults in a wind turbine gearbox equipped with a doubly fed induction generator (DFIG). A localized parameter model was established to simulate the vibratory response of a two-stage gear system under healthy and faulty conditions. The simulation was performed in the MATLAB/Simulink environment. The results include a detailed analysis of the mechanical part of the gearbox, highlighting mesh stiffness, output speed, and accelerations. Additionally, the electrical part was evaluated based on the current supplied by the doubly fed induction generator. The results were presented in the case of healthy gears and in the presence of faults such as a broken or cracked tooth. Fast Fourier transform (FFT) analysis was employed to detect gear defects in the stator current signal. The presence of a crack or broken tooth in the gearbox induces modulation of the DFIG stator current signals according to the shaft frequencies corresponding to the faulty gear. These findings provide a preliminary basis for the detection and diagnosis of this type of failure.

Suggested Citation

  • Idris Issaadi & Kamel Eddine Hemsas & Abdenour Soualhi, 2023. "Wind Turbine Gearbox Diagnosis Based on Stator Current," Energies, MDPI, vol. 16(14), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5286-:d:1190872
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/14/5286/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/14/5286/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Angela Meyer, 2022. "Vibration Fault Diagnosis in Wind Turbines Based on Automated Feature Learning," Energies, MDPI, vol. 15(4), pages 1-13, February.
    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. Liu, W.Y. & Tang, B.P. & Han, J.G. & Lu, X.N. & Hu, N.N. & He, Z.Z., 2015. "The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 466-472.
    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. 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.
    2. Yang, Zhimin & Chai, Yi, 2016. "A survey of fault diagnosis for onshore grid-connected converter in wind energy conversion systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 345-359.
    3. 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.
    4. Masoud Asgarpour & John Dalsgaard Sørensen, 2018. "Bayesian Based Diagnostic Model for Condition Based Maintenance of Offshore Wind Farms," Energies, MDPI, vol. 11(2), pages 1-17, January.
    5. Li, Yanting & Liu, Shujun & Shu, Lianjie, 2019. "Wind turbine fault diagnosis based on Gaussian process classifiers applied to operational data," Renewable Energy, Elsevier, vol. 134(C), pages 357-366.
    6. Liu, Y. & Hajj, M. & Bao, Y., 2022. "Review of robot-based damage assessment for offshore wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    7. Giovanni Rinaldi & Philipp R. Thies & Lars Johanning, 2021. "Current Status and Future Trends in the Operation and Maintenance of Offshore Wind Turbines: A Review," Energies, MDPI, vol. 14(9), pages 1-28, April.
    8. Yang, Ruizhen & He, Yunze & Zhang, Hong, 2016. "Progress and trends in nondestructive testing and evaluation for wind turbine composite blade," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1225-1250.
    9. 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.
    10. 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.
    11. 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.
    12. Xueli An & Dongxiang Jiang, 2014. "Bearing fault diagnosis of wind turbine based on intrinsic time-scale decomposition frequency spectrum," Journal of Risk and Reliability, , vol. 228(6), pages 558-566, December.
    13. Miguel A. Rodríguez-López & Luis M. López-González & Luis M. López-Ochoa & Jesús Las-Heras-Casas, 2018. "Methodology for Detecting Malfunctions and Evaluating the Maintenance Effectiveness in Wind Turbine Generator Bearings Using Generic versus Specific Models from SCADA Data," Energies, MDPI, vol. 11(4), pages 1-22, March.
    14. Le Zhang & Qiang Yang, 2020. "Investigation of the Design and Fault Prediction Method for an Abrasive Particle Sensor Used in Wind Turbine Gearbox," Energies, MDPI, vol. 13(2), pages 1-13, January.
    15. Ding Zhai & Anyang Lu & Jinghao Li & Qingling Zhang, 2016. "Fault detection for singular switched linear systems with multiple time-varying delay in finite frequency domain," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(13), pages 3232-3257, October.
    16. 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.
    17. Gao, Q.W. & Liu, W.Y. & Tang, B.P. & Li, G.J., 2018. "A novel wind turbine fault diagnosis method based on intergral extension load mean decomposition multiscale entropy and least squares support vector machine," Renewable Energy, Elsevier, vol. 116(PA), pages 169-175.
    18. 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.
    19. 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.
    20. Sellak, Hamza & Ouhbi, Brahim & Frikh, Bouchra & Palomares, Iván, 2017. "Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1544-1577.

    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:16:y:2023:i:14:p:5286-:d:1190872. 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.