IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v44y2015icp466-472.html
   My bibliography  Save this article

The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review

Author

Listed:
  • Liu, W.Y.
  • Tang, B.P.
  • Han, J.G.
  • Lu, X.N.
  • Hu, N.N.
  • He, Z.Z.

Abstract

Wind turbines have been developed fast in the recent years and at the same time have brought some problems. It is important to maintain the healthy condition of the running turbine because the consequences after faults are miserable for both the company and owner. There is a constant need to reduce the costs of operating and maintaining the turbines. Therefore, it is very important to detect the faults/failures early so as to minimize downtime and maximize productivity. This paper reviewed the structure of wind turbines and analyzed the different components of wind turbines in order to detect the faults that may happen. Meanwhile, this paper mainly reviewed fault diagnosis methods of wind turbines in the last three years. The main purpose of this paper is to supply some information on structure healthy condition monitoring (SHCM) and fault diagnosis in wind turbines for related researchers.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:rensus:v:44:y:2015:i:c:p:466-472
    DOI: 10.1016/j.rser.2014.12.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032114010570
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.rser.2014.12.005?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. Jiménez, Francisco & Gómez-Lázaro, Emilio & Fuentes, Juan Alvaro & Molina-García, Angel & Vigueras-Rodríguez, Antonio, 2013. "Validation of a DFIG wind turbine model submitted to two-phase voltage dips following the Spanish grid code," Renewable Energy, Elsevier, vol. 57(C), pages 27-34.
    2. Tang, Baoping & Song, Tao & Li, Feng & Deng, Lei, 2014. "Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine," Renewable Energy, Elsevier, vol. 62(C), pages 1-9.
    3. Schubel, P.J. & Crossley, R.J. & Boateng, E.K.G. & Hutchinson, J.R., 2013. "Review of structural health and cure monitoring techniques for large wind turbine blades," Renewable Energy, Elsevier, vol. 51(C), pages 113-123.
    4. Mohseni, Mansour & Islam, Syed M., 2012. "Review of international grid codes for wind power integration: Diversity, technology and a case for global standard," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3876-3890.
    5. Dicorato, M. & Forte, G. & Trovato, M., 2012. "Wind farm stability analysis in the presence of variable-speed generators," Energy, Elsevier, vol. 39(1), pages 40-47.
    6. Zhang, Cai Wen & Zhang, Tieling & Chen, Nan & Jin, Tongdan, 2013. "Reliability modeling and analysis for a novel design of modular converter system of wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 86-94.
    7. Hachicha, Fatma & Krichen, Lotfi, 2012. "Rotor power control in doubly fed induction generator wind turbine under grid faults," Energy, Elsevier, vol. 44(1), pages 853-861.
    8. Zhang, Sufang, 2012. "International competitiveness of China's wind turbine manufacturing industry and implications for future development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3903-3909.
    9. Chong, W.T. & Pan, K.C. & Poh, S.C. & Fazlizan, A. & Oon, C.S. & Badarudin, A. & Nik-Ghazali, N., 2013. "Performance investigation of a power augmented vertical axis wind turbine for urban high-rise application," Renewable Energy, Elsevier, vol. 51(C), pages 388-397.
    10. Feng, Zhipeng & Liang, Ming & Zhang, Yi & Hou, Shumin, 2012. "Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation," Renewable Energy, Elsevier, vol. 47(C), pages 112-126.
    11. Kusiak, Andrew & Verma, Anoop, 2012. "Analyzing bearing faults in wind turbines: A data-mining approach," Renewable Energy, Elsevier, vol. 48(C), pages 110-116.
    12. Farahani, E.M. & Hosseinzadeh, N. & Ektesabi, M., 2012. "Comparison of fault-ride-through capability of dual and single-rotor wind turbines," Renewable Energy, Elsevier, vol. 48(C), pages 473-481.
    13. Ye, Lin & Sun, Hai Bo & Song, Xu Ri & Li, Li Cheng, 2012. "Dynamic modeling of a hybrid wind/solar/hydro microgrid in EMTP/ATP," Renewable Energy, Elsevier, vol. 39(1), pages 96-106.
    14. Ma, Xiandong & Wang, Yifei & Qin, Jianrong, 2013. "Generic model of a community-based microgrid integrating wind turbines, photovoltaics and CHP generations," Applied Energy, Elsevier, vol. 112(C), pages 1475-1482.
    15. Bououden, S. & Chadli, M. & Filali, S. & El Hajjaji, A., 2012. "Fuzzy model based multivariable predictive control of a variable speed wind turbine: LMI approach," Renewable Energy, Elsevier, vol. 37(1), pages 434-439.
    16. Yang, Wenxian & Court, Richard & Jiang, Jiesheng, 2013. "Wind turbine condition monitoring by the approach of SCADA data analysis," Renewable Energy, Elsevier, vol. 53(C), pages 365-376.
    17. Soua, Slim & Van Lieshout, Paul & Perera, Asanka & Gan, Tat-Hean & Bridge, Bryan, 2013. "Determination of the combined vibrational and acoustic emission signature of a wind turbine gearbox and generator shaft in service as a pre-requisite for effective condition monitoring," Renewable Energy, Elsevier, vol. 51(C), pages 175-181.
    18. Feng, Zhipeng & Liang, Ming, 2014. "Fault diagnosis of wind turbine planetary gearbox under nonstationary conditions via adaptive optimal kernel time–frequency analysis," Renewable Energy, Elsevier, vol. 66(C), pages 468-477.
    19. 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.
    20. Entezami, M. & Hillmansen, S. & Weston, P. & Papaelias, M.Ph., 2012. "Fault detection and diagnosis within a wind turbine mechanical braking system using condition monitoring," Renewable Energy, Elsevier, vol. 47(C), pages 175-182.
    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. 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.
    2. Igba, Joel & Alemzadeh, Kazem & Durugbo, Christopher & Henningsen, Keld, 2015. "Performance assessment of wind turbine gearboxes using in-service data: Current approaches and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 144-159.
    3. Mérigaud, Alexis & Ringwood, John V., 2016. "Condition-based maintenance methods for marine renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 53-78.
    4. Igba, Joel & Alemzadeh, Kazem & Durugbo, Christopher & Eiriksson, Egill Thor, 2016. "Analysing RMS and peak values of vibration signals for condition monitoring of wind turbine gearboxes," Renewable Energy, Elsevier, vol. 91(C), pages 90-106.
    5. 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.
    6. 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.
    7. Wang, Yifei & Ma, Xiandong & Joyce, Malcolm J., 2016. "Reducing sensor complexity for monitoring wind turbine performance using principal component analysis," Renewable Energy, Elsevier, vol. 97(C), pages 444-456.
    8. Yuri Merizalde & Luis Hernández-Callejo & Oscar Duque-Perez & Víctor Alonso-Gómez, 2019. "Maintenance Models Applied to Wind Turbines. A Comprehensive Overview," Energies, MDPI, vol. 12(2), pages 1-41, January.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. Ana Rita Nunes & Hugo Morais & Alberto Sardinha, 2021. "Use of Learning Mechanisms to Improve the Condition Monitoring of Wind Turbine Generators: A Review," Energies, MDPI, vol. 14(21), pages 1-22, November.
    16. 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.
    17. 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.
    18. Yingying Zhao & Dongsheng Li & Ao Dong & Dahai Kang & Qin Lv & Li Shang, 2017. "Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data," Energies, MDPI, vol. 10(8), pages 1-17, August.
    19. 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.
    20. Liu, Xianzeng & Yang, Yuhu & Zhang, Jun, 2018. "Resultant vibration signal model based fault diagnosis of a single stage planetary gear train with an incipient tooth crack on the sun gear," Renewable Energy, Elsevier, vol. 122(C), pages 65-79.

    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:rensus:v:44:y:2015:i:c:p:466-472. 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.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.