IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v123y2018icp817-827.html
   My bibliography  Save this article

Condition monitoring of a wind turbine drive train based on its power dependant vibrations

Author

Listed:
  • Romero, Antonio
  • Soua, Slim
  • Gan, Tat-Hean
  • Wang, Bin

Abstract

Increasing the reliability and the downtime of wind turbines is critical to minimise the cost of energy (COE) in the wind sector, especially for offshore wind turbines. Due to the high impact that gearboxes and generator downtimes create on wind turbines, reliable and cost-effective condition monitoring systems (CMS) for the drive train are a great concern to the wind industry. This manuscript presents an approach for condition health monitoring and fault diagnosis in wind turbine gearboxes and generators by means of analysing the power dependant vibrations gathered. This methodology is based on the establishment of the normal operation boundaries for carrying out the identification of deviations related to a defect. The validity of the baseline is studied using q-factor and probability of detection (POD) concepts. Given the nonlinear and nonstationary nature of the faulty vibration signals, envelope analysis is proposed as a demodulation technique to be applied to the signals, prior to the frequency response being extracted. The methodology is validated by field trials in a WINDMASTER300 wind turbine. Baselines for the generator and gearbox were produced as a tool to detect future faults developed within the turbine. Envelope analysis makes the identification of the vibrational frequencies representative of failure very likely.

Suggested Citation

  • Romero, Antonio & Soua, Slim & Gan, Tat-Hean & Wang, Bin, 2018. "Condition monitoring of a wind turbine drive train based on its power dependant vibrations," Renewable Energy, Elsevier, vol. 123(C), pages 817-827.
  • Handle: RePEc:eee:renene:v:123:y:2018:i:c:p:817-827
    DOI: 10.1016/j.renene.2017.07.086
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148117307139
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2017.07.086?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. Tian, Zhigang & Jin, Tongdan & Wu, Bairong & Ding, Fangfang, 2011. "Condition based maintenance optimization for wind power generation systems under continuous monitoring," Renewable Energy, Elsevier, vol. 36(5), pages 1502-1509.
    2. 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.
    3. Ng, Thiam Hee & Tao, Jacqueline Yujia, 2016. "Bond financing for renewable energy in Asia," Energy Policy, Elsevier, vol. 95(C), pages 509-517.
    4. Herbert, G.M. Joselin & Iniyan, S. & Goic, Ranko, 2010. "Performance, reliability and failure analysis of wind farm in a developing Country," Renewable Energy, Elsevier, vol. 35(12), pages 2739-2751.
    5. 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.
    6. 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.
    7. Cambron, P. & Lepvrier, R. & Masson, C. & Tahan, A. & Pelletier, F., 2016. "Power curve monitoring using weighted moving average control charts," Renewable Energy, Elsevier, vol. 94(C), pages 126-135.
    8. Hameed, Z. & Vatn, J. & Heggset, J., 2011. "Challenges in the reliability and maintainability data collection for offshore wind turbines," Renewable Energy, Elsevier, vol. 36(8), pages 2154-2165.
    9. Kusiak, Andrew & Li, Wenyan, 2011. "The prediction and diagnosis of wind turbine faults," Renewable Energy, Elsevier, vol. 36(1), pages 16-23.
    10. Kuang, Yonghong & Zhang, Yongjun & Zhou, Bin & Li, Canbing & Cao, Yijia & Li, Lijuan & Zeng, Long, 2016. "A review of renewable energy utilization in islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 504-513.
    11. 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.
    12. Li, Xin & Chen, Hsing Hung & Tao, Xiangnan, 2016. "Pricing and capacity allocation in renewable energy," Applied Energy, Elsevier, vol. 179(C), pages 1097-1105.
    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. Li, Jianlan & Zhang, Xuran & Zhou, Xing & Lu, Luyi, 2019. "Reliability assessment of wind turbine bearing based on the degradation-Hidden-Markov model," Renewable Energy, Elsevier, vol. 132(C), pages 1076-1087.
    2. Liu, Hongwei & Zhang, Pengpeng & Gu, Yajing & Shu, Yongdong & Song, Jiajun & Lin, Yonggang & Li, Wei, 2022. "Dynamics analysis of the power train of 650 kW horizontal-axis tidal current turbine," Renewable Energy, Elsevier, vol. 194(C), pages 51-67.
    3. Koukoura, Sofia & Scheu, Matti Niclas & Kolios, Athanasios, 2021. "Influence of extended potential-to-functional failure intervals through condition monitoring systems on offshore wind turbine availability," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    4. Zhan, Jun & Wu, Chengkun & Yang, Canqun & Miao, Qiucheng & Wang, Shilin & Ma, Xiandong, 2022. "Condition monitoring of wind turbines based on spatial-temporal feature aggregation networks," Renewable Energy, Elsevier, vol. 200(C), pages 751-766.
    5. Xin, Ge & Hamzaoui, Nacer & Antoni, Jérôme, 2020. "Extraction of second-order cyclostationary sources by matching instantaneous power spectrum with stochastic model – application to wind turbine gearbox," Renewable Energy, Elsevier, vol. 147(P1), pages 1739-1758.
    6. Cheng Yang & Jun Jia & Ke He & Liang Xue & Chao Jiang & Shuangyu Liu & Bochao Zhao & Ming Wu & Haoyang Cui, 2023. "Comprehensive Analysis and Evaluation of the Operation and Maintenance of Offshore Wind Power Systems: A Survey," Energies, MDPI, vol. 16(14), pages 1-39, July.
    7. Li, He & Teixeira, Angelo P. & Guedes Soares, C., 2020. "A two-stage Failure Mode and Effect Analysis of offshore wind turbines," Renewable Energy, Elsevier, vol. 162(C), pages 1438-1461.
    8. 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.
    9. Yang, Wenguang & Liu, Chao & Jiang, Dongxiang, 2018. "An unsupervised spatiotemporal graphical modeling approach for wind turbine condition monitoring," Renewable Energy, Elsevier, vol. 127(C), pages 230-241.
    10. García Márquez, Fausto Pedro & Peco Chacón, Ana María, 2020. "A review of non-destructive testing on wind turbines blades," Renewable Energy, Elsevier, vol. 161(C), pages 998-1010.
    11. Li, Yanting & Wu, Zhenyu, 2020. "A condition monitoring approach of multi-turbine based on VAR model at farm level," Renewable Energy, Elsevier, vol. 166(C), pages 66-80.

    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. 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.
    2. 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.
    3. de Azevedo, Henrique Dias Machado & Araújo, Alex Maurício & Bouchonneau, Nadège, 2016. "A review of wind turbine bearing condition monitoring: State of the art and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 368-379.
    4. 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.
    5. 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.
    6. 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.
    7. Yang, Chunzhen & Liu, Jingquan & Zeng, Yuyun & Xie, Guangyao, 2019. "Real-time condition monitoring and fault detection of components based on machine-learning reconstruction model," Renewable Energy, Elsevier, vol. 133(C), pages 433-441.
    8. 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.
    9. 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.
    10. 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.
    11. Cevasco, D. & Koukoura, S. & Kolios, A.J., 2021. "Reliability, availability, maintainability data review for the identification of trends in offshore wind energy applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
    12. Francisco Bilendo & Angela Meyer & Hamed Badihi & Ningyun Lu & Philippe Cambron & Bin Jiang, 2022. "Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review," Energies, MDPI, vol. 16(1), pages 1-38, December.
    13. Li, He & Teixeira, Angelo P. & Guedes Soares, C., 2020. "A two-stage Failure Mode and Effect Analysis of offshore wind turbines," Renewable Energy, Elsevier, vol. 162(C), pages 1438-1461.
    14. Mallapragada, Dharik S. & Papageorgiou, Dimitri J. & Venkatesh, Aranya & Lara, Cristiana L. & Grossmann, Ignacio E., 2018. "Impact of model resolution on scenario outcomes for electricity sector system expansion," Energy, Elsevier, vol. 163(C), pages 1231-1244.
    15. Shafiee, Mahmood & Sørensen, John Dalsgaard, 2019. "Maintenance optimization and inspection planning of wind energy assets: Models, methods and strategies," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    16. 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.
    17. Fallahi, F. & Bakir, I. & Yildirim, M. & Ye, Z., 2022. "A chance-constrained optimization framework for wind farms to manage fleet-level availability in condition based maintenance and operations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    18. 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.
    19. Pliego Marugán, Alberto & Peco Chacón, Ana María & García Márquez, Fausto Pedro, 2019. "Reliability analysis of detecting false alarms that employ neural networks: A real case study on wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    20. 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.

    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:123:y:2018:i:c:p:817-827. 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.