IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v163y2021icp2053-2067.html

Diagnosis of wind turbine faults with transfer learning algorithms

Author

Listed:
  • Chen, Wanqiu
  • Qiu, Yingning
  • Feng, Yanhui
  • Li, Ye
  • Kusiak, Andrew

Abstract

A framework of using transfer learning algorithms, Inception V3 and TrAdaBoost, for fault diagnosis of two wind turbine faults is presented and verified. Two failure modes, blade icing accretion and gear cog belt fracture, are analyzed using SCADA data. A new index named ‘Comprehensive Index’ is defined to evaluate performance of different algorithms. Traditional machine learning algorithms do not perform well for data sets that are unbalanced and follow different distributions. The former causes bias in classification and the latter leads to poor adaptability of algorithms. A novel transfer learning algorithm studied in this paper, TrAdaBoost, has been proved to have superior performance on dealing with data imbalance and different distributions. A new approach to calibrate data labels using transfer learning algorithms is also proposed, which provides important insights into unsupervised learning for wind turbine fault diagnosis.

Suggested Citation

  • Chen, Wanqiu & Qiu, Yingning & Feng, Yanhui & Li, Ye & Kusiak, Andrew, 2021. "Diagnosis of wind turbine faults with transfer learning algorithms," Renewable Energy, Elsevier, vol. 163(C), pages 2053-2067.
    • Handle: RePEc:eee:renene:v:163:y:2021:i:c:p:2053-2067
    DOI: 10.1016/j.renene.2020.10.121
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148120316931
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2020.10.121?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Helbing, Georg & Ritter, Matthias, 2018. "Deep Learning for fault detection in wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 189-198.
    2. Qiu, Yingning & Feng, Yanhui & Infield, David, 2020. "Fault diagnosis of wind turbine with SCADA alarms based multidimensional information processing method," Renewable Energy, Elsevier, vol. 145(C), pages 1923-1931.
    3. 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.
    4. Lijun Zhang & Kai Liu & Yufeng Wang & Zachary Bosire Omariba, 2018. "Ice Detection Model of Wind Turbine Blades Based on Random Forest Classifier," Energies, MDPI, vol. 11(10), pages 1-15, September.
    5. Fakorede, Oloufemi & Feger, Zoé & Ibrahim, Hussein & Ilinca, Adrian & Perron, Jean & Masson, Christian, 2016. "Ice protection systems for wind turbines in cold climate: characteristics, comparisons and analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 662-675.
    6. Villalpando, Fernando & Reggio, Marcelo & Ilinca, Adrian, 2016. "Prediction of ice accretion and anti-icing heating power on wind turbine blades using standard commercial software," Energy, Elsevier, vol. 114(C), pages 1041-1052.
    7. 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.
    8. 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.
    9. Madi, Ezieddin & Pope, Kevin & Huang, Weimin & Iqbal, Tariq, 2019. "A review of integrating ice detection and mitigation for wind turbine blades," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 269-281.
    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. Jorge Maldonado-Correa & Sergio Martín-Martínez & Estefanía Artigao & Emilio Gómez-Lázaro, 2020. "Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review," Energies, MDPI, vol. 13(12), pages 1-21, June.
    2. Camila Correa-Jullian & Sergio Cofre-Martel & Gabriel San Martin & Enrique Lopez Droguett & Gustavo de Novaes Pires Leite & Alexandre Costa, 2022. "Exploring Quantum Machine Learning and Feature Reduction Techniques for Wind Turbine Pitch Fault Detection," Energies, MDPI, vol. 15(8), pages 1-29, April.
    3. Cristian Velandia-Cardenas & Yolanda Vidal & Francesc Pozo, 2021. "Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA Data," Energies, MDPI, vol. 14(6), pages 1-26, March.
    4. Stoyanov, D.B. & Nixon, J.D. & Sarlak, H., 2021. "Analysis of derating and anti-icing strategies for wind turbines in cold climates," Applied Energy, Elsevier, vol. 288(C).
    5. 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.
    6. Panagiotis Korkos & Jaakko Kleemola & Matti Linjama & Arto Lehtovaara, 2022. "Representation Learning for Detecting the Faults in a Wind Turbine Hydraulic Pitch System Using Deep Learning," Energies, MDPI, vol. 15(24), pages 1-17, December.
    7. 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.
    8. Wang, Shun & Vidal, Yolanda & Pozo, Francesc, 2026. "Recent advances in wind turbine condition monitoring using SCADA data: A state-of-the-art review," Reliability Engineering and System Safety, Elsevier, vol. 267(PA).
    9. Annalisa Santolamazza & Daniele Dadi & Vito Introna, 2021. "A Data-Mining Approach for Wind Turbine Fault Detection Based on SCADA Data Analysis Using Artificial Neural Networks," Energies, MDPI, vol. 14(7), pages 1-25, March.
    10. Hamid, Mohammad & Song, Mengjie & Yu-Hang Chao, Christopher & Qaisrani, Mumtaz A. & Shi, Han & Shao, Keke & Zhen, Zekang & Gao, Runmiao & Zhang, Xuan & Zhang, Long & Hosseini, Seyyed Hossein & Ahmed, , 2026. "Can nature-inspired surface and interface designs offer practical solutions for anti-icing?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 228(C).
    11. Wang, Anqi & Pei, Yan & Qian, Zheng & Zareipour, Hamidreza & Jing, Bo & An, Jiayi, 2022. "A two-stage anomaly decomposition scheme based on multi-variable correlation extraction for wind turbine fault detection and identification," Applied Energy, Elsevier, vol. 321(C).
    12. Ye, Feng & Ezzat, Ahmed Aziz, 2024. "Icing detection and prediction for wind turbines using multivariate sensor data and machine learning," Renewable Energy, Elsevier, vol. 231(C).
    13. Alessandro Murgia & Robbert Verbeke & Elena Tsiporkova & Ludovico Terzi & Davide Astolfi, 2023. "Discussion on the Suitability of SCADA-Based Condition Monitoring for Wind Turbine Fault Diagnosis through Temperature Data Analysis," Energies, MDPI, vol. 16(2), pages 1-20, January.
    14. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2022. "In-situ condition monitoring of wind turbine blades: A critical and systematic review of techniques, challenges, and futures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    15. Lei Fu & Tiantian Zhu & Kai Zhu & Yiling Yang, 2019. "Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy," Energies, MDPI, vol. 12(16), pages 1-20, August.
    16. Huifan Zeng & Juchuan Dai & Chengming Zuo & Huanguo Chen & Mimi Li & Fan Zhang, 2022. "Correlation Investigation of Wind Turbine Multiple Operating Parameters Based on SCADA Data," Energies, MDPI, vol. 15(14), pages 1-24, July.
    17. Adaiton Oliveira-Filho & Ryad Zemouri & Philippe Cambron & Antoine Tahan, 2023. "Early Detection and Diagnosis of Wind Turbine Abnormal Conditions Using an Interpretable Supervised Variational Autoencoder Model," Energies, MDPI, vol. 16(12), pages 1-21, June.
    18. Alan Turnbull & Conor McKinnon & James Carrol & Alasdair McDonald, 2022. "On the Development of Offshore Wind Turbine Technology: An Assessment of Reliability Rates and Fault Detection Methods in a Changing Market," Energies, MDPI, vol. 15(9), pages 1-20, April.
    19. Ivan Kabardin & Sergey Dvoynishnikov & Maxim Gordienko & Sergey Kakaulin & Vadim Ledovsky & Grigoriy Gusev & Vladislav Zuev & Valery Okulov, 2021. "Optical Methods for Measuring Icing of Wind Turbine Blades," Energies, MDPI, vol. 14(20), pages 1-14, October.
    20. 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.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:163:y:2021:i:c:p:2053-2067. 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.