IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v125y2018icp172-181.html
   My bibliography  Save this article

Wind turbine health state monitoring based on a Bayesian data-driven approach

Author

Listed:
  • Song, Zhe
  • Zhang, Zijun
  • Jiang, Yu
  • Zhu, Jin

Abstract

The efficient wind turbine monitoring and the identification of abnormal turbine states are crucial to advance the wind farm operations and management. This paper presents a pioneer study of identifying wind turbine health states based on their SCADA data. A Bayesian framework is introduced to explore the feasibility and potential of identifying abnormal turbine states based on SCADA data only. Three methods, the bin method, the multivariate normal distribution based method, and the Copula method, are applied and compared in the Bayesian framework development based on SCADA data of two commercial wind turbines. A comprehensive study is conducted to analyze the pros and cons of three methods. Computational results demonstrate the effectiveness of the proposed methods and the Copula method outperforms other two after a careful model calibration. Extending the Bayesian Copula model to produce the one-step ahead prediction of turbine health states is also explored. In addition, the advantage of the proposed framework is further validated by comparing with the classical power curve based monitoring methods. Generated results show the feasibility of identifying turbine health states with SCADA data and the great potential of further enhancing the health monitoring function.

Suggested Citation

  • Song, Zhe & Zhang, Zijun & Jiang, Yu & Zhu, Jin, 2018. "Wind turbine health state monitoring based on a Bayesian data-driven approach," Renewable Energy, Elsevier, vol. 125(C), pages 172-181.
    • Handle: RePEc:eee:renene:v:125:y:2018:i:c:p:172-181
    DOI: 10.1016/j.renene.2018.02.096
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148118302404
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2018.02.096?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. Amirat, Y. & Benbouzid, M.E.H. & Al-Ahmar, E. & Bensaker, B. & Turri, S., 2009. "A brief status on condition monitoring and fault diagnosis in wind energy conversion systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(9), pages 2629-2636, December.
    2. Liu, W.Y. & Zhang, W.H. & Han, J.G. & Wang, G.F., 2012. "A new wind turbine fault diagnosis method based on the local mean decomposition," Renewable Energy, Elsevier, vol. 48(C), pages 411-415.
    3. Kusiak, Andrew & Zheng, Haiyang & Song, Zhe, 2009. "Models for monitoring wind farm power," Renewable Energy, Elsevier, vol. 34(3), pages 583-590.
    4. 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.
    5. Kusiak, Andrew & Li, Wenyan, 2010. "Short-term prediction of wind power with a clustering approach," Renewable Energy, Elsevier, vol. 35(10), pages 2362-2369.
    6. Bouye, Eric & Durlleman, Valdo & Nikeghbali, Ashkan & Riboulet, Gaël & Roncalli, Thierry, 2000. "Copulas for finance," MPRA Paper 37359, University Library of Munich, Germany.
    7. 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.
    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. Dong, Xinghui & Gao, Di & Li, Jia & Jincao, Zhang & Zheng, Kai, 2020. "Blades icing identification model of wind turbines based on SCADA data," Renewable Energy, Elsevier, vol. 162(C), pages 575-586.
    2. Raymond Byrne & Davide Astolfi & Francesco Castellani & Neil J. Hewitt, 2020. "A Study of Wind Turbine Performance Decline with Age through Operation Data Analysis," Energies, MDPI, vol. 13(8), pages 1-18, April.
    3. 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.
    4. Jastrzebska, Agnieszka & Morales Hernández, Alejandro & Nápoles, Gonzalo & Salgueiro, Yamisleydi & Vanhoof, Koen, 2022. "Measuring wind turbine health using fuzzy-concept-based drifting models," Renewable Energy, Elsevier, vol. 190(C), pages 730-740.
    5. 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.
    6. Sequeira, C. & Pacheco, A. & Galego, P. & Gorbeña, E., 2019. "Analysis of the efficiency of wind turbine gearboxes using the temperature variable," Renewable Energy, Elsevier, vol. 135(C), pages 465-472.
    7. Urmeneta, Jon & Izquierdo, Juan & Leturiondo, Urko, 2023. "A methodology for performance assessment at system level—Identification of operating regimes and anomaly detection in wind turbines," Renewable Energy, Elsevier, vol. 205(C), pages 281-292.
    8. Sun, Lei & Liu, Tianyuan & Xie, Yonghui & Zhang, Di & Xia, Xinlei, 2021. "Real-time power prediction approach for turbine using deep learning techniques," Energy, Elsevier, vol. 233(C).
    9. Adedipe, Tosin & Shafiee, Mahmood & Zio, Enrico, 2020. "Bayesian Network Modelling for the Wind Energy Industry: An Overview," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    10. Kang Bai & Yong Zhou & Zhibo Cui & Weiwei Bao & Nan Zhang & Yongjie Zhai, 2022. "HOG-SVM-Based Image Feature Classification Method for Sound Recognition of Power Equipments," Energies, MDPI, vol. 15(12), pages 1-12, June.
    11. Jiménez, Alfredo Arcos & García Márquez, Fausto Pedro & Moraleda, Victoria Borja & Gómez Muñoz, Carlos Quiterio, 2019. "Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis," Renewable Energy, Elsevier, vol. 132(C), pages 1034-1048.
    12. Xu, Qifa & Fan, Zhenhua & Jia, Weiyin & Jiang, Cuixia, 2020. "Fault detection of wind turbines via multivariate process monitoring based on vine copulas," Renewable Energy, Elsevier, vol. 161(C), pages 939-955.
    13. Zhou, Jian & Zhang, Wei, 2023. "Coal consumption prediction in thermal power units: A feature construction and selection method," Energy, Elsevier, vol. 273(C).
    14. Ren, He & Liu, Wenyi & Shan, Mengchen & Wang, Xin & Wang, Zhengfeng, 2021. "A novel wind turbine health condition monitoring method based on composite variational mode entropy and weighted distribution adaptation," Renewable Energy, Elsevier, vol. 168(C), pages 972-980.
    15. Chen, Hansi & Liu, Hang & Chu, Xuening & Liu, Qingxiu & Xue, Deyi, 2021. "Anomaly detection and critical SCADA parameters identification for wind turbines based on LSTM-AE neural network," Renewable Energy, Elsevier, vol. 172(C), pages 829-840.

    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. 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.
    2. 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.
    3. Fan Zhang & Juchuan Dai & Deshun Liu & Linxing Li & Xin Long, 2019. "Investigation of the Pitch Load of Large-Scale Wind Turbines Using Field SCADA Data," Energies, MDPI, vol. 12(3), pages 1-20, February.
    4. 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.
    5. Ukashatu Abubakar & Saad Mekhilef & Hazlie Mokhlis & Mehdi Seyedmahmoudian & Ben Horan & Alex Stojcevski & Hussain Bassi & Muhyaddin Jamal Hosin Rawa, 2018. "Transient Faults in Wind Energy Conversion Systems: Analysis, Modelling Methodologies and Remedies," Energies, MDPI, vol. 11(9), pages 1-33, August.
    6. Hsu-Hao Yang & Mei-Ling Huang & Shih-Wei Yang, 2015. "Integrating Auto-Associative Neural Networks with Hotelling T 2 Control Charts for Wind Turbine Fault Detection," Energies, MDPI, vol. 8(10), pages 1-16, October.
    7. Optis, Mike & Perr-Sauer, Jordan, 2019. "The importance of atmospheric turbulence and stability in machine-learning models of wind farm power production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 27-41.
    8. 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.
    9. 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.
    10. 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.
    11. He, Guolin & Ding, Kang & Li, Weihua & Jiao, Xintao, 2016. "A novel order tracking method for wind turbine planetary gearbox vibration analysis based on discrete spectrum correction technique," Renewable Energy, Elsevier, vol. 87(P1), pages 364-375.
    12. Hu, Aijun & Yan, Xiaoan & Xiang, Ling, 2015. "A new wind turbine fault diagnosis method based on ensemble intrinsic time-scale decomposition and WPT-fractal dimension," Renewable Energy, Elsevier, vol. 83(C), pages 767-778.
    13. Miao He & David He & Jae Yoon & Thomas J Nostrand & Junda Zhu & Eric Bechhoefer, 2019. "Wind turbine planetary gearbox feature extraction and fault diagnosis using a deep-learning-based approach," Journal of Risk and Reliability, , vol. 233(3), pages 303-316, June.
    14. 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.
    15. 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.
    16. Liu, W.Y. & Zhang, W.H. & Han, J.G. & Wang, G.F., 2012. "A new wind turbine fault diagnosis method based on the local mean decomposition," Renewable Energy, Elsevier, vol. 48(C), pages 411-415.
    17. 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.
    18. Izquierdo, J. & Márquez, A. Crespo & Uribetxebarria, J. & Erguido, A., 2020. "On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects," Renewable Energy, Elsevier, vol. 153(C), pages 1100-1110.
    19. Rana Muhammad Adnan & Zhongmin Liang & Xiaohui Yuan & Ozgur Kisi & Muhammad Akhlaq & Binquan Li, 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation," Energies, MDPI, vol. 12(2), pages 1-22, January.
    20. Fathi, Abid & Nader, Naifar, 2007. "Price Calibration of basket default swap: Evidence from Japanese market," MPRA Paper 6013, University Library of Munich, Germany.

    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:125:y:2018:i:c:p:172-181. 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.