IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v179y2021icp1969-1989.html
   My bibliography  Save this article

Real-time accurate detection of wind turbine downtime - An Irish perspective

Author

Listed:
  • Mucchielli, P.
  • Bhowmik, B.
  • Ghosh, B.
  • Pakrashi, V.

Abstract

Wind turbines are complex systems that are susceptible to frequent anomalies, faults, and abnormal behaviour. These are caused mainly due to off-nominal conditions, catastrophic events, and major failures, resulting in downtime conditions. An accurate and timely detection of downtime events provides crucial information for planning and decision-making. This study investigates the utility of wind power and wind speed as potential parameters for real-time downtime detection. Early and accurate detection of these anomalies using system outputs collected from monitoring stations is challenging and involved, especially when attempted in real-time. In this article, a real-time downtime detection framework is proposed that maps system outputs to turbine events - faults, scheduled, and unplanned maintenance - through online condition indicators. Without imposing strong distributional assumptions, using available training samples, an optimal, cost-sensitive real-time anomaly detection framework is proposed to determine whether a sample is anomalous. Considering the trade-off between misclassification errors and detection rates, detection studies are performed using wind power and speed - calibrated against available alarm classifiers - obtained from two Irish wind farms. The data cleaning and formatting for analysis was automated and subjected to classification with several levels of complexity. Recursive condition indicators (RCIs) such as Recursive Mahalanobis distance (RMD) and Recursive Residual Error (RRE) are chosen as features for classification. The real-time detection model becomes particularly useful when it is prohibitive to identify in advance the anomalies without a baseline of the system behaviour under such conditions. Case studies involving Irish wind Supervisory Control And Data Acquisition (SCADA) data demonstrate the successful application of the proposed work for early and accurate downtime detection with comparison to a reference machine learning approach.

Suggested Citation

  • Mucchielli, P. & Bhowmik, B. & Ghosh, B. & Pakrashi, V., 2021. "Real-time accurate detection of wind turbine downtime - An Irish perspective," Renewable Energy, Elsevier, vol. 179(C), pages 1969-1989.
    • Handle: RePEc:eee:renene:v:179:y:2021:i:c:p:1969-1989
    DOI: 10.1016/j.renene.2021.07.139
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148121011496
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2021.07.139?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. Wang, Jinjiang & Liang, Yuanyuan & Zheng, Yinghao & Gao, Robert X. & Zhang, Fengli, 2020. "An integrated fault diagnosis and prognosis approach for predictive maintenance of wind turbine bearing with limited samples," Renewable Energy, Elsevier, vol. 145(C), pages 642-650.
    2. 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.
    3. Kusiak, Andrew & Li, Wenyan, 2011. "The prediction and diagnosis of wind turbine faults," Renewable Energy, Elsevier, vol. 36(1), pages 16-23.
    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. Yao, Qingtao & Zhu, Haowei & Xiang, Ling & Su, Hao & Hu, Aijun, 2023. "A novel composed method of cleaning anomy data for improving state prediction of wind turbine," Renewable Energy, Elsevier, vol. 204(C), pages 131-140.
    2. 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.
    3. Long, Huan & Xu, Shaohui & Gu, Wei, 2022. "An abnormal wind turbine data cleaning algorithm based on color space conversion and image feature detection," Applied Energy, Elsevier, vol. 311(C).

    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. Xu, Xuefang & Li, Bo & Qiao, Zijian & Shi, Peiming & Shao, Huaishuang & Li, Ruixiong, 2023. "Caputo-Fabrizio fractional order derivative stochastic resonance enhanced by ADOF and its application in fault diagnosis of wind turbine drivetrain," Renewable Energy, Elsevier, vol. 219(P1).
    2. Mehrjoo, Mehrdad & Jafari Jozani, Mohammad & Pawlak, Miroslaw, 2021. "Toward hybrid approaches for wind turbine power curve modeling with balanced loss functions and local weighting schemes," Energy, Elsevier, vol. 218(C).
    3. 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.
    4. Merainani, Boualem & Laddada, Sofiane & Bechhoefer, Eric & Chikh, Mohamed Abdessamed Ait & Benazzouz, Djamel, 2022. "An integrated methodology for estimating the remaining useful life of high-speed wind turbine shaft bearings with limited samples," Renewable Energy, Elsevier, vol. 182(C), pages 1141-1151.
    5. 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.
    6. Colak, Ilhami & Sagiroglu, Seref & Yesilbudak, Mehmet, 2012. "Data mining and wind power prediction: A literature review," Renewable Energy, Elsevier, vol. 46(C), pages 241-247.
    7. Odofin, Sarah & Bentley, Edward & Aikhuele, Daniel, 2018. "Robust fault estimation for wind turbine energy via hybrid systems," Renewable Energy, Elsevier, vol. 120(C), pages 289-299.
    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. Rasmus Dovnborg Frederiksen & Grzegorz Bocewicz & Grzegorz Radzki & Zbigniew Banaszak & Peter Nielsen, 2024. "Cost-Effectiveness of Predictive Maintenance for Offshore Wind Farms: A Case Study," Energies, MDPI, vol. 17(13), pages 1-24, June.
    10. Ahmed, Umair & Carpitella, Silvia & Certa, Antonella, 2021. "An integrated methodological approach for optimising complex systems subjected to predictive maintenance," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    11. Lydia, M. & Kumar, S. Suresh & Selvakumar, A. Immanuel & Prem Kumar, G. Edwin, 2014. "A comprehensive review on wind turbine power curve modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 452-460.
    12. Phong B. Dao, 2021. "A CUSUM-Based Approach for Condition Monitoring and Fault Diagnosis of Wind Turbines," Energies, MDPI, vol. 14(11), pages 1-19, June.
    13. Jianjun Chen & Weihao Hu & Di Cao & Bin Zhang & Qi Huang & Zhe Chen & Frede Blaabjerg, 2019. "An Imbalance Fault Detection Algorithm for Variable-Speed Wind Turbines: A Deep Learning Approach," Energies, MDPI, vol. 12(14), pages 1-15, July.
    14. Teng, Wei & Ding, Xian & Zhang, Xiaolong & Liu, Yibing & Ma, Zhiyong, 2016. "Multi-fault detection and failure analysis of wind turbine gearbox using complex wavelet transform," Renewable Energy, Elsevier, vol. 93(C), pages 591-598.
    15. Xin Wu & Hong Wang & Guoqian Jiang & Ping Xie & Xiaoli Li, 2019. "Monitoring Wind Turbine Gearbox with Echo State Network Modeling and Dynamic Threshold Using SCADA Vibration Data," Energies, MDPI, vol. 12(6), pages 1-19, March.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.

    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:179:y:2021:i:c:p:1969-1989. 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.