IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v146y2020icp316-328.html
   My bibliography  Save this article

Maintenance management based on Machine Learning and nonlinear features in wind turbines

Author

Listed:
  • Arcos Jiménez, Alfredo
  • Zhang, Long
  • Gómez Muñoz, Carlos Quiterio
  • García Márquez, Fausto Pedro

Abstract

Delamination is a common problem in wind turbine blades, creating stress concentration areas that can lead to the partial or complete rupture of the blade. This paper presents a novel delamination classification approach for reliability monitoring systems in wind turbine blades. It is based on the feature extraction of a nonlinear autoregressive with exogenous input system (NARX) and linear auto-regressive model (AR). A novelty in this paper is NARX as a Feature Extraction method for wind turbine blade delamination classification. Further, the NARX feature is demonstrated to be significantly better than linear AR feature for blade damage detection, and NARX can describe the inherent nonlinearity of blade delamination correctly. A real case study considers different levels of delamination employing ultrasonic guided waves that are sensitive to delamination. Firstly, the signals obtained are filtered and de-noised by wavelet transforms. Then, the features of the signal are extracted by NARX, and the number of features is selected considering the Neighbourhood Component Analysis as main novelties. Finally, six scenarios with different delamination sizes have been performed by supervised Machine Learning methods: Decision Trees, Discriminant Analysis, Quadratic Support Vector Machines, Nearest Neighbours and Ensemble Classification.

Suggested Citation

  • Arcos Jiménez, Alfredo & Zhang, Long & Gómez Muñoz, Carlos Quiterio & García Márquez, Fausto Pedro, 2020. "Maintenance management based on Machine Learning and nonlinear features in wind turbines," Renewable Energy, Elsevier, vol. 146(C), pages 316-328.
    • Handle: RePEc:eee:renene:v:146:y:2020:i:c:p:316-328
    DOI: 10.1016/j.renene.2019.06.135
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148119309735
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2019.06.135?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. Papaelias, Mayorkinos & Cheng, Liang & Kogia, Maria & Mohimi, Abbas & Kappatos, Vassilios & Selcuk, Cem & Constantinou, Louis & Muñoz, Carlos Quiterio Gómez & Marquez, Fausto Pedro Garcia & Gan, Tat-H, 2016. "Inspection and Structural Health Monitoring techniques for Concentrated Solar Power plants," Renewable Energy, Elsevier, vol. 85(C), pages 1178-1191.
    2. Ahn, Sung K. & Reinsel, Gregory C., 1994. "Estimation of partially nonstationary vector autoregressive models with seasonal behavior," Journal of Econometrics, Elsevier, vol. 62(2), pages 317-350, June.
    3. María Pérez-Ortiz & Silvia Jiménez-Fernández & Pedro A. Gutiérrez & Enrique Alexandre & César Hervás-Martínez & Sancho Salcedo-Sanz, 2016. "A Review of Classification Problems and Algorithms in Renewable Energy Applications," Energies, MDPI, vol. 9(8), pages 1-27, August.
    4. Pratumnopharat, Panu & Leung, Pak Sing & Court, Richard S., 2014. "Wavelet transform-based stress-time history editing of horizontal axis wind turbine blades," Renewable Energy, Elsevier, vol. 63(C), pages 558-575.
    5. Fausto Pedro García Márquez & Diego J. Pedregal & Clive Roberts, 2015. "New methods for the condition monitoring of level crossings," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(5), pages 878-884, April.
    6. Men, Zhongxian & Yee, Eugene & Lien, Fue-Sang & Wen, Deyong & Chen, Yongsheng, 2016. "Short-term wind speed and power forecasting using an ensemble of mixture density neural networks," Renewable Energy, Elsevier, vol. 87(P1), pages 203-211.
    7. Márquez, Fausto Pedro García & Pérez, Jesús María Pinar & Marugán, Alberto Pliego & Papaelias, Mayorkinos, 2016. "Identification of critical components of wind turbines using FTA over the time," Renewable Energy, Elsevier, vol. 87(P2), pages 869-883.
    8. 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.
    9. Alberto Pliego Marugán & Fausto Pedro García Márquez & Jesús María Pinar Pérez, 2016. "Optimal Maintenance Management of Offshore Wind Farms," Energies, MDPI, vol. 9(1), pages 1-20, January.
    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.
    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. Han Peng & Songyin Li & Linjian Shangguan & Yisa Fan & Hai Zhang, 2023. "Analysis of Wind Turbine Equipment Failure and Intelligent Operation and Maintenance Research," Sustainability, MDPI, vol. 15(10), pages 1-35, May.
    2. Ren, Zhengru & Verma, Amrit Shankar & Li, Ye & Teuwen, Julie J.E. & Jiang, Zhiyu, 2021. "Offshore wind turbine operations and maintenance: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    3. Hu, Huanling & Wang, Lin & Lv, Sheng-Xiang, 2020. "Forecasting energy consumption and wind power generation using deep echo state network," Renewable Energy, Elsevier, vol. 154(C), pages 598-613.
    4. Hu, Huanling & Wang, Lin & Tao, Rui, 2021. "Wind speed forecasting based on variational mode decomposition and improved echo state network," Renewable Energy, Elsevier, vol. 164(C), pages 729-751.
    5. 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.
    6. Bai-Qiao Chen & Kun Liu & Tongqiang Yu & Ruoxuan Li, 2024. "Enhancing Reliability in Floating Offshore Wind Turbines through Digital Twin Technology: A Comprehensive Review," Energies, MDPI, vol. 17(8), pages 1-23, April.
    7. Hui Li & Xiaolong Lu & Wen Xin & Zhihui Guo & Bo Zhou & Baokuan Ning & Hongbing Bao, 2023. "Repair Parameter Design of Outer Reinforcement Layers of Offshore Wind Turbine Blade Spar Cap Based on Structural and Aerodynamic Analysis," Energies, MDPI, vol. 16(2), pages 1-24, January.
    8. Kaewniam, Panida & Cao, Maosen & Alkayem, Nizar Faisal & Li, Dayang & Manoach, Emil, 2022. "Recent advances in damage detection of wind turbine blades: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    9. Ana María Peco Chacón & Isaac Segovia Ramírez & Fausto Pedro García Márquez, 2020. "False Alarms Analysis of Wind Turbine Bearing System," Sustainability, MDPI, vol. 12(19), pages 1-11, September.
    10. Xiaowen Song & Zhitai Xing & Yan Jia & Xiaojuan Song & Chang Cai & Yinan Zhang & Zekun Wang & Jicai Guo & Qingan Li, 2022. "Review on the Damage and Fault Diagnosis of Wind Turbine Blades in the Germination Stage," Energies, MDPI, vol. 15(20), pages 1-17, October.

    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. 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).
    2. Arcos Jiménez, Alfredo & Gómez Muñoz, Carlos Quiterio & García Márquez, Fausto Pedro, 2019. "Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 2-12.
    3. Huerta Herraiz, Álvaro & Pliego Marugán, Alberto & García Márquez, Fausto Pedro, 2020. "Photovoltaic plant condition monitoring using thermal images analysis by convolutional neural network-based structure," Renewable Energy, Elsevier, vol. 153(C), pages 334-348.
    4. 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.
    5. Fausto Pedro García Márquez & Alberto Pliego Marugán & Jesús María Pinar Pérez & Stuart Hillmansen & Mayorkinos Papaelias, 2017. "Optimal Dynamic Analysis of Electrical/Electronic Components in Wind Turbines," Energies, MDPI, vol. 10(8), pages 1-19, July.
    6. Marugán, Alberto Pliego & Márquez, Fausto Pedro García & Perez, Jesus María Pinar & Ruiz-Hernández, Diego, 2018. "A survey of artificial neural network in wind energy systems," Applied Energy, Elsevier, vol. 228(C), pages 1822-1836.
    7. 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.
    8. Carlos Quiterio Gómez Muñoz & Fausto Pedro García Márquez, 2016. "A New Fault Location Approach for Acoustic Emission Techniques in Wind Turbines," Energies, MDPI, vol. 9(1), pages 1-14, January.
    9. Pliego Marugán, Alberto & García Márquez, Fausto Pedro & Pinar Pérez, Jesús María, 2022. "A techno-economic model for avoiding conflicts of interest between owners of offshore wind farms and maintenance suppliers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    10. Hu, Jiaxiang & Hu, Weihao & Cao, Di & Sun, Xinwu & Chen, Jianjun & Huang, Yuehui & Chen, Zhe & Blaabjerg, Frede, 2024. "Probabilistic net load forecasting based on transformer network and Gaussian process-enabled residual modeling learning method," Renewable Energy, Elsevier, vol. 225(C).
    11. Laihao Ma & Xiaoxue Ma & Jingwen Zhang & Qing Yang & Kai Wei, 2021. "Identifying the Weaker Function Links in the Hazardous Chemicals Road Transportation System in China," IJERPH, MDPI, vol. 18(13), pages 1-17, July.
    12. 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.
    13. Alvaro Furlani Bastos & Surya Santoso, 2021. "Optimization Techniques for Mining Power Quality Data and Processing Unbalanced Datasets in Machine Learning Applications," Energies, MDPI, vol. 14(2), pages 1-21, January.
    14. Melard, G. & Pasteels, J. -M., 2000. "Automatic ARIMA modeling including interventions, using time series expert software," International Journal of Forecasting, Elsevier, vol. 16(4), pages 497-508.
    15. Dorfleitner, Gregor & Wimmer, Maximilian, 2010. "The pricing of temperature futures at the Chicago Mercantile Exchange," Journal of Banking & Finance, Elsevier, vol. 34(6), pages 1360-1370, June.
    16. Wolfram Rozas & Rafael Pastor-Vargas & Angel Miguel García-Vico & José Carpio, 2023. "Consumption–Production Profile Categorization in Energy Communities," Energies, MDPI, vol. 16(19), pages 1-27, October.
    17. Marco Centoni & Gianluca Cubadda, 2011. "Modelling comovements of economic time series: a selective survey," Statistica, Department of Statistics, University of Bologna, vol. 71(2), pages 267-294.
    18. Hongyu Li & Ping Ju & Chun Gan & Feng Wu & Yichen Zhou & Zhe Dong, 2018. "Stochastic Stability Analysis of the Power System with Losses," Energies, MDPI, vol. 11(3), pages 1-11, March.
    19. 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.
    20. Kewei Cai & Belema Prince Alalibo & Wenping Cao & Zheng Liu & Zhiqiang Wang & Guofeng Li, 2018. "Hybrid Approach for Detecting and Classifying Power Quality Disturbances Based on the Variational Mode Decomposition and Deep Stochastic Configuration Network," Energies, MDPI, vol. 11(11), pages 1-18, November.

    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:146:y:2020:i:c:p:316-328. 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.