IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v199y2022icp1568-1579.html
   My bibliography  Save this article

A comprehensive study on Structural Health Monitoring (SHM) of wind turbine blades by instrumenting tower using machine learning methods

Author

Listed:
  • Khazaee, Meghdad
  • Derian, Pierre
  • Mouraud, Anthony

Abstract

In this article, a feasibility study has been carried out in order to detect structural faults in the blade by analyzing the tower vibration. A 5-MW onshore wind turbine was modeled using the NREL FAST code. The structural faults were created on the blade, while only the tower was instrumented by accelerometers and displacement meters. The wind grid was modeled in four Wind Turbulence Intensity (WTI) levels from a highly laminar to a turbulent wind flow by NREL TurbSim code. The tower vibrations were captured in the different WTIs and blade health conditions. Time Series Amplitude (TSA), Discrete Wavelet Transform (DWT), Fast Fourier Transform (FFT) and Statistical Feature Function (SFF) of the tower vibration were calculated and utilized to reveal a faulty blade effect on the tower vibrations. Eventually, a Convolutional Neural Network (CNN) classifier was developed to classify the tower vibrations collected in the blade's healthy and faulty conditions. The results showed that a defective blade has a considerable and detectable effect on the tower vibration. It is observed that blade fault could be precisely tracked and diagnosed mostly in tower Side-to-Side displacements. Also, a reverse relationship between WTI and classification accuracy was concluded.

Suggested Citation

  • Khazaee, Meghdad & Derian, Pierre & Mouraud, Anthony, 2022. "A comprehensive study on Structural Health Monitoring (SHM) of wind turbine blades by instrumenting tower using machine learning methods," Renewable Energy, Elsevier, vol. 199(C), pages 1568-1579.
    • Handle: RePEc:eee:renene:v:199:y:2022:i:c:p:1568-1579
    DOI: 10.1016/j.renene.2022.09.032
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148122013829
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2022.09.032?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. Ashuri, T. & Zaaijer, M.B. & Martins, J.R.R.A. & van Bussel, G.J.W. & van Kuik, G.A.M., 2014. "Multidisciplinary design optimization of offshore wind turbines for minimum levelized cost of energy," Renewable Energy, Elsevier, vol. 68(C), pages 893-905.
    2. Jiang, Yonghua & Tang, Baoping & Qin, Yi & Liu, Wenyi, 2011. "Feature extraction method of wind turbine based on adaptive Morlet wavelet and SVD," Renewable Energy, Elsevier, vol. 36(8), pages 2146-2153.
    3. Yang, Xiyun & Zhang, Yanfeng & Lv, Wei & Wang, Dong, 2021. "Image recognition of wind turbine blade damage based on a deep learning model with transfer learning and an ensemble learning classifier," Renewable Energy, Elsevier, vol. 163(C), pages 386-397.
    4. Md Liton Hossain & Ahmed Abu-Siada & S. M. Muyeen, 2018. "Methods for Advanced Wind Turbine Condition Monitoring and Early Diagnosis: A Literature Review," Energies, MDPI, vol. 11(5), pages 1-14, May.
    5. Ozbek, Muammer & Rixen, Daniel J. & Erne, Oliver & Sanow, Gunter, 2010. "Feasibility of monitoring large wind turbines using photogrammetry," Energy, Elsevier, vol. 35(12), pages 4802-4811.
    6. Chen, Bin & Yu, Songhao & Yu, Yang & Zhou, Yilin, 2020. "Acoustical damage detection of wind turbine blade using the improved incremental support vector data description," Renewable Energy, Elsevier, vol. 156(C), pages 548-557.
    7. Erguido, A. & Crespo Márquez, A. & Castellano, E. & Gómez Fernández, J.F., 2017. "A dynamic opportunistic maintenance model to maximize energy-based availability while reducing the life cycle cost of wind farms," Renewable Energy, Elsevier, vol. 114(PB), pages 843-856.
    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. Bashirzadeh Tabrizi, Amir & Whale, Jonathan & Lyons, Thomas & Urmee, Tania & Peinke, Joachim, 2017. "Modelling the structural loading of a small wind turbine at a highly turbulent site via modifications to the Kaimal turbulence spectra," Renewable Energy, Elsevier, vol. 105(C), pages 288-300.
    10. Wang, Zhenya & Yao, Ligang & Cai, Yongwu & Zhang, Jun, 2020. "Mahalanobis semi-supervised mapping and beetle antennae search based support vector machine for wind turbine rolling bearings fault diagnosis," Renewable Energy, Elsevier, vol. 155(C), pages 1312-1327.
    11. 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.
    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. Mohammad Barooni & Turaj Ashuri & Deniz Velioglu Sogut & Stephen Wood & Shiva Ghaderpour Taleghani, 2022. "Floating Offshore Wind Turbines: Current Status and Future Prospects," Energies, MDPI, vol. 16(1), pages 1-28, December.
    2. Weiwu Feng & Da Yang & Wenxue Du & Qiang Li, 2023. "In Situ Structural Health Monitoring of Full-Scale Wind Turbine Blades in Operation Based on Stereo Digital Image Correlation," Sustainability, MDPI, vol. 15(18), pages 1-17, September.
    3. Artur Bejger & Jan Bohdan Drzewieniecki & Przemysław Bartoszko & Ewelina Frank, 2023. "The Use of Coherence Functions of Acoustic Emission Signals as a Method for Diagnosing Wind Turbine Blades," Energies, MDPI, vol. 16(22), pages 1-17, November.

    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. 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. 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.
    4. Kong, Yun & Qin, Zhaoye & Wang, Tianyang & Han, Qinkai & Chu, Fulei, 2021. "An enhanced sparse representation-based intelligent recognition method for planet bearing fault diagnosis in wind turbines," Renewable Energy, Elsevier, vol. 173(C), pages 987-1004.
    5. Zengyi Zhang & Zhenru Shu, 2024. "Unmanned Aerial Vehicle (UAV)-Assisted Damage Detection of Wind Turbine Blades: A Review," Energies, MDPI, vol. 17(15), pages 1-31, July.
    6. Liu, Y. & Hajj, M. & Bao, Y., 2022. "Review of robot-based damage assessment for offshore wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    7. Reddy, Sohail R., 2021. "A machine learning approach for modeling irregular regions with multiple owners in wind farm layout design," Energy, Elsevier, vol. 220(C).
    8. 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.
    9. Escalera Mendoza, Alejandra S. & Griffith, D. Todd & Jeong, Michael & Qin, Chris & Loth, Eric & Phadnis, Mandar & Pao, Lucy & Selig, Michael S., 2023. "Aero-structural rapid screening of new design concepts for offshore wind turbines," Renewable Energy, Elsevier, vol. 219(P2).
    10. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Tan, Yong & Rao, Lei, 2022. "Anomaly detection and condition monitoring of wind turbine gearbox based on LSTM-FS and transfer learning," Renewable Energy, Elsevier, vol. 189(C), pages 90-103.
    11. 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.
    12. 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.
    13. Bofeng Xu & Yue Yuan & Haoming Liu & Peng Jiang & Ziqi Gao & Xiang Shen & Xin Cai, 2020. "A Pitch Angle Controller Based on Novel Fuzzy-PI Control for Wind Turbine Load Reduction," Energies, MDPI, vol. 13(22), pages 1-16, November.
    14. Battisti, L. & Benini, E. & Brighenti, A. & Dell’Anna, S. & Raciti Castelli, M., 2018. "Small wind turbine effectiveness in the urban environment," Renewable Energy, Elsevier, vol. 129(PA), pages 102-113.
    15. Rakib, M.I. & Evans, S.P. & Clausen, P.D., 2020. "Measured gust events in the urban environment, a comparison with the IEC standard," Renewable Energy, Elsevier, vol. 146(C), pages 1134-1142.
    16. Qian, XiaoYi & Sun, TianHe & Zhang, YuXian & Wang, BaoShi & Awad Gendeel, Mohammed Altayeb, 2023. "Wind turbine fault detection based on spatial-temporal feature and neighbor operation state," Renewable Energy, Elsevier, vol. 219(P1).
    17. 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.
    18. Chen, Z.J. & Stol, K.A. & Mace, B.R., 2017. "Wind turbine blade optimisation with individual pitch and trailing edge flap control," Renewable Energy, Elsevier, vol. 103(C), pages 750-765.
    19. Dan Ling & Chaosong Li & Yan Wang & Pengye Zhang, 2022. "Fault Detection and Identification of Furnace Negative Pressure System with CVA and GA-XGBoost," Energies, MDPI, vol. 15(17), pages 1-19, August.
    20. Michael K. McWilliam & Antariksh C. Dicholkar & Frederik Zahle & Taeseong Kim, 2022. "Post-Optimum Sensitivity Analysis with Automatically Tuned Numerical Gradients Applied to Swept Wind Turbine Blades," Energies, MDPI, vol. 15(9), pages 1-19, April.

    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:199:y:2022:i:c:p:1568-1579. 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.