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Damage detection in operational wind turbine blades using a new approach based on machine learning

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  • Chandrasekhar, Kartik
  • Stevanovic, Nevena
  • Cross, Elizabeth J.
  • Dervilis, Nikolaos
  • Worden, Keith

Abstract

The application of reliable structural health monitoring (SHM) technologies to operational wind turbine blades is a challenging task, due to the uncertain nature of the environments they operate in. In this paper, a novel SHM methodology, which uses Gaussian Processes (GPs) is proposed. The methodology takes advantage of the fact that the blades on a turbine are nominally identical in structural properties and encounter the same environmental and operational variables (EOVs). The properties of interest are the first edgewise frequencies of the blades. The GPs are used to predict the edge frequencies of one blade given that of another, after these relationships between the pairs of blades have been learned when the blades are in a healthy state. In using this approach, the proposed SHM methodology is able to identify when the blades start behaving differently from one another over time. To validate the concept, the proposed SHM system is applied to real onshore wind turbine blade data, where some form of damage was known to have taken place. X-bar control chart analysis of the residual errors between the GP predictions and actual frequencies show that the system successfully identified early onset of damage as early as six months before it was identified and remedied.

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  • Chandrasekhar, Kartik & Stevanovic, Nevena & Cross, Elizabeth J. & Dervilis, Nikolaos & Worden, Keith, 2021. "Damage detection in operational wind turbine blades using a new approach based on machine learning," Renewable Energy, Elsevier, vol. 168(C), pages 1249-1264.
    • Handle: RePEc:eee:renene:v:168:y:2021:i:c:p:1249-1264
    DOI: 10.1016/j.renene.2020.12.119
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    References listed on IDEAS

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    1. Yang, Bin & Sun, Dongbai, 2013. "Testing, inspecting and monitoring technologies for wind turbine blades: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 515-526.
    2. Tang, Jialin & Soua, Slim & Mares, Cristinel & Gan, Tat-Hean, 2016. "An experimental study of acoustic emission methodology for in service condition monitoring of wind turbine blades," Renewable Energy, Elsevier, vol. 99(C), pages 170-179.
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    Cited by:

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    2. 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).
    3. Cui, Bodi & Weng, Yang & Zhang, Ning, 2022. "A feature extraction and machine learning framework for bearing fault diagnosis," Renewable Energy, Elsevier, vol. 191(C), pages 987-997.
    4. 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.
    5. Dimitris Al. Katsaprakakis & Nikos Papadakis & Ioannis Ntintakis, 2021. "A Comprehensive Analysis of Wind Turbine Blade Damage," Energies, MDPI, vol. 14(18), pages 1-31, September.
    6. 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.
    7. Luo, Kai & Chen, Liang & Liang, Wei, 2022. "Structural health monitoring of carbon fiber reinforced polymer composite laminates for offshore wind turbine blades based on dual maximum correlation coefficient method," Renewable Energy, Elsevier, vol. 201(P1), pages 1163-1175.

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