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Automated Quantification of Wind Turbine Blade Leading Edge Erosion from Field Images

Author

Listed:
  • Jeanie A. Aird

    (Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA)

  • Rebecca J. Barthelmie

    (Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA)

  • Sara C. Pryor

    (Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY 14853, USA)

Abstract

Wind turbine blade leading edge erosion is a major source of power production loss and early detection benefits optimization of repair strategies. Two machine learning (ML) models are developed and evaluated for automated quantification of the areal extent, morphology and nature (deep, shallow) of damage from field images. The supervised ML model employs convolutional neural networks (CNN) and learns features (specific types of damage) present in an annotated set of training images. The unsupervised approach aggregates pixel intensity thresholding with calculation of pixel-by-pixel shadow ratio (PTS) to independently identify features within images. The models are developed and tested using a dataset of 140 field images. The images sample across a range of blade orientation, aspect ratio, lighting and resolution. Each model (CNN v PTS) is applied to quantify the percent area of the visible blade that is damaged and classifies the damage into deep or shallow using only the images as input. Both models successfully identify approximately 65% of total damage area in the independent images, and both perform better at quantifying deep damage. The CNN is more successful at identifying shallow damage and exhibits better performance when applied to the images after they are preprocessed to a common blade orientation.

Suggested Citation

  • Jeanie A. Aird & Rebecca J. Barthelmie & Sara C. Pryor, 2023. "Automated Quantification of Wind Turbine Blade Leading Edge Erosion from Field Images," Energies, MDPI, vol. 16(6), pages 1-23, March.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2820-:d:1100780
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    References listed on IDEAS

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    1. Francesco Papi & Lorenzo Cappugi & Simone Salvadori & Mauro Carnevale & Alessandro Bianchini, 2020. "Uncertainty Quantification of the Effects of Blade Damage on the Actual Energy Production of Modern Wind Turbines," Energies, MDPI, vol. 13(15), pages 1-18, July.
    2. 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.
    3. Mishnaevsky, Leon & Hasager, Charlotte Bay & Bak, Christian & Tilg, Anna-Maria & Bech, Jakob I. & Doagou Rad, Saeed & Fæster, Søren, 2021. "Leading edge erosion of wind turbine blades: Understanding, prevention and protection," Renewable Energy, Elsevier, vol. 169(C), pages 953-969.
    4. Rebecca J. Barthelmie & Tristan J. Shepherd & Jeanie A. Aird & Sara C. Pryor, 2020. "Power and Wind Shear Implications of Large Wind Turbine Scenarios in the US Central Plains," Energies, MDPI, vol. 13(16), pages 1-21, August.
    5. Slot, H.M. & Gelinck, E.R.M. & Rentrop, C. & van der Heide, E., 2015. "Leading edge erosion of coated wind turbine blades: Review of coating life models," Renewable Energy, Elsevier, vol. 80(C), pages 837-848.
    6. Herring, Robbie & Dyer, Kirsten & Martin, Ffion & Ward, Carwyn, 2019. "The increasing importance of leading edge erosion and a review of existing protection solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    7. ASM Shihavuddin & Xiao Chen & Vladimir Fedorov & Anders Nymark Christensen & Nicolai Andre Brogaard Riis & Kim Branner & Anders Bjorholm Dahl & Rasmus Reinhold Paulsen, 2019. "Wind Turbine Surface Damage Detection by Deep Learning Aided Drone Inspection Analysis," Energies, MDPI, vol. 12(4), pages 1-15, February.
    8. Matthias Schramm & Hamid Rahimi & Bernhard Stoevesandt & Kim Tangager, 2017. "The Influence of Eroded Blades on Wind Turbine Performance Using Numerical Simulations," Energies, MDPI, vol. 10(9), pages 1-15, September.
    9. Sara C. Pryor & Rebecca J. Barthelmie & Jeremy Cadence & Ebba Dellwik & Charlotte B. Hasager & Stephan T. Kral & Joachim Reuder & Marianne Rodgers & Marijn Veraart, 2022. "Atmospheric Drivers of Wind Turbine Blade Leading Edge Erosion: Review and Recommendations for Future Research," Energies, MDPI, vol. 15(22), pages 1-41, November.
    10. Koodly Ravishankara, Akshay & Özdemir, Huseyin & van der Weide, Edwin, 2021. "Analysis of leading edge erosion effects on turbulent flow over airfoils," Renewable Energy, Elsevier, vol. 172(C), pages 765-779.
    11. Han, Woobeom & Kim, Jonghwa & Kim, Bumsuk, 2018. "Effects of contamination and erosion at the leading edge of blade tip airfoils on the annual energy production of wind turbines," Renewable Energy, Elsevier, vol. 115(C), pages 817-823.
    12. Fraisse, Anthony & Bech, Jakob Ilsted & Borum, Kaj Kvisgaard & Fedorov, Vladimir & Frost-Jensen Johansen, Nicolai & McGugan, Malcolm & Mishnaevsky, Leon & Kusano, Yukihiro, 2018. "Impact fatigue damage of coated glass fibre reinforced polymer laminate," Renewable Energy, Elsevier, vol. 126(C), pages 1102-1112.
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    1. Oscar Best & Asiya Khan & Sanjay Sharma & Keri Collins & Mario Gianni, 2024. "Leading Edge Erosion Classification in Offshore Wind Turbines Using Feature Extraction and Classical Machine Learning," Energies, MDPI, vol. 17(21), pages 1-19, November.

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