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Adaptive Neuro-Fuzzy System for Detection of Wind Turbine Blade Defects

Author

Listed:
  • Lesia Dubchak

    (Faculty of Computer Information Technologies, West Ukrainian National University, 46001 Ternopil, Ukraine)

  • Anatoliy Sachenko

    (Faculty of Computer Information Technologies, West Ukrainian National University, 46001 Ternopil, Ukraine
    Department of Informatics and Teleinformatics, Kazimierz Pulaski University of Technology and Humanities in Radom, 26600 Radom, Poland)

  • Yevgeniy Bodyanskiy

    (Faculty of Computer Science, Kharkiv National University of Radio Electronics, 61166 Kharkiv, Ukraine)

  • Carsten Wolff

    (Faculty of Computer Science, Dortmund University of Applied Science and Arts, 44139 Dortmund, Germany)

  • Nadiia Vasylkiv

    (Faculty of Computer Information Technologies, West Ukrainian National University, 46001 Ternopil, Ukraine)

  • Ruslan Brukhanskyi

    (Education and Research Institute of Innovation, Nature Management and Infrastructure, West Ukrainian National University, 46001 Ternopil, Ukraine)

  • Volodymyr Kochan

    (Faculty of Computer Information Technologies, West Ukrainian National University, 46001 Ternopil, Ukraine)

Abstract

Wind turbines are the most frequently used objects of renewable energy today. However, issues that arise during their operation can greatly affect their effectiveness. Blade erosion, cracks, and other defects can slash turbine performance while also forcing maintenance costs to soar. Modern defect detection applications have significant computing resources needed for training and insufficient accuracy. The goal of this study is to develop the improved adaptive neuro-fuzzy inference system (ANFIS) for wind turbine defect detection, which will reduce computing resources and increase its accuracy. Unmanned aerial vehicles are deployed to photograph the turbines, and these images are beamed back and processed for early defect detection. The proposed adaptive neuro-fuzzy inference system processes the data vectors with lower complexity and higher accuracy. For this purpose, the authors explored grid partitioning and subtractive clustering methods and selected the last one because it uses three rules only for fault detection, ensuring low computational costs and enabling the discovery of wind turbine defects quickly and efficiently. Moreover, the proposed ANFIS is implemented in a controller, which has an accuracy of 91%, that is 1.4 higher than the accuracy of the existing similar controller.

Suggested Citation

  • Lesia Dubchak & Anatoliy Sachenko & Yevgeniy Bodyanskiy & Carsten Wolff & Nadiia Vasylkiv & Ruslan Brukhanskyi & Volodymyr Kochan, 2024. "Adaptive Neuro-Fuzzy System for Detection of Wind Turbine Blade Defects," Energies, MDPI, vol. 17(24), pages 1-20, December.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:24:p:6456-:d:1549566
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    References listed on IDEAS

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    1. Monowar Hossain & Saad Mekhilef & Firdaus Afifi & Laith M Halabi & Lanre Olatomiwa & Mehdi Seyedmahmoudian & Ben Horan & Alex Stojcevski, 2018. "Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-31, April.
    2. Liang Tong & Changlong Fan & Zhongbo Peng & Cong Wei & Shijie Sun & Jie Han, 2024. "WTBD-YOLOv8: An Improved Method for Wind Turbine Generator Defect Detection," Sustainability, MDPI, vol. 16(11), pages 1-17, May.
    3. Álvaro Gómez-Barroso & Asier Alonso Tejeda & Iban Vicente Makazaga & Ekaitz Zulueta Guerrero & Jose Manuel Lopez-Guede, 2024. "Dynamic Programming-Based ANFIS Energy Management System for Fuel Cell Hybrid Electric Vehicles," Sustainability, MDPI, vol. 16(19), pages 1-20, October.
    4. Mahmoud Aref & Almoataz Y. Abdelaziz & Zong Woo Geem & Junhee Hong & Farag K. Abo-Elyousr, 2023. "Oscillation Damping Neuro-Based Controllers Augmented Solar Energy Penetration Management of Power System Stability," Energies, MDPI, vol. 16(5), pages 1-21, March.
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