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Design of a Multi-Robot System for Wind Turbine Maintenance

Author

Listed:
  • Josef Franko

    (MASKOR Institute, Faculty of Mechanical Engineering and Mechatronics, University of Applied Sciences Aachen, 52074 Aachen, Germany)

  • Shengzhi Du

    (Faculty of Engineering and the Built Environment, Tshwane University of Technology, Pretoria 0001, South Africa)

  • Stephan Kallweit

    (MASKOR Institute, Faculty of Mechanical Engineering and Mechatronics, University of Applied Sciences Aachen, 52074 Aachen, Germany)

  • Enno Duelberg

    (MASKOR Institute, Faculty of Mechanical Engineering and Mechatronics, University of Applied Sciences Aachen, 52074 Aachen, Germany)

  • Heiko Engemann

    (MASKOR Institute, Faculty of Mechanical Engineering and Mechatronics, University of Applied Sciences Aachen, 52074 Aachen, Germany)

Abstract

The maintenance of wind turbines is of growing importance considering the transition to renewable energy. This paper presents a multi-robot-approach for automated wind turbine maintenance including a novel climbing robot. Currently, wind turbine maintenance remains a manual task, which is monotonous, dangerous, and also physically demanding due to the large scale of wind turbines. Technical climbers are required to work at significant heights, even in bad weather conditions. Furthermore, a skilled labor force with sufficient knowledge in repairing fiber composite material is rare. Autonomous mobile systems enable the digitization of the maintenance process. They can be designed for weather-independent operations. This work contributes to the development and experimental validation of a maintenance system consisting of multiple robotic platforms for a variety of tasks, such as wind turbine tower and rotor blade service. In this work, multicopters with vision and LiDAR sensors for global inspection are used to guide slower climbing robots. Light-weight magnetic climbers with surface contact were used to analyze structure parts with non-destructive inspection methods and to locally repair smaller defects. Localization was enabled by adapting odometry for conical-shaped surfaces considering additional navigation sensors. Magnets were suitable for steel towers to clamp onto the surface. A friction-based climbing ring robot (SMART— Scanning, Monitoring, Analyzing, Repair and Transportation) completed the set-up for higher payload. The maintenance period could be extended by using weather-proofed maintenance robots. The multi-robot-system was running the Robot Operating System (ROS). Additionally, first steps towards machine learning would enable maintenance staff to use pattern classification for fault diagnosis in order to operate safely from the ground in the future.

Suggested Citation

  • Josef Franko & Shengzhi Du & Stephan Kallweit & Enno Duelberg & Heiko Engemann, 2020. "Design of a Multi-Robot System for Wind Turbine Maintenance," Energies, MDPI, vol. 13(10), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:10:p:2552-:d:359754
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    Citations

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    Cited by:

    1. Oh, So Young & Joung, Chanwoo & Lee, Seonghwan & Shim, Yoon-Bo & Lee, Dahun & Cho, Gyu-Eun & Jang, Juhyeong & Lee, In Yong & Park, Young-Bin, 2024. "Condition-based maintenance of wind turbine structures: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 204(C).
    2. Michaela Gkantou & Carlos Rebelo & Charalampos Baniotopoulos, 2020. "Life Cycle Assessment of Tall Onshore Hybrid Steel Wind Turbine Towers," Energies, MDPI, vol. 13(15), pages 1-21, August.
    3. 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).
    4. 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.

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