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A Human-Cyber-Physical System toward Intelligent Wind Turbine Operation and Maintenance

Author

Listed:
  • Xiao Chen

    (Department of Wind Energy, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark)

  • Martin A. Eder

    (Department of Wind Energy, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark)

  • Asm Shihavuddin

    (EEE Department, Green University of Bangladesh, 220/D, Begum Rokeya Sarani, Dhaka 1207, Bangladesh)

  • Dan Zheng

    (School of Economics and Management, University of Chinese Academy of Sciences, Zhongguancun East Road 80, Haidian District, Beijing 100000, China)

Abstract

This work proposes a novel concept for an intelligent and semi-autonomous human-cyber-physical system (HCPS) to operate future wind turbines in the context of Industry 5.0 technologies. The exponential increase in the complexity of next-generation wind turbines requires artificial intelligence (AI) to operate the machines efficiently and consistently. Evolving the current Industry 4.0 digital twin technology beyond a sole aid for the human decision-making process, the digital twin in the proposed system is used for highly effective training of the AI through machine learning. Human intelligence (HI) is elevated to a supervisory level, in which high-level decisions made through a human–machine interface break the autonomy, when needed. This paper also identifies and elaborates key enabling technologies (KETs) that are essential for realizing the proposed HCPS.

Suggested Citation

  • Xiao Chen & Martin A. Eder & Asm Shihavuddin & Dan Zheng, 2021. "A Human-Cyber-Physical System toward Intelligent Wind Turbine Operation and Maintenance," Sustainability, MDPI, vol. 13(2), pages 1-10, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:561-:d:477251
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
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    Citations

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    Cited by:

    1. Junwei Cao & Jian Jin & Yangyang Ming & Chuang Sun & Xiping Zeng & Zhenzhen Jiao & Songpu Ai, 2023. "Human-Cyber-Physical Systems for Energy Internet—A Review," Energies, MDPI, vol. 16(15), pages 1-28, July.
    2. Stephany Isabel Vallarta-Serrano & Edgar Santoyo-Castelazo & Edgar Santoyo & Esther O. García-Mandujano & Holkan Vázquez-Sánchez, 2023. "Integrated Sustainability Assessment Framework of Industry 4.0 from an Energy Systems Thinking Perspective: Bibliometric Analysis and Systematic Literature Review," Energies, MDPI, vol. 16(14), pages 1-30, July.
    3. Sindhwani, Rahul & Afridi, Shayan & Kumar, Anil & Banaitis, Audrius & Luthra, Sunil & Singh, Punj Lata, 2022. "Can industry 5.0 revolutionize the wave of resilience and social value creation? A multi-criteria framework to analyze enablers," Technology in Society, Elsevier, vol. 68(C).
    4. Ágota Bányai & Tamás Bányai, 2022. "Real-Time Maintenance Policy Optimization in Manufacturing Systems: An Energy Efficiency and Emission-Based Approach," Sustainability, MDPI, vol. 14(17), pages 1-15, August.
    5. Dong, Liang & Lio, Wai Hou & Pirrung, Georg Raimund, 2021. "Analysis and design of an adaptive turbulence-based controller for wind turbines," Renewable Energy, Elsevier, vol. 178(C), pages 730-744.

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