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

Xiao Chen, Martin A. Eder, Asm Shihavuddin and Dan Zheng
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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

Sustainability, 2021, vol. 13, issue 2, 1-10

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.

Keywords: wind turbine; human intelligence; artificial intelligence; machine learning; digital twin; Industry 5.0 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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