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.
Highlights
Introduction to the Bigger PictureOperation and Maintenance.Advancing wind turbine (WT) technology facilitates the attainment of several of the United Nations’ Sustainable Development Goals (SDGs) by providing affordable and clean energy, fostering innovation for sustainable industrialization, and combating climate change [1]
Every WT of this wind farm is among the biggest and most complex machines in the world and is designed to produce energy consistently for its designated lifetime of 30 years in one of the harshest environments on this planet. Approaching one of these WTs more closely, we identify the rotor blades as the world’s largest single component made from fiber composite materials, which, to date, have already exceeded the 100 m length mark and have a tendency for further growth
The rotor blades constitute one of the most critical components in the wind turbine since they directly interact with the outside environment, destined to carry the major load acting on the entire WT system
Summary
As perceived in the wind energy sector, the prime purpose of a DT is to make accurate predictions of the structural health state of individual WT components, eventually enabling a reliable, accurate, and timely assessment of the entire WT For this purpose, a DT needs to predict the evolution of the current damage status by considering the prevailing load history, environmental conditions, and manufacturing imperfections, among others. Credible projections of wind turbine technology development outline an exponential increase in system complexity, rendering pure human-supervised O&M approaches insufficient and obsolete In this situation, it is necessary to introduce more autonomy into the operation of the WT system. Projecting from the current state-of-the-art technologies, we identify and elaborate the KETs that are essential for the realization of the proposed concept
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