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Wind power is an important green and sustainable source of power generation. However, the construction of wind farms does not only need a large amount of initial investment but also highly expensive maintenance cost for their operations during power generation. Therefore, accurately assessing the state of wind turbines and effectively scheduling maintenance to keep them in good operating condition have become crucially important to ensure efficient power generation. Digital twin, as a data-driven digital concept or technology, can be used to effectively address wind power maintenance issues, especially wind turbine state evaluation problem. This article proposes a novel intelligent state evaluation and maintenance arrangement (iSEMA) system based on digital twin, which can accurately evaluate the state of wind turbines, detect faults in the early stage, and provide useful information or warnings to operators and help them to efficiently arrange maintenance tasks. In addition, this article introduces the concept of sub-healthy state of wind turbines, which is very useful for designing the iSEMA system. Experimental results demonstrate that the proposed system can assess the state of wind turbines accurately and provide timely feedback.
Wang et al. (Mon,) studied this question.