Reliability and cost are two important driving factors in the development of wind energy. Automation and digitalization of operation and maintenance (O&M) procedures help to increase turbine reliability and reduce the levelized cost of energy (LCOE). Here, we demonstrate a novel method, coined as AQUADA, which may change the current labor‐intensive and operation‐interfering blade inspection by using thermography and computer vision. We experimentally show that structural damages below the surfaces can be detected and quantified remotely when wind turbine blades are subject to fatigue loads. The data acquisition and analysis are automatically done in one single step, which may shift the current inspection paradigm through more automated O&M procedures. The cost analysis shows that the AQUADA method has a potential to at least half the total inspection cost and reduce the LCOE by 1%–2% when applied to a baseline land‐based wind farm consisting of twenty 2.45‐MW turbines. All data and source codes are published for researchers to reproduce our results and facilitate further development of AQUADA towards more mature industrial applications.

中文翻译：

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AQUADA：自动量化复合风力涡轮机叶片中的损害，以降低LCOE

可靠性和成本是风能发展的两个重要驱动因素。运营和维护（O＆M）程序的自动化和数字化有助于提高涡轮机的可靠性并降低平均能源成本（LCOE）。在这里，我们演示了一种称为AQUADA的新方法，该方法可以通过使用热成像和计算机视觉技术来改变当前的劳动密集型和操作干扰刀片检查。我们的实验表明，当风力涡轮机叶片承受疲劳载荷时，可以远程检测和量化表面以下的结构损伤。数据采集​​和分析在一个步骤中自动完成，这可以通过更自动化的O＆M程序来改变当前的检查范式。成本分析表明，如果将AQUADA方法应用于由20个2.45 MW风机组成的基准陆地风电场，则有可能至少降低总检查成本的一半，并将LCOE降低1％–2％。所有数据和源代码均已发布给研究人员，以重现我们的结果并促进AQUADA向更成熟的工业应用的进一步发展。