Digital twin of wind turbine surface damage detection based on deep learning-aided drone inspection

Weifei Hu, Jianhao Fang, Yaxuan Zhang, Zhenyu Liu, Amrit Shankar Verma, Hongwei Liu, Feiyun Cong and Jianrong Tan

Renewable Energy, 2025, vol. 241, issue C

Abstract: Wind turbine (WT) surface damage detection based on deep learning-aided drone inspection is an important emerging technology. Traditional deep learning algorithms have the issues of low global search capability, low damage detection accuracy, and long inference time. This paper proposes a new real-time detection and semantic segmentation-you only look once (RDSS-YOLO) neural network (NN) for both real-time and accurate detection and semantic segmentation of WT surface damage. A damage size quantification method is further created to calculate the real size of WT surface damage using segmentation results. Moreover, a digital twin (DT) of WT surface damage detection based on the drone inspection aided by the proposed deep learning methods is developed, which can ultimately realize real-time surface damage detection for both standstill and rotating WTs. The proposed RDSS-YOLO NN is tested on an augmented drone inspection dataset and obtains mean average percentage, precision, recall, and mean intersection over union of 95.7 %, 93.9 %, 96.8 %, and 81.5 %, respectively, which are superior to those obtained by some state-of-the-art surface damage detection NNs. The proposed damage size quantification method is tested on another dataset generating an average relative error of 7.24 %, 13.06 %, and 9.44 % for WT blade crack, leading edge erosion, and paint peeling, respectively. The developed DT has been successfully applied in three different wind farms and achieved the real-time detection and semantic segmentation of WT surface features.

Keywords: Wind turbine; Digital twin; Surface damage detection; Drone; Semantic segmentation; YOLO neural network (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:241:y:2025:i:c:s0960148124024005

DOI: 10.1016/j.renene.2024.122332

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