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Study on crack monitoring method of wind turbine blade based on AI model: Integration of classification, detection, segmentation and fault level evaluation

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  • Hang, Xinyu
  • Zhu, Xiaoxun
  • Gao, Xiaoxia
  • Wang, Yu
  • Liu, Longhu

Abstract

The stability of wind turbines is closely related to the economic benefits of wind energy. To improve the stability of the wind turbine, a comprehensive image diagnosis method based on artificial intelligence method called ‘Multivariate Information Perception You Look Only Once’ (MIP-YOLO) is proposed. MIP-YOLO is an improved algorithm based on YOLOv8 that can classify, detect, segment and evaluate the crack damage level, and can be utilized for monitoring surface cracks on wind turbine blades. To improve the detection capability of small, relatively weak targets such as cracks, Multivariate Information Perception and C2TR modules are put forward. Aim at enhancing the ability of extracting objects with edge features, the Haar wavelet attention (HWA) and C2fGhost modules are proposed. In order to make the model extract features better, C2CBAM module is put forward in this paper. For purpose of solving the problem that some samples in the dataset may have poor quality, wise-IOU is introduced into the model. The detection performance of the proposed method is tested using wind turbine's blade images with cracks taken by Unmanned Aerial Vehicles (UAV). The experiment shows that MIP-YOLO can realize the fault diagnosis of blade effectively and improve the economic benefit of wind energy.

Suggested Citation

  • Hang, Xinyu & Zhu, Xiaoxun & Gao, Xiaoxia & Wang, Yu & Liu, Longhu, 2024. "Study on crack monitoring method of wind turbine blade based on AI model: Integration of classification, detection, segmentation and fault level evaluation," Renewable Energy, Elsevier, vol. 224(C).
    • Handle: RePEc:eee:renene:v:224:y:2024:i:c:s0960148124002179
    DOI: 10.1016/j.renene.2024.120152
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    References listed on IDEAS

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    1. Qu, Fuming & Liu, Jinhai & Zhu, Hongfei & Zhou, Bowen, 2020. "Wind turbine fault detection based on expanded linguistic terms and rules using non-singleton fuzzy logic," Applied Energy, Elsevier, vol. 262(C).
    2. Guo, Jihong & Liu, Chao & Cao, Jinfeng & Jiang, Dongxiang, 2021. "Damage identification of wind turbine blades with deep convolutional neural networks," Renewable Energy, Elsevier, vol. 174(C), pages 122-133.
    3. Yang, Xiyun & Zhang, Yanfeng & Lv, Wei & Wang, Dong, 2021. "Image recognition of wind turbine blade damage based on a deep learning model with transfer learning and an ensemble learning classifier," Renewable Energy, Elsevier, vol. 163(C), pages 386-397.
    4. Rezaei, Mohammad M. & Behzad, Mehdi & Moradi, Hamed & Haddadpour, Hassan, 2016. "Modal-based damage identification for the nonlinear model of modern wind turbine blade," Renewable Energy, Elsevier, vol. 94(C), pages 391-409.
    5. Sun, Shilin & Wang, Tianyang & Yang, Hongxing & Chu, Fulei, 2022. "Condition monitoring of wind turbine blades based on self-supervised health representation learning: A conducive technique to effective and reliable utilization of wind energy," Applied Energy, Elsevier, vol. 313(C).
    6. Yang, Jinshui & Peng, Chaoyi & Xiao, Jiayu & Zeng, Jingcheng & Yuan, Yun, 2012. "Application of videometric technique to deformation measurement for large-scale composite wind turbine blade," Applied Energy, Elsevier, vol. 98(C), pages 292-300.
    7. Yang, Wenxian & Court, Richard & Jiang, Jiesheng, 2013. "Wind turbine condition monitoring by the approach of SCADA data analysis," Renewable Energy, Elsevier, vol. 53(C), pages 365-376.
    8. Zhao, Xueyan & Lang, Ziqiang, 2019. "Baseline model based structural health monitoring method under varying environment," Renewable Energy, Elsevier, vol. 138(C), pages 1166-1175.
    9. Xiaoxun, Zhu & Xinyu, Hang & Xiaoxia, Gao & Xing, Yang & Zixu, Xu & Yu, Wang & Huaxin, Liu, 2022. "Research on crack detection method of wind turbine blade based on a deep learning method," Applied Energy, Elsevier, vol. 328(C).
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