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Intelligent ice detection on wind turbine blades using semantic segmentation and class activation map approaches based on deep learning method

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  • Hacıefendioğlu, Kemal
  • Başağa, Hasan Basri
  • Yavuz, Zafer
  • Karimi, Mohammad Tordi

Abstract

Studying the efficacy of intelligent systems to successfully detect ice accumulation on wind turbines in cold climates has been gaining traction in recent years. In this study, both visualization and segmentation techniques were utilized in order to compare their respective results in the detection of ice on wind turbines. In pursuit of this objective, photos of wind turbines, taken under different conditions, were analyzed. To correctly classify objects automatically, pre-trained models of Resnet-50, VGG-16, VGG-19, and Inception-V3, were used. The deep learning approaches used to reliably predict the exact position of icing on wind turbine blades, including the visualization techniques Grad-CAM, Grad-CAM ++, and Score-CAM, proved to have adequate reliability with Score-CAM subsequently identified as the best visualization technique for localization. Additionally, the U-Net segmentation approach was used to delineate icing area boundaries. The U-Net approach was compared with the best visualization technique and pre-trained model to evaluate the visualization efficiency in different situations, including near and far views of a wind turbine, ice density, and light. Results showed that these methods have a high degree of accuracy in detecting ice on wind turbines.

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  • Hacıefendioğlu, Kemal & Başağa, Hasan Basri & Yavuz, Zafer & Karimi, Mohammad Tordi, 2022. "Intelligent ice detection on wind turbine blades using semantic segmentation and class activation map approaches based on deep learning method," Renewable Energy, Elsevier, vol. 182(C), pages 1-16.
    • Handle: RePEc:eee:renene:v:182:y:2022:i:c:p:1-16
    DOI: 10.1016/j.renene.2021.10.025
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    References listed on IDEAS

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    1. Dong, Xinghui & Gao, Di & Li, Jia & Jincao, Zhang & Zheng, Kai, 2020. "Blades icing identification model of wind turbines based on SCADA data," Renewable Energy, Elsevier, vol. 162(C), pages 575-586.
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    2. Cheng Tao & Tao Tao & Xinjian Bai & Yongqian Liu, 2023. "Wind Turbine Blade Icing Prediction Using Focal Loss Function and CNN-Attention-GRU Algorithm," Energies, MDPI, vol. 16(15), pages 1-15, July.
    3. Huan Song & Yongguang Hu & Yongzong Lu & Jizhang Wang & Qingmin Pan & Pingping Li, 2021. "A Review of Methods and Techniques for Detecting Frost on Plant Surfaces," Agriculture, MDPI, vol. 11(11), pages 1-22, November.

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