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Wind and Structures
  Volume 34, Number 6, June 2022 , pages 511-523

Automatic detection of icing wind turbine using deep learning method
Kemal Haciefendioglu, Hasan Basri Basaga, Selen Ayas and Mohammad Tordi Karimi

    Detecting the icing on wind turbine blades built-in cold regions with conventional methods is always a very laborious, expensive and very difficult task. Regarding this issue, the use of smart systems has recently come to the agenda. It is quite possible to eliminate this issue by using the deep learning method, which is one of these methods. In this study, an application has been implemented that can detect icing on wind turbine blades images with visualization techniques based on deep learning using images. Pre-trained models of Resnet-50, VGG-16, VGG-19 and Inception-V3, which are well-known deep learning approaches, are used to classify objects automatically. Grad-CAM, Grad-CAM++, and Score-CAM visualization techniques were considered depending on the deep learning methods used to predict the location of icing regions on the wind turbine blades accurately. It was clearly shown that the best visualization technique for localization is Score-CAM. Finally, visualization performance analyses in various cases which are close-up and remote photos of a wind turbine, density of icing and light were carried out using Score-CAM for Resnet-50. As a result, it is understood that these methods can detect icing occurring on the wind turbine with acceptable high accuracy.
Key Words
    convolutional neural networks; deep learning method; grad-CAM; icing; wind turbine; inception-V3; resnet-50; score-CAM; VGG-16; VGG-19
Kemal Haciefendioglu, Hasan Basri Basaga, Selen Ayas:Department of Civil Engineering, Karadeniz Technical University, 61080, Trabzon, Turkey

Mohammad Tordi Karimi:Department of Computer Engineering, Karadeniz Technical University, 61080, Trabzon, Turkey

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