Wind and Structures

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Automatic detection of icing wind turbine using deep learning method

  • Received : 2021.06.27
  • Accepted : 2022.06.11
  • Published : 2022.06.25
⟨ Previous Next ⟩

Abstract

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.

Keywords

Acknowledgement

The authors would like to thank Goran Ronsten, program coordinator for Winterwind Conferences, for his continuous support and for providing data for carrying out this research.

References

  1. Antikainen, P. (2018), "Retrofitting Anti-icing Blade Heating on Installed Wind Turbines", in International Wind Energy Conference (Winterwind 2018), Feb 5-7, Are, Sweden.
  2. Antikainen, P. (2020), "Megaterends in Blade Heating", In International Wind Energy Conference (Winterwind 2020), Feb 3-5, Are, Sweden.
  3. Chattopadhay, A., Sarkar, A., Howlader, P. and Balasubramanian, V.N. (2018), "Grad-CAM++: Generalized gradient-based visual explanations for deep convolutional networks", Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018, 2018-Janua, 839-847. https://doi.org/10.1109/WACV.2018.00097.
  4. Chen, L., Xu, G., Zhang, Q. and Zhang, X. (2019), "Learning deep representation of imbalanced SCADA data for fault detection of wind turbines", Measure. J. Int. Measure. Confederation, 139, 370-379. https://doi.org/10.1016/j.measurement.2019.03.029.
  5. Dong, X., Gao, D., Li, J., Jincao, Z. and Zheng, K. (2020), "Blades icing identification model of wind turbines based on SCADA data", Renew. Energy, 162, 575-586. https://doi.org/10.1016/j.renene.2020.07.049.
  6. Freytag, R. (2020), "Early Information of Potential Icing and Measuring of Icing Events", Int. Wind Energy Conference (Winterwind 2020), Feb 3-5, Are, Sweden.
  7. Froidevaux, P. (2019), "Benchmark of four Blade-based Ice Detection Systems", In International Wind Energy Conference (Winterwind 2019), Feb 4-6, Umea, Sweden.
  8. Gao, L. and Hong, J. (2021), "Wind turbine performance in natural icing environments: A field characterization", Cold Regions Sci. Technol., 181, 103193. https://doi.org/10.1016/j.coldregions.2020.103193.
  9. Godreau, C. (2019), "Wind turbine rotor icing detectors performance evaluation", In International Wind Energy Conference (Winterwind 2019), Feb 4-6, Umea, Sweden.
  10. Haciefendioglu, K., Basaga, H.B. and Demir, G. (2021), "Automatic detection of earthquake-induced ground failure effects through Faster R-CNN deep learning-based object detection using satellite images". Nat. Haz., 105, 383-403. https://doi.org/10.1007/s11069-020-04315-y.
  11. Han, H., Wang, W.Y. and Mao, B.H. (2005), "Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning", Lecture Notes in Computer Science, 878-887. https://doi.org/10.1007/11538059_91.
  12. He, K., Zhang, X., Ren, S. and Sun, J. (2016), "Deep residual learning for image recognition", Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-Decem, 770-778. https://doi.org/10.1109/CVPR.2016.90.
  13. Hughes, M.J. and Kennedy, R. (2019), "High-quality cloud masking of landsat 8 imagery using convolutional neural networks", Remote Sensing, 11. https://doi.org/10.3390/rs11212591.
  14. Jiang, W. and Jin, J. (2021), Intelligent Icing Detection Model of Wind Turbine Blades Based on SCADA data, 1-10.
  15. Kaikkonen, V. (2020), "Ice and snow management innovations for critical infrastructure", In International Wind Energy Conference (Winterwind 2020), Feb 3-5, Are, Sweden.
  16. Karpathy, A. (2016), "Convolutional Neural Networks (CNNs/ConvNets). Retrieved CS231n Convolutional Neural Networks for Visual Recognition," http://cs231n.github.io/, Available athttps://cs231n.github.io/.
  17. Kreutz, M., Ait-Alla, A., Varasteh, K., Oelker, S., Greulich, A., Freitag, M. and Thoben, K.D. (2019), "Machine learning-based icing prediction on wind turbines", Procedia CIRP, 423-428. https://doi.org/10.1016/j.procir.2019.03.073.
  18. Kreutz, M., Alla, A.A., Eisenstadt, A., Freitag, M. and Thoben, K. D. (2020a), "Ice detection on rotor blades of wind turbines using RGB images and convolutional neural networks", Procedia CIRP, 93, 1292-1297. https://doi.org/10.1016/j.procir.2020.04.107.
  19. Kreutz, M., Alla, A.A., Varasteh, K., Lutjen, M., Freitag, M. and Thoben, K.D. (2020b), "Investigation of icing causes on wind turbine rotor blades using machine learning models, minimalistic input data and a full-factorial design", Procedia Manufacturing, 52, 168-173. https://doi.org/10.1016/j.promfg.2020.11.030.
  20. Krizhevsky, B.A., Sutskever, I. and Hinton, G.E. (2017), "ImageNet classification with deep convolutional neural networks", Communications of the ACM, 60, 84-90. https://doi.org/10.1145/3065386
  21. LeCun, Y., Bengio, Y. and Hinton, G. (2015), "Deep learning", Nature, 51, 436-444. https://doi.org/10.1038/nature14539
  22. Madi, E., Pope, K., Huang, W. and Iqbal, T. (2019), "A review of integrating ice detection and mitigation for wind turbine blades", Renew. Sustaiin. Energy Reviews, 103, 269-281. https://doi.org/10.1016/j.rser.2018.12.019.
  23. Parent, O. and Ilinca, A. (2011), "Anti-icing and de-icing techniques for wind turbines: Critical review", Cold Regions Sci. Technol., 65, 88-96. https://doi.org/10.1016/j.coldregions.2010.01.005.
  24. Piqsels (2021), Wind Turbine, Wind Energy, Turn, Power Generation, Wind Generator, Renewable, Propeller, Wind Power, Piqsels, Available athttps://www.piqsels.com/en/publicdomain-photo-flvmk.
  25. Poppy (2006), "Wind turbine Reading's landmark wind turbine was completed Flickr", https://www.flickr.com/photos/hddod/141018304/.
  26. Rizk, P., Al Saleh, N., Younes, R., Ilinca, A. and Khoder, J. (2020), "Hyperspectral imaging applied for the detection of wind turbine blade damage and icing", Remote Sensing Applications: Soc. Environ., 18, 100291. https://doi.org/10.1016/j.rsase.2020.100291.
  27. Salman, H., Grover, J. and Shankar, T. (2018), "Hierarchical reinforcement learning for sequencing behaviors", 2733, 2709-2733. https://doi.org/10.1162/NECO.
  28. Sarlak, H. (2018), "Numerical simulation of icethrow from wind turbines in cold climate", In International Wind Energy Conference (Winterwind 2018), Feb 5-7, Are, Sweden.
  29. Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D. and Batra, D. (2020), "Grad-CAM: Visual explanations from deep networks via gradient-based localization", Int. J. Comput. Vision, 128, 336-359. https://doi.org/10.1007/s11263-019-01228-7.
  30. Shu, L., Li, H., Hu, Q., Jiang, X., Qiu, G., McClure, G. and Yang, H. (2018), "Study of ice accretion feature and power characteristics of wind turbines at natural icing environment", Cold Regions Sci. Technol., 147, 45-54. https://doi.org/10.1016/j.coldregions.2018.01.006.
  31. Simonyan, K. and Zisserman, A. (2015), "Very deep convolutional networks for large-scale image recognition", In 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 1-14.
  32. Son, C. and Kim, T. (2020), "Development of an icing simulation code for rotating wind turbines", J. Wind Eng. Ind. Aerod., 203, 104239. https://doi.org/10.1016/j.jweia.2020.104239.
  33. Stoyanov, D.B. and Nixon, J.D. (2020), "Alternative operational strategies for wind turbines in cold climates", Renew. Energy, 145, 2694-2706. https://doi.org/10.1016/j.renene.2019.08.023.
  34. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. and Wojna, Z. (2016), "Rethinking the Inception Architecture for Computer Vision", In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, 2818-2826. https://doi.org/10.1109/CVPR.2016.308.
  35. Ullo, S.L., Mohan, A., Sebastianelli, A., Ahamed, S.E., Kumar, B., Dwivedi, R. and Sinha, G.R. (2020), "A new mask R-CNN based method for improved landslide detection", Comput Vision and Pattern Recognition.
  36. Ummers, M. (2019), "Is wind industry ready for disruptive slutions?", In International Wind Energy Conference (Winterwind 2019), Feb 4-6, Umea, Sweden.
  37. Wadham-Gagnon, M. (2018), "Ice Protection System Performance Assessment Methodology," In International Wind Energy Conference (Winterwind 2018), Feb 5-7, Are, Sweden.
  38. Wang, H., Wang, Z., Du, M., Yang, F., Zhang, Z., Ding, S., Mardziel, P. and Hu, X. (2020), "Score-CAM: Score-weighted visual explanations for convolutional neural networks", IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2020-June, 111-119. https://doi.org/10.1109/CVPRW50498.2020.00020.
  39. Xu, J., Tan, W. and Li, T. (2020), "Predicting fan blade icing by using particle swarm optimization and support vector machine algorithm", Comput. Electric. Eng., 87, 106751. https://doi.org/10.1016/j.compeleceng.2020.106751.
  40. Xu, Y., Scott, K. A., Ri, H., Hvljq, V., Ri, Q., Ri, F., Sdwfkhv, L., Qwkhwlf, I.V, Udgdu, D., Zlwk, P., Ihdwxuhv, H.W., Wkh, R.I., Iurp, S., Qdwxudo, I., Wudfwlrq, U.H. and Vkls, D.Q.G. (2017), "Sea ice and open water classification of sar imagery using cnn-based transfer learning," in IEEE International Symposium on Geoscience and Remote Sensing (IGARSS), 5-8.
  41. Yan, Q. and Huang, W. (2018), "Sea ice sensing from GNSS-R data using convolutional neural networks", IEEE Geosci. Remote Sensing Lett., 15, 1510-1514. https://doi.org/10.1109/LGRS.2018.2852143.
  42. Yu, H., Ma, Y., Wang, L., Zhai, Y. and Wang, X. (2017), "A landslide intelligent detection method based on CNN and RSG-R", In 2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017, Institute of Electrical and Electronics Engineers Inc., 40-44. https://doi.org/10.1109/ICMA.2017.8015785.