Icing Thickness Prediction of Overhead Power Transmission Lines Using Parallel Coordinates and Convolutional Neural Networks

  • Baiming Xie
  • Chi Zhang
  • Qing-wu Gong
  • Koyamada Koji
  • Hua-rong Zeng
  • Li-jin Zhao
  • Hu Qiao
  • Liang Huang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 754)


In this paper, a model used for predicting icing thickness of power transmission lines is proposed. An algorithm derived from parallel coordinates is applied to convert the high-dimensional source data, which includes relevant factors about the icing thickness of power transmission lines, to two dimensional images. Then the images are used for training convolutional neural networks (CNNs). Finally, the icing thickness is predicted by the trained CNNs. In this way, our system combines the advantages of information visualization and CNNs. It provides an universal method to process multi-dimensional numerical data with CNNs algorithmically and in a real sense.


Icing thickness Parallel coordinates  Convolutional neural networks Visualization Deep learning 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Baiming Xie
    • 1
  • Chi Zhang
    • 2
  • Qing-wu Gong
    • 3
  • Koyamada Koji
    • 4
  • Hua-rong Zeng
    • 1
  • Li-jin Zhao
    • 1
  • Hu Qiao
    • 3
  • Liang Huang
    • 1
  1. 1.Guizhou Electric Power Research InstituteGuizhou Power Grid Limited Liability CompanyGuiyangChina
  2. 2.Graduate School of EngineeringKyoto UniversityKyotoJapan
  3. 3.School of Electrical EngineeringWuhan UniversityWuhanChina
  4. 4.Academic Center for Computing and Media StudiesKyoto UniversityKyotoJapan

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