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Visual analytics and prediction system based on deep belief networks for icing monitoring data of overhead power transmission lines

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Abstract

In this paper, a system is proposed for visualizing and analyzing icing monitoring data of power transmission lines. The distributions of temperature and humidity are visualized by two-dimensional maps with customizable map layers. The multi-dimensional monitoring data are visualized as parallel coordinates. Moreover, a prediction algorithm that is based on a hybrid deep belief network is integrated into the system for predicting the icing thickness. If the icing thickness of a certain location exceeds the threshold value, the historical meteorological data of the location can be visualized as line graphs, which helps to choose the appropriate de-icing measures. According to the experimental results, our system is capable of reflecting the statistical features of icing monitoring data with high accuracy of icing thickness prediction.

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Acknowledgements

The author (Chi ZHANG) appreciates the financial support of China Scholarship Council during his study at Kyoto University.

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Authors and Affiliations

  1. School of Electrical Engineering and Automation, Wuhan University, Wuhan, China

    Chi Zhang & Qing-wu Gong

  2. Graduate School of Engineering, Kyoto University, Kyoto, Japan

    Chi Zhang

  3. Academic Center for Computing and Media Studies, Kyoto University, Kyoto, Japan

    Koji Koyamada

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  1. Chi Zhang
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  2. Qing-wu Gong
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  3. Koji Koyamada
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Correspondence to Qing-wu Gong.

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Zhang, C., Gong, Qw. & Koyamada, K. Visual analytics and prediction system based on deep belief networks for icing monitoring data of overhead power transmission lines. J Vis 23, 1087–1100 (2020). https://doi.org/10.1007/s12650-020-00670-x

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  • DOI: https://doi.org/10.1007/s12650-020-00670-x

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