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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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References

  • Abdi H, Williams LJ (2010) Principal component analysis. Wiley Interdiscip Rev Comput Stat 2(4):433–459

    Article  Google Scholar 

  • Andrews DF (1972) Plots of high-dimensional data. Biometrics 28:125–136

    Article  Google Scholar 

  • Borg I, Groenen PJ (2005) Modern multidimensional scaling: theory and applications. Springer, Berlin

    MATH  Google Scholar 

  • Chen H, Li H, Fang Y, Chen Y (2016) Anisotropic parallel coordinates with adjustment based on distribution features. J Vis 19(2):327–335

    Article  Google Scholar 

  • Chen S, Dai D, Huang X, Sun M (2012) Short-term prediction for transmission lines icing based on bp neural network. In: Proceedings of IEEE Asia-Pacific power and energy engineering conference (APPEEC) 2012. IEEE, pp 1–5

  • Cigré T (2006) 291 guidelines for meteorological icing models, statistical methods and topographical effects. In: Working Group B, vol 2

  • Crickard P III (2014) Leaflet. Js Essentials. Packt Publishing Ltd, Birmingham

    Google Scholar 

  • De Meester B, De Nies T, Verborgh R, Mannens E, Van de Walle R (2015) Reconnecting digital publications to the web using their spatial information. In: Proceedings of the 24th international conference on world wide web. ACM, pp 749–754

  • Deng L, Yu D et al (2014) Deep learning: methods and applications. Found Trends® Signal Process 7(3–4):197–387

    Article  MathSciNet  MATH  Google Scholar 

  • Donohue RG, Sack CM, Roth RE (2014) Time series proportional symbol maps with Leaflet and JQuery. Cartogr Perspect 76:43–66

    Article  Google Scholar 

  • Elmqvist N, Dragicevic P, Fekete J-D (2008) Rolling the dice: multidimensional visual exploration using scatterplot matrix navigation. IEEE Trans Vis Comput Gr 14(6):1139–1148

    Article  Google Scholar 

  • Farzaneh M (2008) Atmospheric icing of power networks. Springer, Berlin

    Book  Google Scholar 

  • Flanagan D (2006) JavaScript: the definitive guide. O’Reilly Media Inc, Newton

    MATH  Google Scholar 

  • Friendly M, Denis DJ (2001) Milestones in the history of thematic cartography, statistical graphics, and data visualization, vol 32. http://www.datavis.ca/milestones

  • Furnas GW, Buja A (1994) Prosection views: dimensional inference through sections and projections. J Comput Gr Stat 3(4):323–353

    MathSciNet  Google Scholar 

  • Gao J, Wang J (2011) A hybrid quantum-inspired immune algorithm for multiobjective optimization. Appl Math Comput 217(9):4754–4770

    MathSciNet  MATH  Google Scholar 

  • Gong G, Mattevada S, O’Bryant SE (2014) Comparison of the accuracy of kriging and IDW interpolations in estimating groundwater arsenic concentrations in Texas. Environ Res 130:59–69

    Article  Google Scholar 

  • Harman BI, Koseoglu H, Yigit CO (2016) Performance evaluation of idw, kriging and multiquadric interpolation methods in producing noise mapping: a case study at the city of Isparta, Turkey. Appl Acoust 112:147–157

    Article  Google Scholar 

  • Hinton GE (2002) Training products of experts by minimizing contrastive divergence. Neural Comput 14(8):1771–1800

    Article  MATH  Google Scholar 

  • Hinton GE, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554

    Article  MathSciNet  MATH  Google Scholar 

  • Ingram S, Munzner T, Olano M (2009) Glimmer: multilevel MDS on the GPU. IEEE Tran Vis Comput Gr 15(2):249–261

    Article  Google Scholar 

  • Inselberg A (1985) The plane with parallel coordinates. Vis Comput 1(2):69–91

    Article  MathSciNet  MATH  Google Scholar 

  • Kolar V, Ranu S, Subramainan AP, Shrinivasan Y, Telang A, Kokku R, Raghavan, S (2014) People in motion: spatio-temporal analytics on call detail records. In: Proceedings of sixth international conference on communication systems and networks (COMSNETS) 2014. IEEE, pp 1–4

  • Laforte J, Allaire M, Laflamme J (1998) State-of-the-art on power line de-icing. Atmos Res 46(1):143–158

    Article  Google Scholar 

  • Li P, Li N, Li QM, Cao M, Chen HX (2011) Prediction model for power transmission line icing load based on data-driven. Adv Mater Res 143–144:1295–1299

    Article  Google Scholar 

  • Lv X, He Q (2011) Analysis of dicing techniques and methods of overhead transmission line. Proc Eng 15:1135–1139

    Article  Google Scholar 

  • Ma T, Niu D, Fu M (2016) Icing forecasting for power transmission lines based on a wavelet support vector machine optimized by a quantum fireworks algorithm. Appl Sci 6(2):54

    Article  Google Scholar 

  • Makkonen L (1998) Modeling power line icing in freezing precipitation. Atmos Res 46(1):131–142

    Article  Google Scholar 

  • Mehrjardi RT, Jahromi MZ, Mahmodi S, Heidari A (2008) Spatial distribution of groundwater quality with geostatistics (case study: Yazd-ardakan plain). World Appl Sci J 4(1):9–17

    Google Scholar 

  • Myatt GJ, Johnson WP (2011) Making sense of data iii: a practical guide to designing interactive data visualizations, vol 3. Wiley, New York, p 3

    Book  MATH  Google Scholar 

  • Paulovich FV, Nonato LG, Minghim R, Levkowitz H (2008) Least square projection: a fast high-precision multidimensional projection technique and its application to document mapping. IEEE Trans Vis Comput Gr 14(3):564–575

    Article  Google Scholar 

  • Quint A (2003) Scalable vector graphics. IEEE MultiMed 10(3):99–102

    Article  MathSciNet  Google Scholar 

  • Radovic M, Adarkwa O (2015) The us bridge portal-visualization analytics applications for the national bridge inventory (nbi) database. SSRG Int J Civ Eng 2:1–5

    Article  Google Scholar 

  • Robbins JN (2012) Learning web design: a beginner’s guide to HTML. JavaScript, and web graphics. O’Reilly Media Inc, Newton

    Google Scholar 

  • Rumelhart DE, Hinton GE, Williams RJ et al (1988) Learning representations by back-propagating errors. Cognit Model 5(3):1

    MATH  Google Scholar 

  • Stein ML (2012) Interpolation of spatial data: some theory for kriging. Springer, Berlin

    Google Scholar 

  • Tan Q, Xu X (2014) Comparative analysis of spatial interpolation methods: an experimental study. Sens Transducers 165(2):155

    Google Scholar 

  • Tan Y, Yu C, Zheng S, Ding K (2013) Introduction to fireworks algorithm. Int J Swarm Intell Res 4(4):39–70

    Article  Google Scholar 

  • Teller S (2013) Data visualization with D3. Js. Packt Publishing Ltd, Birmingham

    Google Scholar 

  • Valley RD, Drake MT, Anderson CS (2005) Evaluation of alternative interpolation techniques for the mapping of remotely-sensed submersed vegetation abundance. Aquat Bot 81(1):13–25

    Article  Google Scholar 

  • van Wijk JJ, van Liere R (1993) Hyperslice. In: Proceedings of IEEE conference on visualization 1993. IEEE, pp 119–125

  • Wegman EJ (1990) Hyperdimensional data analysis using parallel coordinates. J Am Stat Assoc 85(411):664-675

    Article  Google Scholar 

  • Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2(1–3):37–52

    Article  Google Scholar 

  • Xie B, Zhang C, Gong Q, Koji K, Zeng H, Zhao L,  Qiao H, Huang L (2018) Icing thickness prediction of overhead power transmission lines using parallel coordinates and convolutional neural networks. In: International conference on theory and applications of fuzzy systems and soft computing. Springer, pp 255–267

  • Zeng X-J, Luo X-L, Lu J-Z, Xiong T-T, Pan H (2012) A novel thickness detection method of ice covering on overhead transmission line. Energy Proc 14:1349–1354

    Article  Google Scholar 

  • Zhang L, Zhou W, Jiao L (2004) Wavelet support vector machine. IEEE Trans Syst Man Cybern Part B (Cybern) 34(1):34–39

    Article  Google Scholar 

Download references

Acknowledgements

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

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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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