Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Yi, H.: Analysis and countermeasures for large area accident cause by icing on transmission line. High Volt. Eng. 4, 005 (2005)
Li, Q.-F., Fan, Z., Wu, Q., Gao, J., Su, Z.-Y.: Zhou, W.J.: Investigation of ice-covered transmission lines and analysis on transmission line failures caused by ice-coating in China. Power Syst. Technol. 9, 009 (2008)
Yang, L., Hao, Y., Li, W., Li, Z., Dai, D., Li, L., Luo, B., Zhu, G.: Relationships among transmission line icing, conductor temperature and local meteorology using grey relational analysis. Gaodianya Jishu/High Volt. Eng. 36(3), 775–781 (2010)
Le Cun, Y., Jackel, L., Boser, B., Denker, J., Graf, H., Guyon, I., Henderson, D., Howard, R., Hubbard, W.: Handwritten digit recognition: applications of neural network chips and automatic learning. IEEE Commun. Mag. 27(11), 41–46 (1989)
Lawrence, S., Giles, C.L., Tsoi, A.C., Back, A.D.: Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Netw. 8(1), 98–113 (1997)
Sainath, T.N., Kingsbury, B., Saon, G., Soltau, H., Mohamed, A.R., Dahl, G., Ramabhadran, B.: Deep convolutional neural networks for large-scale speech tasks. Neural Netw. 64, 39–48 (2015)
Inselberg, A.: The plane with parallel coordinates. Vis. Comput. 1(2), 69–91 (1985)
Inselberg, A.: Parallel Coordinates: Visual Multidimensional Geometry and Its Applications. Springer-Verlag, New York (2009). https://doi.org/10.1007/978-0-387-68628-8
Wegman, E.J.: Hyper dimensional data analysis using parallel coordinates. J. Am. Stat. Assoc. 85(411), 664–675 (2012)
Zhang, C., Uenaka, T., Sakamoto, N., Koyamada, K.: Extraction of vortices and exploration of the Ocean Data by Visualization System. In: Xiao, T., Zhang, L., Ma, S. (eds.) ICSC 2012. CCIS, pp. 114–123. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34396-4_14
Wang, S., Yang, Y., Chang, J., Lin, F.: Using penalized regression with parallel coordinates for visualization of significance in high dimensional data. Int. J. Adv. Comput. Sci. Appl. 4(10) (2013)
Yuan, X., Guo, P., Xiao, H., Zhou, H., Qu, H.: Scattering points in parallel coordinates. IEEE Trans. Vis. Comput. Graph. 15(6), 1001–1008 (2009)
Guo, P., Xiao, H., Wang, Z., Yuan, X.: Interactive local clustering operations for high dimensional data in parallel coordinates. In: Visualization Symposium, pp. 97–104 (2010)
Zhang, C., Uenaka, T., Sakamoto, N., Koyamada, K.: Extraction Of vortices and exploration of the ocean data by visualization system. In: Xiao, T., Zhang, L., Ma, S. (eds.) ICSC 2012. CCIS, pp. 114–123. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34396-4_14
Blaas, J., Botha, C.P., Post, F.H.: Extensions of parallel coordinates for interactive exploration of large multi-time point data sets. IEEE Trans. Vis. Comput. Graph. 14(6), 1436–1451 (2008)
Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36(4), 193–202 (1980)
Lecun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Image net classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing coadaptation of feature detectors. Comput. Sci. 3(4), 212–223 (2012)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Li, P., Li, N., Li, Q.M., Cao, M., Chen, H.X.: Prediction model for power transmission line icing load based on data-driven. Adv. Mater. Res. 143–144, 1295–1299 (2010)
Huang, X.B., Jia-Jie, L.I., Ouyang, L.S., Li-Cheng, L.I., Bing, L.: Icing thickness prediction model using fuzzy logic theory. Gaodianya Jishu/High Volt. Eng. 37(5), 1245–1252 (2011)
Zeng, X.J., Luo, X.L., Lu, J.Z., Xiong, T.T., Pan, H.: A novel thickness detection method of ice covering on overhead transmission line. Energy Procedia 14, 1349–1354 (2012)
Ma, T., Niu, D., Fu, M.: Icing forecasting for power transmission lines based on a wavelet support vector machine optimized by a quantum fireworks algorithm. Appl. Sci. 6(2), 54 (2016)
Nielsen, M.A.: Neural networks and deep learning. Determination Press (2015)
Rather, N.N., Patel, C.O., Khan, S.A.: Using deep learning towards biomedical knowledge discovery. Int. J. Math. Sci. Comput. (IJMSC) 3(2), 1–10 (2017). https://doi.org/10.5815/ijmsc.2017.02.01
Sharma, D., Kumar, B., Chand, S.A.: A survey on journey of topic modeling techniques from SVD to deep learning. Int. J. Mod. Educ. Comput. Sci. (IJMECS), 9(7), 50–62 (2017). https://doi.org/10.5815/ijmecs.2017.07.06
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Xie, B. et al. (2019). Icing Thickness Prediction of Overhead Power Transmission Lines Using Parallel Coordinates and Convolutional Neural Networks. In: Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds) Advances in Computer Science for Engineering and Education. ICCSEEA 2018. Advances in Intelligent Systems and Computing, vol 754. Springer, Cham. https://doi.org/10.1007/978-3-319-91008-6_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-91008-6_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91007-9
Online ISBN: 978-3-319-91008-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)