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The Scientific World Journal
Volume 2014 (2014), Article ID 256815, 9 pages
Research Article

Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission Line

1Department of Electronic Engineering, Yunnan University, Kunming 650091, China
2Department of Automation, Tsinghua University, Beijing 100084, China
3Yunnan Electric Power Research Institute, China Southern Power Grid Corp., Kunming 650217, China

Received 27 January 2014; Accepted 14 June 2014; Published 17 July 2014

Academic Editor: Martin Riera-Guasp

Copyright © 2014 Peng Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The design of monitoring and predictive alarm systems is necessary for successful overhead power transmission line icing. Given the characteristics of complexity, nonlinearity, and fitfulness in the line icing process, a model based on a multivariable time series is presented here to predict the icing load of a transmission line. In this model, the time effects of micrometeorology parameters for the icing process have been analyzed. The phase-space reconstruction theory and machine learning method were then applied to establish the prediction model, which fully utilized the history of multivariable time series data in local monitoring systems to represent the mapping relationship between icing load and micrometeorology factors. Relevant to the characteristic of fitfulness in line icing, the simulations were carried out during the same icing process or different process to test the model’s prediction precision and robustness. According to the simulation results for the Tao-Luo-Xiong Transmission Line, this model demonstrates a good accuracy of prediction in different process, if the prediction length is less than two hours, and would be helpful for power grid departments when deciding to take action in advance to address potential icing disasters.