Online prediction method of icing of overhead power lines based on support vector regression. (1st January 2018)

• Record Type: Journal Article
• Authors: Li, Jingjie; Li, Peng; Miao, Aimin; Chen, Yong; Cao, Min; Shen, Xin
• Is Part Of: International transactions on electrical energy systems. Volume 28:Number 3(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-01-01
• Subjects: icing -- multivariate time series -- power lines -- phase‐space reconstruction -- support vector regression -- wavelet; Electric power -- Periodicals; Electric power systems -- Periodicals; Electrical engineering -- Periodicals; 621.3
• DOI: 10.1002/etep.2500
• Languages: English
• ISSNs: 2050-7038

Abstract

Summary: This paper proposes a novel online multivariate time series prediction method, using support vector regression, to build an icing alert system that can forecast short‐term icing accretion load on overhead power lines. Conventional icing alert methods either cannot predict future icing accretion or these methods suffer from inadequate predictive accuracy. To resolve these issues, historical and online micrometeorological data from local observations are used to build a multivariate prediction model. Moreover, data preprocessing based on wavelets is used to prefilter spiking noise within the obtained field signals. In addition, the phase‐space reconstruction theory is applied to find the minimal embedding dimensions of the contributing factors by which the computational complexity of the multivariate model is reduced. Finally, an online adaptive predictive model based on support vector regression is proposed and implemented to further improve the predictive accuracy and predictive length of the icing process of overhead power lines. Experimental results indicate that this method can predict the real‐time icing value on overhead power lines 5 hours in advance, with an acceptable error term.