Journal of South China University of Technology (Natural Science Edition) ›› 2016, Vol. 44 ›› Issue (4): 77-83.doi: 10.3969/j.issn.1000-565X.2016.04.012

• Power & Electrical Engineering • Previous Articles     Next Articles

Calculation of Cable Temperature Based on Support Vector Machine Optimized by Particle Swarm Algorithm

NIU Hai-qing1 YE Kai-fa1 XU Jia1 WU Ju-zhuo1 LUO Jian-bin2 LU Guo-jun2   

  1. 1.School of Electric Power,South China University of Technology,Guangzhou 510640,Guangdong,China; 2.Tests and Research Institute of Guangzhou Power Supply Bureau Co.,Ltd.,Guangzhou 510410,Guangdong,China
  • Received:2015-09-11 Revised:2016-01-06 Online:2016-04-25 Published:2016-04-12
  • Contact: 牛海清(1969-) ,女,博士,副教授,主要从事高压输电线路及高压电气设备等的研究. E-mail:niuhq@scut.edu.cn
  • About author:牛海清(1969-) ,女,博士,副教授,主要从事高压输电线路及高压电气设备等的研究.
  • Supported by:
    Supported by the National High Technology Research and Development of China( 863Program)

Abstract: Cable conductor temperature is a main factor affectingthe life and material utilization of the cable,and is an important parameter reflecting cable's operation state.However,it is difficult to directly measure the conductor temperature of in-use cables,so that a temperature calculation is necessary.In this paper,a model to calculate the transient temperature of cable conductor based on the support vector machine ( SVM) is proposed.In this model,both the load current and the skin temperature are used as the inputs and the conductor temperature is taken as the output.Moreover,in order to improve the calculation accuracy and avoid blind selection of training parameters,the particle swarm optimization ( PSO) algorithmis introduced in the model for optimizing the punishment index C and the core parameter γ.In addition,a comparison between the simulated and the experimental results is made,finding that the proposed PSO-SVM model is superior to the thermal circuit model and the BP neural network because it helps to obtain more accurate transient temperature and possesses good generalization ability.

Key words: cable, conductor temperature, support vector machine, particle swarm optimization, transient calculation