华南理工大学学报(自然科学版) ›› 2016, Vol. 44 ›› Issue (4): 77-83.doi: 10.3969/j.issn.1000-565X.2016.04.012

• 动力与电气工程 • 上一篇    下一篇

基于粒子群优化支持向量机的电缆温度计算

牛海清1 叶开发1 许佳1 吴炬卓1 罗健斌2 陆国俊2   

  1. 1. 华南理工大学 电力学院,广东 广州 510640; 2. 广州供电局有限公司电力试验研究院,广东 广州 510410
  • 收稿日期:2015-09-11 修回日期:2016-01-06 出版日期:2016-04-25 发布日期:2016-04-12
  • 通信作者: 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
  • 作者简介: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.
  • 基金资助:
    国家高技术研究发展计划( 863 计划) 项目( 2015AA050201)

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)

摘要: 导体温度是影响运行电缆使用寿命和材料利用率的最主要因素,也是反映电缆运行状态的参数. 由于技术上尚难以实现对运行电缆导体温度的直接测量,因此有必要进行导体温度计算. 文中以电流和外皮温度作为模型输入,以导体温度作为模型输出,构建基于支持向量机的电缆暂态导体温度的数学模型; 为提高该模型计算的精度,避免盲目选取训练参数,引入粒子群算法对其惩罚因子C 和核参数γ 进行寻优. 仿真与试验对比结果表明: 基于粒子群优化的支持向量机模型( PSO-SVM 模型) 可以用于电缆暂态导体温度计算,且计算误差小于热路模型和BP 神经网络; 模型具有良好的泛化能力.

关键词: 电缆, 导体温度, 支持向量机, 粒子群优化, 暂态计算

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