2025年6月10日 周二
Home > Archive>Volume 35, Issue 6, 2020 >131-137. DOI:10.19781/j.issn.16739140.2020.06.017
PDF HTML XML Export Cite reminder
PSOEMLSSVM forecasting model for the transmission lines icing
CSTR:
[cstr]
Author:
Clc Number:

TM752

  • Article
    • | |
  • Metrics
    • |
  • Reference
    • |
  • Related
    • |
  • Cited by
    • | |
  • Comments
    Abstract:

    According to the fact that the existing icing prediction methods has a slow convergence speed and poor prediction accuracy, a method based on particle swarm optimization with extended memory (PSOEM) is proposed under the consideration of the icing thickness influence to optimize parameters. It is applied to the least squares support vector machine (LSSVM) to predict icing thickness. The proposed method introduces an extended memory factor into the traditional particle swarm algorithm to make the particles have stronger search capabilities, thereby speeding up convergence and improving prediction accuracy. Finally, the actual line icing data is utilized to test the accuracy of the prediction model. It is shown that the average relative error of the prediction model based on PSOEMLSSVM is less than 3%. Compared with other models, the prediction effect is the best.

    Reference
    Related
    Cited by
Get Citation

刘闯,何沁鸿,卢银均,杨凯帆,黄婧,何丽娜,陈磊,孟遂民.输电线路PSOEMLSSVM覆冰预测模型[J].电力科学与技术学报英文版,2020,35(6):131-137. LIU Chuang, HE Qinhong, LU Yinjun, YANG Kaifan, HUANG Jing, HE Lina, CHEN Lei, MENG Suimin. PSOEMLSSVM forecasting model for the transmission lines icing[J]. Journal of Electric Power Science and Technology,2020,35(6):131-137.

Copy
Related Videos

Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Online: April 16,2021
Article QR Code

微信公众号二维码

您是本站第 4754718 访问者

通信地址：

电话/传真： E-mail：

版权所有：Journal of Electric Power Science and Technology ® 2025 All Rights Reserved

京公网安备 11010802026262号