Prediction Method for Time-variant States of Distribution Line Based on Support Vector Machine Algorithm

Hua XIE; Yaxar TURGUN; Hao CHEN; Pei ZHANG

doi:10.7500/AEPS20200209003

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Prediction Method for Time-variant States of Distribution Line Based on Support Vector Machine Algorithm

Basic Research | 更新时间：2025-06-16

- Prediction Method for Time-variant States of Distribution Line Based on Support Vector Machine Algorithm
- Automation of Electric Power Systems Vol. 44, Issue 18, Pages: 74-80(2020)
- 作者机构：
  
  北京交通大学电气工程学院，北京市 100044
- 作者简介：
- 基金信息：
  
  National Key R&D Program of China;State Grid Corporation of China
- DOI：10.7500/AEPS20200209003
  CLC：
- Received：09 February 2020，
  
  Revised：2020-06-08，
  
  Published Online：15 July 2020，
  
  Published：25 September 2020
- 稿件说明：
移动端阅览
谢桦,亚夏尔·吐尔洪,陈昊等.基于支持向量机算法的配电线路时变状态预测方法[J].电力系统自动化,2020,44(18):74-80.

XIE Hua,TURGUN Yaxar,CHEN Hao,et al.Prediction Method for Time-variant States of Distribution Line Based on Support Vector Machine Algorithm[J].Automation of Electric Power Systems,2020,44(18):74-80.
谢桦,亚夏尔·吐尔洪,陈昊等.基于支持向量机算法的配电线路时变状态预测方法[J].电力系统自动化,2020,44(18):74-80. DOI： 10.7500/AEPS20200209003.

XIE Hua,TURGUN Yaxar,CHEN Hao,et al.Prediction Method for Time-variant States of Distribution Line Based on Support Vector Machine Algorithm[J].Automation of Electric Power Systems,2020,44(18):74-80. DOI： 10.7500/AEPS20200209003.

摘要

配电线路状态准确预测是进行配电网调控的基础。提出了基于支持向量机（SVM）算法的配电线路时变状态预测方法。首先，分析影响配电线路状态变化的因素，构建基于Fokker-Planck的配电线路状态转移模型。其次，融合配电信息系统多源海量数据，采用基于相关度的最优特征子集筛选方法构建配电线路状态特征变量集，采用合成少数类过采样技术（SMOTE）算法解决线路故障状态样本数量少而带来的样本集类别不平衡问题。然后，考虑到线路状态二分类的特点，采用SVM算法进行线路运行工况的分类预测，形成了基于SMOTE-SVM算法的状态转移模型求解方法，可实现配电线路时变状态预测。最后，以某实际配电系统为算例验证了所提方法的有效性。

Abstract

Accurate prediction of the states of the distribution line is the basis for the dispatch and control of distribution network. A method based on support vector machine (SVM) is proposed for predicting the time-variant states of distribution lines. Firstly

the factors that affect the state of the distribution lines are analyzed

and a state transition model of the distribution lines is constructed based on the Fokker-Planck equation. Secondly

by fusing the multi-source massive data of the distribution information system

the distribution line state feature variable set is structured using the optimal feature subset selection method based on the correlation. The synthetic minority over-sampling technique (SMOTE) algorithm is used to solve the problem of imbalance sample number between categories caused by the small number of line fault state samples. Then

considering the characteristics of two classifications of the line states

the SVM algorithm is adopted to classify and predict the line operation conditions. A state transition model solution method based on SMOTE-SVM algorithm is formed to realize the time-variant state prediction of distribution lines. Finally

an actual power distribution system is used as an example to verify the effectiveness of the proposed method.

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references

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