Prediction of Leakage Current on Insulator Surface of Transmission Line Based on BP Neural Network

WANG Liming; LI Xu; CAO Bin; GAO Song

doi:10.13296/j.1001-1609.hva.2020.02.011

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Prediction of Leakage Current on Insulator Surface of Transmission Line Based on BP Neural Network

更新时间：2025-04-26

- Prediction of Leakage Current on Insulator Surface of Transmission Line Based on BP Neural Network
- High VoltageApparatus Vol. 56, Issue 2, Pages: 69-76(2020)
- 作者机构：
  
  1. 清华大学深圳研究生院
  2. 江苏省电力公司电力科学研究院
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2020.02.011
  CLC： TM216
- Published：2020
- 稿件说明：
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[1]王黎明,李旭,曹彬,高嵩.基于BP神经网络的线路绝缘子表面泄漏电流预测[J].高压电器,2020,56(02):69-76.

WANG Liming, LI Xu, CAO Bin, et al. Prediction of Leakage Current on Insulator Surface of Transmission Line Based on BP Neural Network[J]. High voltageapparatus, 2020, 56(2): 69-76.
[1]王黎明,李旭,曹彬,高嵩.基于BP神经网络的线路绝缘子表面泄漏电流预测[J].高压电器,2020,56(02):69-76. DOI： 10.13296/j.1001-1609.hva.2020.02.011.

WANG Liming, LI Xu, CAO Bin, et al. Prediction of Leakage Current on Insulator Surface of Transmission Line Based on BP Neural Network[J]. High voltageapparatus, 2020, 56(2): 69-76. DOI： 10.13296/j.1001-1609.hva.2020.02.011.

摘要

污闪事故严重威胁着电力系统的安全稳定运行

而绝缘子表面泄漏电流与污闪之间存在比较密切的联系

文中希望通过神经网络模型对泄漏电流进行预测进而对防污闪工作进行指导。通过对泄漏电流在线监测系统得到的线路多年运行数据进行分析

得到了影响泄漏电流的典型因素及其作用规律。利用BP神经网络算法对某一地区泄漏电流实测数据进行训练

得到较为理想的神经网络模型

使用该模型能够对下一年份线路泄漏电流值进行预测。通过江苏省实际运行线路绝缘子泄漏电流数据的预测结果

验证了该预测方法的有效性和正确性。

Abstract

The leakage current on insulator surface of transmission line has close relation with pollution flashover.Effective prediction of insulator’ s leakage current is of great significance for preventing the flashover.In this paper

the data of leakage current on insulator surface detected in many years by the on-line monitoring system are analyzed

and typical factors influencing the leakage current are obtained.The BP neural network algorithm is used to train the detected data

then an ideal neural network model is obtained.This model can be used to predict the leakage current in next year.The effectiveness of the proposed method is verified by analyzing the leakage current prediction data of a transmission line in operation in Jiangsu Province.

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