Application of BP Neural Network in Lighting Impulse Waveform Adjustment of Power Reactor Based on L-M Method

LI Hua-liang1; SUN Min2; LI Bo1; ZHONG Lei1; LU Jian-ting1; HAO Yu-liang1

doi:10.13296/j.1001-1609.hva.2012.03.012

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Application of BP Neural Network in Lighting Impulse Waveform Adjustment of Power Reactor Based on L-M Method

更新时间：2025-04-26

- Application of BP Neural Network in Lighting Impulse Waveform Adjustment of Power Reactor Based on L-M Method
- High VoltageApparatus Vol. 48, Issue 3, Pages: 52-56+62(2012)
- 作者机构：
  
  1. 西安高压电器研究院有限责任公司
  2. 西安交通大学电气工程学院
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2012.03.012
  CLC： TM47;TM866
- Published：2012
- 稿件说明：
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[1]李华良,孙敏,李博,钟磊,陆建挺,郝宇亮.基于L-M法的BP神经网络在电抗器雷电冲击波形调整中的应用[J].高压电器,2012,48(03):52-56+62.

LI Hua-liang1, SUN Min2, LI Bo1, et al. Application of BP Neural Network in Lighting Impulse Waveform Adjustment of Power Reactor Based on L-M Method[J]. High voltageapparatus, 2012, 48(3): 52-56+62.
[1]李华良,孙敏,李博,钟磊,陆建挺,郝宇亮.基于L-M法的BP神经网络在电抗器雷电冲击波形调整中的应用[J].高压电器,2012,48(03):52-56+62. DOI： 10.13296/j.1001-1609.hva.2012.03.012.

LI Hua-liang1, SUN Min2, LI Bo1, et al. Application of BP Neural Network in Lighting Impulse Waveform Adjustment of Power Reactor Based on L-M Method[J]. High voltageapparatus, 2012, 48(3): 52-56+62. DOI： 10.13296/j.1001-1609.hva.2012.03.012.

摘要

建立了电抗器匝间绝缘雷电冲击调波参数组合寻优模型。为了克服传统BP神经网络训练收敛太慢以及容易陷入局部最小值等问题

采用了正交试验设计方法结合基于Levenberg-Marquardt法的BP神经网络对建立的数学模型进行了仿真训练并对设备雷电冲击调波参数组合进行了寻优

获得了较为精确的标准雷电冲击波形

对实际试验具有一定的指导意义。

Abstract

A reactor’s turn-to-turn insulation lightning impulse waveform parameter mathematical optimization model has been established.In order to overcome the traditional BP neural network training convergence is too slow and easy to fall into local minimum problems.Orthogonal experimental design method together with BP neural network based on Levenberg-Marquardt method have been adopted to train the experiment data and to find the optimum parameter combinations

and a precise standard lightning impulse waveform has been obtained.Research results have certain significance to the actual test.

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