• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International review of electrical engineering >Improvement of Internal Fault Detection Algorithms to Reduce Training Time of Back-Propagation Neural Networks for Transformer Differential Protection Schemes
【24h】

Improvement of Internal Fault Detection Algorithms to Reduce Training Time of Back-Propagation Neural Networks for Transformer Differential Protection Schemes

机译：改进内部故障检测算法以减少用于变压器差动保护方案的反向传播神经网络的训练时间

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper presents an algorithm based on a combination of Discrete Wavelet Transforms (DWT) and back-propagation neural networks for detection and classification of internal faults in a two-winding three-phase transformer. Fault conditions of the transformer are simulated using Electromagnetic Transients Program (EMTP) in order to obtain current signals. The training process for the neural network and fault diagnosis decision are implemented on MATLAB. In addition, the initial number of neurons for the first hidden layer to decrease duration time of train process is taken into account. Various cases based on Thailand electricity transmission and distribution systems are studied to verify the validity of the proposed algorithm. A comparison between the proposed technique and conventional training is presented. The result is shown that the proposed technique is very effective in reduce training time and gives a satisfactory accuracy.
机译：本文提出了一种基于离散小波变换（DWT）和反向传播神经网络的算法，用于两绕组三相变压器的内部故障检测和分类。为了获得电流信号，使用电磁暂态程序（EMTP）对变压器的故障状况进行了仿真。在MATLAB上实现了神经网络的训练过程和故障诊断决策。另外，考虑到第一隐藏层的神经元的初始数量以减少训练过程的持续时间。研究了基于泰国输配电系统的各种情况，以验证该算法的有效性。提出的技术与常规培训之间的比较。结果表明，所提出的技术在减少训练时间上非常有效，并且给出了令人满意的精度。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号