首页> 外文会议>IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems >Fault Classification using Artificial Neural Network in Combined Underground Cable and Overhead Line
【24h】

Fault Classification using Artificial Neural Network in Combined Underground Cable and Overhead Line

机译:地下电缆组合电缆中的人工神经网络故障分类

获取原文

摘要

Indian Power System is equipped mainly with the overhead (OH) line. The inclination towards the use of underground cable (UG) is less. Nowadays with the advent of the XLPE cable with high capacity to transmit power is taking up the interest of the power system engineers to use underground cable along with the overhead line. It is also a solution in the areas where population is more; environmental constraint is present and right of way is a problem. Protection issues are taken into consideration as the fault to be cleared should take up the minimum time and conventional method to detect and classify fault take more time and is a manual approach in case fault occurred in UG cable so digital relay employing Artificial Neural Network is used to classify fault fast and with greater accuracy. The work in this paper is checked for all types of fault taking into account the fundamental components of voltage and current collected from the sending end; the effect of zero sequence currents; the cable parameters and sequence components for OH line using the MATLAB 2013Ra/SIMULINK.
机译:印度电力系统主要配备架空(OH)线。倾向于使用地下电缆(UG)较少。如今,具有高容量传输功率的XLPE电缆的出现正在占据电力系统工程师的兴趣,将地下电缆与架空线一起使用。它也是人口更多的领域的解决方案;环境约束是存在的,是一个问题。考虑到保护问题,因为要清除的故障应占用最小时间和传统方法来检测和分类故障需要更多的时间,并且在UG电缆中发生的情况下发生了手动方法,因此使用了采用人工神经网络的数字继电器快速对故障进行分类,更准确。考虑到从发送端收集的电压和电流的基本组件,检查本文中的所有类型的工作;零序电流的影响;使用MATLAB 2013RA / Simulink的OH行的电缆参数和序列组件。

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