首页> 外文期刊>International Journal of Electrical Power & Energy Systems >A novel intelligent protection system for power transformers considering possible electrical faults, inrush current, CT saturation and over-excitation
【24h】

A novel intelligent protection system for power transformers considering possible electrical faults, inrush current, CT saturation and over-excitation

机译:考虑电力故障,浪涌电流,CT饱和和过励磁的新型电力变压器智能保护系统

获取原文
获取原文并翻译 | 示例
           

摘要

Many electrical events can be easily damage electrical equipments in power systems. Such events or faults can be easily stopped at incipient steps but because of weakness of protecting systems they grow and extend, and consequently impose so many problems and cost to utilities. Power transformers are one of the vital equipments in electrical networks and industries, although many protecting systems have been implemented to prevent dangerous electrical faults, but most of them suffer many problems, such as; time wasting, computational burden, and low speed in response. In addition, whenever patterns of fault signals are similar, their discrimination from each other is so hard. Magnetizing inrush current, internal fault, CT saturation and over excitation are common electrical faults in power transformers so that most of the time are difficult to be separated. In this paper all considerations for designing perfect protecting system for power transformers are considered. Using intelligent approach, Artificial Neural Network (ANN) based method is designed. Indeed, proposed protecting system includes two major sections. In the first section, using Bayesian Classifier (BC) which works based on Bayesian rules and uses knowledge of training data directly; internal fault is detected and is discriminated from three other mentioned faults and normal condition. If the event is not internal fault, second condition of this intelligent system makes a decision. In this section, ANN trained by swarm based algorithms, namely; Improved Gravitational Search Algorithm (IGSA) or Particle Swarm Optimization (PSO) is used to discriminate magnetizing inrush current, current transformer (CT) saturation and over excitation. Obtained results show that proposed system can easily and precisely follow the electrical faults in power transformer and detect them at incipient steps. Such quick and accurate response helps to save so much energy, financial cost and time.
机译:许多电气事件很容易损坏电力系统中的电气设备。此类事件或故障可以很容易地在开始时就停止,但是由于保护系统的弱点,它们会增长和扩展,因此给公用事业带来了很多问题和成本。电力变压器是电气网络和工业中的重要设备之一,尽管已经实施了许多保护系统来防止危险的电气故障,但是它们大多数都遇到许多问题,例如:浪费时间,计算负担大,响应速度慢。另外,每当故障信号的模式相似时,它们之间的区别就非常困难。励磁涌流,内部故障,CT饱和和过励磁是电力变压器中常见的电气故障,因此大部分时间很难分离。本文考虑了设计完善的电力变压器保护系统的所有考虑因素。利用智能方法,设计了基于人工神经网络的方法。实际上,提议的保护体系包括两个主要部分。在第一部分中,使用基于贝叶斯规则的贝叶斯分类器(BC),直接使用训练数据的知识;检测到内部故障,并将其与其他三个提到的故障和正常状况区分开。如果事件不是内部故障,则此智能系统的第二种情况做出决定。在本节中,ANN受基于群体的算法训练,即:改进的引力搜索算法(IGSA)或粒子群优化(PSO)用于区分励磁涌流,电流互感器(CT)饱和度和过励磁。所得结果表明,所提出的系统可以轻松,准确地跟踪电力变压器中的电气故障,并以较早的步骤进行检测。这样快速准确的响应有助于节省大量能源,财务成本和时间。

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