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Neural network based digital differential relay for synchronous generators.

机译:基于神经网络的数字差分继电器,用于同步发电机。

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摘要

Differential protection is the most common method used by electric utilities for generator stator winding protection. Digital relays, used in differential protection, take one cycle or more after fault inception to issue a trip signal. Minimizing the damage due to stator faults, by using high speed relays, is important. A new high speed neural network based digital differential relay for generator stator winding protection is proposed in this dissertation.; A direct 3-phase model that enables an exact study of synchronous machine performance is presented. This model is capable of simulating normal operation and various kinds of external faults. In this dissertation a new method for simulating internal faults in a synchronous generator, using the direct phase quantities, is developed. The internal faults algorithm is capable of simulating internal single phase to ground faults and internal two phase to ground faults in multi-path and single-path generators.; The relay uses two multi-layer feed-forward neural networks (FNNs). One FNN is used by the fault detector module and the other by the fault classifier module. The fault detector module is used to discriminate between three generator states, namely the normal operation, external fault and internal fault states. In the event of an internal fault the relay issues a trip signal and activates the fault classifier module, which identifies the faulted phase(s). In the case of an external fault the relay acts as a backup relay for the main protection against external faults. Simulation results showing the performance of the protection scheme are presented and indicate that it is fast, robust and reliable. The relay tripping time, for the majority of internal faults, is well within half a cycle.; The laboratory experiments are divided in two parts. The first part provides experimental verification of the developed internal faults algorithm. In the second part implementation and real-time experimental verification of the proposed relay are described.
机译:差动保护是电力公司用于发电机定子绕组保护的最常用方法。在发生故障后,用于差动保护的数字继电器要花一个或多个周期才能发出跳闸信号。重要的是,通过使用高速继电器将定子故障造成的损坏降至最低。本文提出了一种新型的基于高速神经网络的数字差分继电器,用于发电机定子绕组的保护。提出了一个直接三相模型,该模型可以精确研究同步电机的性能。该模型能够模拟正常运行和各种外部故障。本文提出了一种利用直接相位量模拟同步发电机内部故障的新方法。内部故障算法能够模拟多径和单径发电机中的内部单相接地故障和内部两相接地故障。中继使用两个多层前馈神经网络(FNN)。一个FNN由故障检测器模块使用,另一个由故障分类器模块使用。故障检测器模块用于区分三种发电机状态,即正常运行,外部故障和内部故障状态。如果发生内部故障,继电器会发出跳闸信号并激活故障分类器模块,该模块识别出故障的相。在发生外部故障的情况下,该继电器充当备用继电器,以提供针对外部故障的主要保护。仿真结果表明了该保护方案的性能,并表明该方案是快速,可靠和可靠的。对于大多数内部故障,继电器跳闸时间在半个周期之内。实验室实验分为两部分。第一部分提供了对开发的内部故障算法的实验验证。在第二部分中,描述了所提出的继电器的实现和实时实验验证。

著录项

  • 作者

    Megahed, Ashraf Ibrahim.;

  • 作者单位

    University of Calgary (Canada).;

  • 授予单位 University of Calgary (Canada).;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 1998
  • 页码 175 p.
  • 总页数 175
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;
  • 关键词

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