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A Multi Agent System Design for Power Distribution Restoration Using Neural Networks.

机译:使用神经网络进行配电恢复的多代理系统设计。

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摘要

The state of the art of power distribution systems is to demand a more accurate response. It also provides more reliability for fault location and restoration respectively. A multi-agent system design for power distribution has been developed using the change of current methodology to detect and locate any type of faults. Employing the artificial intelligence for restoration process is the most important contribution to this study. Since feed-forward neural networks are weight training based back propagation concept, radial basis neural networks showed more efficiency by using the minimum error method to optimize the decision. A Probabilistic radial basis Neural Network (PNN) is designated at each feeder agent to implement the reconfiguration by analyzing the impedance and current values for each zone. The appropriate decision for the optimal reconfiguration case is a vector of activation signals associated with each switch to restore the power to the un-faulted zones of distribution feeder.;This study examines the role of Universal Asynchronous Receiver Transmitter (UART) buffer circuits in the laboratory experiment demonstration of the multi-agent system design. The main approach of a self-healing concept is the protection system. A recloser has been developed and improved for more sensitivity and faster response to detecting a fault where ever it occurs and lead the process of isolating and re-configuration. An electronic buffer circuit using digital microcontroller has been associated with the recloser and agents switches in order to offer a satisfying feedback for the proposed approach. Simulation studies, using MATLAB SimPowerSystems and, Neural Network toolboxes, for the proposed power distribution system showed improved results for fault location and restoration using Radbas neural networks. Hardware implementation with high accurate software data scoping of results has been employed to show the difference in time response using Universal Asynchronous Receiver Transmitter buffers at each switching relay in the design.
机译:配电系统的最新技术水平是要求更准确的响应。它还分别为故障定位和恢复提供了更高的可靠性。已经开发出一种用于配电的多代理系统设计,该设计使用当前方法的变化来检测和定位任何类型的故障。将人工智能用于恢复过程是这项研究的最重要贡献。由于前馈神经网络是基于权重训练的反向传播概念,因此通过使用最小误差方法优化决策,径向基神经网络显示出更高的效率。在每个馈线代理处指定一个概率径向基神经网络(PNN),以通过分析每个区域的阻抗和电流值来实施重新配置。最佳重新配置情况的适当决策是与每个开关相关的激活信号向量,以恢复配电馈线无故障区域的电源。这项研究研究了通用异步收发器(UART)缓冲电路在开关中的作用。多智能体系统设计的实验室实验演示。自我修复概念的主要方法是保护系统。已开发并改进了一种重合器,以提高灵敏度和对故障的响应速度,并能隔离和重新配置。使用数字微控制器的电子缓冲电路已与重合器和代理开关关联,以便为所提出的方法提供令人满意的反馈。使用MATLAB SimPowerSystems和神经网络工具箱对拟议的配电系统进行的仿真研究表明,使用Radbas神经网络进行故障定位和恢复的结果有所改善。在设计中的每个开关继电器处使用通用异步接收器发送器缓冲器,通过对结果进行高精度软件数据范围划分的硬件实现来显示时间响应的差异。

著录项

  • 作者

    Mashta, Mohamad.;

  • 作者单位

    West Virginia University.;

  • 授予单位 West Virginia University.;
  • 学科 Electrical engineering.
  • 学位 M.S.
  • 年度 2015
  • 页码 77 p.
  • 总页数 77
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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