Design and Training of Artificial Neural Networks for Locating Low Current Faults in Distribution Systems

J. Coser, D.T. do Vale, J. Rolim
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引用次数: 21

Abstract

Artificial Neural Networks constitute a suitable approach for pointing out a fault location in radial distribution feeders, even when the fault current has a small value, near the normal load of the system. Some publications have described successful application of artificial neural networks to the fault location problem, but there are still some difficulties that may limit their applicability to a real system, mainly the complexity of the problem when lateral derivations are included as possible fault locations. There are some inherent aspects in distribution networks that prevent the straightforward application of transmission network methodologies to distribution systems. This paper describes a new approach to the use of Artificial Neural Networks for the solution of the fault location problem in energy distribution systems. The objective is to obtain accurate results and to optimize the training stage, all using only the fundamental frequency component of the currents monitored at the substation.
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配电系统小电流故障定位的人工神经网络设计与训练
人工神经网络是一种适用于径向分布馈线故障定位的方法,即使故障电流值很小,接近系统正常负荷。一些出版物描述了人工神经网络在故障定位问题上的成功应用,但仍然存在一些困难,这些困难可能限制了它们在实际系统中的适用性,主要是当横向导数作为可能的故障定位时问题的复杂性。配电网中存在一些固有的问题,阻碍了输电网方法在配电网中的直接应用。本文介绍了一种利用人工神经网络解决配电系统故障定位问题的新方法。目标是获得准确的结果并优化训练阶段,所有这些都只使用变电站监测的电流的基频分量。
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