Prediction for Magnitude of Short Circuit Current in Power Distribution System Based on ANN

Chen Li-an
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引用次数: 15

Abstract

With the growth seen by power systems lately, short circuits have become some of the most common and damaging power system failures. Accurate forecasting of short circuit faults and predicting for magnitude of short circuit current are becoming increasingly important. Power system short circuit fault research is a basic technological problem, but at the same time a rather difficult one, which has played an important role in the design of smart grids. The introduction of ANN prediction has led to improved results over prior art fault diagnosis technologies. Although it has been employed with good results in various fields, reports of its application in power distribution short-circuit current prediction are rather limited. This article introduces the ANN theory in the sphere of short-circuit current prediction in power distribution systems. The prediction model formulated shall serve as theoretical foundation for the design of intelligent switching equipment, with global and selective protection, very likely to be found in future smart grids.
基于人工神经网络的配电系统短路电流大小预测
随着电力系统的发展,短路已成为最常见和最具破坏性的电力系统故障之一。短路故障的准确预测和短路电流大小的预测变得越来越重要。电力系统短路故障的研究是一个基础性的技术问题,同时也是一个较为困难的技术问题,在智能电网的设计中起着重要的作用。人工神经网络预测的引入导致了比现有技术故障诊断技术更好的结果。虽然它在各个领域都得到了很好的应用,但在配电短路电流预测方面的应用报道却相当有限。本文介绍了人工神经网络理论在配电系统短路电流预测中的应用。所建立的预测模型可以作为智能开关设备设计的理论基础,具有全局保护和选择性保护,在未来的智能电网中很有可能实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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