充电暂态故障定位:分析方法与人工神经网络

R. Benato, S. Sessa, G. Rinzo, M. Poli
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引用次数: 0

摘要

本文以意大利60kV地下运行分输电网为例,提出了两种单端故障定位算法。两种算法都处理了声相对地电容充电频率与故障位置的关系。在第一种方法中,集总参数电路在拉普拉斯域中的频率响应与故障距离有关。第二种方法是利用暂态电流波形的频谱作为数据库,训练人工神经网络,通过分析输入数据来识别故障距离。比较了两种方法的故障定位精度,揭示了两种方法的优缺点。所开发的程序已应用于EMTP-rv环境中建模的架空线,而故障定位算法已在Matlab环境中实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Charging Transient for Fault Location: Analytical Method versus Artificial Neural Network
In this paper, two single-ended algorithms for fault location are presented with reference to the unearthed operated sub-transmission Italian grid (60kV). Both algorithms deals with the correlation between the charging frequency of sound phases to ground capacitances with the fault position. In the first one, the frequency response of a lumped parameter circuit in the Laplace domain is linked to the fault distance. In the second one, the frequency spectra of the transient current waveforms are used as a database for the training of an Artificial Neural Network, which identifies the fault distance by analysing the input data. The fault location accuracy of the two proposed methods are compared in order to reveal strengths and weakness of both algorithms. The developed procedures have been applied to an overhead line modelled in EMTP-rv environment, whereas the fault location algorithm has been implemented in Matlab environment.
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