Comparison of artificial neural networks and conventional algorithms in ground fault distance computation

G. Eberl, S. Hanninen, M. Lehtonen, P. Schegner
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引用次数: 20

Abstract

This paper gives a comparison between an artificial neural network method and a differential equation algorithm and wavelet algorithm in transient based earth fault location in the 20 kV radial power distribution networks. The items discussed are earth fault transients. Signal pre-processing and the performance of the proposed distance estimation methods. The networks considered are either unearthed or resonant earthed. The comparison showed that the neural network algorithm was better than the conventional algorithms in the case of very low fault resistance. The mean error in fault location was about 1 km in the field tests using staged faults, which were recorded in real power systems. With higher fault resistances, the conventional algorithms worked better.
人工神经网络与常规算法在接地故障距离计算中的比较
本文比较了人工神经网络方法与微分方程算法和小波算法在20kv径向配电网暂态接地故障定位中的应用。讨论的项目是接地故障暂态。对所提出的距离估计方法进行了信号预处理和性能分析。所考虑的网络要么是接地的,要么是谐振接地的。对比表明,在故障电阻很低的情况下,神经网络算法优于传统算法。在实际电力系统中记录的分段故障现场试验中,故障定位的平均误差约为1 km。在较高的抗故障能力下,传统算法的效果更好。
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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