基于小波神经网络的配电系统故障电流分类

Y. Assef, O. Chaari, M. Meunier
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引用次数: 14

摘要

在中性点谐振接地的配电系统中,基于稳态分析的传统保护算法已不再适用。因此,很好地使用瞬变变得至关重要。本文探讨了在电力系统继电保护算法中使用小波变换作为人工神经网络预处理的可能性。经过训练,人工神经网络决定测量的信号是错误的还是正确的。该神经网络的输入是对电磁瞬变程序产生的故障信号进行递归小波变换后得到的小波系数参数。对小波变换和快速傅里叶变换进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of power distribution system fault currents using wavelets associated to artificial neural networks
In a power distribution system with a resonant neutral grounding, traditional protection algorithms based on a steady state analysis are no longer adapted. Hence a good use of transients becomes essential. This paper deals with the possibility of using wavelet transform as a preprocess for artificial neural networks (ANN) in the algorithm of power system relays. The ANN decides, after training, if the measured signal is faulty or sound. The inputs of the ANN are the arguments of wavelet coefficients obtained after applying a recursive wavelet transform on faulty signals generated with EMTP (ElectroMagnetic Transient Program). A comparison between the wavelets and fast Fourier transform has been made.
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