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Single line to ground-fault detection for unit generator-transformer based on wavelet transform and neutral networks

机译:基于小波变换和神经网络的发电机组变压器单线接地故障检测

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摘要

The aim of this paper is to detect the single line to ground fault on the unit generator- transformer. A new ground fault detection scheme based on the extraction of energy and statistical parameters from wavelet transform based neural network is proposed. The faulty current signals obtained from a simulation were decomposed through wavelet analysis into various approximations and details. The simulation of the unit generator-transformer was carried out using the Sim-PowerSystem Blockset of MATLAB. The energy and statistical parameters analysis involved measured of the dispersion factors (range and standard deviation) of wavelet coefficients. Regarding the ANN performance, the errors in the SLG-fault detection of ANN were under 1 %. The results indicate that the proposed algorithm was accurate enough in differentiating a single line to ground fault and un-fault for a unit generator-transformer.
机译:本文的目的是检测单元发电机变压器上的单线接地故障。提出了一种新的基于小波变换神经网络能量和统计参数提取的接地故障检测方案。通过小波分析将通过仿真获得的故障电流信号分解为各种近似值和细节。使用MATLAB的Sim-PowerSystem Blockset对单元发电机-变压器进行了仿真。能量和统计参数分析涉及对小波系数的色散因子(范围和标准偏差)的测量。关于人工神经网络的性能,人工神经网络的SLG故障检测中的误差小于1%。结果表明,所提出的算法在区分单元发电机-变压器的单线接地故障和无故障方面足够准确。

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