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HVDC transmission line fault localization base on RBF neural network with wavelet packet decomposition

机译:基于小波包分解的RBF神经网络的高压直流输电线路故障定位

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High voltage direct current (HVDC) transmission lines fault location is extremely important in repairing the fault line timely and reducing outage losses, so as to guarantee the reliability of power supply and to improve the stability of power system. Traveling wave, which is a kind of non-stationary signal with mutation, spreads along the line when HVDC transmission line fault happens. The feature of the traveling wave with mutation contains the fault time, fault location. By analyzing the traveling wave, accurate fault location can be derived. In this paper, an HVDC transmission line fault localization algorithm based on radial basis function (RBF) neural network with wavelet packet decomposition (WPD) is proposed. For the sake of simplicity, the proposed algorithm is shorted for WPD-RBF in the rest of this paper. By using WPD algorithm, the feature of the traveling wave can be extracted from the voltage and current signals. These features are then used as the training input sample of RBF neural network to mapping to the line fault location. Simulation result indicates that, line fault position can be accurately localized by the proposed algorithm.
机译:高压直流输电线路的故障定位对于及时修复故障线路,减少停电损失,保证供电可靠性,提高电力系统的稳定性至关重要。行波是一种具有突变的非平稳信号,在高压直流输电线路发生故障时会沿着线路传播。具有突变的行波的特征包括故障时间,故障位置。通过分析行波,可以得出准确的故障位置。提出了一种基于径向基函数神经网络和小波包分解的高压直流输电线路故障定位算法。为了简单起见,本文其余部分将所提出的算法简称为WPD-RBF。通过使用WPD算法,可以从电压和电流信号中提取行波的特征。然后将这些特征用作RBF神经网络的训练输入样本,以映射到线路故障位置。仿真结果表明,该算法可以准确定位线路故障位置。

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