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

机译:具有小波包分解的RBF神经网络的HVDC传输线故障定位基础

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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(HVDC)输电线路故障定位是及时修复故障线路,减少停电损失极其重要的,这样才能保证供电的可靠性和提高电力系统的稳定性。行波,这是一种非平稳信号具有突变的,当直流输电线路发生故障沿着线差。与突变的行波的功能包含故障时,故障定位。通过分析行波,精确故障定位可以的。在本文中,基于径向基函数的HVDC传输线路的故障定位算法(RBF)与小波包分解(WPD)神经网络算法。为简单起见,所提出的算法在本文的其余部分短接WPD-RBF。通过使用WPD算法中,行波的特征可以从电压和电流信号被提取。然后这些特征被用作RBF神经网络的训练输入样本映射到线路故障位置。仿真结果表明,,线路故障位置可通过该算法被精确地定位。

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