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Fault location on high voltage transmission line by applying support vector regression with fault signal amplitudes

机译:通过应用具有故障信号幅度的支持向量回归,在高压输电线路上进行故障定位

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This paper proposes a novel high voltage transmission line fault location scheme based on application of support vector regression (SVR). The proposed scheme just uses the amplitudes of the fault voltage waveforms, measured at a single end of the line. Various types of faults at different locations with different fault impedances and a variety of fault inception angles are studied on a 400 kV-300 km high-voltage transmission line power system. The fault voltages are obtained from 1/8 cycle post-fault signals after the noise has been eliminated using a low-pass filter. The amplitudes of the fault voltage signals are used as features to train the SVR. After training, the SVR is used in the exact location of the fault on the transmission line. When compared with other fault location schemes, the proposed scheme requires less information and a smaller time data window to estimate the fault locations. However, the proposed scheme provides more accurate estimations, irrespective of the fault types, fault inception angles and fault impedances. (C) 2018 Elsevier B.V. All rights reserved.
机译:提出了一种基于支持向量回归(SVR)的高压输电线路故障测距方案。所提出的方案仅使用故障电压波形的幅度,该幅度是在线路的单端测量的。在400 kV-300 km高压输电线路电力系统上研究了不同位置,具有不同故障阻抗和不同故障起始角度的各种类型的故障。使用低通滤波器消除噪声后,可从1/8周期故障后信号获得故障电压。故障电压信号的幅度用作训练SVR的功能。训练后,将SVR用于传输线上故障的确切位置。与其他故障定位方案相比,该方案需要较少的信息和较小的时间数据窗口来估计故障位置。然而,所提出的方案提供了更准确的估计,而与故障类型,故障接收角度和故障阻抗无关。 (C)2018 Elsevier B.V.保留所有权利。

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