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首页> 外文期刊>Electric power systems research >A double ended AC series arc fault location algorithm for a low-voltage indoor power line using impedance parameters and a neural network
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A double ended AC series arc fault location algorithm for a low-voltage indoor power line using impedance parameters and a neural network

机译：使用阻抗参数和神经网络的低压室内电力线双端交流串联电弧故障定位算法

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摘要

This paper presents a novel method for the distance estimation of a series arc fault in a low-voltage indoor power line. This method is based on the SIMULINK modeling of an electrical line by using its RLCG parameters. Rather than using an arc fault model, arc faults are inserted at different points across the line, using measured data. Faults using carbonized paths and opening contacts between two copper electrodes are considered. The algorithm estimates the arc fault distances by employing both the voltages and currents at two ends of the line, calculating their (Discrete Fast Fourier Transform) and then inserting these magnitudes, into Kirchhoff equations which take into account impedance parameters of the simplified approach line model. The arc fault is generated at an unknown distance. As the impedance parameters depend on the fault location, sets of supposed fault distances (varying by one-meter steps) are inserted in these equations. Thus, the extraction of currents peak values at the fundamental frequency (50 Hz) generates a signature vector. A neural network is trained using signature vectors as inputs (for faults generated at different distances). Finally, the algorithm thus developed is validated in two steps. First, the fault distances are estimated using distances not considered in the training process. Secondly, the fault distances are estimated using other series arc faults data not considered in the learning process. The results obtained in this work show that fault location can be successfully estimated both for faults generated by opening contacts and for carbonized paths. Additionally, the algorithm has successfully tested on different line lengths and considering also changes on the impedance parameters of the line.
机译：本文提出了一种低压室内电力线串联电弧故障距离估计的新方法。该方法基于电线的SIMULINK建模，方法是使用其RLCG参数。并非使用电弧故障模型，而是使用测量数据在整个线路的不同点插入电弧故障。考虑使用碳化路径和两个铜电极之间断开触点的故障。该算法通过同时利用线路两端的电压和电流来估算电弧故障距离，计算其（离散快速傅立叶变换），然后将这些幅度插入考虑了简化进近线模型的阻抗参数的基尔霍夫方程中。电弧故障是在未知距离处产生的。由于阻抗参数取决于故障位置，因此在这些方程式中插入了一组假定的故障距离（以一米为步长变化）。因此，提取基频（50 Hz）上的电流峰值将生成特征向量。使用签名向量作为输入来训练神经网络（用于在不同距离处生成的故障）。最后，分两步验证了由此开发的算法。首先，使用训练过程中未考虑的距离来估计故障距离。其次，使用在学习过程中未考虑的其他串联电弧故障数据估计故障距离。这项工作获得的结果表明，对于断开触点和碳化路径所产生的故障，都可以成功地估计故障位置。此外，该算法已成功测试了不同的线路长度，并考虑了线路阻抗参数的变化。

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