After a transmission line fails, quickly and accurately find the fault point and deal with it, which is of great significance to maintaining the normal operation of the power system. Aiming at the problems of low accuracy of traditional traveling wave fault location methods and many affected factors, this paper relies on distributed traveling wave monitoring points arranged on transmission lines to study methods to improve the accuracy of traveling wave fault location on transmission lines. First, when a line fails, a traveling wave signal that moves to both ends will be generated and transmitted along the transmission line. We use the Radon transform algorithm to process the traveling wave signal. Then, this paper uses ant colony algorithm to analyze and verify the location and extent of transmission line faults and then optimizes high-precision collection and processing. Finally, the simulation distance measurement is carried out on double-terminal transmission lines and multiterminal transmission lines (T-shaped lines) with branches. The results show that, for double-ended transmission lines, the algorithm increases the speed of matrix calculations and at the same time makes the fault location error of the transmission grid still maintain the improved effect.

中文翻译：

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基于机器学习和人工智能融合的输电线路故障定位研究

输电线路发生故障后，快速准确地找到故障点并进行处理，对于维护电力系统的正常运行具有重要意义。针对传统行波故障测距方法精度低，影响因素多的问题，本文以分布在传输线上的行波监测点为研究对象，研究了提高行波故障测距精度的方法。首先，当线路发生故障时，将生成移动到两端的行波信号，并沿着传输线传输。我们使用Radon变换算法来处理行波信号。然后，本文采用蚁群算法对传输线故障的位置和范围进行分析和验证，然后对高精度的采集和处理进行优化。最后，在具有分支的双端传输线和多端传输线（T形线）上进行模拟距离测量。结果表明，对于双端输电线路，该算法提高了矩阵计算的速度，同时使输电网的故障定位误差仍保持了改善的效果。