论文标题

通过使用机器学习方法在混合传输线中使用机器学习方法估算的位置估算

Fault Location Estimation by Using Machine Learning Methods in Mixed Transmission Lines

论文作者

Budak, Serkan, Akbal, Bahadir

论文摘要

高架线通常用于电能传输。此外,XLPE地下电缆线通常在市中心和拥挤的区域提供电气安全性,因此高压地下电缆线与传输线中的高架线一起使用,这些线被称为混合线。距离保护继电器用于根据传输线中的电流和电压幅度来确定基于阻抗的故障位置。但是,由于单位长度不同的特征阻抗,在混合传输线中无法正确检测到故障位置,因为高压电缆线的特征阻抗与高架线有显着不同。因此,在混合传输线中,难以确定故障部分和具有距离保护继电器的位置。在这项研究中,检查了154 kV的架空传输线和地下电缆线作为距离保护继电器的混合传输线。相位断层是在混合传输线中创建的,并且使用PSCAD模拟了台面部分和地下电缆部分。短路故障图像是在距离防传输线和地下电缆传输线故障的距离保护继电器中生成的。这些图像包括故障的RX阻抗图,并且通过应用图像处理步骤检测到Rx阻抗图。回归方法用于预测故障位置,图像处理的结果用作回归方法训练过程的输入参数。将回归方法的结果进行比较,以在本研究结束时选择最合适的方法,以预测传输线中的故障位置。

Overhead lines are generally used for electrical energy transmission. Also, XLPE underground cable lines are generally used in the city center and the crowded areas to provide electrical safety, so high voltage underground cable lines are used together with overhead line in the transmission lines, and these lines are called as the mixed lines. The distance protection relays are used to determine the impedance based fault location according to the current and voltage magnitudes in the transmission lines. However, the fault location cannot be correctly detected in mixed transmission lines due to different characteristic impedance per unit length because the characteristic impedance of high voltage cable line is significantly different from overhead line. Thus, determinations of the fault section and location with the distance protection relays are difficult in the mixed transmission lines. In this study, 154 kV overhead transmission line and underground cable line are examined as the mixed transmission line for the distance protection relays. Phase to ground faults are created in the mixed transmission line, and overhead line section and underground cable section are simulated by using PSCAD. The short circuit fault images are generated in the distance protection relay for the overhead transmission line and underground cable transmission line faults. The images include the RX impedance diagram of the fault, and the RX impedance diagram have been detected by applying image processing steps. The regression methods are used for prediction of the fault location, and the results of image processing are used as the input parameters for the training process of the regression methods. The results of regression methods are compared to select the most suitable method at the end of this study for forecasting of the fault location in transmission lines.

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