Comparative Fault Location Estimation by Using Image Processing in Mixed Transmission Lines

TL;DR

This paper proposes a novel image processing approach combined with neural networks and regression methods to accurately locate faults in mixed transmission lines comprising overhead and underground cables.

Contribution

It introduces a new fault location estimation method using image analysis of impedance diagrams in mixed transmission lines, enhancing accuracy over traditional impedance-based methods.

Findings

ANN outperforms regression in fault location accuracy.

Image processing effectively extracts impedance features for fault prediction.

Method successfully locates faults in mixed transmission line simulations.

Abstract

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. overhead line section and underground cable section are simulated by using PSCAD-EMTDC.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 R-X impedance diagram of the fault, and the R-X impedance diagram have been detected by applying image processing steps. Artificial neural network (ANN) and 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 ANN and the regression methods. The results of ANN and 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.