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

• URL: http://arxiv.org/abs/2011.03238v1
• Date: Fri, 6 Nov 2020 08:56:30 GMT
• Title: Fault Location Estimation by Using Machine Learning Methods in Mixed Transmission Lines
• Authors: Serkan Budak, Bahadir Akbal
• Abstract summary: 154 kV overhead transmission line and underground cable line are examined as the mixed transmission line for the distance protection relays. Fault location cannot be correctly detected in mixed transmission lines due to different characteristic impedance per unit length.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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