Estimation of High Impedance Fault Location in Electrical Transmission Lines Using Artificial Neural Networks and R-X Impedance Graph
Serkan Budak, Bahadir Akbal

TL;DR
This paper presents a method using artificial neural networks and R-X impedance graphs to accurately locate high impedance faults in 154 kV transmission lines, enhancing protection relay performance.
Contribution
It introduces a novel approach combining ANN with impedance graph images for precise fault location in high voltage transmission lines.
Findings
High accuracy fault location achieved with ANN models.
Simulation data effectively used for training and testing.
Method improves speed and reliability of fault detection.
Abstract
It is very important to ensure continuity in the process from generation of electricity to transmission to cities. The most important part of the system is energy transmission lines and distance protection relays that protect these lines. The main function of the protection relays in electrical installations should be deactivated as soon as possible in the event of short circuits in the system. An accurate error location technique is required to make fast and efficient work. Distance relays are widely used as main and backup protection in transmission and distribution lines. Basically, distance protection relays determine the impedance of the line by comparing the voltage and current values. In this study, artificial neural network (ANN) has been used to accurately locate high impedance short circuit faults in 154 kV power transmission lines. The impedance diagram (R-X) of the circuit…
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Taxonomy
TopicsPower Systems Fault Detection · Vehicle License Plate Recognition · Electricity Theft Detection Techniques
