A Comparative Study of Symmetrical Method and Artificial Neural Network in Faults Detection in Power Transmission Lines

Authors : Joseph Owolabi; Ojadi Pius

Volume/Issue : Volume 7 - 2022, Issue 5 - May

Google Scholar : https://bit.ly/3IIfn9N

Scribd : https://bit.ly/3OhIFiV

DOI : https://doi.org/10.5281/zenodo.6716134

The most challenges to electrical power system supply is mainly faults in transmission line and there is need for quick faults isolation in order to remove damages as a result of power outage. This paper compared together the two methods of symmetrical component method of (1) and artificial neural network method of (11) to determine their effectiveness. The two methods were subjected to simpower system,under normal and fault conditions using Akure – Ikeji Arakeji – Ilesha transmission line . Three phases were used, single line, double line, and line to line, all to the qround faults.In symmetrical component, faults in both the currents and impedance were detected, also in the artificial neural network both the faulty voltages and currents were detected. The comparison between the two methods show that the symmetrical component method, needs computation of faults in the impedance, this does not have genuine application for isolation of faults compared to artificial neural network method which has fault isolation application but no impedance calculation and have datathat gives correct results as quickly as possible. Also this method is very fast, effective and simple. Therefore, the artificial neural method is better because of it is simplicity and accuracy than the symmetrical component method.


Paper Submission Last Date
29 - February - 2024

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