Transmission line fault classification of multi-dataset using CatBoost classifier

Ogar, V. N., Hussain, S. and Gamage, K. A.A. (2022) Transmission line fault classification of multi-dataset using CatBoost classifier. Signals, 3(3), pp. 468-482. (doi: 10.3390/signals3030027)

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Transmission line fault classification forms the basis of fault protection management in power systems. Because faults have adverse effects on transmission lines, adequate measures must be implemented to avoid power outages. This paper focuses on using the categorical boosting (CatBoost) algorithm classifier to analyse and train multiple voltage and current data from a 330 kV and 500 km-long simulated faulty transmission line model designed using Matlab/Simulink. From it, 93,340 fault data sizes were extracted. The CatBoost classifier was employed to classify the faults after different machine learning algorithms were used to train the same data with different parameters. The trainer achieved the best accuracy of 99.54%, with an error of 0.46% for 748 iterations out of 1000. The algorithm was selected for its high performance in classifying faults based on accuracy, precision and speed. In addition, it is easy to use and handles multiple data-sets. In contrast, a support vector machine and an artificial neural network each has a longer training time than the proposed method’s 58.5 s. Proper fault classification techniques assist in the effective fault management and planning of power system control thereby preventing energy waste and providing high performance.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Gamage, Professor Kelum and Ogar, VINCENT and Hussain, Dr Sajjad
Creator Roles:
Hussain, S.Supervision
Gamage, K. A.A.Supervision
Ogar, V. N.Writing – original draft
Authors: Ogar, V. N., Hussain, S., and Gamage, K. A.A.
College/School:College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Signals
ISSN (Online):2624-6120
Published Online:05 July 2022
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in Signals 3(3): 468-482
Publisher Policy:Reproduced under a Creative Commons License

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