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)

Abstract

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
• Status: Published
• Refereed: Yes
• 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
• Publisher: MDPI
• ISSN: 2624-6120
• 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