fault classification in electrical power transmission system Using Machine Learning Algorithms

Authors

  • راما الختيار Tishreen University
  • George Isber Tishreen University
  • Ali Alsayed Damascus University

Abstract

As high-voltage lines are an important component of the electrical power system, it is necessary to accurately classify faults on them, thus improving the quality of electrical power and increasing the stability of the system.

In this research, machine learning algorithms were relied upon to classify faults in electrical power transmission lines due to their reliability and accuracy of their results. The performance of two machine learning algorithms was compared: Decision Tree (DT) and Support Vector Machine (SVM) in distinguishing fault conditions. From the normal working condition and determining the type of fault on a 400 kV high tension line. The line was modeled and faults simulated in the SIMULINK environment in MATLAB, to generate fault type data at different values of fault resistance. The data was filtered and processed in a Python environment to be used in training these algorithms.

The results showed that the DT algorithm outperformed the SVM algorithm in fault classification under different system conditions..

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Published

2024-08-12

How to Cite

1.
الختيار ر, جورج اسبر, علي السيد. fault classification in electrical power transmission system Using Machine Learning Algorithms. Tuj-eng [Internet]. 2024Aug.12 [cited 2024Aug.13];46(3):375-88. Available from: https://journal.tishreen.edu.sy/index.php/engscnc/article/view/17075

Issue

Vol. 46 No. 3 (2024): Tishreen University Journal of Research and Scientific Studies - Engineering Sciences Series

Section

Master student researches

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Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0/

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