fault classification in electrical power transmission system Using Machine Learning Algorithms
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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