Machine Learning-Based Electrical Fault Classification: A Comparative Analysis of Logistic Regression and Random Forest
DOI:
https://doi.org/10.48047/g9ph5v39Keywords:
Electrical fault detection, machine learning, fault classification, Random Forest, Logistic Regression, hyper parameter tuning.Abstract
Fault classification plays a vital role in electrical systems, ensuring reliability and minimizing downtime. This study assesses how well two machine learning models—Random Forest and Logistic Regression—distinguish between fault and no-fault situations. To assess their performance, we analyzed accuracy, precision, recall, and F1-score, both before and after hyper parameter tuning.
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References
S. Mandal and A. K. Bohre, “Fault Classification in Electrical Systems Using Machine Learning Algorithms,” Aug. 2022. doi: 10.1109/ICICICT54557.2022.9917976.
H. Mirshekali, R. Dashti, and H. R. Shaker, “A Novel Fault Location Algorithm for Electrical Networks Considering Distributed Line Model and Distributed Generation Resources,” in IEEE PES Innovative Smart Grid Technologies Europe, Oct. 2020. doi: 10.1109/ISGTEUROPE47291.2020.9248755.
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