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- Title
Using Machine Learning Algorithms For Classifying Transmission Line Faults.
- Authors
TANYILDIZI AĞIR, Tuba
- Abstract
The faults in transmission lines should be identified for attaining high quality energy in electrical power systems. Savings can be made in both time and energy if the transmission line faults are classified accurately. The present study examined phase-ground, phase-phase-ground, phase-phase, phase-phase-phase and no fault cases. Support Vector Machine (SVM), K-Nearest Neighbours Algorithm (KNN), Decision Tree (DT), Ensemble, Linear discriminant analysis (LDA) classifiers were used for classifying the transmission line faults. These algorithms were compared with regard to parameters such as accuracy, error rate, prediction speed and training time. The accuracy and minimum error of SVM and KNN classifiers were 99.7 % and 0.0011 respectively. DT classifier is faster than the other classifiers with a predicted speed of 29000 obs/sec. Whereas LDA had the shortest training time of 0.76992 sec. The results have indicated that SVM, KNN classifiers have similar performances. In addition, the classifiers SVM, KNN acquired minimum error with the highest accuracy compared with the other classifiers. While DT has the highest estimation speed, LDA has the shortest training time.
- Subjects
MACHINE learning; K-nearest neighbor classification; DECISION trees; DISCRIMINANT analysis; SUPPORT vector machines
- Publication
Dicle University Journal of Engineering / Dicle Üniversitesi Mühendislik Dergisi, 2022, Vol 13, Issue 2, p227
- ISSN
1309-8640
- Publication type
Article
- DOI
10.24012/dumf.1096691