Journal of Research in Engineering and Applied Sciences | |
ANALYSIS AND COMPARISON OF MACHINE LEARNING APPROACHES FOR TRANSMISSION LINE FAULT PREDICTION IN POWER SYSTEMS | |
Meera Viswavandya1  Shashwat Patel2  Kaushik Sahoo2  | |
[1] Department, Electrical Engineering, College of Engineering and Technology, Bhubaneswar, India;Electrical Engineering, College of Engineering and Technology, Bhubaneswar, India; | |
关键词: transmission line faults; k-nearest neighbors; multi-layer perceptron; support vector machines; decision tree; random forest; | |
DOI : https://doi.org/10.46565/jreas.2021.v06i01.005 | |
来源: DOAJ |
【 摘 要 】
The transmission lines suffer from various faults subjected to numerous natural as well as manmade causes. This paper presents aproposedMATLAB-SIMULINKmodelforgenerationofsuchrandomdisturbances.Theoutputofthesystemisinputto another python-based model in order to detect and predict the exact nature of disturbances using various artificial neural networks with their respective accuracy scores. This paper provides a brief comparison between Decision Tree Classifier, Random Forest Classifier, Support Vector Machines, K-Nearest Neighbors and Multi-Layer Perceptron methodologies for detection of line to ground fault, as an example in this model-based approach.
【 授权许可】
Unknown