ANALYSIS AND COMPARISON OF MACHINE LEARNING APPROACHES FOR TRANSMISSION LINE FAULT PREDICTION IN POWER SYSTEMS

Journal of Research in Engineering and Applied Sciences

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