Identifying line-to-ground faulted phase in low and medium voltage AC microgrid using principal component analysis and supervised machine-learning

Muhammad Uzair; Li Li; Jian Guo Zhu

doi:10.1109/AUPEC.2018.8757918

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Conference proceeding

Identifying line-to-ground faulted phase in low and medium voltage AC microgrid using principal component analysis and supervised machine-learning

Muhammad Uzair, Li Li and Jian Guo Zhu

2018 Australasian Universities Power Engineering Conference (AUPEC), pp.1-6

11/2018

DOI: https://doi.org/10.1109/AUPEC.2018.8757918

Abstract

Computational complexity

Computational modeling

line-to-ground fault

machine-learning

Malware

Mathematical model

microgrid

Peer-to-peer computing

principal component analysis

protection

YouTube

A supervised machine-learning based approach for faulted phase identification in bolted, low- and high-impedance line-to-ground faults using principal component analysis for feature extraction from multiple input signals is presented in this paper. DIgSILENT PowerFactory is used for simulating the underlying microgrid to obtain fault related data, while MATLAB is used for machine learning application. A 15-fold cross validation is applied to the training dataset for evaluation of different machine learning models and the results show supreme performance compared to previous methods.

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Title: Identifying line-to-ground faulted phase in low and medium voltage AC microgrid using principal component analysis and supervised machine-learning
Creators - without role: Muhammad Uzair - University of Technology Sydney
Li Li - University of Technology Sydney
Jian Guo Zhu - University of Sydney
Publication Details: 2018 Australasian Universities Power Engineering Conference (AUPEC), pp.1-6
Publisher: IEEE
Identifiers: 9916764208331
Academic Unit: Islamic University of Al Madinah
Language: English
Resource Type: Conference proceeding