Machine Learning Model Applications for Fault Detection and Classification in Distributed Power Networks

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Jose Eduardo Urrea Cabus
https://orcid.org/0000-0001-7733-4939
İsmail Hakkı Altaş
https://orcid.org/0000-0001-9298-4091

Abstract

This paper compares various unsupervised feature extraction techniques and supervised machine learning models for fault detection and classification over a power distributed generation system. The modified IEEE 34 bus test feeder was implemented for the study case simulated through PowerFactory DigSILENT software. Data analysis results from three-phase voltages and currents collected were performed in Python. Simulation results confirm that by applying dimensionality reduction techniques as feature extraction and wavelet family selection adequately, a high identification and classification accuracy can be obtained, excluding the less essential characteristics and preventing the machine learning models from overfitting or underfitting the datasets.

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How to Cite
[1]
J. Urrea Cabus and İsmail Altaş, “Machine Learning Model Applications for Fault Detection and Classification in Distributed Power Networks”, DataSCI, vol. 4, no. 2, pp. 11-18, Jun. 2022.
Section
Research Articles

References

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