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Machine Learning–Based Protection and Fault

Milad Beikbabaei, Michael Lindemann

2024Englishelectricityelectronicsmachine learningmicrogridsinverter-based resourcesfault detection

Abstract

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100% inverter-based renewable units are becoming more prevalent, introducing new challenges in the protection of microgrids that incorporate these resources. This is particularly due to low fault currents and bidirectional flows. Previous work has studied the protection of microgrids with high penetration of inverter-interfaced distributed generators; however, very few have studied the protection of a 100% inverter-based microgrid. This work proposes machine learning (ML)–based protection solutions using local electrical measurements that consider implementation challenges and effectively combine short-circuit fault detection and type identification. A decision tree method is used to analyze a wide range of fault scenarios. PSCAD/EMTDC simulation environment is used to create a dataset for training and testing the proposed method. The effectiveness of the proposed methods is examined under seven distinct fault types, each featuring varying fault resistance, in a 100% inverter-based microgrid consisting of four inverters.

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Cite This Work

@article{97e7b965-41c6-4a76-945c-517c37ac6bd7,
  title={Machine Learning–Based Protection and Fault},
  author={Milad Beikbabaei and Michael Lindemann},
  year={2024},
  language={English}
}
TY  - JOUR
TI  - Machine Learning–Based Protection and Fault
AU  - Milad Beikbabaei
AU  - Michael Lindemann
PY  - 2024
LA  - English
ER  -

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