Machine Learning-Based Protection and Fault Identification of 100% Inverter-Based Microgrids
<|reference_start|>Machine Learning-Based Protection and Fault Identification of 100% Inverter-Based Microgrids: 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.<|reference_end|>
@article{beikbabaei2024machine,
    title={Machine Learning-Based Protection and Fault Identification of 100%
  Inverter-Based Microgrids},
    author={Milad Beikbabaei, Michael Lindemann, Mohammad Heidari Kapourchali, and
  Ali Mehrizi-Sani},
    journal={arXiv preprint arXiv:2405.07310},
    year={2024},
    doi={10.1109/ISIE54533.2024.10595742},
    archivePrefix={arXiv},
    eprint={2405.07310},
    primaryClass={eess.SY cs.SY}
}