Testing Machine Learned Fault Detection and Classification on a DC Microgrid
Author:
Affiliation:
1. Sandia National Laboratories,Electric Power System Research,Albuquerque,NM,USA
2. Sandia National Laboratories,Renewable, Distributed Systems Integration,Albuquerque,NM,USA
3. Emera Technologies LLC,Block Energy Labs,Burlington,ON,Canada
Funder
Office of Electricity
National Nuclear Security Administration
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9817414/9817460/09817517.pdf?arnumber=9817517
Reference23 articles.
1. Overcurrent Protection on Voltage-Source-Converter-Based Multiterminal DC Distribution Systems
2. Hybrid Passive-Overcurrent Relay for Detection of Faults in Low-Voltage DC Grids
3. High-Speed Differential Protection for Smart DC Distribution Systems
4. Centralized Protection Strategy for Medium Voltage DC Microgrids
5. Validation of Fast and Selective Protection Scheme for an LVDC Distribution Network
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1. An advanced graph algorithm-based protection strategy for detecting kilometric and cross-country faults in DC microgrid;Heliyon;2024-06
2. Comparative Analysis of Machine Learning Techniques for Fault Detection;2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management (IC-RVITM);2023-11-28
3. Prognostic Health Monitoring of DC Microgrid with Fault Detection and Localization using Machine Learning Techniques;2023 IEEE Energy Conversion Congress and Exposition (ECCE);2023-10-29
4. Time Series Classification for Detecting Fault Location in a DC Microgrid;2023 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge);2023-04-10
5. Advancements in DC Microgrids: Integrating Machine Learning and Communication Technologies for a Decentralized Future;Smart Grid 3.0;2023
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