Machine Learning-Based Protection and Fault Identification of 100% Inverter-Based Microgrids
Author:
Affiliation:
1. Computer Engineering, Virginia Tech,The Bradley Department of Electrical,Blacksburg,VA,24061
2. University of Alaska,Department of Electrical Engineering,Anchorage,AK,99508
Funder
National Science Foundation
Office of Energy Efficiency and Renewable Energy
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10595551/10595675/10595742.pdf?arnumber=10595742
Reference12 articles.
1. A new protection scheme for feeders of microgrids with inverter-based resources
2. Mitigating False Data Injection Attacks on Inverter Set Points in a 100% Inverter-Based Microgrid
3. Adaptive Protection Scheme for a Real-World Microgrid with 100% Inverter-Based Resources
4. Transient Wavelet Energy-Based Protection Scheme for Inverter-Dominated Microgrid
5. Intelligent Fault Diagnosis Using Deep Learning for a Microgrid with High Penetration of Renewable Energy Sources
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