Integrating fault detection and classification in microgrids using supervised machine learning considering fault resistance uncertainty
Abstract Microgrids (MGs) can enhance the consumers’ reliability. Nevertheless, besides significant outcomes, some challenges arise. Regarding the intermittent nature of Renewable Energy Resources (RESs), MGs are not operated radially. Accordingly, the reliable protection of MGs considering uncertai...
Saved in:
Main Authors: | Morteza Barkhi, Javad Pourhossein, Seyed Ali Hosseini |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2024-11-01
|
Series: | Scientific Reports |
Subjects: | |
Online Access: | https://doi.org/10.1038/s41598-024-77982-7 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Author Correction: Integrating fault detection and classification in microgrids using supervised machine learning considering fault resistance uncertainty
by: Morteza Barkhi, et al.
Published: (2025-02-01) -
Graph attention convolution network for power flow calculation considering grid uncertainty
by: Haochen Li, et al.
Published: (2025-04-01) -
Experimental identification of I-mode characteristics at the edge of FIRE mode in KSTAR
by: Chweeho Heo, et al.
Published: (2025-01-01) -
Covering for uncertainty; family as the complex adaptive system in times of polycrisis
by: Sunil Kumar Raina
Published: (2025-01-01) -
On the exact Maxwell evolution equation of resonator dynamics
by: Wu Tong, et al.
Published: (2025-01-01)