End-to-End Deep Graph Convolutional Neural Network Approach for Intentional Islanding in Power Systems Considering Load-Generation Balance.
- DOI
- 10.3390/s21051650
- Published
- 2021 Feb 27
- Container
- Sensors (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/s21051650,
title = {End-to-End Deep Graph Convolutional Neural Network Approach for Intentional Islanding in Power Systems Considering Load-Generation Balance.},
author = {Sun Z and Spyridis Y and Lagkas T and Sesis A and Efstathopoulos G and Sarigiannidis P},
year = {2021},
journal = {Sensors (Basel, Switzerland)},
doi = {10.3390/s21051650},
url = {https://doi.org/10.3390/s21051650}
}RIS
TY - JOUR TI - End-to-End Deep Graph Convolutional Neural Network Approach for Intentional Islanding in Power Systems Considering Load-Generation Balance. AU - Sun Z AU - Spyridis Y AU - Lagkas T AU - Sesis A AU - Efstathopoulos G AU - Sarigiannidis P PY - 2021 JO - Sensors (Basel, Switzerland) DO - 10.3390/s21051650 UR - https://doi.org/10.3390/s21051650 ER -
APA
Z, S., Y, S., T, L., A, S., G, E., & P, S. (2021). End-to-End Deep Graph Convolutional Neural Network Approach for Intentional Islanding in Power Systems Considering Load-Generation Balance.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s21051650
Source records
- pubmed · retrieved 2026-11-10T21:27:06.752Z