End-to-end deep graph convolutional neural network approach for intentional islanding in power systems considering load-generation balance

Sun, Z., Spyridis, Y., Lagkas, T. orcid.org/0000-0002-0749-9794 et al. (3 more authors) (2021) End-to-end deep graph convolutional neural network approach for intentional islanding in power systems considering load-generation balance. Sensors, 21 (5). 1650.

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2021 The Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Keywords: deep learning; grap h partition; graph convolutional networks; intentional islanding; load-generation balance; power system; spectral clustering
Dates:
  • Accepted: 17 February 2021
  • Published (online): 27 February 2021
  • Published: 27 February 2021
Institution: The University of Sheffield
Academic Units: The University of Sheffield > International Faculty (Sheffield) > City College, Computer Science Department (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 28 Apr 2021 06:37
Last Modified: 28 Apr 2021 06:37
Status: Published
Publisher: MDPI AG
Refereed: Yes
Identification Number: https://doi.org/10.3390/s21051650
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