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
Intentional islanding is a corrective procedure that aims to protect the stability of the power system during an emergency, by dividing the grid into several partitions and isolating the elements that would cause cascading failures. This paper proposes a deep learning method to solve the problem of intentional islanding in an end-to-end manner. Two types of loss functions are examined for the graph partitioning task, and a loss function is added on the deep learning model, aiming to minimise the load-generation imbalance in the formed islands. In addition, the proposed solution incorporates a technique for merging the independent buses to their nearest neighbour in case there are isolated buses after the clusterisation, improving the final result in cases of large and complex systems. Several experiments demonstrate that the introduced deep learning method provides effective clustering results for intentional islanding, managing to keep the power imbalance low and creating stable islands. Finally, the proposed method is dynamic, relying on real-time system conditions to calculate the result.
Metadata
Item Type: | Article |
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Authors/Creators: |
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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: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > International Faculty (Sheffield) > City College - Computer Science |
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: | 10.3390/s21051650 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:173540 |