Physics-Informed Fully Convolutional Network-Based Power Flow Analysis for Multi-Terminal MVDC Distribution Systems

Sun, P; Wu, R; Wang, H; Li, G; Khalid, M; Konstantinou, G

Physics-Informed Fully Convolutional Network-Based Power Flow Analysis for Multi-Terminal MVDC Distribution Systems

Sun, P Wu, R Wang, H Li, G Khalid, M Konstantinou, G

Permalink

Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Power Systems, 2024, 39, (6), pp. 7389-7402
Issue Date:: 2024-01-01

Closed Access

	Filename	Description	Size
	1718420.pdf	Published version	2.51 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Sun, P
dc.contributor.author	Wu, R
dc.contributor.author	Wang, H
dc.contributor.author	Li, G
dc.contributor.author	Khalid, M
dc.contributor.author	Konstantinou, G
dc.date.accessioned	2025-01-28T08:34:23Z
dc.date.available	2025-01-28T08:34:23Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Transactions on Power Systems, 2024, 39, (6), pp. 7389-7402
dc.identifier.issn	0885-8950
dc.identifier.issn	1558-0679
dc.identifier.uri	http://hdl.handle.net/10453/184448
dc.description.abstract	Numerical methods in power flow (PF) studies for medium-voltage direct current (MVDC) distribution systems require repetitive computations, particularly in scenarios with time-variable facilities that often alter the system operation points. Conventional neural networks (NNs), though efficient in rapid PF calculations, face accuracy challenges with untrained data distributions and varied topology structures. This highlights the need for more robust approaches to improve reliability in diverse scenarios. This paper proposes a physics-informed fully convolutional network (PI-FCN) to address this issue. The architecture of the PI-FCN is enhanced with the inclusion of two additional layers: i) a channel combination layer and ii) a physics operation layer. The former channel combination layer strengthens the model feature extraction capability by converting all input channels constituted by initial PF data matrix into dc voltage, current and line conductance matrix channels. The latter physics operation layer reformulates the combined input channels by physical connections in MVDC systems. The new layers enhance the prediction accuracy and allow generalization of the model. Five multi-terminal MVDC (MT-MVDC) distribution networks with different dc voltage levels and network layouts are used to verify the superiority of proposed PI-FCN compared to other NNs in fixed and varied topology structures.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Power Systems
dc.relation.isbasedon	10.1109/TPWRS.2024.3382266
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Energy
dc.subject.classification	4008 Electrical engineering
dc.title	Physics-Informed Fully Convolutional Network-Based Power Flow Analysis for Multi-Terminal MVDC Distribution Systems
dc.type	Journal Article
utslib.citation.volume	39
utslib.for	0906 Electrical and Electronic Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2025-01-28T08:34:22Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	39
utslib.citation.issue	6

Abstract:

Numerical methods in power flow (PF) studies for medium-voltage direct current (MVDC) distribution systems require repetitive computations, particularly in scenarios with time-variable facilities that often alter the system operation points. Conventional neural networks (NNs), though efficient in rapid PF calculations, face accuracy challenges with untrained data distributions and varied topology structures. This highlights the need for more robust approaches to improve reliability in diverse scenarios. This paper proposes a physics-informed fully convolutional network (PI-FCN) to address this issue. The architecture of the PI-FCN is enhanced with the inclusion of two additional layers: i) a channel combination layer and ii) a physics operation layer. The former channel combination layer strengthens the model feature extraction capability by converting all input channels constituted by initial PF data matrix into dc voltage, current and line conductance matrix channels. The latter physics operation layer reformulates the combined input channels by physical connections in MVDC systems. The new layers enhance the prediction accuracy and allow generalization of the model. Five multi-terminal MVDC (MT-MVDC) distribution networks with different dc voltage levels and network layouts are used to verify the superiority of proposed PI-FCN compared to other NNs in fixed and varied topology structures.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/184448