A Hybrid Monotonic Neural Network Approach for Multi-Area Power System Load Frequency Control Against FGSM Attack

Liu, X; Jiao, Q; Qiao, S; Yan, Z; Wen, S; Wang, P

A Hybrid Monotonic Neural Network Approach for Multi-Area Power System Load Frequency Control Against FGSM Attack

Liu, X Jiao, Q Qiao, S Yan, Z Wen, S

Wang, P

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Circuits and Systems II: Express Briefs, 2024, PP, (99), pp. 1-1
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Liu, X
dc.contributor.author	Jiao, Q
dc.contributor.author	Qiao, S
dc.contributor.author	Yan, Z
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.contributor.author	Wang, P
dc.date.accessioned	2024-08-07T05:00:01Z
dc.date.available	2024-08-07T05:00:01Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Transactions on Circuits and Systems II: Express Briefs, 2024, PP, (99), pp. 1-1
dc.identifier.issn	1549-7747
dc.identifier.issn	1558-3791
dc.identifier.uri	http://hdl.handle.net/10453/180258
dc.description.abstract	The integrity of data-driven load frequency control (LFC) in power system is increasingly threatened by adversarial attack. Addressing this concern, this paper introduces a novel hybrid approach that integrates adversarial reinforcement learning and monotonic neural network (ARL-HMNN) for LFC in multi-area power system. To holistically counter unforeseen uncertainties and to withstand the prevalent adversarial attack, the proposed ARL-HMNN approach builds a stable neural network structure with monotonicity constraints, and optimizes this neural network for LFC with adversarial training and deep deterministic policy gradient algorithm. By enforcing the deviation-command monotonicity constraints, the HMNN is enabled to satisfy Lyapunov stability conditions for LFC, which significantly enhances the stability and robustness of the power system. To further enhance the robustness of LFC, the classic fast gradient sign method (FGSM) adversarial attack is applied during the reinforcement learning training process. Through the integration of adversarial training, our method improves the system’s resilience to FGSM attack under malicious threat from the communication network, while at the same time maintaining provable frequency stability. The superior performance of the developed approach is demonstrated by comparison to existing data-driven control methods on the IEEE 39-bus power system.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Circuits and Systems II: Express Briefs
dc.relation.isbasedon	10.1109/TCSII.2024.3367184
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.title	A Hybrid Monotonic Neural Network Approach for Multi-Area Power System Load Frequency Control Against FGSM Attack
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Australian Artificial Intelligence Institute (AAII)
utslib.copyright.status	recently_added	*
dc.date.updated	2024-08-07T04:59:58Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

The integrity of data-driven load frequency control (LFC) in power system is increasingly threatened by adversarial attack. Addressing this concern, this paper introduces a novel hybrid approach that integrates adversarial reinforcement learning and monotonic neural network (ARL-HMNN) for LFC in multi-area power system. To holistically counter unforeseen uncertainties and to withstand the prevalent adversarial attack, the proposed ARL-HMNN approach builds a stable neural network structure with monotonicity constraints, and optimizes this neural network for LFC with adversarial training and deep deterministic policy gradient algorithm. By enforcing the deviation-command monotonicity constraints, the HMNN is enabled to satisfy Lyapunov stability conditions for LFC, which significantly enhances the stability and robustness of the power system. To further enhance the robustness of LFC, the classic fast gradient sign method (FGSM) adversarial attack is applied during the reinforcement learning training process. Through the integration of adversarial training, our method improves the system’s resilience to FGSM attack under malicious threat from the communication network, while at the same time maintaining provable frequency stability. The superior performance of the developed approach is demonstrated by comparison to existing data-driven control methods on the IEEE 39-bus power system.

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http://hdl.handle.net/10453/180258