Optimized predictive control for AGC cyber resiliency

Nafees, M. N., Saxena, N. ORCID: https://orcid.org/0000-0002-6437-0807 and Burnap, P. ORCID: https://orcid.org/0000-0003-0396-633X 2021. Optimized predictive control for AGC cyber resiliency. Presented at: 2021 ACM Conference on Computer and Communications Security (CCS), Seoul, South Korea, 14-19 November 2021. CCS '21: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security. ACM, pp. 2450-2452. 10.1145/3460120.3485358

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Abstract

Automatic Generation Control (AGC) is used in smart grid systems to maintain the grid's frequency to a nominal value. Cyber-attacks such as time delay and false data injection on the tie-line power flow, frequency measurements, and Area Control Error (ACE) control signals can cause frequency excursion that can trigger load shedding, generators' damage, and blackouts. Therefore, resilience and detection of attacks are of paramount importance in terms of the reliable operation of the grid. In contrast with the previous works that overlook ACE resiliency, this paper proposes an approach for cyber-attack detection and resiliency in the overall AGC process. We propose a state estimation algorithm approach for the AGC system by utilizing prior information based on Gaussian process regression, a non-parametric, Bayesian approach to regression. We evaluate our approach using the PowerWorld simulator based on the three-area New England IEEE 39-bus model. Moreover, we utilize the modified version of the New England ISO load data for the three-area power system to create a more realistic dataset. Our results clearly show that our resilient control system approach can mitigate the system using predictive control and detect the attack with a 100 percent detection rate in a shorter period using prior auxiliary information.

Item Type:	Conference or Workshop Item (Poster)
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	ACM
Date of First Compliant Deposit:	24 November 2021
Date of Acceptance:	7 September 2021
Last Modified:	27 Aug 2024 05:59
URI:	https://orca.cardiff.ac.uk/id/eprint/143926

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