Programmable intrusion detection for distributed energy resources in cyber-physical networked microgrids

Ma, Shuyang, Lia, Yan, Du, Liang, Wu, Jianzhong ORCID: https://orcid.org/0000-0001-7928-3602, Zhou, Yue ORCID: https://orcid.org/0000-0002-6698-4714, Zhang, Yichen and Xu, Tao 2022. Programmable intrusion detection for distributed energy resources in cyber-physical networked microgrids. Applied Energy 306 (PartB) , 118056. 10.1016/j.apenergy.2021.118056

PDF - Accepted Post-Print Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (2MB)

Abstract

A programmable intrusion detection method is presented to identify the malicious attacks to distributed energy resources (DERs) in the cyber–physical networked microgrids. The proposed method injects small programmable signals into the system and uses the response to identify abnormal conditions. Because of the low or even zero inertia induced by integrations of DER power-electronic-interfaces, microgrids have very limited resilience capability; and thus, being sensitive to attacks. One microgrid’s malfunction caused by attacks can easily propagate to its neighboring systems when several microgrids are connected, leading to catastrophic electricity supply failures. Through the presented method, malicious intrusions can be effectively detected, located, and defended for securing microgrids. Theoretical derivations are provided to define the programmable detection rules. The detection rule is easy and flexible to update, making it difficult for attack actors to gain the knowledge of the detection rules, in order to avoid being detected. Numerical results on a cyber–physical networked microgrids system show that the proposed method is effective and efficient in precisely locating intrusion attacks to the microgrids system.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	1068-7181
Date of First Compliant Deposit:	12 October 2021
Date of Acceptance:	11 October 2021
Last Modified:	29 Nov 2024 09:45
URI:	https://orca.cardiff.ac.uk/id/eprint/144820

Citation Data

Cited 3 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item