Enhancing Cyber-Physical Resiliency of Microgrid Control under Denial-of-Service Attack with Digital Twins

Mahmoud S. Abdelrahman, Ibtissam Kharchouf, Hossam M. Hussein, Mustafa Esoofally and Osama A. Mohammed ()
Additional contact information
Mahmoud S. Abdelrahman: Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA
Ibtissam Kharchouf: Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA
Hossam M. Hussein: Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA
Mustafa Esoofally: Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA
Osama A. Mohammed: Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA

Energies, 2024, vol. 17, issue 16, 1-25

Abstract: Microgrids (MGs) are the new paradigm of decentralized networks of renewable energy sources, loads, and storage devices that can operate independently or in coordination with the primary grid, incorporating significant flexibility and supply reliability. To increase reliability, traditional individual MGs can be replaced by networked microgrids (NMGs), which are more dependable. However, when it comes to operation and control, they also pose challenges for cyber security and communication reliability. Denial of service (DoS) is a common danger to DC microgrids with advanced controllers that rely on active information exchanges and has been recorded as the most frequent cause of cyber incidents. It can disrupt data transmission, leading to ineffective control and system instability. This paper proposes digital twin (DT) technology as an integrated solution, with new, advanced analytics technology using machine learning and artificial intelligence to provide simulation capabilities to predict and estimate future states. By twinning the cyber-physical dynamics of NMGs using data-driven models, DoS attacks targeting cyber-layer agents will be detected and mitigated. A long short-term memory (LSTM) model data-driven digital twin approach for DoS attack detection and mitigation is implemented, tested, and evaluated.

Keywords: smart grid; cyber-physical microgrid; digital twin; denial-of-service attack (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/16/3927/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/16/3927/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:16:p:3927-:d:1452430

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().