Reinforcement Learning for the Cybersecurity of Grid-Forming and Grid-Following Inverters

The U.S. movement toward clean energy generation has increased the number of installed inverter-based resources (IBR) in the grid, introducing new challenges in IBR control and cybersecurity. IBRs receive their set point through the communication link, which may expose them to cyber threats. Previous work has developed various techniques to detect and mitigate cyberattacks on IBRs, developing schemes for new inverters being installed in the grid. This work focuses on developing model-free control techniques for already installed IBR in the grid without the need to access IBR internal control parameters. The proposed method is tested for both the grid-forming and grid-following inverter control. Separate detection and mitigation algorithms are used to enhance the accuracy of the proposed method. The proposed method is tested using the modified CIGRE 14-bus North American grid with 7 IBRs in PSCAD/EMTDC. Finally, the performance of the detection algorithm is tested under grid normal transients, such as set point change, load change, and short-circuit fault, to make sure the proposed detection method does not provide false positives.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Electrical Engineering

Department dc:contributor.department

Electrical Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Kwiatkowski, Brian Michael

Chair dc:contributor.committeechair

• Mehrizi-Sani, Ali

Committee members dc:contributor.committeemember

• Centeno, Virgilio A.
• Liu, Chen-Ching

• Cyberattack
• inverter-based resources (IBR)
• power system control
• reinforcement learning (RL)
• renewable energy sources

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:42081

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/123754