Cross-Layered Cyber-Physical Power System State Estimation towards a Secure Grid Operation

Conference · Thu Oct 27 04:00:00 EDT 2022

DOI:https://doi.org/10.1109/PESGM48719.2022.9916756· OSTI ID:1958547

Aljohani, Nader ^[1]; Agnew Jr., Dennis ^[1]; Nagaraj, Keerthiraj ^[1]; Boamah, Sharon ^[1]; Mathieu, Reynold ^[1]; Bretas, Arturo Suman ^[2]; McNair, Janise ^[1]; Zare, Alina ^[1]

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1. University of Florida
2. BATTELLE (PACIFIC NW LAB)

In the Smart Grid paradigm, this critical infrastructure operation is increasingly exposed to cyber-threats due to the increased dependency on communication networks. An adversary can launch an attack on a power grid operation through False Data Injection into system measurements and/or through attacks on the communication network, such as flooding the communication channels with unnecessary data or intercepting messages. A cross-layered strategy that combines power grid data, communication grid monitoring and Machine Learning based processing is a promising solution for detecting cyberthreats. In this paper, an implementation of an integrated solution of a cross-layer framework is presented. The advantage of such a framework is the augmentation of valuable data that enhances the detection of anomalies in the operation of power grid. IEEE 118-bus system is built in Simulink to provide a power grid testing environment and communication network data is emulated using SimComponents. The performance of the framework is investigated under various FDI and communication attacks.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1958547
• Report Number(s): PNNL-SA-170852
• Country of Publication: United States
• Language: English

Related Subjects

cyber security
network security
cyber-physical systems
cross-layered
power systems
machine learning