Abstract
This paper describes an approach to apply the dynamic data-driven applications systems (DDDAS) paradigm to enhance cyber security and resilience of wide-area monitoring systems in electrical grids. In particular, we explore a DDDAS-aware application to self-heal phasor measurement unit (PMU) networks that monitor the states of power systems in real-time. The application is built on top of a novel software-defined networking (SDN) architecture. The main components include a dynamic data-driven model that efficiently abstracts the PMU network behavior at run time and an optimization-based solution to quickly reconfigure network connections to restore the power system observability. The application also compresses network updates of the recovery plan to further reduce the recovery time. We develop a prototype system in a container-based network testbed and evaluate the recovery time of the self-healing application using the IEEE 30-bus system.
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Acknowledgment
This work is partly sponsored by the Air Force Office of Scientific Research (AFOSR) under Grant YIP FA9550-17-1-0240, the National Science Foundation (NSF) under Grant CNS-1618631, and the Maryland Procurement Office under Contract No. H98230-18-D-0007.
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Qu, Y., Liu, X., Yan, J., Jin, D. (2020). Dynamic Data-Driven Self-healing Application for Phasor Measurement Unit Networks. In: Darema, F., Blasch, E., Ravela, S., Aved, A. (eds) Dynamic Data Driven Applications Systems. DDDAS 2020. Lecture Notes in Computer Science(), vol 12312. Springer, Cham. https://doi.org/10.1007/978-3-030-61725-7_12
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DOI: https://doi.org/10.1007/978-3-030-61725-7_12
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