Visible to the public Scalable, Physical Effects Measurable Microgrid for Cyber Resilience Analysis (SPEMMCRA)

TitleScalable, Physical Effects Measurable Microgrid for Cyber Resilience Analysis (SPEMMCRA)
Publication TypeConference Paper
Year of Publication2020
AuthorsUlrich, Jacob, McJunkin, Timothy, Rieger, Craig, Runyon, Michael
Conference Name2020 Resilience Week (RWS)
Date PublishedOct. 2020
PublisherIEEE
ISBN Number978-1-7281-8693-1
KeywordsAutomated Response Action, automated security, composability, control systems, cyber-physical system, industrial control, industrial control system, Inverters, Load modeling, Mathematical model, Microgrids, pubcrawl, resilience, Resiliency, Scalability, scalable Microgrid, Scalable Security
Abstract

The ability to advance the state of the art in automated cybersecurity protections for industrial control systems (ICS) has as a prerequisite of understanding the trade-off space. That is, to enable a cyber feedback loop in a control system environment you must first consider both the security mitigation available, the benefits and the impacts to the control system functionality when the mitigation is used. More damaging impacts could be precipitated that the mitigation was intended to rectify. This paper details networked ICS that controls a simulation of the frequency response represented with the swing equation. The microgrid loads and base generation can be balanced through the control of an emulated battery and power inverter. The simulated plant, which is implemented in Raspberry Pi computers, provides an inexpensive platform to realize the physical effects of cyber attacks to show the trade-offs of available mitigating actions. This network design can include a commercial ICS controller and simple plant or emulated plant to introduce real world implementation of feedback controls, and provides a scalable, physical effects measurable microgrid for cyber resilience analysis (SPEMMCRA).

URLhttps://ieeexplore.ieee.org/document/9241267
DOI10.1109/RWS50334.2020.9241267
Citation Keyulrich_scalable_2020