Multi-level optimization with the koopman operator for data-driven, domain-aware, and dynamic system security

Oster, Matthew R.; King, Ethan; Bakker, Craig; Bhattacharya, Arnab; Chatterjee, Samrat; Pan, Feng

doi:10.1016/j.ress.2023.109323

Multi-level optimization with the koopman operator for data-driven, domain-aware, and dynamic system security

Journal Article · Sat May 06 00:00:00 EDT 2023 · Reliability Engineering and System Safety

DOI:https://doi.org/10.1016/j.ress.2023.109323· OSTI ID:1988409

^[1]; King, Ethan ^[1]; Bakker, Craig ^[1]; ^[1]; ^[1]; Pan, Feng ^[1]

Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Cyber-Physical Systems (CPSs) like the power grid are critically important but also increasingly vulnerable; ensuring reliable system operation in the face of disruptions is becoming more and more challenging. Multi-Level Optimization (MLO) is a powerful way to model adversarial interactions, which naturally makes it applicable to studying CPS security. However, MLO typically does not address underlying system dynamics, and incorporating nonlinear dynamics is generally infeasible. In this paper, we show how to combine MLO with the Koopman Operator (KO) to remedy this. The KO maps nonlinear dynamics to a lifted space in which those dynamics are linear, thus making it ideal for use with MLO. Moreover, the structure of the KO also provides convenient ways to incorporate domain knowledge into the data-driven process of learning the KO representation of a given system. Here we then demonstrate the use of MLO-KO on a small example problem taken from the power grid domain, discuss the scalability and computational cost of MLO-KO, and identify future research directions for this work.

View Accepted Manuscript (DOE)

Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 1988409

Report Number(s):: PNNL-SA-169379

Journal Information:: Reliability Engineering and System Safety, Journal Name: Reliability Engineering and System Safety Vol. 237; ISSN 0951-8320

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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42 ENGINEERING
Koopman operator
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machine learning
multi-level optimization
power grid

Multi-level optimization with the koopman operator for data-driven, domain-aware, and dynamic system security

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