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Arthur-Durett, Kristine M.

Publications and source records attributed to Arthur-Durett, Kristine M..

A Data-driven approach to Determining the Fidelity in the Hardware-in-the-loop Systems using Subspace Identification Method

One of the major questions in any Hardware-in-the-loop (HiL) simulation is to understand the fidelity of the HiL simulation itself which is most often indicated qualitatively as either high or low instead of quantifying it. Being cognizant of the level of fidelity forms the crux to assess the validity and credibility of the HiL simulation. In this work, we address this issue by developing a systematic, data-driven approach to assess the fidelity of any HiL simulation, and more specifically, the fidelity of the interface between the simulator and hardware in an HiL simulation. Applying a subspace identification method, a linear system representing the interface is obtained from the time-series data captured between the hardware and simulator in the HiL simulation. Finally, the fidelity is defined based on this linear system properties. The proposed data-driven fidelity quantification framework is illustrated on the IEEE 123 node feeder system running on HYPERSIM and interacting with virtual protection relays.

97 MATHEMATICS AND COMPUTING↗

Complementary workflows for identifying one-hop network behavior and multi-hop network dependencies

A network analysis tool evaluates network flow information in complementary workflows to identify one-hop behavior of network assets and also identify multi-hop dependencies between network assets. In one workflow (e.g., using association rule learning), the network analysis tool can identify significant one-hop communication patterns to and/or from network assets, taken individually. Based on the identified one-hop behavior, the network analysis tool can discover patterns of similar communication among different network assets, which can inform decisions about deploying patch sets, mitigating damage, configuring a system, or detecting anomalous behavior. In a different workflow (e.g., using deep learning or cross-correlation analysis), the network analysis tool can identify significant multi-hop communication patterns that involve network assets in combination. Based on the identified multi-hop dependencies, the network analysis tool can discover functional relationships between network assets, which can inform decisions about configuring a system, managing critical network assets, or protecting critical network assets.

97 MATHEMATICS AND COMPUTING↗