Dynamic Anomaly Detection with High-fidelity Simulators: A Convex Optimization Approach

Kaikai Pan*, Peter Palensky, Peyman Mohajerin Esfahani

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
19 Downloads (Pure)

Abstract

The main objective of this article is to develop scalable dynamic anomaly detectors with high-fidelity simulators of power systems. On the one hand, models in high-fidelity simulators are typically 'intractable' if one opts to describe them in a mathematical formulation in order to apply existing model-based approaches from the anomaly detection literature. On the other hand, pure data-driven methods developed primarily in the machine learning literature neglect our knowledge about the underlying dynamics of power systems. In this study, we combine tools from these two mainstream approaches to develop a data-assisted model-based diagnosis filter utilizing both the knowledge from a picked abstract model and also the data of simulation results from high-fidelity simulators. The proposed diagnosis filter aims to achieve two desired features: (i) performance robustness with respect to model mismatch; (ii) high scalability. To this end, we propose a tractable (convex) optimization-based reformulation in which decisions are the filter parameters, the model-based information introduces feasible sets, and the data from the simulator forms the objective function to-be-minimized regarding the effect of model mismatch on the filter performance. To validate the theoretical results, we implement the developed diagnosis filter in DIgSILENT PowerFactory to detect false data injection attacks on the Automatic Generation Control measurements in the three-area IEEE 39-bus system.

Original languageEnglish
Pages (from-to)1500-1515
Number of pages16
JournalIEEE Transactions on Smart Grid
Volume13
Issue number2
DOIs
Publication statusPublished - 2022

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Anomaly detection
  • convex optimization.
  • Data models
  • data-assisted model-based
  • diagnosis filter
  • high-fidelity simulator
  • Mathematical models
  • model mismatch
  • Numerical models
  • Power system dynamics
  • System dynamics
  • Tools

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