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Dynamic Anomaly Detection with High-fidelity Simulators: A Convex Optimization Approach
Optimization and Control · Submitted 29 Apr 2020
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim In this study, we combine tools from these two mainstream approaches to develop a diagnosis filter that utilizes the knowledge of both the dynamical system as well as the simulation data of the high-fidelity simulators.
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The abstract, sentence by sentence
The main objective of this article is to develop scalable dynamic anomaly detectors when high-fidelity simulators of power systems are at our disposal. On the one hand, mathematical models of these high-fidelity simulators are typically "intractable" to apply existing model-based approaches. On the other hand, pure data-driven methods developed primarily in the machine learning literature neglect our knowledge about the underlying dynamics of the systems. In this study, we combine tools from these two mainstream approaches to develop a diagnosis filter that utilizes the knowledge of both the dynamical system as well as the simulation data of the 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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | The main objective of this article is to develop scalable dynamic anomaly detectors when high-fidelity simulators of power systems are at our disposal. | contribution 0.82 | 0.27 | 0.08 | asserted 0.96 |
| s1 | On the one hand, mathematical models of these high-fidelity simulators are typically "intractable" to apply existing model-based approaches. | background 0.78 | 0.09 | 0.02 | asserted 0.99 |
| s2 | On the other hand, pure data-driven methods developed primarily in the machine learning literature neglect our knowledge about the underlying dynamics of the systems. | background 0.55 | 0.06 | 0.02 | asserted 1.00 |
| s3 | In this study, we combine tools from these two mainstream approaches to develop a diagnosis filter that utilizes the knowledge of both the dynamical system as well as the simulation data of the high-fidelity simulators. | contribution 0.85 | 0.91 | 0.15 | asserted 1.00 |
| s4 | The proposed diagnosis filter aims to achieve two desired features: (i) performance robustness with respect to model mismatch; (ii) high scalability. | contribution 0.96 | 0.41 | 0.11 | asserted 0.92 |
| s5 | 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. | method 0.95 | 0.92 | 0.18 | asserted 1.00 |
| s6 | 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. | method 0.84 | 0.95 | 0.10 | asserted 0.98 |
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Original abstract
The main objective of this article is to develop scalable dynamic anomaly detectors when high-fidelity simulators of power systems are at our disposal. On the one hand, mathematical models of these high-fidelity simulators are typically "intractable" to apply existing model-based approaches. On the other hand, pure data-driven methods developed primarily in the machine learning literature neglect our knowledge about the underlying dynamics of the systems. In this study, we combine tools from these two mainstream approaches to develop a diagnosis filter that utilizes the knowledge of both the dynamical system as well as the simulation data of the 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.
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