Foundational and state-of-the-art anomaly-detection methods through power system state estimation are reviewed. Traditional components for bad data detection, such as chi-square testing, residual-based methods, and hypothesis testing, are discussed to explain the motivations for recent anomaly-detection methods given the increasing complexity of power grids, energy management systems, and cyber-threats. In particular, state estimation anomaly detection based on data-driven quickest-change detection and artificial intelligence are discussed, and directions for research are suggested with particular emphasis on considerations of the future smart grid.
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This content will become publicly available on February 1, 2025
C0 interior penalty methods for an elliptic distributed optimal control problem with general tracking and pointwise state constraints
We consider C0 interior penalty methods for a linear-quadratic elliptic distributed optimal control problem with pointwise state constraints in two spatial dimensions, where the cost function tracks the state at points, curves and regions of the domain. Error estimates and numerical results that illustrate the performance of the methods are presented.
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- Award ID(s):
- 2208404
- PAR ID:
- 10508359
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Computers & Mathematics with Applications
- Volume:
- 155
- Issue:
- C
- ISSN:
- 0898-1221
- Page Range / eLocation ID:
- 80 to 90
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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