The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
A Review on Artificial Intelligence for Grid Stability Assessment
Open accessAbstract
Artificial intelligence provides a convenient route for power grid stability assessment. Compared with simulation-based approaches, artificial intelligence can potentially save time on model development and numerical computation in stability assessment. This paper first reviewed existing literature on using artificial intelligence for power grid stability assessment. Then a machine-leaning-based tool is presented and developed to assess power grid transient stability, frequency stability, and small signals stability. Test results verified the accuracy and effectiveness of the AI tool for power grid stability assessment.
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“You et al. [97] have proposed ES-based approaches for supervisory control and data acquisition (SCADA) systems.”
From A Comprehensive Review on the Role of Artificial Intelligence in Power System Stability, Control, and Protection: Insights and Future Directions (2024) · cites this paper as [97]
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“An ANN-based self-tuning PSS was suggested by [97] to circumvent this issue.”
From A Comprehensive Review on the Role of Artificial Intelligence in Power System Stability, Control, and Protection: Insights and Future Directions (2024) · cites this paper as [97]
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Cites
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- xiao, 2019, Data-Driven Security Assessment of Power Grids Based on Machine Learning Approach, International Council on Large Electric Systems (CIGRE) Proceedings of the United States National Committee for Grid of the Future Symposium, 1
- 10.1109/TPWRS.2004.826018 · DOI
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Cited by
Sorted by citations
Alhamrouni, I., Abdul Kahar, N. H., Salem, M., Swadi, M., Zahroui, Y., Kadhim, D. J., Mohamed, F. A., & Alhuyi Nazari, M. (2024) · Applied Sciences · doi:10.3390/app14146214 · Cited by 217
Metadata
- Authors
- You, S., Zhao, Y., Mandich, M., Cui, Y., Li, H., Xiao, H., Fabus, S., Su, Y., Liu, Y., Yuan, H., Jiang, H., Tan, J., & Zhang, Y.
- Affiliations
- University of Tennessee at Knoxville (US)
- Oak Ridge National Laboratory (US)
- National Laboratory of the Rockies (US)
- ORCID
- Venue
- 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
- Publisher
- IEEE
- Published
- 2020-11-11
- Pages
- 1-6
- OpenAlex
- W3116572356
- Cited by
- 70
- References
- 44
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 14 · 2023: 17 · 2022: 15 · 2021: 10
- FWCI
- 14.76
- Type
- conference paper
- Language
- en
- Open access
- green
- www.osti.gov
- Licence
- Locations
- IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- U.S. Department of Energy
- Office of Energy
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Power System Optimization and Stability
- Physical Sciences › Engineering › Control and Systems Engineering › Microgrid Control and Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Frequency Control in Power Systems
- Keywords
- artificial intelligence, machine learning, transient stability, frequency stability, small-signal stability, power system simulation
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