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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Citations
Cites
Kundur, P., Paserba, J. J., Ajjarapu, V., Andersson, G., Bose, A., Cañizares, C. A., Hatziargyriou, N. D., Hill, D. J., Stanković, A. M., Taylor, C. W., et al. (2004) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2004.825981 · Cited by 3,727
Intelligent Time-Adaptive Transient Stability Assessment System
Yu, J. J. Q., Hill, D. J., Lam, A. Y. S., Gu, J., & Li, V. O. K. (2017) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2017.2707501 · Cited by 379
Support Vector Machines for Transient Stability Analysis of Large-Scale Power Systems
Moulin, L. S., daSilva, A. P. A., El-Sharkawi, M. A., & MarksII, R. J. (2004) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2004.826018 · Cited by 360
A Reliable Intelligent System for Real-Time Dynamic Security Assessment of Power Systems
Xu, Y. H., Dong, Z. Y., Zhao, J. H., Zhang, P., & Wong, K. P. (2012) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2012.2183899 · Cited by 222
Rotor Angle Instability Prediction Using Post-Disturbance Voltage Trajectories
Rajapakse, A. D., Gomez, F. R., Nanayakkara, K., et al. (2010) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2009.2036265 · Cited by 185
Transient Stability Prediction by a Hybrid Intelligent System
Amjady, N., & Majedi, S. F. (2007) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2007.901667 · Cited by 181
Transient Instability Prediction Using Decision Tree Technique
Amraee, T., & Ranjbar, S. (2013) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2013.2238684 · Cited by 178
Diao, R., Vittal, V., & Logic, N. (2009) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2009.2035507 · Cited by 156
Liu, Y., You, S., Tan, J., et al. (2018) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2018.2799744 · Cited by 134
Self-citationZhang, Y., Xu, Y. H., Dong, Z. Y., Xu, Z., & Wong, K. P. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2676879 · Cited by 126
Self-citation
Research works printed but not linked: 3
- 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
- 10.1109/TPWRS.2014.2353048 · DOI
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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