A Framework for Robust Assessment of Power Grid Stability and Resiliency
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Abstract (arXiv)
Security assessment of large-scale, strongly nonlinear power grids containing thousands to millions of interacting components is a computationally expensive task. Targeting at reducing the computational cost, this paper introduces a framework for constructing a robust assessment toolbox that can provide mathematically rigorous certificates for the grids' stability in the presence of variations in power injections, and for the grids' ability to withstand a bunch sources of faults. By this toolbox we can "off-line" screen a wide range of contingencies or power injection profiles, without reassessing the system stability on a regular basis. In particular, we formulate and solve two novel robust stability and resiliency assessment problems of power grids subject to the uncertainty in equilibrium points and uncertainty in fault-on dynamics. Furthermore, we bring in the quadratic Lyapunov functions approach to transient stability assessment, offering real-time construction of stability/resiliency certificates and real-time stability assessment. The effectiveness of the proposed techniques is numerically illustrated on a number of IEEE test cases.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Apr 2015.
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Citing papers per year: 2022: 3; 2023: 0; 2024: 0; 2025: 0; 2026: 1.
4 papers.
- 2022Gradient-Enhanced Physics-Informed Neural Networks for Power Systems Operational Supportcited by 6
- 2022Attention-based Deep Neural Networks for Battery Discharge Capacity Forecastingcited by 4
- 2022Fast Transient Stability Prediction Using Grid-informed Temporal and Topological Embedding Deep Neural Networkcited by 0
- 2026Knowledge Integration in Differentiable Models: A Comparative Study of Data-Driven, Soft-Constrained, and Hard-Constrained Paradigms for Identification and Control of the Single Machine Infinite Bus Systemcited by 0
- 2026Knowledge Integration in Differentiable Models: A Comparative Study of Data-Driven, Soft-Constrained, and Hard-Constrained Paradigms for Identification and Control of the Single Machine Infinite Bus System10 Feb 2026
- 2022Gradient-Enhanced Physics-Informed Neural Networks for Power Systems Operational Support21 Jun 2022
- 2022Attention-based Deep Neural Networks for Battery Discharge Capacity Forecasting14 Feb 2022
- 2022Fast Transient Stability Prediction Using Grid-informed Temporal and Topological Embedding Deep Neural Network23 Jan 2022