Neural Lyapunov Control for Power System Transient Stability: A Deep Learning-Based Approach
Abstract
Power system control and transient stability analysis play essential roles in secure system operation. Control of power systems typically involves highly nonlinear and complex dynamics. Most of the existing works address such problems with additional assumptions in system dynamics, leading to a requirement for a complete and general solution. This paper, therefore, proposes a novel control framework for various power system control and stability problems leveraging a learning-based approach. The proposed framework includes a two-module structure that iteratively and jointly learns the candidate Lyapunov function and control law via deep neural networks in a learning module. Meanwhile, it guides the learning procedure towards valid results satisfying Lyapunov conditions in a falsification module. The introduced termination criteria ensure provable system stability. This control framework is verified through several studies handling different types of power system control problems. The results show that the proposed framework is generalizable and can simplify the control design for complex power systems with the stability guarantee and enlarged region of attraction.
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The Lyapunov stability-based update overcomes limitations of gradient descent methods such as instability and local minima trapping.
“which overcomes the limitations of gradient descent (GD) methods such as instability and local minima trapping.”
From Deep reinforcement learning-based adaptive fuzzy control for electro-hydraulic servo system (2025) · cites this paper as [26]
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Neural Network Based Approach for Steady-State Stability Assessment of Power Systems
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“To overcome the limitations of the GD-based update equations like instability or stocking in local minima [25, 26], the Lyapunov stability method is used to derive the adaptation law of weights.”
Li, Q., Liu, S., Yang, C., Chaoui, H., & Saddik, A. E. (2026) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2026.3670916
- Authors
- Zhao, T., Wang, J., Lu, X., & Du, Y.
- Affiliations
- Brookhaven National Laboratory (US)
- Southern Methodist University (US)
- Temple University (US)
- Venue
- IEEE Transactions on Power Systems
- Published
- 2022-03
- Volume
- 37
- Issue
- 2
- Pages
- 955-966
- ISSN
- 0885-8950 (ISSN-L)
- 1558-0679
- OpenAlex
- W3196789737
- Cited by
- 75
- References
- 39
- Citations by year
- 2026: 17 · 2025: 26 · 2024: 15 · 2023: 11 · 2022: 6
- FWCI
- 2.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Power Systems (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy
- Solar Energy Technologies Office (34230)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Power System Optimization and Stability
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Frequency Control in Power Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Control and Stability of Dynamical Systems
- Keywords
- power system transient stability, deep neural network, domain of attraction, power system control, learning control
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