Neural Lyapunov Control for Power System Transient Stability: A Deep Learning-Based Approach

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 requ...

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Bibliographic Details
Published inIEEE transactions on power systems Vol. 37; no. 2; pp. 955 - 966
Main Authors Zhao, Tianqiao, Wang, Jianhui, Lu, Xiaonan, Du, Yuhua
Format Journal Article
LanguageEnglish
Published New York IEEE 01.03.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Artificial neural networks
Asymptotic stability
Control stability
Control systems
Control theory
Deep learning
Liapunov functions
Lyapunov function
Lyapunov methods
Machine learning
Modules
neural Lyapunov control
Nonlinear dynamics
Power system dynamics
Power system stability
Stability analysis
Stability criteria
System dynamics
Systems stability
Transient analysis
Transient stability
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Summary: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.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2021.3102857