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

Zhao, Tianqiao; Wang, Jianhui; Lu, Xiaonan; Du, Yuhua

doi:10.1109/tpwrs.2021.3102857

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

Journal Article · 04 August 2021 · IEEE Transactions on Power Systems

DOI: https://doi.org/10.1109/tpwrs.2021.3102857 · OSTI ID:1868519

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Brookhaven National Lab. (BNL), Upton, NY (United States)
Southern Methodist Univ., Dallas, TX (United States)
Temple Univ., Philadelphia, PA (United States)

We report that 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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Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

Grant/Contract Number:: SC0012704

OSTI ID:: 1868519

Report Number(s):: BNL-222935-2022-JAAM

Journal Information:: IEEE Transactions on Power Systems, Journal Name: IEEE Transactions on Power Systems Journal Issue: 2 Vol. 37; ISSN 0885-8950

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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