Abstract
This paper presents a partially model-free adaptive optimal tracking control method for power systems, specifically targeting a synchronous generator connected through a reactive transmission line. By integrating the tracking error dynamics with reference trajectory dynamics, an augmented system is created. A discounted performance function is introduced to address the nonlinear tracking problem optimally. Unlike traditional methods that compute feedforward and feedback terms separately, the proposed approach calculates both simultaneously by minimizing the discounted performance function. The discrete-time tracking Bellman and Hamilton-Jacobi-Bellman (HJB) equations are derived, and a reinforcement learning (RL)-based technique is employed to solve the optimal policy online without requiring prior knowledge of system drift dynamics. Finally, the proposed method is validated through a real-time digital simulator (RTDS) with a standard power system representation.
Recommended Citation
V. K. Singh et al., "Reinforcement Learning-Based Nonlinear Optimal Discrete-Time Control of Power Systems," 2025 IEEE Conference on Control Technology and Applications Ccta 2025, pp. 748 - 753, Institute of Electrical and Electronics Engineers, Jan 2025.
The definitive version is available at https://doi.org/10.1109/CCTA53793.2025.11151272
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Keywords and Phrases
adaptive optimal control; neural networks; Power systems; RDTS; reinforcement learning; synchronous generators
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2025
Included in
Computer Sciences Commons, Electrical and Computer Engineering Commons, Medicine and Health Sciences Commons
Comments
Office of Naval Research, Grant N00014-21-1-2232