LAPSE:2023.20098v1
Published Article
LAPSE:2023.20098v1
Load Frequency Active Disturbance Rejection Control for Multi-Source Power System Based on Soft Actor-Critic
Yuemin Zheng, Jin Tao, Hao Sun, Qinglin Sun, Zengqiang Chen, Matthias Dehmer, Quan Zhou
March 10, 2023
Abstract
To ensure the safe operation of an interconnected power system, it is necessary to maintain the stability of the frequency and the tie-line exchanged power. This is one of the hottest issues in the power system field and is usually called load frequency control. To overcome the influences of load disturbances on multi-source power systems containing thermal power plants, hydropower plants, and gas turbine plants, we design a linear active disturbance rejection control (LADRC) based on the tie-line bias control mode. For LADRC, the parameter selection of the controller directly affects the response performance of the entire system, and it is usually not feasible to manually adjust parameters. Therefore, to obtain the optimal controller parameters, we use the Soft Actor-Critic algorithm in reinforcement learning to obtain the controller parameters in real time, and we design the reward function according to the needs of the power system. We carry out simulation experiments to verify the effectiveness of the proposed method. Compared with the results of other proportional−integral−derivative control techniques using optimization algorithms and LADRC with constant parameters, the proposed method shows significant advantages in terms of overshoot, undershoot, and settling time. In addition, by adding different disturbances to different areas of the multi-source power system, we demonstrate the robustness of the proposed control strategy.
Keywords
linear active disturbance rejection control, load frequency control, multi-source power system, reinforcement learning, soft actor-critic
Suggested Citation
Zheng Y, Tao J, Sun H, Sun Q, Chen Z, Dehmer M, Zhou Q. Load Frequency Active Disturbance Rejection Control for Multi-Source Power System Based on Soft Actor-Critic. (2023). LAPSE:2023.20098v1
Author Affiliations
Zheng Y: College of Artificial Intelligence, Nankai University, Tianjin 300350, China [ORCID]
Tao J: College of Artificial Intelligence, Nankai University, Tianjin 300350, China; Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland [ORCID]
Sun H: College of Artificial Intelligence, Nankai University, Tianjin 300350, China
Sun Q: College of Artificial Intelligence, Nankai University, Tianjin 300350, China
Chen Z: College of Artificial Intelligence, Nankai University, Tianjin 300350, China; Key Laboratory of Intelligent Robotics of Tianjin, Nankai University, Tianjin 300350, China [ORCID]
Dehmer M: Department of Computer Science, Swiss Distance University of Applied Sciences, 3900 Brig, Switzerland
Zhou Q: Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland [ORCID]
Journal Name
Energies
Volume
14
Issue
16
First Page
4804
Year
2021
Publication Date
2021-08-06
ISSN
1996-1073
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Original Submission
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PII: en14164804, Publication Type: Journal Article
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LAPSE:2023.20098v1
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https://doi.org/10.3390/en14164804
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