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

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)
93 Downloads (Pure)


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 ActorCritic 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.

Original languageEnglish
Article number4804
Number of pages17
Issue number16
Publication statusPublished - 6 Aug 2021
MoE publication typeA1 Journal article-refereed


  • Linear active disturbance rejection control
  • Load frequency control
  • Multisource power system
  • Reinforcement learning
  • Soft actor-critic


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