Deep Q-Network based real-time active disturbance rejection controller parameter tuning for multi-area interconnected power systems
Deep Q-Network based real-time active disturbance rejection controller parameter tuning for multi-area interconnected power systems
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DOI：
10.1016/j.neucom.2021.06.063
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发表时间：
2021-10
期刊：
影响因子：
6
通讯作者：
Yuemin Zheng;Qinglin Sun;Zengqiang Chen;Mingwei Sun;Jin Tao;Hao Sun
中科院分区：
文献类型：
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作者：
Yuemin Zheng;Qinglin Sun;Zengqiang Chen;Mingwei Sun;Jin Tao;Hao Sun
针对多区域互联电力系统中的负载频率控制(LFC)问题，设计了一种利用深度Q网络(DQN)实时获取控制器参数的线性自抗扰控制(LADRC)方法。 DQN和LADRC相结合的主要思想是将强化学习（RL）环境下的状态和动作等同于电力系统的输出和控制器的参数，这将使控制器具有学习能力和更好的适应性。为了达到更好的训练效果，对动作选择策略和奖励函数进行了一些改进。将该方法应用于扰动影响下的三区和四区互联电力系统，仿真结果表明了该方法的有效性。与比例积分微分（PID）控制器和固定参数的LADRC相比，本文提出的方法在电力系统的超调和稳定时间方面具有更好的响应效果。
For the problem of load frequency control (LFC) in multi-area interconnected power systems, a method of linear active disturbance rejection control (LADRC) that uses Deep Q-Network (DQN) to obtain controller parameters in real-time is designed. The main idea of combining the DQN and the LADRC is to equate the state and action in the environment of Reinforcement Learning (RL) to the output of the power system and the parameters of the controller, which will make the controller have learning ability and better adaptability. In order to achieve a better training effect, some improvements have been made to the action selection strategy and reward function. The proposed method is applied to the three-area and four-area interconnected power systems under the influence of disturbances, and the simulation results show the effectiveness of the proposed method. Compared with Proportional-Integral-Derivative (PID) controller and LADRC with fixed parameters, the method proposed in this paper has a better response effect in terms of overshoot and settling time of the power system.