Distributed Secondary Control Strategy Based on Q-learning and Pinning Control for Droop-controlled Microgrids

作     者：Wei Liu Jun Shen Sicong Zhang Na Li Ze Zhu Liang Liang Zhen Wen Wei Liu;Jun Shen;Sicong Zhang;Na Li;Ze Zhu;Liang Liang;Zhen Wen

作者机构：IEEE Department of Electrical Engineering School of Automation Nanjing University of Science and TechnologyNanjing 210094China State Grid Zhejiang Electric Power Co. Ltd. Jiaxing Power Supply CompanyJiaxing230022China 

出 版 物：《Journal of Modern Power Systems and Clean Energy》 (现代电力系统与清洁能源学报（英文）)

年 卷 期：2022年第10卷第5期

页      面：1314-1325页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 080802[工学-电力系统及其自动化] 0808[工学-电气工程] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China (No. 52077103) 

主　　题：Microgrid distributed secondary control pinning control Q-learning 

摘      要：A distributed secondary control(DSC) strategy that combines Q-learning and pinning control is originally proposed to achieve a fully optimal DSC for droop-controlled microgrids(MGs). It takes advantages of cross-fusion of the two algorithms to realize the high efficiency and self-adaptive control in MGs. It has the following advantages. Firstly, it adopts the advantages of reinforcement learning in autonomous learning control and intelligent decision-making, driving the action value of pinning control for feedback adaptive correction. Secondly, only a small part of points selected as pinned points needs to be controlled and pre-learned, hence the actual control problem is transformed into a synchronous tracking problem and the installation number of controllers is further ***, the pinning matrix can be modified to adapt to plugand-play operation under the distributed control ***, the effectiveness and versatility of the proposed strategy are demonstrated with a typical droop-controlled MG model.