Automatic Voltage Control of Differential Power Grids Based on Transfer Learning and Deep Reinforcement Learning

作     者：Tianjing Wang Yong Tang 

作者机构：Laboratory of Power Grid Safety and Energy ConservationChina Electric Power Research InstituteBeijing 100192China School of Electrical and Electronic EngineeringNanyang Technological UniversitySingapore 

出 版 物：《CSEE Journal of Power and Energy Systems》 (中国电机工程学会电力与能源系统学报（英文）)

年 卷 期：2023年第9卷第3期

页      面：937-948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 Science Foundation of China(U1866602) 

主　　题：Deep reinforcement learning differential power grids transfer voltage control 

摘      要：In terms of model-free voltage control methods,when the device or topology of the system changes,the model’s accuracy often decreases,so an adaptive model is needed to coordinate the changes of *** overcome the defects of a model-free control method,this paper proposes an automatic voltage control(AVC)method for differential power grids based on transfer learning and deep reinforcement ***,when constructing the Markov game of AVC,both the magnitude and number of voltage deviations are taken into account in the ***,an AVC method based on constrained multiagent deep reinforcement learning(DRL)is *** further improve learning efficiency,domain knowledge is used to reduce action ***,distribution adaptation transfer learning is introduced for the AVC transfer circumstance of systems with the same structure but distinct topological relations/parameters,which can perform well without any further training even if the structure ***,for the AVC transfer circumstance of various power grids,parameter-based transfer learning is created,which enhances the target system’s training speed and ***,the method’s efficacy is tested using two IEEE systems and two real-world power grids.