A DDPG Algorithm Based Reinforcement Learning Controller for Three-Phase DC-AC Inverters

Jian Ye, Sen Mei, Huanyu Guo, Yingjie Hu, Xinan Zhang

Research output: Chapter in Book/Conference paperConference paperpeer-review

5 Citations (Scopus)

Abstract

This paper proposes a DC-AC inverter controller based on reinforcement learning (RL) algorithm. Compared with the traditional PID control method, the structure of the RL algorithm based controller is simpler. The deep deterministic policy gradient (DDPG) algorithm in RL algorithm is used to realize model-free control of inverters, so that the control algorithm has adaptive ability to different types of DC-AC inverters. It can avoid the dependence of the control strategy on the system model. Through simulation, the control strategy of three-phase two-level DC-AC inverter based on DDPG algorithm is compared with the traditional PID control. The simulation results show that the total harmonic distortion (THD) is reduced by 12% and the current tracking performance is improved by at least 75%.

Original languageEnglish
Title of host publication2023 International Conference on Power Energy Systems and Applications (ICoPESA 2023)
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages429-434
Number of pages6
ISBN (Electronic)9798350345605
DOIs
Publication statusPublished - 2023
Event2023 International Conference on Power Energy Systems and Applications - Nanjing, China
Duration: 24 Feb 202326 Feb 2023

Conference

Conference2023 International Conference on Power Energy Systems and Applications
Abbreviated titleICoPESA 2023
Country/TerritoryChina
CityNanjing
Period24/02/2326/02/23

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