Data-Driven Coordinated Control of AVR and PSS in Power Systems: A Deep Reinforcement Learning Method

Arman Oshnoei, Omid Sadeghian , Behnam Mohammadi-Ivatloo, Frede Blaabjerg, Amjad Anvari-Moghaddam*

*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

4 Citations (Scopus)
126 Downloads (Pure)

Abstract

In this paper, a strategy based on deep reinforcement learning (DRL) as an intelligent coordinator for power system stabilizer (PSS) and automatic voltage regulator (AVR) in a two-are power grid is proposed. The proposed coordinator is developed to provide accurate online modification of the gains appearing in the structure of PSS and AVR which avoids unfavorable interactions between PSS and AVR under significant changes in the working point and thereby guaranteeing the stability of the power grid. A Markov decision manner is used to formulate the DRL problem and it is solved through a deep deterministic policy gradient approach with an actor-critic framework. Since the intelligent coordinator relies on the expert's science, some scaling coefficients are added to the coordinator body to achieve optimal performance. To confirm the effectiveness of the presented DRL approach, the design is conducted on Kundur's power grid. Simulations illustrate that the proposed DRL-based control can confirm the stability of the system and attain desired dynamic responses.
Original languageEnglish
Title of host publication2021 IEEE International Conference on Environment and Electrical Engineering : EEEIC 2021
PublisherIEEE Press
Publication date2021
Pages1-6
ISBN (Print)978-1-6654-3614-4
ISBN (Electronic)978-1-6654-3613-7
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) - Bari, Italy
Duration: 7 Sept 202110 Sept 2021

Conference

Conference2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)
Country/TerritoryItaly
CityBari
Period07/09/202110/09/2021

Keywords

  • Power System Stability
  • Deep Reinforcement Learning
  • Coordinated Control
  • Interconnected power systems

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