Q-learning based dynamic optimal CPS control methodology for interconnected power systems

Tao Yu, Bin Zhou, Ka Wing Chan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

29 Citations (Scopus)

Abstract

The NERC's control performance standard (CPS) based automatic generation control (AGC) problem is a stochastic multistage decision problem, which can be suitably modeled as a reinforcement learning (RL) problem based on Markov decision process (MDP) theory. The paper chose the Q-learning method as the RL algorithm regarding the CPS values as the rewards from the interconnected power systems. By regulating a closed-loop CPS control rule to maximize the total reward in the procedure of on-line learning, the optimal CPS control strategy can be gradually obtained. An applicable semi-supervisory pre-learning method was introduced to enhance the stability and convergence ability of Q-learning controllers. Two cases show that the proposed controllers can obviously enhance the robustness and adaptability of AGC systems while the CPS compliances are ensured. Soc. for Elec. Eng.
Original languageChinese (Simplified)
Pages (from-to)13-19
Number of pages7
JournalZhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering
Volume29
Issue number19
Publication statusPublished - 5 Jul 2009

Keywords

  • Automatic generation control
  • Control performance standard
  • Markov decision process
  • Optimal control
  • Q-learning

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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