R(λ) imitation learning for automatic generation control of interconnected power grids

T. Yu, B. Zhou, Ka Wing Chan, Y. Yuan, B. Yang, Q. H. Wu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

53 Citations (Scopus)


The goal of average reward reinforcement learning is to maximize the long-term average rewards of a generic system. This coincides with the design objective of the control performance standards (CPS) which were established to improve the long-term performance of an automatic generation controller (AGC) used for real-time control of interconnected power systems. In this paper, a novel R(λ) imitation learning (R(λ)IL) method based on the average reward optimality criterion is presented to develop an optimal AGC under the CPS. This R(λ)IL-based AGC can operate online in real-time with high CPS compliances and fast convergence rate in the imitation pre-learning process. Its capability to learn the control behaviors of the existing AGC by observing system variations enable it to overcome the serious defect in the applicability of conventional RL controllers, in which an accurate power system model is required for the offline pre-learning process, and significantly enhance the learning efficiency and control performance for power generation control in various power system operation scenarios.
Original languageEnglish
Pages (from-to)2130-2136
Number of pages7
Issue number9
Publication statusPublished - 1 Sept 2012


  • Automatic generation control
  • Average reward optimality criterion
  • Control performance standards
  • Imitation pre-learning
  • R(λ) learning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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