Supplementary damping controller of grid connected dc micro-grids based on Q-learning

Tianqi Hong, Tao Bian, Francisco De León

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, a new near optimal supplementary damping controller is proposed for grid connected dc microgrids with small power capacities using the Q-learning algorithm. A novel system discretization approach is presented to formulate the power system stability problem into a Q-learning solvable format. A numerical example is provided for illustration of the performance of the proposed supplementary damping controller.

Original languageEnglish (US)
Title of host publication2016 IEEE Power and Energy Society General Meeting, PESGM 2016
PublisherIEEE Computer Society
ISBN (Electronic)9781509041688
DOIs
StatePublished - Nov 10 2016
Event2016 IEEE Power and Energy Society General Meeting, PESGM 2016 - Boston, United States
Duration: Jul 17 2016Jul 21 2016

Publication series

NameIEEE Power and Energy Society General Meeting
Volume2016-November
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933

Other

Other2016 IEEE Power and Energy Society General Meeting, PESGM 2016
CountryUnited States
CityBoston
Period7/17/167/21/16

Keywords

  • Damping
  • Micro-grids
  • Q-learning
  • Small signal stability

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering

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  • Cite this

    Hong, T., Bian, T., & De León, F. (2016). Supplementary damping controller of grid connected dc micro-grids based on Q-learning. In 2016 IEEE Power and Energy Society General Meeting, PESGM 2016 [7741189] (IEEE Power and Energy Society General Meeting; Vol. 2016-November). IEEE Computer Society. https://doi.org/10.1109/PESGM.2016.7741189