Stability enhancement through reinforcement learning: Load frequency control case study

Sara Eftekharnejad, Ali Feliachi

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

12 Scopus citations

Abstract

A multi-agent based control architecture using reinforcement learning is proposed to enhance power system stability. It consists of a layer of local agents and a global agent that coordinates the behavior of the local agents. Load frequency control is chosen as a case study to demonstrate the viability of the proposed concept. Simulation results illustrate the effectiveness of this controller as an online automatic generation controller (AGC) for a two area system, with and without generation rate constraints (GRC).

Original languageEnglish (US)
Title of host publication2007 iREP Symposium- Bulk Power System Dynamics and Control - VII, Revitalizing Operational Reliability
DOIs
StatePublished - Dec 1 2007
Externally publishedYes
Event2007 iREP Symposium- Bulk Power System Dynamics and Control - VII, Revitalizing Operational Reliability - Charleston, SC, United States
Duration: Aug 19 2007Aug 24 2007

Publication series

Name2007 iREP Symposium- Bulk Power System Dynamics and Control - VII, Revitalizing Operational Reliability

Other

Other2007 iREP Symposium- Bulk Power System Dynamics and Control - VII, Revitalizing Operational Reliability
CountryUnited States
CityCharleston, SC
Period8/19/078/24/07

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Control and Systems Engineering

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    Eftekharnejad, S., & Feliachi, A. (2007). Stability enhancement through reinforcement learning: Load frequency control case study. In 2007 iREP Symposium- Bulk Power System Dynamics and Control - VII, Revitalizing Operational Reliability [4410552] (2007 iREP Symposium- Bulk Power System Dynamics and Control - VII, Revitalizing Operational Reliability). https://doi.org/10.1109/IREP.2007.4410552