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Stability enhancement through reinforcement learning: Load frequency control case study

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2 Author(s)
Eftekharnejad, S. ; West Virginia Univ. Morgantown, Morgantown ; Feliachi, A.

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).

Published in:

Bulk Power System Dynamics and Control - VII. Revitalizing Operational Reliability, 2007 iREP Symposium

Date of Conference:

19-24 Aug. 2007

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