Stability enhancement through reinforcement learning: load frequency control case study
S Eftekharnejad, A Feliachi - 2007 iREP Symposium-Bulk …, 2007 - ieeexplore.ieee.org
2007 iREP Symposium-Bulk Power System Dynamics and Control-VII …, 2007•ieeexplore.ieee.org
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).
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).
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).
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