通过强化学习增强稳定性:负荷频率控制案例研究

2007 iREP Symposium - Bulk Power System Dynamics and Control - VII. Revitalizing Operational Reliability Pub Date : 2007-12-26 DOI:10.1109/IREP.2007.4410552

Sara Eftekharnejad, A. Feliachi

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引用次数: 18

摘要

为了提高电力系统的稳定性，提出了一种基于多智能体的强化学习控制体系结构。它由一层本地代理和一个全局代理组成，全局代理负责协调本地代理的行为。选择负载频率控制作为案例研究来证明所提出概念的可行性。仿真结果验证了该控制器作为两区系统在线自动生成控制器(AGC)的有效性，该控制器具有和不具有生成速率约束(GRC)。

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Stability enhancement through reinforcement learning: Load frequency control case study

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

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