Adaptive Q-Learning Algorithm for Optimal Load Frequency Control in Single-Area Power Systems with Unknown Parameters

This paper presents a novel Q-learning algorithm to address the optimal load frequency control (LFC) problem in a single-area power system with unknown parameters. LFC is a critical issue for ensuring the stability and reliability of power systems. Traditional control methods often rely on precise mathematical models of the system, which are challenging to obtain or continuously changing in practical applications. To overcome this challenge, we design an adaptive Q-learning con-trol algorithm. This algorithm continuously learns and optimizes control strategies through interactions between the agent and the power system environment, achieving efficient and stable frequency control of the system. Simulation results demonstrate that the proposed Q-learning algorithm effectively maintains system frequency stability under conditions of parameter un-certainty, proving its applicability and effectiveness in solving LFC problems in power systems.