Reinforcement Learning based Load Frequency Control for Power Systems

Load Frequency Control (LFC) holds significant importance in the operation of power systems. It is a critical system that requires intelligent methods to address its associated challenges. In this article, we employ Reinforcement Learning (RL) as a solution to tackle the LFC problem specifically for two turbines.RL proves to be a promising approach for optimizing LFC due to its ability to learn from experience and make decisions accordingly. By utilizing RL, we aim to enhance the performance and efficiency of LFC in power systems.The application of RL in LFC involves training an RL agent to make decisions based on observed states, such as frequency and tie-line power deviations. The agent learns from feedback in the form of rewards and updates its policy accordingly. Through this iterative learning process, the RL agent aims to find an optimal control strategy for maintaining system frequency and power balance.By employing RL techniques, we strive to improve the effectiveness and reliability of LFC in power systems with two turbines, ultimately contributing to the stability and operational efficiency of the overall power grid.

کلیدواژه ها:

LFC ، reinforcement learning ، non-reheated and reheated turbines.