In this study, a particle swarm optimization (PSO) based Multi-Agent Reinforcement Learning (MARL) approach for load frequency control (LFC) of multi-area power system over communication network considering communication delay and packet loss is presented. A networked control system (NCS) is framed to model a constant delay and packet loss using Markovian approach. The controller using PSO based MARL technique having two levels of controlled action in each area of power system act as an estimator agent and controller agent respectively. The proposed method for frequency regulation of multi-area power system is studied in detail and verified for different types of load disturbances. In addition, to check the system dynamic performance of electric power system a mean square errors (MSEs) of states with different value of communication delay are computed. The simulation results of this study is validated and compared with some pre-design methods available from literatures, along with capacity of multi agent system (MAS) for application of LFC in power system over communication network.

在这项研究中，提出了一种基于粒子群优化（PSO）的多智能体强化学习（MARL）方法，用于在通信网络上考虑通信延迟和丢包的负载频率控制（LFC）。使用Markovian方法对网络控制系统（NCS）进行建模，以模拟恒定延迟和数据包丢失。使用基于PSO的MARL技术的控制器在电力系统的每个区域中具有两个级别的受控动作，分别充当估计器代理和控制器代理。对提出的多区域电力系统频率调节方法进行了详细研究，并针对不同类型的负载扰动进行了验证。此外，为了检查电力系统的系统动态性能，计算了具有不同通信延迟值的状态的均方误差（MSE）。验证了本研究的仿真结果，并与文献中提供的一些预先设计方法进行了比较，并验证了多代理系统（MAS）在通信网络上将LFC应用于电力系统的能力。