## 1 Introduction  
The power system, a critical component of national infrastructure, provides stable and reliable electrical energy services to diverse socio-economic sectors. It plays a crucial role in driving modernization and serves as a safeguard for it. The system's stability, quality, and safety significantly impact national energy security, living standards, and sustainable development. Operational disruptions due to unexpected events and uncertainties can cause frequency deviations from the nominal value in the system. Prolonged frequency deviations not only affect user experience and damage system equipment but can also trigger grid collapse, leading to widespread power outages and significant societal losses. Extensive research has focused on improving the reliable and stable operation of power systems through the study of LFC methods. This research aims to identify more effective frequency control strategies to improve the economic and safety aspects of power systems [1]. Recently, the academic community has proposed diverse control strategies for LFC in multi-area power systems, employing various theoretical frameworks. These strategies encompass Model Predictive Control [2, 3], Robust Control [4, 5], Fuzzy Logic Control [6, 7, 8], Sliding Mode Control [9, 10], Linear Matrix Inequality (LMI) Control [11, 12], Reinforcement Learning [13, 14], and other methods. Reference [2] integrates dynamic event-triggered mechanisms and a hybrid H2 performance index to design a robust Model Predictive Control (MPC) strategy for LFC in power systems, capable of effectively handling network attacks and disturbances. Reference [3] introduces a novel dynamic event-based model predictive control strategy designed to enhance the robustness and stability of power system load frequency control in the presence of cyber attacks. Reference [4] designs a robust Proportional-Integral (PI)-type LFC scheme for power systems, taking into account sampling periods and transmission delays in communication networks. Simultaneously, this scheme introduces an Exponential Decay Rate (EDR) as a design parameter. Adjusting the value of EDR can achieve robust performance evaluation regarding parameter uncertainty, load fluctuation, and communication networks. Reference [5] presents a robust LFCstrategy for power systems that effectively accounts for transmission delays and varying sampling periods, ensuring improved system stability and performance. Reference [7] presents a sampled memory-event-triggered fuzzy load frequency control method for wind power systems. This approach is specifically designed to address outliers and transmission delays, therefore improving system reliability and performance. Reference [10] introduces a novel sliding model LFC strategy for renewable power systems, addressing time-delay uncertainty, parameter uncertainty, and load disturbances. Subsequently, the sliding mode switching surface and controller are designed based on the standard form. Using the isokinetic convergence law, the system state is directed to reach the switching surface within a finite timeframe, ensuring stable sliding motion on this surface. Reference [11] contributes to power system stability by designing a robust load frequency control (LFC) system capable of coping with inherent time delays by utilizing Linear Matrix Inequalities (LMI). It introduces a novel delay margin estimation technique to ascertain the maximum permissible delay for maintaining system stability, which enhances the control system's robustness compared to traditional methods. Using brain-inspired deep meta-reinforcement learning, reference [13] enhances multi-area grids' load frequency control (LFC). This approach develops a fault-tolerant LFC system that adapts to disturbances and faults, showing superior adaptability and robustness compared to traditional methods. Multi-area interconnected power systems depend on power communication networks for exchanging information and transmitting control commands across regions.