## 1 Introduction
Therefore, it is necessary to find better solutions to monitor the DCS main controller for distributed control. In order to describe the novel overload control algorithm in greater detail--along with its optimal real-time utilization--it is necessary to investigate the reliability of the DCS and address associated safety concerns [4; 5; 6]. \begin{table}
\begin{tabular}{c c c} \hline
**Specifications** & **MFP (Multi-Functional Processor) Module** & **BRC (Bridge Controller Card) Module** \\ \hline Microprocessor & 32 bit & 32 bit \\ \hline NVRAM & 64 K Byte & 1 M Byte \\ \hline RAM & 256 K Byte & 2 M Byte \\ \hline Power consumption & 5 V/2 A & 5 V/2 A \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of MFP and BRC modules in terms of processor type, memory capacity, and power consumption. In this paper, to study the overall DCS system, it is necessary to recognize the system-level architecture and have in-depth knowledge of the hardware level, so that simulations that calculate overload are possible. Therefore, Figure 1 illustrates the system-level and hardware-level architecture of a DCS as applied to energy infrastructure environments. In the context of this paper, the DCS serves as the foundational platform for implementing and evaluating a novel overload control algorithm. The architecture highlights the importance of modular control, real-time data acquisition, and distributed processing capabilities, which are critical for maintaining operational stability and resilience under dynamic load conditions in large-scale energy systems. Figure 1a illustrates the architecture of a DCS, where multiple process control units (PCUs) are connected via a central DCS data highway to DCS operator stations, engineering workstations (EWSs), and peripheral devices such as printers. The system enables decentralized control by distributing computational tasks across multiple PCUs, improving fault tolerance and scalability. The configuration includes multiple PCUs connected via a centralized DCS data highway to operator stations, EWSs, and peripheral devices [7]. This hierarchical structure enables modular, distributed control and real-time supervisory monitoring. Figure 1b provides a detailed view of the internal hardware architecture, showing how microprocessors interface with RAM, ROM, I/O sections, and communication buses to handle process I/O signals and control functions efficiently. This architecture supports deterministic signal processing and robust execution of control algorithms, providing the computational foundation for implementing advanced overload management strategies in distributed energy systems. It should be noted that Figure 1 is a diagram illustrating the real-time operation of each module installed in an actual thermal power plant in Tae-an, Korea. To examine this further, it is necessary to examine more details [8; 9]. This paper focuses on the best utilization and transmission of alternative methods to prevent the overload situation of DCS in advance. For this purpose, a new MFP and module are designed and applied to detect overload. Figure 1: Overall DCS system design including diagram of MFP module: (**a**) system-level architecture of a DCS designed for energy infrastructure applications; (**b**) hardware-level architecture of a typical DCS controller, illustrating the internal interaction between the microprocessor, memory units and peripheral I/O components such as serial ports, DMA, and bus interfaces. This paper makes several significant contributions to the field of overload control and reliability enhancement in DCSs, particularly in thermoelectric and nuclear power plants. The following are the key contributions of this paper:  
1. Proposal of a real-time overload detection and mitigation algorithm that enhances the stability and reliability of DCS. 2. Introduction of a modular, multi-functional logic processor (MFP) to improve fault tolerance and load balancing and ensure optimal resource utilization while maintaining operational efficiency. 3.