A Novel Overload Control Algorithm for Distributed Control Systems to Enhance Reliability in Industrial Automation

This paper presents a novel real-time overload detection algorithm for distributed control systems (DCSs), particularly applied to thermoelectric power plant environments. The proposed method is integrated with a modular multi-functional processor (MFP) architecture, designed to enhance system reliability, optimize resource utilization, and improve fault resilience under dynamic operational conditions. As legacy DCS platforms, such as those installed at the Tae-An Thermoelectric Power Plant, face limitations in applying advanced logic mechanisms, a simulation-based test bench was developed to validate the algorithm in anticipation of future DCS upgrades. The algorithm operates by partitioning function code executions into segment groups, enabling fine-grained, real-time CPU and memory utilization monitoring. Simulation studies, including a modeled denitrification process, demonstrated the system’s effectiveness in maintaining load balance, reducing power consumption to 17 mW under a 2 Gbps data throughput, and mitigating overload levels by approximately 31.7%, thereby outperforming conventional control mechanisms. The segmentation strategy, combined with summation logic, further supports scalable deployment across both legacy and next-generation DCS infrastructures. By enabling proactive overload mitigation and intelligent energy utilization, the proposed solution contributes to the advancement of self-regulating power control systems. Its applicability extends to energy management, production scheduling, and digital signal processing—domains where real-time optimization and operational reliability are essential.