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"Distributed control systems"
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Blockchain and Random Subspace Learning-Based IDS for SDN-Enabled Industrial IoT Security
The industrial control systems are facing an increasing number of sophisticated cyber attacks that can have very dangerous consequences on humans and their environments. In order to deal with these issues, novel technologies and approaches should be adopted. In this paper, we focus on the security of commands in industrial IoT against forged commands and misrouting of commands. To this end, we propose a security architecture that integrates the Blockchain and the Software-defined network (SDN) technologies. The proposed security architecture is composed of: (a) an intrusion detection system, namely RSL-KNN, which combines the Random Subspace Learning (RSL) and K-Nearest Neighbor (KNN) to defend against the forged commands, which target the industrial control process, and (b) a Blockchain-based Integrity Checking System (BICS), which can prevent the misrouting attack, which tampers with the OpenFlow rules of the SDN-enabled industrial IoT systems. We test the proposed security solution on an Industrial Control System Cyber attack Dataset and on an experimental platform combining software-defined networking and blockchain technologies. The evaluation results demonstrate the effectiveness and efficiency of the proposed security solution.
Journal Article
Modeling of Distributed Control System for Network of Mineral Water Wells
The article is devoted to solving the problem of designing a distributed control system for a network of production wells on the example of mineral water deposits in the Caucasus Mineral Waters region, Russia. The purpose was to determine the set of parameters of the control system to ensure technologically effective and safe operating modes of mineral water deposits. A mathematical model of the deposit was developed taking into account the given configuration and production rate of the network of the wells. The detailed algorithm is presented for designing the control system under consideration based on the frequency concept of analysis and synthesis for distributed control systems. The experimental tests and model validation were performed at the production wells facility of “Narzan”, Kislovodsk, Russia. The results of modeling and field experiments confirmed the adequacy of the mathematical model and the effectiveness of the proposed algorithm. The authors came to the conclusion that the adapted mathematical model can be used to create a regional automated field cluster management system for monitoring, operational management and forecasting the nature of real hydrogeological processes and ensuring their stability.
Journal Article
Controller Hardware-in-the-Loop Simulation of SOFC-GT Hybrid System
The solid oxide fuel cell–gas turbine (SOFC-GT) hybrid system is confronted with challenges related to system integration and coordinated control. In this study, a Controller Hardware-in-the-Loop Simulation (C-HILS) platform is constructed to validate its digital solutions. The C-HILS platform integrates the Advanced Process Simulation System (APROS), LabVIEW 2020 programming software, NI PXI hardware, and a distributed control system (DCS). Specifically, bidirectional data transmission between the simulation software and the DCS is facilitated through LabVIEW and PXI, leveraging the OLE for Process Control (OPC) protocol and physical Input and Output (I/O) channels. The dynamic SOFC-GT model developed in APROS demonstrates good consistency with design values, with relative errors below 4%. The DCS configuration employs PID controllers to achieve control over total power, SOFC fuel utilization, and gas turbine rotational speed. Experiments under transient conditions reveal that, despite discrepancies in dynamic responses between C-HILS and full-digital simulations, both can achieve stable control. This C-HILS platform effectively integrates virtual models with physical hardware, offering a reliable environment for verifying SOFC-GT control strategies and digital solutions, and thus facilitating the digital transformation of energy systems.
Journal Article
A multi-agent RFID-enabled distributed control system for a flexible manufacturing shop
Flexible manufacturing systems are complex, stochastic environments requiring the development of innovative, intelligent control architectures that support flexibility, agility, and reconfigurability. Distributed manufacturing control system addresses this challenge by introducing an adaptive production control approach supported by the presence of autonomous control units that are cooperating with each other. Most of the currently distributed control systems still suffer from lack of flexibility and agility when the product verity is high and is not reconfigured in case of ad hoc events. To overcome this limitation, a drawback of an excessive dependence on up-to-date information about the products and other elements that move within the system is essential. Radio frequency identification (RFID) is a new emerging technology which uses radio frequency waves to transfer data between a reader and movable item for identification, tracking, and categorization purpose. This paper discusses the architecture devised to deploy RFID-enabled distributed control and monitoring system by means of a set of agents that are responsible for the realization of different control and monitoring tasks and for cooperating to enhance agility, flexibility, and reconfigurability of manufacturing system.
Journal Article
A Data-Driven Framework for FDI Attack Detection and Mitigation in DC Microgrids
This paper proposes a Data-Driven (DD) framework for the real-time monitoring, detection, and mitigation of False Data Injection (FDI) attacks in DC Microgrids (DCMGs). A supervised algorithm is adopted in this framework to continuously estimate the output voltage and current for all Distributed Generators (DGs) with acceptable accuracy. Accordingly, among the various evaluated supervised DD algorithms, Adaptive Neuro-Fuzzy Inference Systems (ANFISs) are utilized because of their low computational burden, efficiency in operation, and simplicity in design and implementation in a distributed control system. The proposed framework is based on the residual analysis of the generated error signal between the estimated and actual sensed signals. The proposed framework detects and mitigates the cyber-attack depending on trends in generated error signals. Moreover, by applying Online Change Point Detection (OCPD), the need for a static user-defined threshold for the residual analysis of the generated error signal is dispelled. Finally, the proposed method is validated in a MATLAB/Simulink testbed, considering the resilience, effectiveness, accuracy, and robustness of multiple case study scenarios.
Journal Article
Development of a Simulator for Steam Turbine Generator Protection System Based on a Distributed Control System
A steam turbine generator is a very complex machine and very dangerous because it has the potential to explode. Therefore, it should be equipped with a control system and protection system. A protection system for steam turbine generators is more complex than other facilities in the power-generating industry. Due to its complexity, a high-skill operator is required to operate the facility. In this study, a simulator of a protection system for a steam turbine generator based on a distributed control system DCS ABB 800xA has been developed. The system was developed considering eight parameters to ensure safety, including turbine speed, inlet temperature, vibration, turbine shaft position, steam drum tank level, and lubricating oil pressure. The system also provides a manual emergency push button to anticipate an uncontrollable condition. The developed simulator has been tested to ensure it works properly and protects the steam turbine generator from abnormal conditions. Tests were performed to check interlocking responses caused by a single variable. All the variables have been tested. Another test was performed to check the ability of the simulator to detect abnormal conditions and respond to those conditions. All the tests showed that the simulator system could operate properly. The simulator system is very comprehensive in detecting the potential of turbine trips. This system considered all the variables that were highly reported as the factors of the turbine malfunction. It is the main advantage of the proposed system. The developed system provides significant benefits for training the operator without interrupting the operation of power-generating facilities.
Journal Article
Influence of Time Delays on Network-Controlled Diesel Generator Performance
In the paper the influence of Ethernet network dynamics on the quality of diesel-generator control is considered. The control quality indicators depend on time delays in the transmission of data packets over the Ethernet network. The optimization task of minimizing such time delays to improve the control quality was resolved. The Lagrange's method of undetermined multipliers and Bellman optimality rule were used for the analytical solution of the problem of minimizing time delays. A Matlab-model was developed for the research of the impact of time delays on the diesel-generator control quality, in which the Ethernet network is used as a data transmission channel between control objects and regulators. The scientific novelty of the results is in the improvement of the analytical method for analysing the characteristics of the automated control systems information processing network to study the influence of network dynamics on the quality of control of diesel-generators and determining the intensity of transmission of information and control packets, as well as using the proposed optimal conflict resolution rule, using which the transmission time delays data are minimal. This reduces the number of conflicts between the processes claiming the resources by almost 2 times and increases the quality of control.
Journal Article
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.
Journal Article