Control Room Operators Research Articles

Fault Detection in Transmission Line

Abstract: This project focuses on the design and implementation of an advanced fault detection system for overhead transmission lines, utilizing key components such as the ESP 8266 microcontroller, current sensors, a GPS module, and IoT integration. The system is capable of detecting and classifying four major fault types: single line to ground (L-G), line to line (LL), double line to ground (L-L-G), and three-phase faults (L-L-LG). By placing current sensors at the transmission line's sending end, the system identifies faults and accurately determines their location through GPS coordinates .Simulations using protect us software were conducted to verify the system’s performance prior to building a hardware prototype. Upon fault detection, the system provides alerts via an LCD display and a buzzer, while real-time fault data is transmitted to an Android app and web server through the arduino IoT cloud. Testing confirmed the system’s precision in fault identification and location tracking, ensuring timely notifications for control room and remote device operators. This successful implementation highlights the system's potential for improving safety and efficiency in power transmission networks. the project lays the groundwork for further exploration into advanced fault detection and real-time monitoring solutions in electrical engineering.

Team-centered IDAC: Modeling and simulation of operating crew in complex systems - Part 2: Simulation aspects and application

Complex systems operations, such as Nuclear Power Plants (NPPs), generally require professional operating teams. Factors associated with teamwork, such as inappropriate communication and coordination, are important contributing factors to accidents and unsafe behavior. The impact of crew interactions on team effectiveness and, consequently, on the entire system, has yet to be fully and quantitatively explored in high-risk environments such as NPPs. Since a team is an interactive social system, team-specific issues must be studied and evaluated from a “team perspective”—based on team dynamics and processes. This paper is a part of a two-papers series that presents a simulation-based Team Model for NPP control room operations. Part 1 describes the theoretical fundaments of the model and details its elements. The current paper, Part 2, describes the simulation aspects and a full application of the method to an NPP four-steam generator feedwater system pipe break. It presents how to set up the simulation elements, such as hardware and humans within a team, using MATLAB Simulink. The method is demonstrated through a case study of an NPP four-steam generator feed water system pipe break. The results are discussed and assessed against theoretical and experimental findings.

Scrutinizing the effect of task complexity on operators’ task performance time in a digitalized main control room of a nuclear power plant

The TACOM (Task Complexity) measure was previously developed to quantify the complexity of tasks conducted by human operators in the main control room of nuclear power plants. The appropriateness of the TACOM measure was then confirmed in various validation studies mainly by comparing TACOM scores and operators’ task performance times in analog-type main control rooms. However, the suitability of the measure has not yet been validated using operators’ task performance times in digital-type main control rooms. This study aimed to further validate the suitability of the TACOM measure by analyzing task performance time data obtained from a digitalized full-scope simulator of a Korean domestic nuclear power plant and comparing the data with TACOM scores. The findings reveal a positive correlation between the task performance times in the digitalized main control room and the TACOM scores, meaning that the operators’ task performance time increases proportionally as the TACOM score increases. This strongly implies that the TACOM measure is also a reliable tool for predicting task complexity in a digitalized main control room.

Team-centered IDAC: Modeling and simulation of operating crew in complex systems – Part 1: Team model and fundamentals

Operation of highly complex systems such as Nuclear Power Plants (NPPs) generally require highly trained professional operating teams. Factors associated with teamwork, such as ineffective communication and coordination, can be important contributing factors to accidents and unsafe behavior. The impact of crew interactions on team effectiveness and, consequently, on the entire system, has not been fully and quantitatively explored in high-risk environments such as NPPs. Since a team is an interactive social system, team-specific issues must be studied and evaluated from a “team perspective”—based on team dynamics and processes. This paper is part of a two-papers series that presents a simulation-based Team Model for NPP control room operations. The current paper, Part 1, describes the theoretical fundaments of the model and details its elements. The accompanying paper describes the simulation aspects, and a full application of the method to a pipe break accident in a four- four-loop steam generator feedwater system. The proposed model is based on the IDAC (Information, Decision, and Action in Crew context) cognitive model framework. The resulting model, Team-centered IDAC (Tc-IDAC), examines the team activities “Collaborative information collection,” “Shared decision making,” and “Distributed action execution” through specific modules for Team Error Management. These modules include error detection, error indication and error correction, and team performance shaping factors.

Design challenges and response plans for intelligent decision support systems for Korean nuclear power plants under normal and abnormal conditions

In nuclear power plants (NPPs), an intelligent decision support system (IDSS) aids the decision-making process of main control room operators. It achieves this by monitoring and diagnosing conditions, predicting progress, and providing preventive advice during both normal and abnormal operations. Despite ongoing research in Korea, few IDSS for NPPs have been effectively applied and verified. Furthermore, established designs and validation guidelines for IDSSs for Korean NPPs remain lacking. To address this gap, this study systematically identifies problems and corresponding countermeasures for applying artificial intelligence-based design and validation technologies, while complying with licensing regulatory standards. The aim is to develop a practical and effective IDSS. This study identifies design challenges in the practical application of fundamental IDSS technology, outlining necessary design and validation technologies. Additionally, it suggests technology-specific response plans for the identified design challenges.

Factors Affecting Control Room Operator Situation Awareness and ITS Fuzzy Logic Model

Factors Affecting Control Room Operator Situation Awareness and ITS Fuzzy Logic Model

The Correlation Between Lighting Intensity, Eye Fatigue, Occupational Stress, and Sleep Quality in the Control Room Operators of Abadan Refinery

Background: The control room is a critical work environment where optimal performance is essential to minimize errors. Lighting is one of the key environmental factors influencing the performance of control room operators. Inadequate sleep quality and job stress also significantly impact job performance. Poor sleep quality, eye fatigue, inappropriate lighting, and occupational stress can lead to errors and decreased accuracy in work. Objectives: The present study aims to determine the correlation between lighting intensity, eye fatigue, occupational stress, and sleep quality among control room operators at the Abadan Refinery. The study was conducted in 2022. Methods: This cross-sectional descriptive-analytical study was conducted on 190 control room operators at the Abadan Refinery in 2022. A lux meter device was used to evaluate the intensity of lighting. Standard Pittsburgh sleep quality, eye fatigue, and Occupational Stress questionnaires were also administered. The results were analyzed using SPSS software. Results: The average light intensity of the work surface was 347.95 ± 147.34 lux. The mean and standard deviation of job stress score and sleep quality were 155.42 ± 9.18 and 9.38 ± 1.62, respectively. The average eye fatigue score was 5.09 ± 0.821 in people without glasses and 4.47 ± 0.828 in people with glasses. A significant correlation was found between brightness intensity and visual fatigue (ρ = 0.201, P = 0.029). The correlation between eye fatigue and sleep quality score was also significant (ρ = 0.225, P = 0.002). However, no significant correlation was observed between lighting intensity and sleep quality (ρ = - 0.005, P = 0.946). The correlation between the overall score of occupational stress and sleep quality was statistically significant (r = - 0.148, P = 0.042). Conclusions: The results showed a significant correlation between lighting intensity and the factors of sleep quality and eye fatigue. Additionally, there was a significant correlation between eye fatigue and sleep quality, as well as between occupational stress and sleep quality. Therefore, it is suggested that planning to minimize job stress, and improve lighting and sleep quality for gas refinery control room personnel should be designed and implemented by the authorities.

Exploring the Influence of Human System Interfaces: Introducing Support Tools and an Experimental Study

Situational awareness and decision support tools such as procedures and alarm systems are vital for effective interaction among control room operators, especially in safety-critical situations. In safety-critical environments such as process plants, there remains a gap in evaluating specific tools during actual operations, or ”work-as-done.” Additionally, the underlying factors that might impact operators’ cognitive states and performance concerning safety have not been thoroughly explored. The need for such an evaluation is further bolstered by current interaction configurations where operators are more passive than active, thus reducing their cognitive performance. Therefore, this experimental study addresses the highlighted evaluation gap by introducing and comparing three human system interfaces/decision support tools in four human-in-the-loop configurations. The supports include two alarm design formats (prioritized vs. non-prioritized) and three procedure representation formats (paper, screen-based digitized, and an AI-based support system built with an integrated Bayesian network and reinforcement learning model). Ninety-two people (n = 92) participated voluntarily in the test. They were divided equally into four groups. Each group tested three safety-related events in a simulated formaldehyde production facility. Individuals belonging to the group with prioritized alarms and utilized paper procedures rated procedural support slightly higher on average than others in different groups. Unlike the other groups, their assessment of alarm prioritization support remained consistent across all scenarios. Further analysis of the impact of the setup on cognitive states and actual performance will be performed.

Hybrid Optimal Power System Recovery and Load Pick-Up With DGAllocation In Electrical Power System

The increasing demand for electricity, the expansion of complex power networks, and the operation of power systems near their capacity have heightened the risk of power outages. Consequently, control room operators must develop comprehensive restoration plans to minimize recovery time. Key steps in power system restoration include starting non-black-start units with black-start units and initiating load pick-up. This process requires identifying the shortest route between nodes to expedite repairs. This research presents an expert plan employing the Modified Cuckoo Search algorithm (MCSA) to determine optimal pathways. Additionally, it explores the optimal allocation of distributed generators (DG) to enhance the system's voltage profile and reduce power losses using particle swarm optimization (PSO). The results demonstrate that DG significantly reduces losses during recovery. The study prioritizes the restoration of DGs with strong black start capability, considering network and DG constraints. The effectiveness of the proposed restoration approach is validated through MATLAB simulations on theIEEE 39-bus system.

Identification of propagation path and root cause of faults based on generative adversarial networks in industrial systems

Fault diagnosis and detection (FDD) improves product quality, process safety, and profitability in modern industrial plants. Faults can propagate between different parts of plants due to connections between devices and control loops. As a result, control room operators may receive multiple alarms that distract their attention from addressing the main causes of abnormal situations. Therefore, investigating causal relationships between process variables is essential in determining the root cause of faults. This study proposes a new systematic approach for causality analysis to identify the propagation path and root cause of faults in industrial plants. The research's innovations are categorized into two main parts. Initially, the study utilizes machine learning algorithms to identify causal relationships using probabilistic concepts. Additionally, it introduces a novel architecture by merging the generative adversarial network (GAN) with the variational auto-encoder (VAE). The process variables are mapped to a latent space, where an adversarial generative network will investigate the causal relationship between variables. Finally, the Tennessee Eastman Process simulation and gas turbine industrial data are used to evaluate the proposed algorithm.

Perancangan Monitoring Coal Handling System PLTU Kaltim Teluk Berbasis Internet of Things

Abstract— Balikpapan Bay PLTU is a large capacity power plant located in the city of Balikpapan, which is located in East Kalimantan. This PLTU with a capacity of 2X110 MW meets the electricity needs of the Barito Mahakam network, which consists of the East Kalimantan, North Kalimantan, Central Kalimantan and South Kalimantan regions. The main fuel for electricity generation is low grade coal (LRC) or low calorie coal. High Speed Diesel (HSD) oil is used as initial fuel. DCS (Distributed Control System) is used for wood management at PLTU Kaltim Teluk. DCS helps operations by monitoring and controlling the main and supporting components, however, management, supervisors and other staff have difficulty knowing the operating conditions of the generating unit remotely because the monitoring system can only be accessed by the control room operator at the CCR. The aim of this research is to design an Internet of Things-based coal handling system control system for PLTU Kaltim Teluk. This real time based digital monitoring system can collect and display data from every sensor in the field. The research method is a literature study method and a field method to apply the design results and collect monitoring data at the two Balikpapan Bay PLTUs. The results show that testing, which includes integration testing with MySQL, functional testing, and URL and Endpoint testing, was carried out successfully. As expected, all the main features and functions of the PLTU monitoring system are running well. This system has averagepercentage error 0.1%. So this study has an accuracy rate of 99.9%. This system has the ability to make it easier for managers to monitor the system.

Predicting the duration of motorway incidents using machine learning

AbstractMotorway incidents are frequent & varied in nature. Incident management on motorways is critical for both driver safety & road network operation. The expected duration of an incident is a key parameter in the decision-making process for control room operators, however, the actual duration for which an incident will impact the network is never known with true certainty. This paper presents a study which compares the ability of different machine learning algorithms to estimate the duration of motorway incidents on Ireland’s M50 motorway, using an extensive historical incident database. Results show that the support vector machine has the best performance in most cases, but a different method may need to be used to improve accuracy in some situations. Results highlight the main challenges in accurately forecasting incident durations in real time & recommendations are made for improving prediction accuracy through systematic recording of various additional incident details.

Environmental Risk Assessment of a Hydrocracker Unit in Abadan Oil Refinery Using the EFMEA Analysis

Background: The aim of risk assessment is to use a rational method to analyze risks and to identify the possible hazards and outcomes for people, equipment, materials, and the environment. Methods: The criteria and indices for assessing the environmental, health, and safety risks of the hydrocracker unit were determined by a Delphi questionnaire in this study. Then, an Environmental Failure Mode and Effects Analysis was applied to evaluate, score, and rank the risks based on their probability of occurrence, severity, probability of detection, extent of pollution, and potential of recycling. Results: According to the results of the Delphi process, 19 out of the 22 items were identified as the main criteria in the environmental, health, and safety risk evaluation of the hydrocracker unit at the Abadan Oil Refinery. However, the results indicated that 67% of the risks associated with the life cycle operation were low while 33% of them were high in terms of intensity. In contrast, 75% of the risks associated with control room operators were low and 25% were high in terms of intensity. On the other hand, 64, 7, and 29% of the risks associated with the activities of site employees were low, moderate, and high in terms of intensity, respectively, while the corresponding figures were 14, 29, and 57% in the case of risks associated with repairs. Conclusion: Based on the results of techniques of environmental failure modes and effects analysis (EFMEA) and Delphi, appropriate methods can be used to identify and reduce risks in similar industries.

Collecting and Organizing the Influencing Factors of Team Communications to Handle Nuclear Power Plant Emergencies

A nuclear power plant (NPP), as a complex safety-critical system, requires qualified operators working in teams. Interactions between operators in the main control room (MCR) team are important to ensure safe operation. Since communication is the basis of the operators’ interactions, team communication is a significant factor affecting teamwork performance. Especially during NPP emergencies, poor team communication may lead to incorrect decisions and countermeasures, causing deterioration toward accidents. Moreover, in an emergency situation, emergency response teams are assembled. This multi-team and critical work condition further emphasizes the need for effective and accurate team communication. We collected the factors influencing team communication in NPP emergencies using a literature review combined with text mining. Our method for extracting the influencing factors consists of four steps; then, by applying topic modeling from text mining, we complemented the influencing factors. The resulting list of influencing factors of team communications for handling NPP emergencies is organized into five elements: individual, team, communication, NPP tasks, and external elements. Discussions on the team communication model, applicability, communication errors, and emergency response teams are also presented.

Enhancing Control Room Operator Decision Making

In the dynamic and complex environment of industrial control rooms, operators are often inundated with numerous tasks and alerts, leading to a state known as task overload. This condition can result in decision fatigue and increased reliance on cognitive biases, which may compromise the decision-making process. To mitigate these risks, the implementation of decision support systems (DSSs) is essential. These systems are designed to aid operators in making swift, well-informed decisions, especially when their judgment may be faltering. Our research presents an artificial intelligence (AI)-based framework utilizing dynamic influence diagrams and reinforcement learning to develop a powerful decision support system. The foundation of this AI framework is the creation of a robust, interpretable, and effective DSS that aids control room operators during critical process disturbances. By incorporating expert knowledge, the dynamic influence diagram provides a comprehensive model that captures the uncertainties inherent in complex industrial processes. It excels in anomaly detection and recommending optimal actions. Furthermore, this model is improved through a strategic collaboration with reinforcement learning, which refines the recommendations to be more context-specific and accurate. The primary goal of this AI framework is to equip operators with a live, reliable DSS that significantly enhances their response during process upsets. This paper describes the development of the AI framework and its implementation in a simulated control room environment. Our results show that the DSS can improve operator performance and reduce cognitive workload. However, it also uncovers a trade-off with situation awareness, which may decrease as operators become overly dependent on the system’s guidance. Our study highlights the necessity of balancing the advantages of decision support with the need to maintain operator engagement and understanding during process operations.

The potential impact of emotionally loaded stimuli on over/under-estimating neutral situations among power plant control-room operators.

Human emotions vary on a contextual basis. The significance of emotions becomes even more salient in Control Room Operators (CROs) in power plants. This study investigated the effects of emotionally loaded pictures on over/underestimating neutral situations. Twenty CROs voluntarily enrolled in the present study. Twenty-one blocks were considered, including ten emotionally loaded and 11 neutral blocks. The stimuli were alternatively submitted to subjects in random order. Each block comprised 13 images from the International Affective Picture System (IAPS), which were shown for 5 seconds. Subjects were required to complete the Self-Assessment Manikin (SAM) after exposure to the first and the last neutral blocks, which were identical. Our analyses showed significant differences between IAPS arousal and SAM1 and SAM2 arousal ratings (p SAM1&IAPS = 0.00, p SAM2&IAPS = 0.02). There was no significant relationship between the first and the second arousals, and emotionally loaded images corresponded to no significant difference in terms of valence. The findings suggested that the participants overestimated neutral situations compared with IAPS only in arousal level. Furthermore, CROs can still retain their ability to assess neutral situations in the case of viewing emotional stimuli, especially in valence level, at least half an hour after the first rating. A study design with pure negative/positive and high arousal levels may still provide even more significant results.

Multi-class classification of control room operators’ cognitive workload using the fusion of eye-tracking and electroencephalography

Chemical process industries are hazard intensive. Most industrial accidents occur today due to human error. For safe and efficient operation, it is therefore critical to ensure optimal operator performance. With the advent of Industry 4.0 and concomitant digitalization, the role of operators has become cognitively challenging. Therefore, it is imperative to assess the cognitive performance of operators. One of the major constructs to understand cognitive performance is the cognitive workload. An increase in cognitive workload often leads to degradation in performance. Traditional assessment techniques fail to capture cognitive aspects of performance. Recently, researchers in various domains such as aviation, driving, marine, and nuclear power have started to utilize physiological measures to gauge the cognitive workload of their operators. In our previous works, we have used electroencephalography (EEG) and eye-tracking separately to assess the cognitive workload of operators in the process industries. In contrast, in this paper, we explore the benefits of their fusion in classifying process industry control room operators’ workload into low, medium, and high classes while they tackle abnormal situations. The methodology employs the fusion of metrics derived from pupil, gaze, and EEG data to train a decision tree-based model for workload classification. Our results reveal that fusion leads to an increase in classification accuracy of upto 22 %. The work has the potential to identify the expertise level of operators and hence, can be critical in ensuring their optimal performance.

Design and research of suburban railway fire alarm system

With the rapid development of suburban rail transit, fire safety issues have received more and more attention. As the first line of the Shanghai Suburban Railway, the fire safety problem of the Shanghai Suburban Railway Airport Link Line is more prominent. In this paper, an automatic fire alarm system is designed and researched for the fire safety problem of the Shanghai Railway Airport Liaison Line, which adopts a hierarchical distributed structure and can realize accurate fire detection thorough immediately sending fire alarm information to the station control room and line operation control center and informing the location of the fire area. At the same time, the system also coordinates with the BAS system and ISCS system or independently realizes the linkage control of fire fighting equipment. The research results of this paper are of great significance for improving the fire safety level of Shanghai railway airport links.

Ecological interface design and evaluation for feedwater dearating system in NPPs

Abstract A good human-computer interface can improve the efficiency of operators in the main control room of a nuclear power plant (NPP) and reduce operational errors. It has been shown that ecological interface design (EID) can effectively reduce the cognitive load of users, improve the level of situation awareness, and help users to make decisions quickly and effectively. In this paper, we analyzed the feedwater deaeration system (ADG) of nuclear power plants, constructed the work domain analysis according to the abstraction hierarchy theory. The factors that affect the balance of the system are clarified, and the ecological interface is designed based on it, so that it can present the system status and present the future development trend more intuitively, and support the operator to predict the system situation. In this study, 10 volunteers with relevant knowledge background were selected for operational experiments, with subjective evaluation based on SART scale and grey theory, which verified that EID interface has significant advantages over the original interface in supporting both operator response time and accuracy.

An ergonomic focus evaluation of work-related musculoskeletal disorders amongst operators in the UAE network control centres

Work-related musculoskeletal disorders (WMSDs) have rapidly increased during the last decade, but only a few descriptive surveys have been conducted in the United Arab Emirates (UAE). This study investigated the prevalence of WMSDs and analysed their ergonomic risks amongst operators in the network control rooms across two government organisations, X and Y, in the UAE. Essential data were collected by the Nordic Musculoskeletal Questionnaire (NMQ) and the Maastricht Upper Extremity Questionnaire (MUEQ) from online surveys and direct observations based on the Rapid Office Strain Assessment (ROSA) and the Rapid Upper Limb Assessment (RULA) form and ergonomic measurements for the working environments, respectively. Fifty-three and eighteen operators participated from Organisations X and Y. This study found a high presence of WMSDs in both organisations over the past 12 months. In Organisation X, individual, work-related physical and psychosocial risk factors of high BMI, educational level, morning work shift, high job duration, lack of exercise habit, awkward body posture, high job demand, low job control, and low work social support were associated with WMSDs in different body areas (p < 0.05). In Organisation Y, older age, high BMI, high job duration, lack of exercise habits, unergonomic workstations, awkward body posture, low break time, high job demand, and stress level were associated with WMSDs in different body areas (p < 0.05). The control room operators' most affected body areas were the back, eyes, and neck. Several efficient ergonomic intervention ideas were explored to lessen the detrimental effects of WMSDs and preclude the development of WMSDs amongst the control centre operators.