Advanced Main Control Room Research Articles

A Novel Communication Framework for Reduction of Staffs in Advanced MCRs of SMRs: A Prototype Development of a NLP-Based System

As small modular reactors are gaining attention as the future of nuclear energy, it has become increasingly imperative to minimize the number of required operators in order to improve economic competitiveness. Although the tasks of operators have changed and their workloads have been relatively reduced as digital technologies have been applied to advanced main control rooms (MCRs), no change in the number of operators has been made when compared to conventional MCRs in the Republic of Korea. As a solution, the introduction of natural language processing (NLP) technology to replace certain operator tasks in advanced MCRs can be a valuable means of reducing MCR staff. In this paper, we suggest a novel communication framework utilizing NLP technology to minimize the number of operators in advanced MCRs. To do this, we analyze operator tasks in advanced MCRs and select those that can be replaced by NLP technology. We then develop a prototype NLP-based system and analyze the process and characteristics of the suggested communication framework.

An Approach to Analyze Diagnosis Errors in Advanced Main Control Room Operations Using the Cause-Based Decision Tree Method

Advancements in the nuclear industry have led to the development of fully digitized main control rooms (MCRs)—often termed advanced MCRs—for newly built nuclear power plants (NPPs). Diagnosis is a major part of the cognitive activity in NPP MCRs. Advanced MCRs are expected to improve the working environment and reduce human error, especially during the diagnosis of unexpected scenarios. However, with the introduction of new types of tasks and errors by digital MCRs, a new method to analyze the diagnosis errors in these new types of MCRs is required. Task analysis for operator diagnosis in an advanced MCR based on emergency operation was performed to determine the error modes. The cause-based decision tree (CBDT) method—originally developed for analog control rooms—was then revised to a modified CBDT (MCBDT) based on the error mode categorizations. This work examines the possible adoption of the MCBDT method for the evaluation of diagnosis errors in advanced MCRs. We have also provided examples of the application of the proposed method to some common human failure events in emergency operations. The results show that with some modifications of the CBDT method, the human reliability in advanced MCRs can be reasonably estimated.

Modeling digital main control room operator’s resilience under extreme conditions: An Experiment design scheme

Human reliability is one of the most important factors that make effects in nuclear power plant(NPP) operation. In advanced digital NPP main control room with high levels of automation, the systematic operation which require a sufficient mental workload to address those undesired events has become a critical challenge for operators. The aim of this research is to identify the operator’s reliability by developing a resilience model. In this work, a seven-stage technique framework is proposed, which includes the skeleton of theoretical analysis, experimental design and hardware setting to how to establish the model for NPP operator in a downsize main control room cabin. The resilience model for operators’ reliability via assessing their basic skill tasks performance and evaluating their cognitive workload in the framework hence can be used for assessing the level of training of the new employed operators as well as human reliability in other critical process industries.

Biosignal-Based Attention Monitoring to Support Nuclear Operator Safety-Relevant Tasks.

In the main control room (MCR) of a nuclear power plant (NPP), the quality of an operator's performance can depend on their level of attention to the task. Insufficient operator attention accounted for more than 26% of the total causes of human errors and is the highest category for errors. It is therefore necessary to check whether operators are sufficiently attentive either as supervisors or peers during reactor operation. Recently, digital control technologies have been introduced to the operating environment of an NPP MCR. These upgrades are expected to enhance plant and operator performance. At the same time, because personal computers are used in the advanced MCR, the operators perform more cognitive works than physical work. However, operators may not consciously check fellow operators' attention in this environment indicating potentially higher importance of the role of operator attention. Therefore, remote measurement of an operator's attention in real time would be a useful tool, providing feedback to supervisors. The objective of this study is to investigate the development of quantitative indicators that can identify an operator's attention, to diagnose or detect a lack of operator attention thus preventing potential human errors in advanced MCRs. To establish a robust baseline of operator attention, this study used two of the widely used biosignals: electroencephalography (EEG) and eye movement. We designed an experiment to collect EEG and eye movements of the subjects who were monitoring and diagnosing nuclear operator safety-relevant tasks. There was a statistically significant difference between biosignals with and without appropriate attention. Furthermore, an average classification accuracy of about 90% was obtained by the k-nearest neighbors and support vector machine classifiers with a few EEG and eye movements features. Potential applications of EEG and eye movement measures in monitoring and diagnosis tasks in an NPP MCR are also discussed.

Functional Information of System Components Influenced by Counteractions on Computer-Based Procedure

In an emergency condition of nuclear power plant, operators have to mitigate the accident in order to remove the decay heat and to prevent the release of radioactive material to the environment following the emergency operating procedures (EOPs). The action of operators on a component, for example, changing the parameter level of a component, which is described in a procedure step, will impact other components of the plant and the plant behavior. Nowadays, the advanced main control rooms have been equipped with the computer-based procedures (CBPs) which provide some features and benefits which are not available in paper-based procedures. However, most of CBPs do not provide information of the impact of the counteractions on each procedure step (components influenced and future plant behavior) although it is useful for operators to understand the purpose of the procedure steps before making decisions and taking the actions. This paper discusses the functional information and the method to generate the information using multilevel flow modeling (MFM) model of operator actions on some procedure steps of a simplified EOP of pressurized water reactor (PWR) plant, as an example.

Preliminary Investigation of Time Remaining Display on the Computer-based Emergency Operating Procedure

One of the important thing in the mitigation of accidents in nuclear power plant accidents is time management. The accidents should be resolved as soon as possible in order to prevent the core melting and the release of radioactive material to the environment. In this case, operators should follow the emergency operating procedure related with the accident, in step by step order and in allowable time. Nowadays, the advanced main control rooms are equipped with computer-based procedures (CBPs) which is make it easier for operators to do their tasks of monitoring and controlling the reactor. However, most of the CBPs do not include the time remaining display feature which informs operators of time available for them to execute procedure steps and warns them if the they reach the time limit. Furthermore, the feature will increase the awareness of operators about their current situation in the procedure. This paper investigates this issue. The simplified of emergency operating procedure (EOP) of steam generator tube rupture (SGTR) accident of PWR plant is applied. In addition, the sequence of actions on each step of the procedure is modelled using multilevel flow modelling (MFM) and influenced propagation rule. The prediction of action time on each step is acquired based on similar case accidents and the Support Vector Regression. The derived time will be processed and then displayed on a CBP user interface.

Development of an EEG-based workload measurement method in nuclear power plants

The environment of main control rooms of large scale process control systems such as nuclear power plants (NPPs) has been changed from the conventional analog type to the digital type. In digitalized advanced main control rooms, human operators conduct highly cognitive work rather than physical work compared to the case of the original control rooms in NPPs. Various operating support systems (OSSs) have been developed to reduce an operator’s workload. Most representative techniques to evaluate the workload are based on subjective ratings. However, there are some limitations including the possibility of skewed results due to self-assessment of the workload and the impossibility of continuously measuring the workload due to freezing simulation for workload assessment. As opposed to subjective ratings techniques, physiological techniques can be used for objective and continuous measurements of a human operator’s mental status by sensing the physiological changes of the autonomic or central nervous system. In this study, electroencephalogram (EEG) was used to measure the operator’s mental workload because it had been proven to be sensitive to variations of mental workload in other studies, and it allows various types of analysis. Based on various research reviews on the characteristics of brainwaves, EEG-based Workload Index (EWI) was suggested and validated through experiments. As a result, EWI is concluded to be valid for measuring an operator’s mental workload and preferable to subjective techniques. Furthermore, EWI was applied to evaluate the effects of OSSs on human operators through experiments. Finally, it is expected that the results of this study can be used to measure the operator’s workload in NPPs.

A framework to estimate probability of diagnosis error in NPP advanced MCR

Recently, a new type of main control room (MCR) has been adopted in nuclear power plants (NPPs). The new MCR, known as the advanced MCR, consists of digitalized human-system interfaces (HSIs), computer-based procedures (CPS), and soft controls while the conventional MCR includes many alarm tiles, analog indicators, hard-wired control devices, and paper-based procedures. These changes significantly affect the generic activities of the MCR operators, in relation to diagnostic activities. The aim of this paper is to suggest a framework to estimate the probabilities of diagnosis errors in the advanced MCR by updating a time reliability correlation (TRC) model. Using Bayesian inference, the TRC model was updated with the probabilities of diagnosis errors. Here, the diagnosis error data were collected from a full-scope simulator of the advanced MCR. To do this, diagnosis errors were determined based on an information processing model and their probabilities were calculated. However, these calculated probabilities of diagnosis errors were largely affected by context factors such as procedures, HSI, training, and others, known as PSFs (Performance Shaping Factors). In order to obtain the nominal diagnosis error probabilities, the weightings of PSFs were also evaluated. Then, with the nominal diagnosis error probabilities, the TRC model was updated. This led to the proposal of a framework to estimate the nominal probabilities of diagnosis errors in the advanced MCR.

Study on a new framework of Human Reliability Analysis to evaluate soft control execution error in advanced MCRs of NPPs

Since the Three Mile Island (TMI)-2 accident, human error has been recognized as one of the main causes of Nuclear Power Plant (NPP) accidents, and numerous studies related to Human Reliability Analysis (HRA) have been carried out. Most of these methods were developed considering the conventional type of Main Control Rooms (MCRs). However, the operating environment of MCRs in NPPs has changed with the adoption of new Human-System Interfaces (HSIs) that are based on computer-based technologies. The MCRs that include these digital technologies, such as large display panels, computerized procedures, and soft controls, are called advanced MCRs. Among the many features of advanced MCRs, soft controls are a particularly important feature because operating actions in NPP advanced MCRs are performed by soft control. Due to the differences in interfaces between soft control and hardwired conventional type control, different Human Error Probabilities (HEPs) and a new HRA framework should be considered in the HRA for advanced MCRs.To this end, a new framework of a HRA method for evaluating soft control execution human error is suggested by performing a soft control task analysis and the literature regarding widely accepted human error taxonomies is reviewed. Moreover, since most current HRA databases deal with operation in conventional MCRs and are not explicitly designed to deal with digital HSIs, empirical analysis of human error and error recovery considering soft controls under an advanced MCR mockup are carried out to collect human error data, which is essential to estimate the final HEP using the proposed framework of the HRA method. Finally, final HEPs in advanced MCRs are estimated by integrating the suggested HRA framework, and a nominal HEP and recovery failure probability database developed from empirical studies.

Operations strategy for workload balancing of crews in an advanced main control room

The advanced main control room (advanced-MCR) is the one that allows for reactor operations based on digital instrumentation and control (I&C) technology. Thus, the operators of an advanced-MCR operate the plant through digital I&C interfaces, and for this purpose, an additional digital manipulation task for the operating equipment should be performed that cannot be observed in a conventional-MCR. As a prior study proposing the cognitive, communicative, and operational activity measurement approach (COCOA), COCOA enables an evaluation of the operator's workload in an advanced-MCR, which includes newly generated tasks for Man-Machine Interface System based secondary operation under a digital environment, which does not exist in a conventional-MCR. As a result of observations on the workload level by utilizing COCOA for a reference plant with an advanced-MCR when conducting an emergency operating procedure, it was observed that the workload of the shift supervisor is about two times greater than that of other operators. This is because operators therein stuck to the old guidelines customized to a conventional-MCR and failed to accomplish load balancing in consideration of the operation environment that an advanced-MCR provides. In this context, it would be imperative to develop and apply an operations strategy for an advanced-MCR operation. This study proposes an operations strategy in an attempt to make a balanced workload of operators in an advanced-MCR.

Human error and the associated recovery probabilities for soft control being used in the advanced MCRs of NPPs

Since the Three Mile Island (TMI)-2 accident, human error has been recognized as one of the main causes of Nuclear Power Plant (NPP) accidents, and numerous studies related to Human Reliability Analysis (HRA) have been carried out. Most of these studies were focused on considering the conventional Main Control Room (MCR) environment. However, the operating environment of MCRs in NPPs has changed with the adoption of new human-system interfaces (HSI) largely based on up-to-date digital technologies. The MCRs that include these digital and computer technologies, such as large display panels, computerized procedures, and soft controls, are called advanced MCRs. Among the many features of advanced MCRs, soft controls are a particularly important because operating actions in advanced MCRs are performed by soft control. Due to the difference in interfaces between soft control and hardwired conventional controls, different HEP should be used in the HRA for advanced MCRs. Unfortunately, most current HRA databases deal with operations in conventional MCRs and are not explicitly designed to deal with digital Human System Interface (HSI). For this reason, empirical human error and the associated error recovery probabilities were collected from the mockup of an advanced MCR equipped with soft controls. To this end, small-scaled experiments are conducted with 48 graduated students in the department of nuclear engineering in Korea Advanced Institute of Science and Technology (KAIST) are participated, and accident scenarios are designed with respect to the typical Design Basis Accidents (DBAs) in NPPs, such as Steam Generator Tube Rupture (SGTR), Loss of Coolant Accident (LOCA) and Excess Steam Demand Event (ESDE). After that, the Bayesian update is conducted in order to extract the 5% and 95% quantiles of empirical human error and the associated recovery failure probabilities.

Development of task analysis method for operator tasks in main control room of an advanced nuclear power plant

Task analysis methods provide an insight for quantitative and qualitative predictions of how people will use a proposed system, though the different versions have different emphases. Most of the methods can attest to the coverage of the functionality of a system and all provide estimates of task performance time. However, most of the tasks that operators deal with in a digital work environment in the main control room of an advanced nuclear power plant require high mental activity. Such mental tasks overlap and must be dealt with at the same time; most of them can be assumed to be highly parallel in nature. Therefore, the primary aim to be addressed in this paper was to develop a method that adopts CPM-GOMS (cognitive perceptual motor-goals operators methods selection rules) as the basic pattern of mental task analysis for the advanced main control room. A within-subjects experiment design was used to examine the validity of the modified CPM-GOMS. Thirty participants participated in two task types, which included high- and low-compatibility types. The results indicated that the performance was significantly higher on the high-compatibility task type than on the low-compatibility task type; that is, the modified CPM-GOMS could distinguish the difference between high- and low-compatibility mental tasks.

An optimal range of information quantity on computer-based procedure interface design in the advanced main control room

The quantification of information in the interface design is a critical issue. Too much information on an interface can confuse a user while executing a task, and too little information may result in poor user performance. This study focused on the quantification of visible information on computer-based procedures (CBPs). Levels of information quantity and task complexity were considered in this experiment. Simulated CBPs were developed to consist of three levels: high (at least 10 events, i.e. 3.32 bits), medium (4–8 events, i.e. 2–3 bits), and low information quantity (1 or 2 events, i.e. 0 or 1 bits). Task complexity comprised two levels: complex tasks and simple tasks. The dependent variables include operation time, secondary task performance, and mental workload. Results suggested that medium information quantity of five to eight events has a remarkable advantage in supporting operator performance under both simple and complex tasks. This research not only suggested the appropriate range of information quantity on the CBP interface, but also complemented certain deficient results of previous CBP interface design studies. Additionally, based on results obtained by this study, the quantification of information on the CBP interface should be considered to ensure safe operation of nuclear power plants.

An empirical study on the human error recovery failure probability when using soft controls in NPP advanced MCRs

It is well known that probabilistic safety assessments (PSAs) today consider not just hardware failures and environmental events that can impact upon risk, but also human error contributions. Consequently, the focus on reliability and performance management has been on the prevention of human errors and failures rather than the recovery of human errors. However, the recovery of human errors is as important as the prevention of human errors and failures for the safe operation of nuclear power plants (NPPs). For this reason, many researchers have tried to find a human recovery process or step. However, modeling the human recovery process is not sufficient enough to be applied to human reliability analysis (HRA), which requires human error and recovery probabilities. In this study, therefore, human error recovery failure probabilities based on predefined human error modes were investigated by conducting experiments in the operation mockup of advanced/digital main control rooms (MCRs) in NPPs. To this end, 48 subjects majoring in nuclear engineering participated in the experiments. In the experiments, using the developed accident scenario based on tasks from the standard post trip action (SPTA), the steam generator tube rupture (SGTR), and predominant soft control tasks, which are derived from the loss of coolant accident (LOCA) and the excess steam demand event (ESDE), all error detection and recovery data based on human error modes were checked with the performance sheet and the statistical analysis of error recovery/detection was then carried out to obtain the recovery failure probabilities.

Operator’s cognitive, communicative and operative activities based workload measurement of advanced main control room

An advanced man–machine interface system (MMIS) with a computer-based procedure system and high-tech control/alarm system is installed in the advanced main control room (MCR) of a nuclear power plant. Accordingly, though the task of the operators has been changed a great deal, owing to a lack of appropriate guidelines on the role allocation or communication method of the operators, operators should follow the operating strategies of conventional MCR and the problem of an unbalanced workload for each operator can be raised. Thus, it is necessary to enhance the operation capability and improve the plant safety by developing guidelines on the role definition and communication of operators in an advanced MCR. To resolve this problem, however, a method for measuring the workload according to the work execution of the operators is needed, but an applicable method is not available. In this research, we propose a COgnitive, Communicative and Operational Activities measurement approach (COCOA) to measure and evaluate the workload of operators in an advanced MCR. This paper presents the taxonomy for additional operation activities of the operators to use the computerized procedures and soft control added to an advanced MCR, which enables an integrated measurement of the operator workload in various dimensions of cognition, communication, and operation. To check the applicability of COCOA, we evaluated the operator workload of an advanced MCR of a reference power plant through simulation training experiments. As a result, the amount of high workload of the shift supervisor (SS) was observed in relation to the computerized procedure system newly introduced into the environment of an advanced MCR, and the overall increase in the workload was also observed owing to the screen operation for the soft control of MMIS. On the basis of these observations, it is necessary to reestablish the role and communication method of MCR operators suitable to the new operational environment and changed work, and develop appropriate operating guidelines.

디지털 기기 기반 직무 수행 시 인적오류위험성에 대한 실험적 평가

The application of advanced Main Control Room(MCR) is accompanied with lots of changes and different forms and features through the virtue of new digital technologies. The characteristics of these digital technologies and devices give many opportunities to the interface management, and can be integrated into a compact single workstation in advanced MCR so that workers can operate the plant with minimum physical burden under any operation conditions. However, these devices may introduce new types of human errors and thus a means to evaluate and prevent such errors is needed, especially those related to characteristics of digital devices. This paper reviewed the new type of human error hazards of tasks based on digital devices and surveyed researches on physiological assessment related to human error. An experiment was performed to verify human error hazards by physiological responses such as EEG which was measured to evaluate the cognitive workload of operators. And also, the performances of four tasks which are representative in human error hazard tasks based on digital devices were compared. Response time, <TEX>${\beta}$</TEX> power spectrum rate of each task by EEG, and mental workload by NASA-TLX were evaluated. In the results of the experiment, the rate of the <TEX>${\beta}$</TEX> power was increased in the task 1 and task 4 which are searching and navigating task and memory task of hierarchical information, respectively. In case of the mental workload, in most of evaluation items, task 1 and 4 were highly rated comparatively. In this paper, human error hazards might be identified by highly cognitive workload. Conclusively, it was concluded that the predictive method which is utilized in this paper and an experimental verification can be used to ensure the safety when applying the digital devices in Nuclear Power Plants (NPPs).

Applying the Skill-Rule-Knowledge Framework to Understanding Operators’ Behaviors and Workload in Advanced Main Control Rooms

For the past years, a number of researches have focused on operators’ behaviors and workloads in advanced main control rooms (MCRs) in either the procedure-domain or knowledge-domain and in either workload-increased or workload-decreased conditions. Different job positions connote different responsibilities and abilities that are required to perform the job successfully. However, it may be inappropriate to apply a dichotomy in these issues. In this study, we clarified these controversial points through the analysis of the time, frequency, and workload of the behaviors based on Rasmussen's skill–rule–knowledge classification (SRK framework) according to the supervisor operator (SRO), reactor operator (RO), and assistant reactor operator (ARO). The results showed that, for the SRO, rule- and knowledge-based behaviors occurred more often than skill-based behavior in terms of time and frequency, and knowledge-based behavior was the main source of workload. For the RO, no significant differences were found among the three behavior types in terms of frequency and workload, but more time was spent on rule-based behaviors than on skill- and knowledge-based behaviors. The ARO spent more time performing skill-based behaviors than rule- and knowledge-based behaviors, but in terms of frequency and workload, rule-based behavior was the predominant type. Operators’ behaviors contribute to a plant's defense-in-depth approach to safety and serve a vital function in ensuring its safe operation. Research on behavioral taxonomies of advanced MCRs has many significant benefits in both scientific-theoretical and applied practical fields.

Development of staffing evaluation principle for advanced main control room and the effect on situation awareness and mental workload

Situation awareness and mental workload, both of which influence operator performance in the advanced main control room of a nuclear power plant, can be affected by staffing level. The key goal of staffing is to ensure the proper number of personnel to support plant operations and events. If the staffing level is not adaptive, the operators may have low situation awareness and an excessive mental workload, which lead to human error. Accordingly, this study developed a staffing evaluation principle based on CPM-GOMS modeling for operations in the advanced main control room. A within-subject experiment was designed to examine the validity of the staffing evaluation principle. The results indicated that the situation awareness, mental workload, and operating performance of the staffing level determined by the staffing evaluation principle was significantly better than that of the non-evaluated staffing level; thus, the validity of the staffing evaluation technique is acceptable. The implications of the findings of this study on managerial practice are discussed.

Quantitative estimation of the human error probability during soft control operations

In this work, a method was proposed for quantifying human errors that can occur during operation executions using soft controls. Soft controls of advanced main control rooms have totally different features from conventional controls, and thus they may have different human error modes and occurrence probabilities. It is important to identify the human error modes and quantify the error probability for evaluating the reliability of the system and preventing errors. This work suggests an evaluation framework for quantifying the execution error probability using soft controls. In the application result, it was observed that the human error probabilities of soft controls showed both positive and negative results compared to the conventional controls according to the design quality of advanced main control rooms.

An empirical study on the basic human error probabilities for NPP advanced main control room operation using soft control

By adopting new human–system interfaces that are based on computer-based technologies, the operation environment of main control rooms (MCRs) in nuclear power plants (NPPs) has changed. The MCRs that include these digital and computer technologies, such as large display panels, computerized procedures, soft controls, and so on, are called Advanced MCRs. Among the many features in Advanced MCRs, soft controls are an important feature because the operation action in NPP Advanced MCRs is performed by soft control. Using soft controls such as mouse control, touch screens, and so on, operators can select a specific screen, then choose the controller, and finally manipulate the devices.However, because of the different interfaces between soft control and hardwired conventional type control, different basic human error probabilities (BHEPs) should be considered in the Human Reliability Analysis (HRA) for advanced MCRs. Although there are many HRA methods to assess human reliabilities, such as Technique for Human Error Rate Prediction (THERP), Accident Sequence Evaluation Program (ASEP), Human Error Assessment and Reduction Technique (HEART), Human Event Repository and Analysis (HERA), Nuclear Computerized Library for Assessing Reactor Reliability (NUCLARR), Cognitive Reliability and Error Analysis Method (CREAM), and so on, these methods have been applied to conventional MCRs, and they do not consider the new features of advance MCRs such as soft controls. As a result, there is an insufficient database for assessing human reliabilities in advanced MCRs.In this paper, BHEPs in a soft control operation environment are investigated empirically for BHEPs to apply advanced MCRs. A soft control operation environment is constructed by using a compact nuclear simulator (CNS), which is a mockup for advanced MCRs. Before the experiments, all tasks that should be performed by subjects are analyzed using one of the task analysis methods, Systematic Human Error Reduction and Prediction Approach (SHERPA). Human errors are then checked to analyze BHEPs, human error mode, and the cause of human error when using soft control.