Digital Main Control Rooms Research Articles

Develop a framework of human reliability analysis to evaluate the human error probability in DMCR of NPPs

ABSTRACT Human reliability analysis (HRA) has an important effect on the safety of nuclear power plants (NPPs). However, the operating environment in the digital main control room of NPPs has changed with the application of digital interface. Therefore, this paper developed a framework of HRA to evaluate human error probability (HEP) under the condition of digital interface control. Firstly, the hierarchical task analysis method was used to decompose the task to determine a series of sub-tasks. Secondly, the HEPs of diagnosis and execution were calculated based on the standardized plant analysis risk-human reliability analysis method. Finally, the final HEP of task was calculated by considering the recovery failure probability of human error. The experimental results showed that the HEP evaluation result of the developed method was consistent with the statistical method result. It indicated that the developed HRA method can be used to evaluate the HEP of task.

Comparisons of human reliability data between analog and digital environments

It is obvious that the management of operational risk for nuclear power plants (NPPs) is critical for their sustainability. In this regard, it should be stressed that the establishment of digital main control rooms (MCRs) in NPPs is gradually increasing these days, which are distinguished from analog MCRs equipped with conventional devices. Since the operational conditions of human operators working in digital MCRs are entirely different from those of analog MCRs, human reliability data should be clarified from the viewpoint of the raw data to be included in the related probabilistic safety assessment (PSA). In this study, two sets of human reliability data collected from both analog and digital MCRs were compared with respect to 21 basic task types that are observable during the performance of proceduralized tasks (e.g., emergency operating procedures). As a result, it seems that human error probabilities (HEPs) of the digital MCR are lower than those of the analog MCR overall. However, since certain digital MCR HEPs are higher for several particular task types, uncertainty in PSA could be enlarged if human reliability data secured from an analog MCR are directly used to conduct the PSA of a digital MCR. This paper is an extended version of the paper submitted to the ESREL 2020-PSAM15 conference.

A Fuzzy CREAM Method for Human Reliability Analysis in Digital Main Control Room of Nuclear Power Plants

Human error is an important factor leading to nuclear power plant (NPP) accidents. The increasing of the amount of information improves the operators’ human error probability (HEP) in the digital main control room of NPPs. Human reliability analysis (HRA) is considered to be an effective method to reduce human error. The Cognitive Reliability and Error Analysis Method (CREAM) is one of the widely accepted HRA methods. However, there are shortcomings that weaken the applicability of this method. Therefore, this research proposes a fuzzy CREAM method based on a combination of fuzzy logic theory and the CREAM method. The study considers the weight of common performance conditions (CPCs) and constructs a logical relationship between CPCs and control modes. Finally, the effectiveness of the proposed method is determined using a widely accepted method to validate the evaluation results. The validated results showed that the evaluation result has a consistency between the fuzzy CREAM method and the traditional CREAM method. They indicated that the fuzzy CREAM method can obtain reliable HEP.

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.

Electroencephalography-Based Intention Monitoring to Support Nuclear Operators’ Communications for Safety-Relevant Tasks

The safe operation of a nuclear power plant (NPP) can be guaranteed through the team effort of operators in the main control room (MCR). Among the various features, peer checks, concurrent verification, independent verification, and communication reconfirmation are major contributors to effective operations in the MCR. In the digital MCR environment of advanced NPPs, there are potential emerging issues of concern related to these contributors resulting from the use of PC-soft controls for reactor operations. The objective of this study is to investigate the development of quantitative indicators for estimating the implicit intentions of reactor operators as a way to mitigate such concerns. The proposed quantitative indicators support peer checks and concurrent/independent verifications for diagnosing and preventing human errors through communication enhancement in a digital technology-based MCR. A machine learning–based algorithm was used to classify two implicit intentions of agreement and disagreement. The classification was based on electroencephalography data measured from human subjects while they performed mock operational tasks using soft controls. The mock operational tasks were based on using a Windows-based nuclear plant performance analyzer (Win-NPA). Statistical analysis was performed on the measured data to identify significant differences between the agreement and disagreement judgments by the operators. An average classification accuracy of 72% was achieved by using a support vector machine classifier for the Win-NPA task with a low number of features across the various Brodmann areas. The methodology proposed in this study may also serve to enhance communications in conventional MCRs for human error minimization.

Using Artificial Neural Network for Predicting and Evaluating Situation Awareness of Operator

The decrease of situation awareness (SA) is one of reasons leading to human factor accidents in nuclear power plants. The main purpose of this paper is to the evaluation and prediction the operators’ SA in digital main control room. Firstly, this paper used both the entropy weight method and variation coefficient method to determine the relevant influencing factors. Secondly, principal component analysis (PCA) was used to concentrate the input variables into common component. Then, an artificial neural network (ANN) model was conducted based on influencing factors and SA data. The result showed that there are identified fifteen factors that have a greater impact on SA reliability, accounting for 65.2% of the weight of all factors. The PCA result showed that the contribution rate of eight common factors reached 80.6% for the total variance of variables and the cumulative variance. Therefore, these variables were explained by eight common components. The 8-14-1 network structure was can obtain the minimum of the MSE (0.0058) and the maximum of R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (0.9814). The predicted data can obtain the minimal MSE value (0.0035) and maximum R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (0.9886) when the ‘Relu’ function was used as the activation function of both the hidden layer and output layer. The average prediction accuracy of the constructed ANN model is more than exceeded 92% for the test data. This result indicated that the developed ANN model can accurately evaluate operator’s SA.

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.

Effects of Cognitive Style and Information Acquisition Method on Diagnosis Task Performance

ABSTRACT Recently, conventional main control rooms are being gradually replaced by digital main control rooms in nuclear power plants, which results in changes of factors that have effects on diagnosis task performance. The experiment was conducted under four interfaces with different information acquisition methods (Query, Filter, Query-Filter combined, and Visual Search) to explore the influence of cognitive style on diagnosis task performance and possible interaction effects with information acquisition methods. Cognitive style was measured by Index of Learning Styles Questionnaire from four dimensions (Reflective/Active, Intuitive/Sensing, Verbal/Visual, and Global/Sequential). The results showed that cognitive style significantly affected diagnosis task performance, and had interaction effects with information acquisition method. Reflective/Active dimension had significant effect on diagnosis performance under Filter and Query-Filter combined interfaces (active participants performed better). Intuitive/Sensing dimension had significant effect on diagnosis performance under Query and Filter interfaces (intuitive participants performed better). Verbal/Visual dimension had significant effect on diagnosis performance under conventional Visual Search interface (visual participants performed better) and Query interface (verbal participants performed better). The results could be useful for human-machine interface design and the selection of operators in complex industrial systems.

Human reliability analysis for operators in the digital main control rooms of nuclear power plants

ABSTRACT As the main control room of nuclear power plants (NPPs) has been gradually digitized, new human reliability problems may emerge because of a series of new changes in the cognitive processes, behavioral patterns, and error mechanisms of operators. Aiming to address this situation, this paper proposes a method as guidance for human reliability analysis (HRA) of different cognitive Stages. This method first constructs the influencing factors of three cognitive processes, including monitoring, decision-making, and execution of actions, and then evaluates the weights of these influencing factors through an analytic hierarchy process (AHP). In this study, the parameters used in the proposed HRA method were determined by analyzing the test data obtained from a simulation model, and the results demonstrated the rationality and feasibility of the proposed method. A case example using this HRA method was given in which the human error probabilities at three stages in a nuclear power plant (NPP) steam generator tube ruptures (SGTR) accident were obtained. In summary, the proposed method is a simple and feasible HRA tool that can be applied in digital NPP main control rooms (MCRs).

The effect of communication quality on team performance in digital main control room operations

A team of operators is required for nuclear power plant operation, and communication between the operators is an important aspect of the team's ability to successfully carry out tasks. It has been difficult to evaluate the quality of this communication though, and as the relationship between communication quality and team performance has yet to be clarified, it has not been applied to most human reliability analysis (HRA) methodologies. This study investigates the relationship between the quality of communication and team performance using data from a full-scope training simulator of a digital main control room (MCR). Two important characteristics of communication were considered to determine quality: each operator's ability to self-confirm the status of a given task in a digital MCR, and the type of communication, as divided into 1-way, 2-way, and 3-way between operators. To measure team performance, the concept of an unsafe act was employed, which is defined as a human error that has the potential to negatively affect plant safety. Analysis results showed that the communication quality and team performance were related to each other. With this more clearly defined relationship, the results of this study can be applied to related performance shaping factors to improve HRA.

Analysis of interface management tasks in a digital main control room

Development of digital main control rooms (MCRs) has greatly changed operating environments by altering operator tasks, and thus the unique characteristics of digital MCRs should be considered in terms of human reliability analysis. Digital MCR tasks can be divided into primary tasks that directly supply control input to the plant equipment, and secondary tasks that include interface management conducted via soft controls (SCs). Operator performance regarding these secondary tasks must be evaluated since such tasks did not exist in previous analog systems. In this paper, we analyzed SC-related tasks based on simulation data, and classified the error modes of the SCs following analysis of all operational tasks. Then, we defined the factors to be considered in human reliability analysis methods regarding the SCs; such factors are mainly related to interface management and computerized operator support systems. As these support systems function to reduce the number of secondary tasks required for SC, we conducted an assessment to evaluate the efficiency of one such support system. The results of this study may facilitate the development of training programs as well as help to optimize interface design to better reflect the interface management task characteristics of digitalized MCRs.

Application of a process mining technique to identifying information navigation characteristics of human operators working in a digital main control room – feasibility study

It is well known that one of the major contributors to the significant events of socio-technical systems is a human error. Accordingly, various kinds of human reliability analysis techniques have been proposed for many decades, which allow us to properly estimate human error probabilities. However, one of the urgent challenges is to quantify human error probabilities when required tasks are conducted in a control room equipped with up-to-date digital equipment (e.g., digital main control room) because existing techniques are mainly focusing on an analog control room. In this regard, it is necessary to carefully understand how professional operators in the digital control room carry out the required tasks. Therefore, in this study, information navigation characteristics (i.e., information gathering behaviors observable from professional operators who need to conduct the required tasks by using given digital equipment) were analyzed based on process mining techniques. To this end, event logs were collected from the professional operators of domestic nuclear power plants who were exposed to simulated off-normal conditions in a digital main control room. As a result, it seems that process mining techniques are useful for extracting crucial information needed for the human reliability analysis in the digital main control room.

Identifying key performance shaping factors in digital main control rooms of nuclear power plants: A risk-based approach

Various human reliability analysis (HRA) methods use performance shaping factors (PSFs) to model operator performance. The tendency in HRA is to increase the number of PSFs. However, using many PSFs may negatively affect the quality of HRA outcomes. An elite-set PSF model with key PSFs is lacking. We therefore suggest a risk-based approach for identifying the key PSFs by ranking their risks to operator performance. In this approach, we elicit nuclear power plant (NPP) operators’ perceptions of the frequency and impact of PSF indicators in main control rooms (MCRs), quantify the perceived risk scores of PSFs by multiplying their perceived frequency and impact, and classify PSF risk levels according to the suggested PSF risk matrix. Licensed operators (N = 33) from a digital NPP MCR participated in our survey. The top-ranked PSFs in terms of perceived risk score and the PSFs at the High Risk level were identified as the key PSFs from the surveyed NPP MCR. A comparison with other studies indicates that the importance of these key PSFs has been previously recognized, implying that the risk-based approach can yield meaningful results for ranking and identifying the key PSFs.

Study on operator’s SA reliability in digital NPPs. Part 3: A quantitative assessment method

Situational awareness (SA) is a key element impacting operators’ and decision-making and performance in nuclear power plants. SA reliability is an important part of human reliability analysis. In order to quantitatively assess SA reliability, the Bayesian network model of operators’ SA reliability analysis in digital main control rooms is built by data analysis. Furthermore, a quantitative assessment procedure of SA reliability is established, and the conditional probability distribution of influencing factors are more objectively determined on the basis of the full-size simulator experiment data of nuclear power plants and Bayesian theory. It is used to quantitatively calculate SA reliability, and a case is used to illustrate specific application of the established model. The results show that the model can provide more reliable data and standardized analysis procedures to support operators’ SA reliability evaluation in digital nuclear power plants.

An experimental investigation on relationship between PSFs and operator performances in the digital main control room

This study designs an experiment to investigate the relationship between performance shaping factors (PSFs) and operator performances. This study involves selecting three PSFs that are controllable in the experiments: (1) experience, (2) complexity, and (3) urgency. Six scenarios are developed to reflect the PSFs. The experiment involves the participation of licensed operators and the use of an APR1400 simulator. During the experiment, operator performances, such as completion time, error, secondary task, workload, and situation awareness, are measured and collected. The experimental result indicates that the operator’s experience is most effective on the overall performances. The task complexity influences the secondary tasks and situation awareness.

Measuring Situation Awareness of Operating Team in Different Main Control Room Environments of Nuclear Power Plants

Abstract Environments in nuclear power plants (NPPs) are changing as the design of instrumentation and control systems for NPPs is rapidly moving toward fully digital instrumentation and control, and modern computer techniques are gradually introduced into main control rooms (MCRs). Within the context of these environmental changes, the level of performance of operators in a digital MCR is a major concern. Situation awareness (SA), which is used within human factors research to explain to what extent operators of safety-critical systems know what is transpiring in the system and the environment, is considered a prerequisite factor to guarantee the safe operation of NPPs. However, the safe operation of NPPs can be guaranteed through a team effort. In this regard, the operating team's SA in a conventional and digital MCR should be measured in order to assess whether the new design features implemented in a digital MCR affect this parameter. This paper explains the team SA measurement method used in this study and the results of applying this measurement method to operating teams in different MCR environments. The paper also discusses several empirical lessons learned from the results.

Comparison between conventional and digital nuclear power plant main control rooms: A task complexity perspective, Part II: Detailed results and analysis

Part II of this study aims to provide detailed, diagnostic information about the complexity difference between conventional and digital main control rooms (MCRs) in nuclear power plants. Complexity factors were classified according to task components and complexity dimensions. The effects of operator experience and plant type on complexity factors were statistically analyzed from three levels, i.e., task components, complexity dimensions, and individual factors. Interface management complexity factors were compared with other factors in digital MCRs. The results suggest that generally operator experience had effects on several task components and complexity dimensions only in abnormal/emergency situations. Plant type affected several task component and complexity dimensions in both abnormal/emergency and normal situations. Complexity factors in the affected task components and complexity dimensions had higher frequency, complexity, or impact in digital MCRs than those in conventional MCRs. Factors related to crew activity and the dimensions of overabundance, temporal demand, and variability had relatively high frequency, complexity, or impact. Compared with other factors, interface management complexity factors had marginally higher frequency, but significantly lower complexity and impact. Relevance to industryThis study quantitatively addresses the complexity difference between conventional and digital MCRs in detail. It may provide rich information for how to improve operator working environments in NPPs. It may also contribute to other applied domains, such as human reliability analysis and interface design.

Comparison between conventional and digital nuclear power plant main control rooms: A task complexity perspective, part I: Overall results and analysis

Digitalization is a trend in safety-critical complex systems. It changes the way human interacts with systems. We have less empirical knowledge about its potential negative effects on human. In our study, we compared conventional and digital main control rooms (MCRs) in nuclear power plants (NPPs) from a task complexity perspective. Complexity factors in MCRs were quantified in terms of three aspects, frequency of occurrence, complexity induced by their being, and impact caused by them. A total of 69 licensed operators participated in the study. The study consists of two parts. In Part I, overall results and analysis were reported. Generally, operators in digital MCRs perceived higher frequency and higher impact of complexity factors than those in conventional MCRs, no matter in abnormal/emergency or normal situations. Operators in digital MCRs perceived higher complexity than those in conventional MCRs in abnormal/emergency situations. These findings suggest that operators in digital MCRs experience higher complexity and workload which may reduce their reliability. These findings imply that we should caution the side-effects of ubiquitous digitalization in complex industrial systems. Relevance to industryDigital technologies are widely deployed in the nuclear industry. They change the working environments in which operators interact with NPP systems. There is insufficient research on operator experience on the changes brought by technological developments in NPP control rooms. Our findings imply that we should take care of the potential negative effect of digitalization on operator working environments.

STATE TOKEN PETRI NET MODELING METHOD FOR FORMAL VERIFICATION OF COMPUTERIZED PROCEDURE INCLUDING OPERATOR'S INTERRUPTIONS OF PROCEDURE EXECUTION FLOW

The Computerized Procedure System (CPS) is one of the primary operating support systems in the digital Main Control Room. The CPS displays procedure on the computer screen in the form of a flow chart, and displays plant operating information along with procedure instructions. It also supports operator decision making by providing a system decision. A procedure flow should be correct and reliable, as an error would lead to operator misjudgment and inadequate control. In this paper we present a modeling for the CPS that enables formal verification based on Petri nets. The proposed State Token Petri Nets (STPN) also support modeling of a procedure flow that has various interruptions by the operator, according to the plant condition. STPN modeling is compared with Coloured Petri net when they are applied to Emergency Operating Computerized Procedure. A converting program for Computerized Procedure (CP) to STPN has been also developed. The formal verification and validation methods of CP with STPN increase the safety of a nuclear power plant and provide digital quality assurance means that are needed when the role and function of the CPS is increasing.

Modeling of computerized procedure execution with State Token Petri Net for formal verification of procedure flow

Computerized procedure system (CPS) is one of the primary operating support systems in the digital main control room (MCR) of a nuclear power plant. CPS displays the procedure at computer screen in the form of a flow chart and displays plant operating information along with procedure instructions. It also supports the operator to make a decision by providing system decision with logic. Most importantly, CPS guides the operator to follow a procedure flow that is predefined when the computerized procedure (CP) is written. A procedure flow should be correct and reliable, as error would lead to operator misjudgment and inadequate control. As such, the procedure flow should be verified before being applied to the MCR. In this article, we present a model of CPS that enables formal verification based on Petri nets. The proposed State Token Petri Net also supports modeling of a procedure flow that has various interruptions by the operator according to the plant condition.