scholarly journals Human Reliability Studies With Microworld Simulators

2019 ◽  
Vol 63 (1) ◽  
pp. 1829-1833 ◽  
Author(s):  
Ronald Boring ◽  
Thomas Ulrich ◽  
Roger Lew ◽  
Martin Rasmussen Skogstad
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Data Collection ◽  
Nuclear Power ◽  
Human Error ◽  
Data Sources ◽  
Human Reliability ◽  
Reliability Data ◽  
Experimental Control ◽  
Advantages And Disadvantages

The authors have recently developed a microworld, a simplified process control simulator, to simulate a nuclear power plant. The microworld provides an environment that can be readily manipulated to gather data using a range of participants, from students to fully qualified operators. Because the microworld represents a simplified domain, it is possible to have more precise experimental control compared with the complex and confounding environment afforded by a full-scope simulator. In this paper, we discuss collecting human reliability data from a microworld. We review the generalizability of human error data from the microworld compared to other data sources like full-scope simulator studies and compare advantages and disadvantages of microworld simulator studies to support human reliability data collection needs.

Bohunice Simulator Data Collection Project

2002 ◽  
Author(s):  
Ivan Cˇilli´k ◽  
Ja´n Procha´ska
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Data Collection ◽  
Nuclear Power ◽  
Human Error ◽  
Slovak Republic ◽  
Simulator Training ◽  
Human Reliability ◽  
Training Process ◽  
Technological Parameters

The paper describes the way and results of human reliability data analysis collected as a part of the Bohunice Simulator Data Collection Project (BSDCP), which was performed by VUJE Trnava, Inc. with funding support from the U.S. DOE, National Nuclear Security Administration. The goal of the project was to create a methodology for simulator data collection and analysis to support activities in probabilistic safety assessment (PSA) and human reliability assessment for Jaslovske Bohunice nuclear power plant consisting of two sets of twin units: two VVER 440/V-230 (V1) and two VVER 440/V-213 (V2) reactors. During the project training of V-2 control room crews was performed at VUJE-Trnava simulator. The simulator training and the data collection were done in parallel. The main goal of BSDCP was to collect suitable data of human errors under simulated conditions requiring the use of symptom-based emergency operating procedures (SBEOPs). The subjects of the data collection were scenario progress time data, operator errors, and real-time technological parameters. The paper contains three main parts. The first part presents preparatory work and semi-automatic computer-based methods used to collect data and to check technological parameters in order to find hidden errors of operators, to be able to retrace the course of each scenario for purposes of further analysis, and to document the whole training process. The first part gives also an overview of collected data scope, human error taxonomy, and state classifications for SBEOP instructions coding. The second part describes analytical work undertaken to describe time distribution necessary for execution of various kinds of instructions performed by operators according to the classification for coding of SBEOP instructions. It also presents the methods used for determination of probability distribution for different operator errors. Results from the data evaluation are presented in the last part of the paper. An overview of observed human error probabilities (HEP) according to the developed taxonomy is given. HEP observed during training process were used as reference input data for HRA (Human Reliability Assesment) within existing PSAs performed by VUJE. Observing two different training seasons offered an opportunity to compare a progress achieved through the training process. This paper shows us how it is possible to make this kind of comparison in order to establish an objective measure of training quality and to determine training weaknesses. Results gained during the project-evoked interest of different NPPs (Nuclear Power Plant) in Slovak Republic to collect and process simulator data for further improvement of human factor safety, operational procedures, training process, etc.

Human reliability in non-destructive inspections of nuclear power plant components: modeling and analysis

2019 ◽  
Vol 7 (2B) ◽  
Author(s):  
Vanderley Vasconcelos ◽  
Wellington Antonio Soares ◽  
Raissa Oliveira Marques ◽  
Silvério Ferreira Silva Jr ◽  
Amanda Laureano Raso
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Human Factors ◽  
Nuclear Power ◽  
Power Plants ◽  
Failure Modes ◽  
Cooling System ◽  
Human Reliability ◽  
Non Destructive ◽  
Plant Components

Non-destructive inspection (NDI) is one of the key elements in ensuring quality of engineering systems and their safe use. This inspection is a very complex task, during which the inspectors have to rely on their sensory, perceptual, cognitive, and motor skills. It requires high vigilance once it is often carried out on large components, over a long period of time, and in hostile environments and restriction of workplace. A successful NDI requires careful planning, choice of appropriate NDI methods and inspection procedures, as well as qualified and trained inspection personnel. A failure of NDI to detect critical defects in safety-related components of nuclear power plants, for instance, may lead to catastrophic consequences for workers, public and environment. Therefore, ensuring that NDI is reliable and capable of detecting all critical defects is of utmost importance. Despite increased use of automation in NDI, human inspectors, and thus human factors, still play an important role in NDI reliability. Human reliability is the probability of humans conducting specific tasks with satisfactory performance. Many techniques are suitable for modeling and analyzing human reliability in NDI of nuclear power plant components, such as FMEA (Failure Modes and Effects Analysis) and THERP (Technique for Human Error Rate Prediction). An example by using qualitative and quantitative assessesments with these two techniques to improve typical NDI of pipe segments of a core cooling system of a nuclear power plant, through acting on human factors issues, is presented.

U.S. Nuclear Power Plant Performance Assessment Using the Versatile Economic Risk Tool (VERT)

2021 ◽  
Author(s):  
Jaden C. Miller ◽  
Spencer C. Ercanbrack ◽  
Chad L. Pope
Keyword(s):  
Risk Assessment ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Fault Tree ◽  
Plant Performance ◽  
Economic Risk ◽  
Reliability Data ◽  
Water Reactor ◽  
Tree Models

Abstract This paper addresses the use of a new nuclear power plant performance risk analysis tool. The new tool is called Versatile Economic Risk Tool (VERT). VERT couples Idaho National Laboratory’s SAPHIRE and RAVEN software packages. SAPHIRE is traditionally used for performing probabilistic risk assessment and RAVEN is a multi-purpose uncertainty quantification, regression analysis, probabilistic risk assessment, data analysis and model optimization software framework. Using fault tree models, degradation models, reliability data, and economic information, VERT can assess relative system performance risks as a function of time. Risk can be quantified in megawatt hours (MWh) which can be converted to dollars. To demonstrate the value of VERT, generic pressurized water reactor and boiling water reactor fault tree models were developed along with time dependent reliability data to investigate the plant systems, structures, and components that impacted performance from the year 1980 to 2020. The results confirm the overall notion that US nuclear power plant industry operational performance has been improving since 1980. More importantly, the results identify equipment that negatively or positively impact performance. Thus, using VERT, individual plant operators can target systems, structures, and components that merit greater attention from a performance perspective.

Based on Human Behavior Process of Human Error Defensive Management Research for NPP

2017 ◽  
Author(s):  
Shen Yang ◽  
Geng Bo ◽  
Li Dan
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Control Chart ◽  
Human Behavior ◽  
Nuclear Power ◽  
Human Error ◽  
Human Performance ◽  
Human Factor ◽  
Error Management ◽  
Root Cause

According to the research of nuclear power plant human error management, it is found that the traditional human error management are mainly based on the result of human behavior, the event as the point cut of management, there are some drawbacks. In this paper, based on the concept of the human performance management, establish the defensive human error management model, the innovation point is human behavior as the point cut, to reduce the human errors and accomplish a nip in the bud. Based on the model, on the one hand, combined with observation and coach card, to strengthen the human behavior standards expected while acquiring structured behavior data from the nuclear power plant production process; on the other hand, combined with root cause analysis method, obtained structured behavior data from the human factor event, thus forming a human behavior database that show the human performance state picture. According to the data of human behavior, by taking quantitative trending analysis method, the P control chart of observation item and the C control chart of human factor event is set up by Shewhart control chart, to achieve real-time monitoring of the process and result of behavior. At the same time, development Key Performance Indicators timely detection of the worsening trend of human behavior and organizational management. For the human behavior deviation and management issues, carry out the root cause analysis, to take appropriate corrective action or management improvement measures, so as to realize the defense of human error, reduce human factor event probability and improve the performance level of nuclear power plant.

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