Main Control Room Research Articles

EPICS application for ITER RH supervisory control system

The purpose of the Remote Handling System is to carry-out remote maintenance of the ITER machine and components where the environment radiation levels are greater than 100 μSv/hr. The general approach to the remote maintenance operations is to remove components from the ITER machine and to replace with new or refurbished components. The refurbishment and waste processing operations take place in the Hot-Cell, also using the RH System. Remote maintenance operations are man-in-the-loop. Trained RH operators, using specialized interfaces to remotely operated RH equipment, carry-out a maintenance activity in the hazardous location. A dedicated suite of control rooms, located in the Personnel Access Control Building (B24), is used to control the RH operations in the Tokamak and Hot-Cell.The Remote Handling (RH) Supervisory Control System provides the basic control infrastructure to integrate the RH equipment systems into a single ITER Plant System operating from the RH control room(s) under the authority of the ITER central control system. The RH Supervisory Control System (RHSCS) has six relatively independent sub-systems fulfilling three major functions: a) to provide basic control infrastructure of the RH maintenance system; b) to support central monitoring and coordination of RH maintenance activities; c) to support management of RH maintenance campaigns.The RHSCS, currently in Final Design phase, comprehends the RH Plant Controller System (RHPC), responsible to provide the functionalities to control RH plant activities at a level suitable for coordination with other ITER activities. This sub-system, developed using the EPICS framework, will allow the connection between the main control room (MCR) and the RH Plant Controller.The system implements the EPICS Channel Access Protocol and it uses a set of process variable to communicate RH Plant System operating states and ITER rooms states between MCR and RH System via CODAC interface. Behind the simple approach based on the HMI created in BOY, the sub-system implements a mechanism to allow the flow-down and application of MCR work program to the RH System.

An experimental study on the effects of task complexity and knowledge and experience level on SA, TSA and workload

With the improvement of digital level of main control rooms (MCRs) of nuclear power plants (NPPs), the issue of situation awareness (SA) of operator becomes particularly prominent in NPP operations. More and more scholars have done some research on SA and team situation awareness (TSA). In order to explore the effects of performance shaping factors (PSFs) on SA, TSA and workload, the effects of task complexity and operators’ knowledge and experience level on operators’ SA, TSA and workload were studied through simulator experiments. The results show significant effects on the operators’ SA and TSA levels. The higher the complexity of task, the lower the level of operators’ SA and TSA; and the higher the level of knowledge and experience, the higher the level of operators’ SA and TSA. Experiments also show that task complexity and the level of knowledge and experience have a significant impact on the operator's workload. The higher the task complexity is, the higher the operator’s workload level is, and the higher the knowledge and experience level is, the lower the workload level of the operator is. The research results provide a theoretical basis for improving the level of SA and reducing workload, and provide theoretical and experimental guidance for identifying the impacts of PSF on SA, TSA and workload.

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.

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.

Simulation Emergency Fire Evacuation Model for Nuclear Power Plant

Simulation of egress is vital for minimizing losses during fire disasters, however accurate simulations are scarce and real-life data is hard to come by. In this paper, a Proposed Wireless Fire Evacuation Model (PWFEM) is proposed to simulate fire evacuation process in a short time to minimize evacuee’s exposure to the harmful radiation and fire hazards. The PWFEM simulation realistic by supposing fire scenario at cabined contains electrical cables inside a standard Main Control Room (MCR) in a Nuclear Power Plant (NPP) building. In addition, a new Hybrid Safest Shortest Exit (HSSE) is developed which consists of three stages: safest route based on rules-based technique, evacuees location based on the DV-hop technique [1] and shortest route that depends on Dijkstra technique. The PWFEM Simulations are appreciated to yield a realistic fire scenario by using a telecommunications TCP/IP network in form of server and client sides that help in transfer data inside internal networks in the NPP building. On the server-side, suppose that Consolidated Model of Fire Growth and Smoke Transport (CFAST) fire modeling is applied to simulate the fire scenario in MCR through CFAST model to generate fire products as output data in excel sheets and sends them to the client-side. The client-side then runs HSSE to produce the tree map for safest and shortest routes to help the evacuee for safe exit from his/her location. HSSE can be implemented on evacuee’s watches. From the results, it is concluded that PWFEM can simulate the fire scenario inside MCR, furthermore it is validated that HSEE can be used as an efficient emergency fire evacuation technique that can produce safest and shortest exit route for evacuee in any location inside NPP in very small time. In addition, PWFEM can be used for simulating fire evacuation inside any high-risk buildings and can appreciate yielding any realistic fire scenario with many types of fire sources in different places inside buildings.

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.

An intelligent diagnosis model of accidents in nuclear power plant

This essay focus on the situation when the operator of the main control room can not handle the accident in time or make wrong judgment under accident condition in nuclear power plant, which may lead to the occurrence of major accidents or even more accidents concurrently. An intelligent fuzzy diagnosis model based on Grey Relational degree is constructed in this paper, by establishing accident-state correlation matrix and accident-state probability matrix, defining the vector of information weights for accident characteristic vector and monitoring result vector with linear transform, using Grey Relation to measure the relevance degree between monitoring result vector and accident characteristic vector, Searching for suspected accidents approaching to real solutions in iterative recursive algorithms, realizing automatic fuzzy diagnosis of multiple accidents. The results of simulation experiments of LOCA accidents indicate that the model and algorithm can diagnose various common accidents accurately and rapidly in complex nuclear power systems, providing strategy for operator’s diagnosis decision.

Substation Indoor Wheeled Robot Inspection Visual Blind Pressure Plate Recognition

In order to solve the problem of wheeled robots in the main control room of the substation due to the height of the robot and the shooting distance, the main camera cannot take a complete picture of the bottom pressure plate of the relay protection pressure plate cabinet during the inspection process and perform open/close state and label image recognition. Propose install a wide-angle secondary camera on the side of wheeled robot, and targeted proposal for the correction of distorted images taken with a wide-angle lens using a division model, improving the accuracy of decompression plate checks. Experiments have shown that, the proposed approach is practical and feasible, effective solution for indoor wheeled robot relay panel cabinet inspection visual blind spot shooting problem, enhanced the effectiveness of wheeled robotic inspections in the main control room of the substation, improved platen verification accuracy.

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.

A SYSTEMS APPROACH TO ANALYSING WORK EFFICIENCY IN POWER CONTROL ROOMS: A CASE STUDY

The research model presented in this article, of control room operators’ work activities in an electric power system, includes an analysis of the main control room’s characteristics, the performance of the operators, and the influence of the environment on the operators’ efficiency. For the purpose of group ranking the most important aspects describing the risk factors, fuzzy numbers are used. The influence of all the experts on the final decision is based on their previous experience. Human system, control information system, and supporting system elements are used as assessment criteria. In the analysed control room, the major impact on the operators’ efficiency is the causes and effects of stress and the presentation of information. Display panels and control desks are crucial aspects of the control information system, as they affect the efficient presentation of information. In relation to the indoor environment, lighting conditions are identified as the most important. Based on the results obtained, we propose changes that decrease the risks by enabling better work positions for operators and the presentation of information on display panels and graphic screens.

Quantitative Evaluation on the Effect of Experience Under Emergency Situations in NPP Main Control Room Based on Multimodal Data

The safety of the main control room in a nuclear power plant is an important research topic with practical implications. In this study, we used virtual reality technology and multimodal data to investigate the effect of experience on operators’ responses under emergency conditions. We asked participants to perform a series of tasks in a virtual fire emergency environment while simultaneously recording their behavioral, eye-tracking, and physiological data. The results showed a significant effect of experience on participants’ behavioral performance, total fixation time, and skin electrical response. Participants from a high-experience group showed a longer total fixation duration and lower skin conductance level compared to those from a low-experience group. This suggested that experience could have an impact on operators’ visual information extraction and mental stress under emergency situations in an NPP. Our study also provides a reference method for using virtual experimental settings and objective measurements for future human factor research in the main control room of an NPP.

INFORMATION PROCESSING IN THE REACTOR PROTECTION SYSTEMS OF HIGH TEMPERATURE GAS-COOLED REACTORS

Reactor protection systems (RPS) transform process variable signals from the sensors into initiation and actuation signals to trip the reactor if the signal's value exceeds the predefined trip setpoints of the RPS. Information on the current value of the process variables signals and the trip setpoint should be displayed properly on the visual display unit (VDU) in order to maintain the situation awareness of the operators in main control rooms (MCR). In addition, it is also helpful for them to investigate the cause of an accident after the reactor trip and to mitigate the accident based on the appropriate emergency operating procedures. This paper investigates how the information is processed in the RPS of Experimental Power Reactor (EPR) based on high temperature reactor (HTR) technology, and how the information is displayed on the human machine interface (HMI) of the MCR of the EPR. It is conducted by classifying the RPS into three layers based on its components and their functions, followed by the investigation of the type and the information processing in each layer. The results show that the form of the information has been changed throughout the RPS, started from the sensors and until it is displayed on the VDU. The results of the investigation are necessary to understand the concept of RPS, especially for new operators, and to prepare the mitigation actions based on the process variable that cause the reactor trip.Keywords: Experimental power reactor, Reactor protection system, Human machine interface, Information processing, Situation awareness

Workload measurement using physiological and activity measures for validation test: A case study for the main control room of a nuclear power plant

In design of safety-critical and social-technical systems such as a nuclear power plant, practitioners are required to conduct a performance-based Integrated System Validation (ISV) test to verify that the system design could support the safe operation of the plant. Measurement of workload should be included. However, subjective workload measurements could not provide detailed information and continuous monitoring of the changing workload. This study compared physiological (heart rate difference, heart rate variability, respiration rate and breathing wave amplitude) and activity (number of walking steps, peak acceleration, activity level, and inclination) measures with workload defined as intensity of task demand and estimated with a task complexity measure in an ISV test. The test was conducted on a full-scale simulator using a beyond design-basis accident scenario. The results show that heart rate difference and respiration rate are positively correlated with the estimated workload, while heart rate variability and breathing wave amplitude are negatively correlated with the estimated workload. For operations using traditional panels, high workload is accompanied by larger number of walking steps, higher activity level, and smaller angles of inclination. It is suggested that continuous monitoring of cardiovascular, respiration, and activity measures can detect workload change during the ISV test. Relevance to industry: This study provides recommendations for continuous monitoring of workload during an ISV test of a nuclear power plant. The identified physiological and activity measures can be applied in detecting workload change. The findings are supportive in meeting regulatory requirements and improving system design in the nuclear domain.

Real-Time Streaming of Surgery Performance and Intraoperative Imaging Data in the Hybrid Operating Room: Development and Usability Study.

BackgroundThe trend of quick evolution and increased digital data in today’s operating rooms (ORs) has led to the construction of hybrid ORs. There is often a main control room with monitors for integrating intraoperative data from multiple devices in the hybrid OR. However, there is no adequate solution for communicating the data with people outside the OR.ObjectiveThe objective of this study was to design an intelligent operating room (iOR) system, augmented onto the existing information technology (IT) infrastructure of hybrid ORs, to stream surgery performance and intraoperative imaging data.MethodsIn this study, an all-in-one device with synergetic encoder and decoder was used. The device was able to stream multiple sources to one display. The lossless video and images from specific surgical workflows were streamed outside the hybrid OR through network protocols and were further managed by a streaming server and wireless control system. The steps of this study included the following: (1) defining the requirements and feasibility of an iOR system in the hybrid OR, (2) connecting multiple sources, (3) setting up equipment across the hybrid OR and a conference room, (4) designing a video management system, and (5) real-time streaming under specific surgical workflows.ResultsThe wired streamed video was shown simultaneously on the display in the hybrid OR and the display in the conference room with near-zero latency. Additionally, an interactive video between the hybrid OR and the conference room was achieved through the bidirectional wireless control system. The functions of recording, archiving, and playback were successfully provided by the streaming server. The readily available hardware components and open-access programming reduced the cost required to construct this streaming system.ConclusionsThis flexible and cost-effective iOR system not only provided educational benefits, but also contributed to surgical telementoring.

Research on the Cognitive Reliability Model of DCS+SOP in the Ling’ao Phase II Nuclear Power Plant’s Main Control Room

The digital control system (DCS)+state-oriented procedure (SOP) system adopted by China’s Ling’ao Phase II nuclear power plant’s main control room requires changes to the cognitive process, behavior mode, and error mode while triggering new human factors. Therefore, in this paper we present a cognitive reliability model for the DCS+SOP system in the Ling’ao Phase II Nuclear Power Plant’s main control room and conduct a human reliability analysis. The model is based on the cognitive process with respect to considering the coordinator’s accident recovery effect and obtaining the method of calculating cognitive reliability. We determine impact factors for the three cognitive stages of the operator’s and the coordinator’s diagnosis, decision making, and operation. We obtain the operator’s and the coordinator’s weights for each process through an analytic hierarchy process. Using methods of simulation and analyzing the experiment data, we obtain revised coefficients for the cognitive reliability model. Additionally, the trend of the simulation curve indicates the rationality of the model. Finally, we provide an example based on the proposed cognitive reliability model. The process of analyzing the example demonstrates that this method provides a feasible analysis method for the cognitive reliability of the DCS+SOP system in the main control room.

Human error probability quantification for NPP post-accident analysis using Cognitive-Based THERP method

Human Reliability Analysis (HRA) studies human actions from the safety point of view, modeling the most important of them and assessing their probabilities. The Technique for Human Error Rate Prediction (THERP) is a classical HRA approach that models Human Error Probability (HEP) for a specific action regardless of the human cognitive process. Another HRA method is Human Cognitive Reliability (HCR) that expresses the non-response probability as a function of available time regarding the performance type and ignores other affecting factors. In this study, the strengths of the Deterministic Safety Analysis (DSA), THERP and HCR are integrated to form a systematic framework introduced as Cognitive Based THERP (CB-THERP) method to calculate HEP for the Nuclear Power Plant (NPP) post-accident analysis. The calculated values of HEP during the Total Loss of Feedwater (TLFW) accident reveal that a better Human-Machine Interface (HMI) condition reduces HEP. However, operators' higher training level compensates even the worst HMI. In addition, the HMI condition has the greatest impact on an averaged-knowledge Main Control Room (MCR) operator and then an expert and a novice. Dependency almost doesn't influence the worst HMI condition and it much affects highly trained operators when the HMI condition is excellent. Also, the calculated HEP values from the Standardized Plant Analysis Risk-Human Reliability (SPAR-H) and CB-THERP methods for different cases of the scenario have similar trend while THERP calculates a fixed value for them.

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).

Understanding individualistic response patterns when assessing expert operators on nuclear power plant control tasks

We evaluated the performance of three highly practiced participants on three task types that comprised a simulated nuclear power plant control operation. Multiple subjective, physiological, and objective performance measures were collected on these three highly-practiced individuals. Results indicated ceiling effects in terms of performance accuracy, yet each individual adopted a unique response strategy across the respective sub-tasks. Their maximised accuracy was achieved at the expense of longer response times across differing sub-tasks. The measures which proved diagnostic and predictive of performance capacity were explored. The current conclusion presents us with an invidious problem in that performance and workload associations, insensitivities, and dissociations may be unique to each individual operator, and may well depend also upon the overall task in context. Such findings push our science away from seeking nomothetic assertions and toward individuated concerns. In consequence, the age of the idiographic may well be upon us. Practitioner summary: The importance and relevance of nuclear power control is self-evident. Concerns here have centred around the safety of the technology and its operators. Our work informs practitioners in this industry, and in Ergonomics in general, of the response of highly trained individuals in these safety-critical, operational domains. We show that even experts engage in personal and individual strategies, an observation critical to the assessment of this specific workplace, and potentially all others. Abbreviations: NPP: nuclear power plant; ROs: reactor operators; MCR: main control room; LOA: levels of automation; EOP: emergency operating procedure; OP: operating procedures; ISA: instantaneous self-assessment; DSSQ: Dundee stres state questionnaire

CFD code-to-code benchmarking on simulation of fire and smoke propagation for nuclear applications

Abstract The nuclear industry has seen an increased use of Computational Fluid Dynamics (CFD) technology as a high-fidelity tool for design-basis and beyond design-basis accident simulations. For fire hazards, detailed experimental investigation in a realistic environment is often impractical. Therefore, the use of an advanced 3D CFD modeling approach to predict smoke and fire propagation has risen, especially for nuclear power plant fire modeling applications. CFD simulations can include major contributing and mitigating mechanisms regarding the fire propagation and its consequences, in fine detail and under realistic geometry conditions. In addition, accurate simulations of unsteady 3D fire and smoke spread, in conjunction with evacuation analyses, can be used to establish effective emergency evacuation strategies. In the present study, the fire and smoke propagation in a main control room was modelled using Reynolds-Averaged Navier-Stokes Equation based twoequation turbulence models and a Large Eddy Simulation model implemented in a commercial CFD code, Siemens STAR-CCM+. The predictions from STAR-CCM+ were compared with those obtained in a previous study using the NIST Fire Dynamic Simulator (FDS) code. Although higher temperature was predicted at the fire source by STAR-CCM+, the current study predicted trends similar to the FDS results with some minor differences at the operator location. A follow-up study is currently underway to investigate the factors affecting the differences, including further assessment of codes against available benchmark experiments.