Nuclear Power Plant Control Research Articles

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

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

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

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

Multi-Stage Evaluation Using GONUKE

Multi-stage validation (MSV) has become an important concept in nuclear power plant control room design and modernization. MSV consists of multiple stages of collecting evidence that establish reasonable confidence in the design. The Guideline for Operational Nuclear Usability and Knowledge Elicitation (GONUKE) was created as a framework to support different types of human factors evaluations across different stages of the design and development life cycle. The GONUKE framework did not originally address MSV, but it espoused safety case approaches that build different evidences toward design completion, which is a complementary approach to MSV. In this paper, we show how GONUKE specifically aligns to MSV. We then expand the discussion to include multi-stage evaluation. As defined in GONUKE, there are three types of evaluation. Validation is empirical evidence collected through human-in-the-loop studies, whereas verification is expert comparison of a design against formalized standards, and epistemiation is expert knowledge transfer to design.

Self-Healing Control of Nuclear Power Plants Under False Data Injection Attacks

The transition from analog to digital instrumentation and control (I&C) systems introduces new threats caused by cyberattacks in the nuclear industry. This paper proposes a self-healing strategy to respond to a false data injection attack that targets digital I&C systems, which is a type of cyberattack commonly targeting nuclear power plants with the potential to cause serious physical impacts. This resilience strategy for self-healing control contains three components: (1) an anomaly detection model that can detect false data injection attacks, (2) a device-level control that utilizes inferred values to perform control under a detected false data injection, and (3) a system-level control that leverages another controller that is not under attack to lead the system back to a safe operation state when the device-level control is unavailable. Anomaly detection and device-level control use an autoencoder while system-level control utilizes reinforcement learning. The proposed self-healing resilience strategy is demonstrated with a generic pressurized water reactor (GPWR) simulator under false data injections, targeting the steam generator water level. The results show that the proposed strategy effectively leads the system back to a normal operation state under various false data injection cases.

ANALYSIS OF THE DESTRUCTION OF UKRAINIAN NUCLEAR FUEL CYCLE ENERGY FACILITIES AS A RESULT OF RUSSIAN MILITARY OPERATIONS

Problem statement. Due to Russia's ongoing terrorist attacks on Ukraine's energy facilities, the energy system was damaged, resulting in blackouts and rolling blackouts across the country. The destruction of power capacities resulted in the unstable operation of certain critical infrastructure units for electricity generation at thermal, nuclear and hydroelectric power plants and the introduction of forced hourly blackout schedules. Seizure of the Chornobyl Nuclear Power Plant, occupation and full control of the Zaporizhzhia Nuclear Power Plant and the Zaporizhzhia Thermal Power Plant (TPP). Complete destruction of the Kakhovka hydroelectric power station (HPP), partial destruction of the Dnipro HPP, Dniester HPP and Kaniv HPP. During the hostilities, Kryvyi Rih and Prydniprovia TPPs were damaged, Luhansk TPP (90–100 %), Vuhlehirsk TPP (90–100 %), Zmiiv TPP (up to 90 %) were completely destroyed, Burshtyn TPP (80–90 %) and Ladyzhyn TPP (70–95 %) were almost completely destroyed. The purpose of the article is to identify and analyse the damage to Ukrainian TPPs, HPPs and NPPs caused by the Russian military actions against Ukraine and to determine possible consequences for the nuclear fuel cycle of our country and possible prospects for the transition to new nuclear reactors of European and global fuel cell manufacturers, to improve equipment and stabilise the energy market in the context of military operations. Conclusion. In the course of the analysis of potential hazards at radiation hazardous facilities (RHF) in Ukraine, it was determined that the main threat is accidents at RHF with release of radiation into the atmosphere, lithosphere and hydrosphere. In the light of recent events regarding the military seizure of the Chornobyl NPP and Zaporizhzhya NPP, measurements using standard methods of research, screening and monitoring are not possible, and access of IAEA representatives is limited, which is why the latest measurement methods should be used with the use of autonomous remote-controlled ground, surface and air drones in automatic object or sample measurement of radiation parameters at RNS. It is the use of autonomous vehicles (drones) that is currently the most pressing issue of radiation safety not only in the nuclear power industry but also in the entire nuclear fuel cycle as part of the environmental safety of Ukraine. In case of seizure of a nuclear facility or limited access to it, research methods should be changed. If the facility is partially seized or access is available, then express methods of radiation contamination registration or sampling can be used and the method of laboratory research can be used in the future. However, if a nuclear power plant or a facility that is part of the nuclear fuel cycle of Ukraine is captured and access to it is impossible, then only methods of remote measurement of the radiation situation at and around nuclear facilities using ground and airborne autonomous vehicles remain. For further research, it is necessary to identify technical possibilities for using Ukraine's own mineral resources and switching from Soviet RBMK-1000, VVER-440 and VVER-1000 reactors with fuel elements supplied to us from the Russian Federation to newer types of European and American reactors with appropriate fuel elements.

Evaluating the reliability of machine-learning-based predictions used in nuclear power plant instrumentation and control systems

The field of data-driven, neural-network-based machine learning (ML) has seen significant growth, with applications in various information and control systems. Despite promising real-world uses, the reliability of models remains questionable. Conventionally, reliability is assessed based on predictive fidelity, accuracy, and training effectiveness; however, quality developmental procedures and excellent training performance metrics do not guarantee operational reliability. Instead, an ML model's predictive performance depends on the training set's representativeness to the intended operational space. It is known that ML algorithms excel at interpolation but struggle with extrapolation tasks. Anomalies and feature drift can also reduce operational performance. Determining whether a new sample is an interpolation or extrapolation task involves out-of-distribution (OOD) detection for assessing its proximity to the existing training data. Thus, we present a real-time, model-agnostic individual prediction reliability evaluation method called Data Auditing for Reliability Evaluation (DARE) for applying OOD detection to the training dataset. We demonstrate on a feedforward neural network ML-integrated digital twin for predicting fuel centerline temperatures during loss-of-flow transients. DARE acts as a “data supervisor” in determining the model's applicability under different operating conditions. In this manner, we demonstrate how training data can serve as inductive evidence to support the reliability of ML predictions.

Novel Approach for Network Data Exchange Among Safety-Related Control Systems of Nuclear Reactors

Industrially mature digital networking technologies form the backbone of the distributed control architectures of nuclear reactors. Typically, hundreds of control systems responsible for assuring safer and secure plant operations are located in the field and interconnected via wired isolated local area networks, which are also connected to centralized servers and operating consoles located in the main control room. Many of such control systems require data processed by other systems situated far away in the plant, which mandates multiple concurrent reliable TCP sessions to be active or live for such field-level control systems. Multi-socket communication beyond 10 is practically difficult for embedded systems with limited memory and processing capabilities; hence, data accumulation, extraction, and routing are typically done with the help of commercially available high-performance servers and switches. The servers and operating consoles of a plant are generally Linux-based commercial-off-the-shelf (COTS) industrial computers, whose development lifecycle is not accessible for review and in accordance with safety guides; hence, qualifying such components for safety operations is extremely difficult. This paper presents a novel way of independently offloading safety-related data transactions from such COTS components (reducing dependency) with the help of very simple, reliable, and secure hardware, which is referred to as a data exchange and processing unit (DEPU). An approach is proposed to realize distributed control architecture with this proposed hardware (to simplify qualification tasks) along with existing servers and operating consoles without compromising powerful and convenient human-machine interface as well as the data logging capabilities of such components. Hardware and software design perspectives, performance characterization, and qualification testing of the DEPU in accordance with nuclear power plant instrumentation and control system design guidelines are also briefed in this paper.

Analysis of hydrogen control in a Small Modular Reactor during TLOFW severe accident

During the Fukushima Daiichi nuclear accident in 2011, hydrogen explosions occurred in all units from Unit 1 to Unit 3. Consequently, one of the lessons learned from the Fukushima Daiichi accident is the necessity of implementing hydrogen control and mitigation strategies for most Nuclear Power Plants (NPPs). This paper focuses on the incorporation of Passive Autocatalytic Recombiners (PARs) during the design phase of a small modular Pressurized Water Reactor (SMR-PWR) project. The numerical analyses are conducted using the MELCOR v. 2.2 code. Two scenarios are compared: the Total Loss of Feed Water (TLOFW) severe accident with and without PARs. Saphiro’s diagram is utilized to investigate whether the mixture's composition (hydrogen, air, steam) is flammable for both scenarios. It has been observed that the inclusion of PARs leads to a reduction in hydrogen risk (detonative or deflagrative) as the final hydrogen concentration values fall below the flammability limit. This study is preliminary, and further research is required.

Measuring team situation awareness through team communication: A study on nuclear main control room crews.

Situation awareness (SA) in complex socio-technical systems is considered a key cognitive activity, as control crews are often engaged in simultaneous task processes and are required to monitor and evaluate system parameters, making decisions and projections for the future accordingly. However, measuring SA at the team level is still a challenging area of research within the topic. In our research, we applied verbal protocol analysis as an alternative method to assess nuclear power plant control room crews' team SA. We conducted a study of 10 control room crews, examining their intrateam communication and its relatedness to team performance. We have found that communication categories related to the second and third levels of team SA increased significantly after the onset of an emergency event. Furthermore, while none of the team communication categories was related to team performance before the emergency event, all of them showed a strong positive correlation with team performance after the emergency situation occurred. Our results underline the importance of adequate verbalization of key information within the team, so as to support the rapid and accurate development of team SA during emergency situations.

Dynamics optimization of small branch pipes in nuclear power plants based on machine learning algorithms

Vibration fatigue failure of small branch pipes poses a great threat to the safe operation of nuclear power plants. However, the transient and wide-band vibration problems are not adequately considered in ASME and RCC-M code, resulting in repeated fatigue failures. In addition, the current research mainly focuses on vibration test methods and fatigue analysis methods, neglecting the study of pipeline vibration characteristics. Therefore, innovative approaches were essential for effectively managing complex dynamic loads. In this study, an innovative approach combining backpropagation artificial neural networks (BP-ANN) and non-dominated sorting Genetic Algorithm II (NSGA-II) was proposed to optimize the vibration of these pipes. The goal was mitigating vibration-induced failures by enhancing operational stability. The methodology progressed through several key stages. Firstly, BP-ANN was utilized for regression analysis, correlating pipe characteristics to vibration effects. Through regression analysis, the complex interrelationships governing the pipes' dynamic behavior was revealed. Subsequently, based on the regression model, NSGA-II was used to derive an optimal combination of design parameters to minimize the vibration response. The proposed technique was validated on an L-shaped cantilevered pipe via finite element simulations and physical experiments. The analysis case shows that the BP-ANN model demonstrated excellent accuracy in predicting vibration responses. Meanwhile, NSGA-II successfully revealed the trade-offs between conflicting objectives, generating a Pareto-optimal set balancing stability under different excitation directions. This study highlights the potential of machine learning methods for dynamic optimization of small branch pipes in nuclear power plants, and verifies the accuracy and effectiveness of the method through experiments. The research results provide a new idea for small branch pipe design and vibration control of nuclear power plant, which contributes to enhancing the safety and reliability of small branch pipes.

Verification and validation optimization method for signal quality bits in digital control system application software of nuclear power plant

Abstract The digitalization enhances nuclear power plant (NPP) instrumentation and control (I&C) system performance, while simultaneously introducing safety and reliability challenges. To enhance the stability of distributed control system (DCS), and minimize the dangerous failure rate and safe failure rate of safety system, signal quality bits (SQBs) are widely used in NPP DCS to identify the validity of important signals. However, in the past decade or so, numerous unscheduled turbine shutdowns and/or reactor trips have resulted from incorrect SQB design or settings. Therefore, it is necessary to conduct optimization research on the design methods and setting principles of SQBs, establish an optimization method for DCS application software SQB verification and validation (V&V), then minimize DCS faults attributed to SQB. With the aim, this work conducts an in-depth investigation and analysis of the definition, design, setting and transmission of SQB in NPP. A specific optimization method for software V&V to conduct SQB on NPP safety DCS application software is proposed. Twelve concerns in the SQB V&V work are detailed. The reactor trip system ΔT protection logic is utilized as a case to illustrate the detail implementation process of this optimization method. This work provides an engineering technical reference and operational guidance for SQB software V&V, conducive to ensuring the safe and economical operation of NPPs.

Application of artificial intelligence technologies and big data computing for nuclear power plants control: a review

Nuclear power plants produce a massive amount of clean energy and necessitate safe operation through intelligence technologies. Recently, the rapid advancements in communication infrastructures including artificial intelligence, big data computing, and Internet of Things devices moving the nuclear industries towards digitalization and intelligence to improve safety. The integration of these technologies into the nuclear sector offers effective tactics in addressing several challenges in the control and safe operation of nuclear power plants. This can be achieved through the insights generated from massive amounts of data. This paper comprehensively reviews the literature on artificial intelligence technologies and big data, seeking to provide a holistic perspective on their relations and how they can be integrated with nuclear power plants. The utilization of computing platforms boosts the deployment of artificial intelligence and big data analytics effectively in nuclear power plants. Further, this review also points out the future opportunities as well as challenges for applying artificial intelligence and big data computing in the nuclear industry.

Transient phenomena and their consideration in load-following control of nuclear power plants

Radical changes have been happening on the source side of the electricity system due to the rolling back of large-sized fossil-fueled power plants and the coming to the front of uncontrollable renewables. As a worldwide consequence, to ensure the permanent balance of the grid, even big-sized units have to be operated in load-following mode, including also nuclear power plants. This mode is conducted by the control system, which must also consider the side effects of this rather dynamic operating mode. In this report, we show up all components of such a control system, including the process model, structural integrity assessment, and control algorithm. All the aforementioned elements are discussed on the basis of a concrete example, and the resulting control behavior of the entire system is also shown. The results demonstrated that a significant reduction in the caused thermal stresses could be achieved while keeping the control quality practically unchanged.

A Reliable People Tracking in Nuclear Power Plant Control Room Monitoring System Using Particle Filter

A Reliable People Tracking in Nuclear Power Plant Control Room Monitoring System Using Particle Filter

Exhaustive Test Case Generation for Nuclear Safety Software Based on the Software Logic Model

An issue regarding the incorporation of software reliability within the nuclear power plant (NPP) probabilistic risk assessment model has emerged in the licensing processes of digitalized NPPs. Since software failure induces common-cause failure of the processor modules, the reliability of the software used in the NPP safety-critical instrumentation and control systems must be quantified and verified with proper test cases and environments. In this study, a software testing method based on the minimal cut set (MCS)–based exhaustive test case generation scheme is proposed where the software logic model is developed from available information on the software development and the MCSs that represent the necessary and sufficient conditions for the software variables’ states to produce safety software outputs are generated. The MCSs are then converted into the test cases, which can be used as inputs to the test bed to verify that the test cases produce correct outputs after software execution. The effectiveness of the proposed method is demonstrated with the safety-critical trip logic software of the APR-1400 reactor protection system. The method provides a systematic way to conduct exhaustive software testing and prove the functionality of the nuclear safety software based on the test result without uncertainties.

Simulator Feature Framework: Requirements to Support Training, Research, and Education

There is an expansion of nuclear power plant control room simulator use beyond traditional operator training. Modern simulator designs require more robust capabilities to serve the diverse needs of multiple user groups including researchers and educators. A common framework for evaluating simulator features to support training, research, and education is critical to ensure future simulators enable research for immediate and future plant modernization and advanced reactor deployment needs. An initial framework comprised of 8 feature categories was developed by reviewing published simulator-based research and analyzing simulator features against research objectives and results. Future research will focus on evaluating the suitability of this framework in characterizing and differentiating simulators across training, research, and education use cases.

Technical Recommendations of Interface for Safety and Security Aspects of NPP Instrumentation and Control Systems in Indonesia

Instrumentation and control (I&C) systems are crucial in ensuring the safe operation of power reactors. New and modern nuclear facilities commonly use digital I&C systems. However, the application of digital technologies within I&C systems has made these systems vulnerable to cyber-attack. Literature study shows that cyber-attack is one of the threats that could occur at any time in the lifetime of the I&C system. Indonesia regulates cyber security and interface of safety and security through BAPETEN Chairman Regulation (BCR) number 1 of 2009 on Nuclear Installation Physical Protection and BCR number 6 of 2012 on Design Provision of Computer-Based Important Safety System on Power Reactor. The provision related to interface safety and security against cyber-attack has a significant gap compared to IAEA Requirements and Internationals practice. Therefore, this paper tries to identify technical recommendations for BCR number 1 of 2009 and BCR number 6 of 2012 related to the interface of safety and security and cyber security in the Nuclear Power Plant (NPP) I&C system using IAEA requirements and international practice. The paper concludes that some technical recommendations must be added to BCR 1 of 2009 and BCR 6 of 2012. These recommendations are required in establishing cyber security program, integrating a cyber-security program with physical protection system, implementing control networks with reduced interconnectivity, implementing defense-in-depth as an essential concept in designing the NPP I&C system, developing a highly integrated protection system platform that utilizes FPG, and integrating passive safety system into the safety system. Keywords: cyber-attack, safety and security interfaces, instrumentation and control system

Descriptor sliding mode observer based fault tolerant control for nuclear power plant with actuator and sensor faults

In sophisticated and complex system such as nuclear power plant, fault estimation and fault tolerant control always play an important role in maintaining the system stability and assuring satisfactory and safe operation. Thus, in this work a fault estimation and fault tolerant control scheme based on sliding mode theory is proposed for a pressurized water reactor type nuclear power plant considering simultaneous actuator and sensor faults. First, using descriptor sliding mode observer approach, an accurate estimation of the system states and sensor fault vector have been obtained simultaneously. Then, based on the estimated information, an integral type sliding mode control scheme is proposed to stabilize the resulting faulty system. With the help of Lyapunov stability theory, reachabilities of the proposed sliding mode surfaces are shown in both the state estimation space and the error estimation space, simultaneously. Finally, the efficacy of the proposed control scheme is shown by applying it to a nuclear power plant.

Reliability Measurement of Control and Instrumentation Systems of Nuclear Power Plants

Control and instrumentation (C&I) systems are used to measure the critical parameters to take decisions and, hence, such systems do have high reliability target. Therefore, it is essential to measure the reliability of such systems with high accuracy. Petri net (PN) is a mathematical tool that can model the stochastic behavior of the systems. The research work is devoted to propose a framework to measure the reliability of these systems using PN. The proposed framework contains three phases: 1) state-space identification, 2) probability of a system being in each state, and 3) reliability measurement. The proposed approach has been applied to 21 C&I systems of nuclear power plant (NPP) and is demonstrated on a case study of NPP to prove its effectiveness.

Autonomous control model for emergency operation of small modular reactor

Advancements in computer performance and Artificial Intelligence, especially Deep Neural Networks and reinforcement learning, have caught the attention of many researchers. Specifically, the potential for achieving autonomy (a higher cognitive concept than legacy automation systems) has garnered significant interest. Thus, many autonomous systems like self-driving cars, are in the research/ development phase. Despite its numerous advantages, the adoption of autonomous control for Nuclear Power Plants is deficient mainly due to stringent safety concerns. Especially, autonomous control for Small Modular Reactors is a desirous capability to accrue its full potential. This study proposes an AI-based model for autonomous control of IP-200 NPP, a Small Modular Reactor (SMR) under development. The model adopts a modular architecture for performing different functions of monitoring/ diagnosis, strategy formulation and assessment. Despite following knowledge-based rules, the network decisions are optimized through supervised learning and fine-tuned through reinforcement learning. The results demonstrate that the proposed model can handle plant emergencies while ensuring plant safety at optimal performance.