Operation Of Nuclear Power Plants Research Articles

Exploring signal interactions in PWR nuclear power plants through noise-based signal processing

Nuclear power plants operate as complex, interconnected systems with tightly interlinked variables. Monitoring systems within such plants gather data from various process parameters. This paper systematically analyzes key signals in a typical PWR power plant. We develop a dynamic model of the PWR plant encompassing mathematical models for the reactor core, steam generator, pressurizer, turbine, and condenser. This model is rigorously validated against reference data. We use this model to analyze signal perturbations in hypothetical scenarios, employing both time-domain and frequency-domain analyses. Our results demonstrate that noise in one signal noticeably impacts others. Furthermore, the extent of this impact varies depending on the nature of their connection. Direct connections exhibit a higher susceptibility to noise-induced interference, whereas intermediate connections typically show a diminished impact. Through signal analysis, we identify correlated signal pairs, offering insights into processes during abnormal conditions. These findings have implications for enhancing the safety and reliability of nuclear power plant operations.

Current Progress in the Application of Artificial Intelligence for Nuclear Power Plant Operation

Current Progress in the Application of Artificial Intelligence for Nuclear Power Plant Operation

Explainable, Deep Reinforcement Learning–Based Decision Making for Operations and Maintenance

This paper presents research that integrates condition monitoring and prognostics with decision making for nuclear power plant operations and maintenance aimed at reducing lifetime maintenance and repair costs. Additionally, a focal point of this research is to make the decisions explainable to operators, improving the trustworthiness of the decisions from what can be considered a black box model. In this work, we develop and evaluate an explainable, online asset management methodology to help reduce lifetime maintenance and repair costs. Using the latest advancements in condition monitoring, inventory management, deep reinforcement learning, and explainable artificial intelligence methods, we create a predictive maintenance methodology that can optimize the maintenance and spare part management of a repairable nuclear power plant system. To demonstrate these methods, preliminary studies were conducted on a representative maintenance system undergoing a stochastic degradation process that requires repairs or replacement to continue operation. Using deep reinforcement learning, we were able to reduce maintenance spending by approximately 50% compared to optimized, time-based maintenance strategies for the chosen system. A key component of our methodology is the integration of Shapley values to quantify the contribution of various factors to the decision-making process. This addition enhances the explainability and trustworthiness of our decisions, providing operators with transparent and understandable insights into the rationale behind maintenance strategies. The robustness and resiliency of our decision policy against observation noise were also thoroughly evaluated, demonstrating its effectiveness in uncertain operational environments.

A computationally efficient approach of tuned mass damper design for a nuclear cabinet based on two-step machine learning and optimization methods

Enhancing nuclear power plant (NPP) safety is demanded because of the recent beyond-design-basis earthquake near a NPP. Therefore, research on improving the seismic performance of the electrical cabinet, which ensures the safe operation of NPPs, is needed. In this paper, a tuned mass damper (TMD) is employed to control the seismic response of cabinet. To design the TMD, we employ existing design equations or perform numerical model–based optimization. However, limitations, such as inconsistencies with targeted control of the load and structure, the possibility of converging a local solution, and the high cost of numerical analysis. Therefore, this paper proposes a two-step machine learning and optimization method. Such an approach is utilized to find the optimal global design solution and reduce numerical analysis costs. Each step involves the design of experiment (DOE), response surface, and optimization. Notably, range setting in the DOE accounts for the difference between each step. In the first step, the sampling range is widened to determine the relationship between the design variables and the cabinet's response, and in the second step, the sampling range is narrowed depending on the result of the first step. Consequently, the proposed method reduced the cabinet's response by 35.4 % on average and numerical analysis cost declined by 1/3.

Behaviour of dissolved inorganic salts in the cooling water of a nuclear power plant open recirculation system and formation of water discharge.

The main problem in the operation of nuclear power plants (NPPs) is the scale formation of mineral impurities in an open recirculating system (ORS). The discharge of water from an ORS into natural water bodies can alter the chemical equilibrium of wastewater components, necessitating continuous monitoring. The purpose of this study was to analyse the behaviour of dissolved inorganic salts (DIS) in water within an ORS during water treatment, using the Rivne Nuclear Power Plant (RNPP) as a case study. Moreover, the analysis impact of their discharge with return water in the Styr River. The DIS concentration has a significant impact on the efficiency of the system and the environmental of an ORS power plant. Altogether, each of the DIS components was analysed separately using the standard measurement methods, statistical methods of data processing and correlation analysis. In addition, the annual discharge of the DIS components was calculated, and the amount of discharge was assessed for compliance with the maximum discharge limit. Thus, the impact of the formation of DIS and the variations in their concentration levels upon the discharge of wastewater into a natural water body were examined.

The application of time series deep learning model to the fast prediction of parameters in the MSLB accident

The Main Steam Line Break Accident (MSLB) threatens the safe operation of nuclear power plants. The transient safety parameters of the Passive Containment Cooling System (PCCS) in the MSLB accident are predicted by the time series deep learning model based on LSTM or RNN. In the dataset preprocessing, the linear normalization and two of feature label segmentation methods are used. The neural network models and the other models, namely the GBRT, RF and SVR, are used to construct the multi-parameter time series deep learning models to predict the transient safety parameters. The performance of the different models and the impact of two segmentation methods on models are compared, and it is obtained that the prediction accuracy of LSTM is higher than that of other models.

Uncertainty-Aware and Explainable Human Error Detection in the Operation of Nuclear Power Plants

The timely and accurate identification of incidents, such as human factor error, is important to restore nuclear power plants (NPPs) to a stable state. However, the identification of abnormal operating conditions is difficult because of the existence of multiple scenarios. In addition, to implement mitigation actions rapidly after an incident occurs, operators must accurately identify an incident by monitoring the trends of many variables. The mental burden posed by this can increase human error and cause failure in identifying incidents. Failure to identify incidents directly results in erroneous mitigation measures, which are detrimental to NPPs. In this study, we leverage uncertainty-aware models to identify such errors and thereby increase the chances of mitigating them. We use the data collected from a physical test bed. The goal is to identify both certain and accurate models. For this, the two main aspects of focus in this study are explainable artificial intelligence (XAI) and uncertainty quantification (UQ). While XAI elucidates the decision pathway, UQ evaluates decision reliability. Their integration paints a comprehensive picture, signifying that understanding decisions and their confidence should be interlinked. Thus, in this study we leverage UQ measures (e.g. entropy and mutual information) along with Shapley additive explanations to gain insights into the features contributing to both accuracy and uncertainty in error identification. Our results show that uncertainty-aware models combined with XAI tools can explain the artificial intelligence–prescribed decisions, with the potential of better explaining errors for the operators.

System theory safety analysis of network malfunction in nuclear power plant distributed control systems

Abstract A secure and highly reliable communication network is a crucial component of the distributed control system (DCS) in nuclear power plants (NPPs), particularly for the protection system. However, there have been numerous reported cases of cyber-attacks in the field of nuclear power, and the cybersecurity threats faced by a DCS are increasing. Given that the DCS is a complex integrated system combining computers, communication, and control with a large number of interactions between components and external interferences, traditional linear safety analysis methods fail to meet its requirements. To minimize cybersecurity accidents caused by potential vulnerabilities, it is necessary to conduct systematic safety analysis on DCS cybersecurity accidents in NPPs. Therefore, this paper selected a specific case of cybersecurity accidents in a NPP for abstract modeling and reproduction. The causal analysis based on system theory (CAST) method was applied to conduct an analysis of the accident causes. It can identify existing vulnerabilities and potential factors impacting the safe operation of NPPs, and countermeasures and recommendations can be proposed. The reference for assessing potential risks and devising solutions can be provided for other DCS communication networks.

A Novel Intelligent Condition Monitoring Framework of Essential Service Water Pumps

Essential service water pumps are necessary safety devices responsible for discharging waste heat from containments through seawater; their condition monitoring is critical for the safe and stable operation of seaside nuclear power plants. However, it is difficult to directly apply existing intelligent methods to these pumps. Therefore, an intelligent condition monitoring framework is designed, including the parallel implementation of unsupervised anomaly detection and fault diagnosis. A model preselection algorithm based on the highest validation accuracy is proposed for anomaly detection and fault diagnosis model selection among existing models. A novel information integration algorithm is proposed to fuse the output of anomaly detection and fault diagnosis. According to the experimental results of modules, a kernel principal component analysis using mean fusion processing multi-channel data (AKPCA (fusion)) is selected, and a support vector machine using mean fusion processing multi-channel data (SVM (fusion)) is selected. The overall test accuracy and false negative rate of AKPCA (fusion) are 0.83 and 0.144, respectively, and the overall test accuracy and f1-score of SVM (fusion) are 0.966 and 1, respectively. The test results of AKPCA (fusion), SVM (fusion), and the proposed information integration algorithm show that the information integration algorithm successfully avoids a lack of abnormal status information and misdiagnosis. The proposed framework is a meaningful attempt to achieve the intelligent condition monitoring of complex equipment.

Probabilistic typhoon hazard and sensitivity analysis for nuclear power plant sites in Korea using logic tree

There has been an increase in typhoon intensity, and high-intensity typhoons can affect the safety of nuclear power plants (NPPs). Typhoons can damage the structures, systems, and components (SSCs) of NPPs, causing core damage and the release of radioactive materials into the environment. Therefore, design criteria for the effects of natural phenomena are demanded to ensure the safety-critical SSC functions of NPPs. Also, there is a need for safety assessments of typhoons in operational NPPs, which require typhoon hazard analysis. In this study, we derived typhoon hazards for NPPs in Korea using Logic Tree and Monte Carlo simulations. We performed sensitivity analysis on the typhoon hazards based on the distances (up to 100 km) from the NPP sites. Among other NPP sites, the typhoon hazard of the Kori site showed the highest wind speed, and the Hanul site showed the lowest wind speed. The uncertainty of the wind speed increased as the recurrence interval of the typhoon hazard increased. We compared the typhoon hazard in the areas of interest to that within 100 km. The range of the distance deviation for the Hanbit site's typhoon hazard was within μ±σ up to 75 km, while for the other sites, it was within μ±σ up to 100 km.

Radial reflector macroscopic cross sections preparation impact on the low neutron leakage core Beavrs

Full core nodal calculations are essential for safe nuclear power plant operation. The accuracy and compliance with the calculation and the experiment are based on the proper macroscopic data preparation model. While fuel assembly data preparation is usually based on the simple infinite lattice model simulation, the reflector data preparation is not standardised and the influence of the reflector macroscopic data on full core simulation is not quantified. The paper evaluates the large core with low neutron leakage behaviour with the different reflector cross section preparation models with respect to criticality and power distribution prediction. However, in opposition to the first insight, the different reflector model cannot increase the compliance with the power prediction near the fuel assembly reflector boundary. However, the usage of a special rehomogenization model can be helpful for pin power reconstruction compliance near the edge of the core.

Imbalanced data fault diagnosis method for nuclear power plants based on convolutional variational autoencoding Wasserstein generative adversarial network and random forest

Data-driven fault diagnosis techniques are significant for the stable operation of nuclear power plants (NPPs). However, in practical applications, the fault diagnosis of NPPs usually faces imbalance data problems with small fault samples and much redundant data which results in low model training efficiency and poor generalization performance. Thus, this paper proposes a convolutional variational autoencoding gradient-penalty Wasserstein generative adversarial network with random forest (CVGR) to reduce the impact of imbalanced samples on fault diagnosis. Firstly, a feature selection method based on the random forest is used to identify the most relevant measurements and reduce the impact of redundant data on fault diagnosis. Then, variational autoencoding is introduced into gradient-penalty Wasserstein generative adversarial to effectively extract original sample features and generate high-quality samples with high rationality and diversity. In addition, the convolutional neural network is used to extract the features of mixed samples to realize intelligent fault diagnosis. Finally, several experiments based on the Fuqing Unit 2 full-scope simulator under different operating conditions are used to validate the performance of the CVGR in data enhancement and intelligent fault diagnosis. The results show that the proposed method can effectively mitigate the imbalance data problem, which gives insights into intelligent fault diagnosis of NPPs.

Optimal design of circulating water system in pressurized water reactor nuclear power plant

The optimization of the circulating water system of pressurized water reactor nuclear power plants can effectively improve the profit without affecting the primary circuit operation, which is of great significance in enhancing the market competitiveness of nuclear power plants. The circulating water system of the nuclear power plant is modeled, and the circulating water system optimization scheme is formed after the nuclear power unit is reformed from three aspects: condenser heat transfer area, condenser connection mode, and circulating water pump operation mode. The circulating water system optimization calculation is carried out for each scheme by using the minimum annual cost method. Based on the sensitivity analysis of the factors that affect the profits of nuclear power plants, the best circulating water system optimization scheme is finally given for new nuclear power plants and nuclear power plants in operation, which provides a reference for the construction of related nuclear power projects.

Low-Power Wireless Sensor Module for Machine Learning-Based Continuous Monitoring of Nuclear Power Plants.

This paper introduces the novel design and implementation of a low-power wireless monitoring system designed for nuclear power plants, aiming to enhance safety and operational efficiency. By utilizing advanced signal-processing techniques and energy-efficient technologies, the system supports real-time, continuous monitoring without the need for frequent battery replacements. This addresses the high costs and risks associated with traditional wired monitoring methods. The system focuses on acoustic and ultrasonic analysis, capturing sound using microphones and processing these signals through heterodyne frequency conversion for effective signal management, accommodating low-power consumption through down-conversion. Integrated with edge computing, the system processes data locally at the sensor level, optimizing response times to anomalies and reducing network load. Practical implementation shows significant reductions in maintenance overheads and environmental impact, thereby enhancing the reliability and safety of nuclear power plant operations. The study also sets the groundwork for future integration of sophisticated machine learning algorithms to advance predictive maintenance capabilities in nuclear energy management.

MODELING THE DISTRIBUTION OF EMERGENCY RELEASE PRODUCTS AT A NUCLEAR POWER PLANT UNIT

Despite the fact that much attention is paid to the safe operation of nuclear power plants, there is a possibility of an accident with the release of radionuclides. This is especially true in Ukraine, where there is a threat of the damage to nuclear reactors as a result of military operations. It is impossible to research the distribution of products emergency releases radioactive substances in laboratory conditions. Therefore, the only tool for the development predicting of an accident is the modeling the spread of a radionuclides cloud. The purpose of the research is a modeling the distribution of emergency release products in a nuclear power plant unit, suitable for the operative assessment of a development an accident. Results of the research: The mathematical model of the distribution emission products of a nuclear power plant has been developed, which takes into account the value of the initial activity of emission products, the rate of the settling radioactive particles, the wind speed components, the intensity changes radionuclide emission over time. The technique for solving the boundary value problem of modeling in conditions of a complex shape of the computational domain, taking into account the presence of obstacles to the spread of emission products has been developed. The use of the velocity potential equation in evolutionary form allows us to speed up the calculation process. The chosen splitting scheme of an alternating-triangular method allows to find the speed potential according to the explicit form at each splitting step. This allowed software implementation of the CFD model. The visualized models of the emission cloud distribution allow to determine the radiation situation in any place of the emission product distribution zone. The developed model makes it possible to quickly predict the development of an accident in space and time, which makes it possible to take measures to protect people from exposure in the shortest possible time. Conclusions: The obtained emission cloud propagation models and their visualization make it possible to determine the state of environmental pollution under various initial conditions during the development of the accident.

Optimized FOPID controller for nuclear research reactor using enhanced planet optimization algorithm

Nuclear reactor control is pivotal for the safe and efficient operation of nuclear power plants, facilitating the regulation of reactor reactivity. This study introduces an optimized fractional-order proportional-integral-derivative (FOPID) controller tailored for maintaining reactivity levels in nuclear power plants, particularly during load-following operations. The controller adjusts the position of control rod to regulate power output effectively. We enhance FOPID controller's performance using a modification of Planet Optimization Algorithm (POA-M), leveraging the strengths of the Arithmetic Optimization Algorithm (AOA) to improve its exploitation capabilities. We evaluate the efficacy of POA-M-FOPID controller against traditional techniques, including POA, AOA, and Particle Swarm Optimization (PSO). We assess its performance using the Egyptian Testing Research Reactor (ETRR-2) as a case study. Our results demonstrate that the POA-M-FOPID controller outperforms alternative algorithms across various control metrics, exhibiting superior resilience and efficiency. Notably, the utilization of the POA-M-FOPID controller yields remarkable improvements in reactor power performance, achieving significantly reduced settling time (25.27 sec) and maximum overshoot (0.67 %) compared to conventional FOPID controllers incorporating POA, AOA, and PSO methods. These findings underscore the effectiveness of POA-M-FOPID in enhancing nuclear reactor control systems, offering potential benefits for broader nuclear power industry in terms of safety, stability, and operational efficiency.

A Study on One-Time Inspection for Aging Management of Operating Nuclear Power Plants

One-time inspection is used to confirm the effectiveness of nuclear power plant aging management programs such as water chemistry, fuel oil chemistry, and selective leaching program that is designed to prevent or minimize aging degradation during the plant operation. This paper represents one-time inspection experience of selected components and susceptible locations within the scope of water chemistry and fuel oil chemistry aging management programs. Prior to performing one-time inspection, sample sizes of components to be inspected were determined based on combination of materials, environment and aging effect. Examination techniques and relevant operating experience were analysed to obtain technical basis for further implementation of one-time inspection aging management programs. Existing correlation curves between sample size and total population number were reviewed to prepare further inspections for entire operating plants and ensure safety level of components subject to one-time inspection program.

How Cs-137 dispersions due to atmospheric deposition to East Vietnam Sea: a hypothetical level 7 incident from the Fangchenggang Nuclear Power Plant (China)

Only about 50 km from the Quang Ninh coastal area in Vietnam, the Fangchenggang Nuclear Power Plant (China) operation causes excellent concerns for the environment, especially the marine environment, if an incident occurs. Based on topographic and hydrometeorological data and assumptions about radiation release conditions when an incident occurs, the Delft3D-Part modeling system was set up to assess/forecast the effect of Cs-137 radioactive activity on the source of air deposition in the water environment of Vietnam. The results showed that the sources of the drop from the air significantly affected and caused a faster impact than direct radioactive sources after the incident. Under the influence of marine circulation, wind radioactive material after falling into the sea will quickly disperse and may affect the entire East Vietnam Sea after 3–6 months. The area with high radioactivity is concentrated mainly in the Tonkin Gulf and along the coast of Vietnam. Especially in the Gulf of Tonkin, the radioactivity can reach 300–1,200 Bq/m3 after the 5-day incident. The amount of radiation then gradually decreases to almost less than 100 Bq/m3 after one year and below 20 Bq/m3 after two years. According to depth, the amount of radioactive Cs-137 tends to shift between layers from the surface to the bottom: higher at first in the upper layers, then gradually decreasing and increasing in the lower layers. Radioactivity significantly reduced after two years of the incident but still exceeded QCVN 10: 2008/BTNMT.

Convolutional preprocessing Transformer-based fault diagnosis for rectifier-filter circuits in nuclear power plants

Rectifier-filter circuit, as a vital electronic component in nuclear power plants (NPPs), serves the role of converting alternating current into smooth direct current. Affected by intense radiation environmental factors and variable operating profiles, the rectifier-filter circuit is subject to degradation and soft failures, namely, a derated circuit performance but not yet to failure. It is urgent to diagnose the soft failures of the rectifier-filter circuit, so as to ensure the safe operations of NPPs. In this article, an ensemble empirical mode decomposition (EEMD) algorithm is first employed to decompose the three-phase current signals and electrical signals of electronic components. The decomposed signals are fed into a convolutional preprocessing Transformer (CPT) to deeply extract the fault features of the rectifier-filter circuit. Specifically, the multi-directional, multi-scale, and extremely sensitive long-range time-dependent features in the EEMD feature data are extracted by the multi-head attention mechanism of the Transformer. A deep Softmax classifier is, then, devised to reduce the feature dimensionality and identify the soft fault modes of the rectifier-filter circuit. The fault simulation of the rectifier-filter circuit is conducted, and a real case experiment and multiple comparative studies are conducted to validate the effectiveness and diagnosis accuracy of the CPT-based method.

Legal regulation of nuclear and radiation safety during operation of nuclear power plants

The article examines the problems of legal regulation of nuclear and radiation safety that arise during the operation of nuclear power plants. On the basis of the legal definition of such legal categories as radiation and nuclear safety, radiation protection, which must be guaranteed by the state to human life and health, the main attention is focused on the analysis of the legal regime of lands as the basis of human life activities that have undergone radioactive contamination, which are legally classified as man-made polluted. The genesis of the legislation that secures the definition of these lands is analyzed, it is emphasized that these lands are the result of human economic activity, that is, it is about the consequences of anthropogenic activity, when highlighting the peculiarities of the legal regime of man-made contaminated lands, their division into radioactively dangerous and radioactively contaminated is taken as a basis. The mentioned lands, being in state ownership, cannot be transferred to private ownership, are included in the exclusion zone and the zone of mandatory (unconditional) resettlement of the territory that has undergone radioactive contamination as a result of the Chernobyl disaster. Residence and permanent stay of people in the territory of these zones is prohibited by law. Land plots located in the zone of guaranteed voluntary resettlement are also radioactively contaminated and are used in accordance with the procedure determined by the Cabinet of Ministers of Ukraine. The amendments to the current legislation are analyzed separately, which relate to the inclusion of radioactively contaminated lands in the ecological network for the purpose of restoration and are related to the creation of the Chernobyl radiation- ecological biosphere reserve. It is emphasized that part of the territory of the reserve, created at the expense of the zone of exclusion and the zone of unconditional (mandatory) resettlement, being on the border with the Republic of Belarus, causes additional restrictions of its legal regime. In this case, we are talking about changing the purpose of a part of the relevant lands, which have been transferred to defense lands. The conclusions emphasize that lands that have undergone radioactive contamination need priority restoration, which today is carried out naturally. The legal mechanism for restoring the state of such lands is imperfect and needs significant changes. It is time to develop and approve the Strategy for returning radioactively contaminated land to economic use.