Operation Of Nuclear Power Plants Research Articles

A nuclear power plant digital twin for developing robot navigation and interaction

As robot technologies are rapidly improving, an increasing number of new ideas on utilizing robots for automated operation and maintenance tasks in nuclear power plants (NPPs) are being studied. However, due to safety concerns, researchers hardly found opportunities to test their new robot solutions on physical NPPs. In that sense, an efficient and realistic simulation environment plays a vital role in the development of automation systems for an NPP. In this paper, we propose the design of a 3D digital twin system capable of simulating NPP in real-time. This system obtains the data from a full-scope NPP simulator to reproduce the operating conditions of the plant. In addition, a scenario of a team of robots performing inspection tasks like temperature and pressure measurements will highlight its usability. This system enables development of intelligent robot swarms to deploy for inspection and maintenance purposes. It will positively impact autonomous control and operations for many types of reactors by reducing uncertainty in autonomous control and providing the tools necessary for remote intervention.

Technical Language Processing of Nuclear Power Plants Equipment Reliability Data

Operating nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) element data that contain information about the status of components, assets, and systems. Some of this information is in textual form where the occurrence of abnormal events or maintenance activities are described. Analyses of NPP textual data via natural language processing (NLP) methods have expanded in the last decade, and only recently the true potential of such analyses has emerged. So far, applications of NLP methods have been mostly limited to classification and prediction in order to identify the nature of the given textual element (e.g., safety or non-safety relevant). In this paper, we target a more complex problem: the automatic generation of knowledge based on a textual element in order to assist system engineers in assessing an asset’s historical health performance. The goal is to assist system engineers in the identification of anomalous behaviors, cause–effect relations between events, and their potential consequences, and to support decision-making such as the planning and scheduling of maintenance activities. “Knowledge extraction” is a very broad concept whose definition may vary depending on the application context. In our particular context, it refers to the process of examining an ER textual element to identify the systems or assets it mentions and the type of event it describes (e.g., component failure or maintenance activity). In addition, we wish to identify details such as measured quantities and temporal or cause–effect relations between events. This paper describes how ER textual data elements are first preprocessed to handle typos, acronyms, and abbreviations, then machine learning (ML) and rule-based algorithms are employed to identify physical entities (e.g., systems, assets, and components) and specific phenomena (e.g., failure or degradation). A few applications relevant from an NPP ER point of view are presented as well.

HK-SPSA based performance optimization method for steam generator liquid level control

Steam generator level control systems play a crucial role in ensuring safe, economical, and stable operation of nuclear power plants. However, traditional control systems exhibit limited efficiency in commissioning and are susceptible to human error. Additionally, traditional parameter tuning methods are typically experienced-based, cumbersome, and time-consuming, making it challenging to obtain optimal parameters. To address these issues, we propose a hybrid knowledge-guided improved simultaneous perturbation stochastic approximation algorithm (HK-SPSA) to optimize the control parameters of steam generator level control systems. Firstly, the algorithm utilizes iteration point adjacency information to approximate the current optimization process status and guide the adaptive adjustment of the iteration step size. Secondly, it utilizes composite gradient information generated by the estimated historical and current gradients to guide the optimization direction and the tuning of iteration step size. Simulation experiments have shown that HK-SPSA improves optimization performance and reduces the costs of optimization and adjustment compared to traditional methods.

Prolongation of Safe Operation of the K-1000-60/3000 Turbine Power Unit after Damage to the HPC Rotor

Currently, when large-scale military actions are taking place on the territory of Ukraine, the inclusion of the domestic energy system in the European one is a reliable component of providing electricity to the country's energy market. However, according to experts, uninterrupted and safe operation of nuclear power plants is still considered a prerequisite for the stable operation of the energy sector of Ukraine. The purpose of the paper is to assess the damageability and individual resource of the rotor of high-pressure cylinder (HPC) of the K-1000-60/3000 turbine power unit of the LMZ after damage to the blades in order to prolong the operation of the power unit in the conditions of a stressed state of the power system. One of the most effective ways to partially solve the problem of replacement of generating capacities is to extend the operating periods of NPP power units after the end of the project operating period, provided that nuclear and radiation safety standards are met. The review of the previously established service life of the energy equipment of NPP power units involves the assessment of the residual resource of the energy equipment in accordance with the regulatory documents. After the accidental damage of the blades of the last stage of the HPC rotor of the K-1000-60/3000 turbine power unit of the LMZ, there was a need to study the cyclic and static damage, the individual residual resource of the HPC rotor. In the process of achieving the goal, studies were carried out for three design options: the original option (five stages of the HPC rotor), the option without the blades of the last stage and the option without the fifth stage (with four first stages). The calculation of the resource indicators of the rotor in the execution of the HPC without blades of the 5th stage shows that the static damage accumulated in the main metal is 52%, the cyclic damage is 5% when applying the standard strength reserves for the number of cycles and for deformations at the level of nN=10 and nε=1.5 according to the recommendations of SOU-N MEV 40.1-21677681-52:2011. Thus, the total damage to the base metal is 57%, which sets the residual resource of the HPC rotor at the level of 88.4 thousand hours. The calculation of the resource indicators of the rotor in the execution of the HPC without entire 5th stage shows that the static damage accumulated in the base metal is 52%, the cyclic damage is 6% when applying the standard strength reserves for the number of cycles and deformations at the above-mentioned level. The total damage to the base metal is 58%, which determines the residual resource of the HPC rotor at the level of 85.6 thousand hours.

Methods of determining internal damage in power transformers

Due to the variety of defects that arise in electrical machines, it is necessary to use more effective methods for monitoring their condition. All over the world research and development of new means and methods for monitoring powerful electrical machines during their operation is underway. Some examples of recent advances are vibration diagnostic methods for assessing the compaction of components inside a transformer, acoustic and electrical systems for monitoring partial discharges, data processing using digital methods, and new sensors for continuous monitoring of gases and moisture in oil, as well as hot spot temperatures. Additionally, thermal imaging testing of power equipment is also an important tool to ensure reliable operation. It is believed that the most effective method is gas chromatographic oil analysis, which can identify most defects in oil-filled equipment. During the operation of powerful electrical machines, the use of existing non-destructive testing methods does not allow a complete assessment of the condition of the main parts of the equipment, as an analysis of damage locations shows, 25% are damage to the core and windings. Effective monitoring of the condition and determination of the performance of transformers is of particular importance, since they are key elements in the operation of nuclear power plants. The paper considers the possibility of using the eddy current testing method; determining a defect in magnetic core steel is based on fixing the unevenness of the magnetic field on the horizontal or vertical planes of a yoke or rod consisting of electrical steel plates. Monitoring and evaluating the functioning of existing equipment, detecting deficiencies in the early stages of their development, when repair costs are still minimal, and preventing emergency failures become a priority. In accordance with the growth rate of detected deficiencies, monitoring is carried out from time to time or continuously, the maximum number of monitored characteristics is achieved when the transformer is fully tested to determine its functionality.

Evaporator with horizontal water plane stabilization

Evaporation plays an important role in maintaining water balance and managing water resources in reservoirs. This problem is relevant in hot regions with a lack of fresh water. In addition, reliable operation of evaporators plays an important role in preventing overheating and ensuring safe cooling of reactors which is critical to maintaining nuclear safety. The complete control and accurate measurement of the evaporation process provided by the developed device enables efficient water management ensuring the stability of nuclear power plants and minimising environmental risks. Evaporators are used to control the evaporation process in reservoirs. Based on the analysis of existing evaporators and their shortcomings, an evaporator with horizontal plane stabilization in the pan is developed. Due to this function the developed device allows to control the evaporation process with higher accuracy by reducing fluctuations in the water level in the evaporator. The developed device contains an autonomous power supply that uses energy-saving technology based on a solar battery which ensures operation without additional sources. The use of such a power supply increases the evaporator reliability. One of the important advantages of this device is the use of a cellular-based information transmission channel to the control center which allows to transmit the results of evaporation quickly monitoring and make water management decisions accordingly. In addition, the use of a GPS navigation system allows to set or determine the coordinates of the evaporator installation in the reservoir accurately. The developed device can be included in an intelligent energy resource management system; its use will significantly improve the safety and sustainability of nuclear power plants by improving evaporation process control. It will also optimize the nuclear power plants operation, promote efficient management of water resources and ensure safety in the nuclear energy field.

Safety evaluation of Multiple Steam Generator Tube rupture accident using the best estimate plus uncertainty approach

Following the disaster in Fukushima Nuclear Power Plant (NPP) in 2011, the awareness of securing the safety of NPP under extreme events has been raised. For this purpose, the concept of Design Extension Conditions (DECs) was introduced, to enhance the plant’s capability to withstand events that are more severe than Design Basis Accidents (DBA), such as Multiple Steam Generator Tube Rupture (MSGTR) accident, given its rapid progression and significant potential for large release of radioactive materials to the environment. It is worth noting that MSGTR has never occurred during the commercial operation of nuclear power plant, therefore the knowledge of consequences of the event and potential mitigation strategy is limited. In this study, a postulated MSGTR (simultaneous rupture of 5 U-tubes in a single SG) accident is analysed using the best-estimate thermal hydraulics code RELAP5/SCDAPSIM/MOD3.4. The main focus of this work is evaluation of appropriate operator mitigation actions under various inherent uncertainties. To this end, the best estimate plus uncertainty (BEPU) approach is adopted, whereby RELAP5/SCDAPSIM/MOD3.4 was coupled to the statistical analysis software, DAKOTA, to conduct the uncertainty quantification (UQ). Using BEPU analysis, an ensemble of system responses under various epistemic and aleatory uncertainties is obtained and used to validate the efficacy of operator actions in bringing the plant to a safe shutdown condition.

An Association Rule Mining-Based Method for Revealing the Impact of Operational Sequence on Nuclear Power Plants Operating

The operations of the operators are important for nuclear safety, but conventional operating experience feedback and common data-driven methods make it difficult to explicitly find valuable information hidden in these operational sequences that can help the operator to provide advice at the operational level. During the nuclear power plant (NPP) operation, a large amount of historical operating data is accumulated, which records the operational sequences of the operators and the state parameters of equipment. Therefore, this paper proposes the use of association rule techniques to mine the NPP operating data to discover the operational characteristics of operators and reveal their possible impact on the NPP operation. This work helps to improve the operational performance of operators and prevent human-factor events. To this end, the concept of state switching values for describing the operating states of NPPs is proposed to enable the proposed method to be adapted to different practical application scenarios. A sequence segmentation method is proposed to be able to transform historical NPP operating data into a sequence data set for association rule mining. Furthermore, an ensemble algorithm based on sequence pattern mining and sequence rule mining and its postprocessing method are designed. The empirical study was carried out using 20 batches of historical operating data of the cold start-up. A total of 164 original association rules are generated using the proposed method and were analyzed by experts. The recommendations were made for 4 different cases that would improve the operational performance of the operators.

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.

Mobile Robot for Security Applications in Remotely Operated Advanced Reactors

This review paper addresses the escalating operation and maintenance costs of nuclear power plants, primarily attributed to rising labor costs and intensified competition from renewable energy sources. The paper proposes a paradigm shift towards a technology-centric approach, leveraging mobile and automated robots for physical security, aiming to replace labor-intensive methods. Focusing on the human–robot interaction principle, the review conducts a state-of-the-art analysis of dog robots’ potential in infrastructure security and remote inspection within human–robot shared environments. Additionally, this paper surveys research on the capabilities of mobile robots, exploring their applications in various industries, including disaster response, exploration, surveillance, and environmental conservation. This study emphasizes the crucial role of autonomous mobility and manipulation in robots for diverse tasks, and discusses the formalization of problems, performance assessment criteria, and operational capabilities. It provides a comprehensive comparison of three prominent robotic platforms (SPOT, Ghost Robotics, and ANYmal Robotics) across various parameters, shedding light on their suitability for different applications. This review culminates in a research roadmap, delineating experiments and parameters for assessing dog robots’ performance in safeguarding nuclear power plants, offering a structured approach for future research endeavors.

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.

Fragility and Leakage Risk Assessment of Nuclear Containment Structure under Loss-of-Coolant Accident Conditions Considering Liner Corrosion

The steel liner plays a key role in ensuring the leak-tightness of nuclear containment structures (NCSs). Once the liner is subjected to corrosion, its corrosion effects can severely impact the leak-tight function of the NCS, especially in severe accident scenarios. Therefore, evaluating the corrosion effects of the liner is essential to guaranteeing the safe operation of nuclear power plants. This paper presents a probabilistic safety analysis of the NCS under liner corrosion conditions. Firstly, the corrosion mechanism of the liner is elucidated, and a refined simulation method is developed to investigate the localized corrosion effects of the liner. Utilizing a probabilistic finite element method, the fragility of the NCS under loss-of-coolant accident (LOCA) conditions is evaluated. Finally, the leakage risk of the NCS at different degrees of liner corrosion is discussed. The results indicate that liner corrosion has a significant impact on the median pressure capacity and high-confidence–low-probability pressure capacity of the NCS. With the aggravation of liner corrosion, the safety margin of the NCS decreases, and the total probability of failure increases. Within the scope of this paper, the NCS can fulfill the probabilistic safety requirements.

Methodology to Assist in Training Maneuvers of Operation of Pumps of a Nuclear Plant Using Virtual Reality

In power plants, one of the important maneuvers is the switching of pumps, where incorrect execution can even cause the unit to shut down. Therefore, the objective of this paper is to create a virtual environment for training the critical maneuver of the pump switching operation. The method used as reference was the specific procedure for the actual execution of the pump switching maneuver. Then the procedure was interpreted in order to reproduce in the virtual environment the same behaviors as in the real environment. Subsequently, visits to the field, control room, and control room simulator were undertaken in order to obtain material and data for the assembly of the virtual environment. Finally, with all the information collected, a virtual environment was created. The result was the creation of a system and equipment of interest for switching pumps interactively in the virtual environment, with great fidelity to the real environment. Tests were done by nuclear power plant operators and instructors on this virtual environment. The test results of this group demonstrated that the virtual environment created is a good tool to significantly assist in this type of training, as it promotes the experience of imposed challenges, which are common to several other systems of a nuclear power plant. In this way, the choice of a system to be reproduced in a virtual environment that represents the challenge of training critical maneuvers that affect the reactor was fundamental.

Nuclear power plant pipeline detection robot based on a new radiation-proof material

Soft robots are gradually gaining recognition in recent years for their ability to work in extreme environments. However, radiation shielding is a major issue when designing soft robots for use in nuclear environments. In this paper we present an effective soft robot design and develop new radiation shielding materials that enable the robot to perform a variety of tasks in the complex pipelines of a nuclear power plant while being sufficiently radiation shielded. To achieve this, we used a mixture of silicone and lead powder with excellent radiation shielding capabilities and designed and built a soft robot prototype consisting of multiple pneumatic actuators and a resin frame. It is capable of moving through complex pipelines and performing inspection tasks, while also providing radiation shielding for the internal components of the robot. Finite element simulations and prototype experiments were then carried out to evaluate the robot's performance in various scenarios. Simulation results show that the soft robot is able to perform inspection of nuclear power plant pipelines efficiently and effectively. Furthermore, the radiation shielding provided by the radiation-proof material is effective in protecting the internal components of the robot from radiation exposure. In conclusion, we have successfully developed a soft robot prototype that is capable of performing pipeline inspections in extreme environments such as nuclear power plants, while providing adequate radiation shielding. The proposed approach can be extended to other soft robotics applications in the nuclear environment and has the potential to improve the safety and efficiency of nuclear power plant operations.

Sorptive Removal of 133Ba from Aqueous Solution Using a Novel Cellulose Hydroxyapatite Composite Derived from Cigarette Waste

133Ba is a hazardous radionuclide generated during the operation of nuclear power plants. 133Ba needs to be removed from waste solutions because its half-life (10.55 years) and gamma energy pose a significant threat to human health. Cigarette butt (CB) is a waste that causes serious environmental problems. Various types of adsorbent materials are prepared based on the cellulose in its structure. The focus of the present study is to synthesize a novel composite material derived from CBs and to investigate its 133Ba removal capability. Microfibrillated cellulose (MFC) obtained from CBs was modified with hydroxyapatite (HAp) via the co‐precipitation method and converted into a composite adsorbent for the removal of 133Ba ions. Response surface methodology (RSM) based on Box‐Behnken Design (BBD) was employed for the examination of process variables such as initial pH, metal concentration, and adsorbent amount on 133Ba sorption. XRD and FTIR data confirmed the successful isolation of cellulose and the modification of the cellulose surface with HAp. The model F-value (100.04) and R2 (0.99) suggested that the proposed model was significant. Optimum conditions were determined as initial pH of 8, contact time of 134 min, and concentration of 0.01 mol/L, and the barium sorption capacity of MFC‐HAp was found to be 0.75 mmol/g under these conditions. The maximum monolayer barium sorption capacity was determined to be 2.92 mmol/g. Combining cellulose and HAp to be a novel composite adsorbent is useful for reusing CB waste and promising for removing 133Ba ions from aqueous solutions.

Research on the removal of charged colloidal filter materials from coolants in nuclear power plants

In this paper, nano-grade aluminum powder was used as raw material to prepare glass fiber filter material with positive Zeta potential by hydrothermal method. Since colloids of activated corrosion products in primary circuit of nuclear power plant are mainly electronegative, the filter material prepared in this experiment was used to remove colloidal particles in coolant by combining traditional mechanical filtration with electrostatic adsorption, thus improving the removal efficiency of colloidal particles. The leaching problem of soluble silicon in glass fiber filter material was studied. The leaching amount of soluble silicon in glass fiber filter material was decreased while the filtration efficiency was improved. Consequently reduce the risk of safe operation of nuclear power plants.

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.

Research on Nuclear Power Plant Operational Task Complexity Assessment Method Based on Group Decision Making and Deep Learning of EEG Signals

This study aims to explore the correlation between the operational task complexity of nuclear power plant (NPP) operators and electroencephalogram (EEG) features. Initially, we segmented EEG signals according to operational steps and extracted a total of 120 time domain, frequency domain, and time-frequency domain features. Subsequently, we applied an adaptive principal component analysis (PCA) dimensionality reduction method to process the features. On the other hand, three experts were invited to evaluate the complexity of the operational tasks, and their evaluation data were synthesized using a group decision-making approach. A correlation analysis was performed between these data and the PCA-reduced feature data, identifying the features with the highest correlation coefficient for each participant. Then we built a long short-term memory model with the data of the first group of participants to predict the task complexity value and tested it with the data of the second group of participants. Testing the model with data from the second group yielded favorable results, with a training set mean squared error (MSE) of 0.025 and a testing set MSE of 0.078. The results of this study indicate a significant correlation between specific EEG features and task complexity in the operational tasks of NPP operators. The model established through a combination of group decision making and machine learning methods effectively predicted the task complexity levels for operators in different operational tasks. This research provides a new perspective on NPP operators’ cognitive load and operational tasks, holding practical significance for operator training and workload management.

Investigations on seismic performance of nuclear power plants equipped with an optimal BIS-TMDI considering FSI effects

This paper introduces a base isolation system-tuned mass damper inerter (BIS-TMDI) hybrid system to the AP1000 nuclear power plant (NPP), which reduces seismic damage potential of the NPP structure. The effects of fluid-structure interaction (FSI) caused by the passive containment cooling system water storage tank (PCCWST) on NPP's seismic performance are investigated. The FSI of water tank theoretical model is considered based on the Housner's model, and a series of time history analyses are performed to prove the rationality of the proposed model. Three single-objective optimization strategies are employed to minimize the relative displacement variance and absolute acceleration variance of the upper structure, as well as the filtered energy index (FEI). Furthermore, a multi-objective optimization strategy considering all these three indexes is proposed to obtain optimal parameters of vibration control. The influence of vibration control strategies on the relative deformation and acceleration of the upper structure is explored with various water level ratios. The analytical results indicate that the proposed BIS-TMDI strategy has significantly reduced the NPP structure's seismic response. The effectiveness of the vibration control strategy is influenced by the water level ratio, emphasizing the significance of designing an appropriate water level ratio to reduce NPP structure's seismic response.

The development of the international nuclear power plant construction market in the context of nuclear power social acceptability research

Aim. To determine the vector of influence of public opinion on the prospects of development of the international market of nuclear power plant (NPP) construction in the conditions of global energy transition and to formulate the most priority directions of communication work in order to increase the Russian export of nuclear power technologies in the markets of developing countries.Objectives. To identify the current trends of social acceptability of nuclear power in the world; to consider a number of tools used to work with public opinion in the field of improving the reputation of nuclear power in countries-importers of nuclear power technologies; to substantiate the importance of communication work in order to increase Russian exports of nuclear power technologies in the markets of developing countries.Methods. Data from various sociological surveys were used to analyze the current state of social acceptability of nuclear power plants. The key methods of this study are comparative method and case study method.Results. The analysis of the international experience of society’s influence on energy policy has shown that it is impossible to implement international projects in the field of NPP construction without public support for the initiative on nuclear power development. The analysis revealed that one of the competitive advantages of ROSATOM in the international NPP construction market is the inclusion of public communication work in the export offer. Based on the analysis of the results of sociological surveys, it was found out that within the framework of communication work with the public in the importer country it is reasonable to emphasize the economic benefits of NPP operation and continuity of its functioning.Conclusions. The article shows the possibilities of increasing the Russian export of nuclear power technologies in the markets of developing countries, due to the global trend to increase the social acceptability of nuclear power and the presence of relevant competitive advantages, including within the framework of the implementation of communication strategy.