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1 July 2024

The Evolving Technological Framework and Emerging Trends in Electrical Intelligence within Nuclear Power Facilities

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1
China Nuclear Power Engineering Co., Ltd., Beijing 100840, China
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School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
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Processes2024, 12(7), 1374;https://doi.org/10.3390/pr12071374
This article belongs to the Special Issue Sustainable and Intelligent Energy Systems and Processes: Recent Advances and Challenges
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Abstract

This paper thoroughly explores the feasibility of integrating a variety of intelligent electrical equipment and smart maintenance technologies within nuclear power plants to enhance the currently limited level of intelligence of these systems and better support operational and maintenance tasks. Initially, this paper outlines the demands and challenges of intelligent electrical systems in nuclear power plants, highlighting the current state of development of intelligent electrical systems, including new applications of artificial intelligence and big data technologies in power grid companies, such as intelligent defect recognition through image recognition, intelligence-assisted inspections, and intelligent production commands. This paper then provides a detailed introduction to the architecture of intelligent electrical equipment, encompassing the smart electrical equipment layer, the smart control system layer, and the cloud platform layer. It discusses the intelligentization of medium- and low-voltage electrical equipment, such as smart circuit breakers, smart switchgear, and low-voltage distribution systems, emphasizing the importance of intelligentization in improving the safety, reliability, and maintenance efficiency of medium- and low-voltage distribution equipment in nuclear power plants. Furthermore, this paper addresses issues in the intelligentization of nuclear power plant electrical systems, such as information silos, the inefficiency of traditional manual inspection processes, and the lack of comprehensive intelligent design and evaluation standards, proposing corresponding solutions. Additionally, this paper presents the trends in intelligent operation and maintenance technology and applications, including primary and secondary fusion technology, intelligent patrol system architecture, intelligent inspection based on non-destructive testing, and a comprehensive solution based on inspection robots. The application of these technologies aids in achieving automated inspection, real-time monitoring, and the intelligent diagnosis of electrical equipment in nuclear power plants. Finally, this paper proposes basic principles for the development of intelligent electrical systems in nuclear power plants, including intelligent architecture, the evolutionary path, and phased goals and key technologies. It emphasizes the gradual transition from automation to digitization and then to intelligentization and presents a specific implementation plan for the intelligentization of the electrical systems in nuclear power plants. This paper concludes with a summary of short-term and long-term goals for improving the performance of nuclear power plant electrical systems through intelligent technologies and prospects for the application of intelligent technologies in the operation and maintenance of nuclear power plants in the future.

1. Introduction

Intelligence has emerged as a pivotal topic across diverse industries, and nuclear power, being a cornerstone for national security and economic growth, has responded promptly to national initiatives [1,2,3]. Endeavors have been directed towards advancements in digital nuclear power, industry databases, and the evolution of smart power plants. Nonetheless, in the realm of electrical intelligence in nuclear power plants, we are still navigating the initial stages of exploration. A comprehensive framework for intelligent operation and maintenance technologies in nuclear power plants is yet to be formulated, pivotal technologies for intelligent electrical operations and maintenance remain unsystematically identified, and in-depth research into preliminary solutions for key technological breakthroughs is still lacking [4,5,6].
Currently, both domestically and internationally, there exists practical experience in the application of electrical intelligence in industrial sectors, thermal power plants, and nuclear power plant design [7]. By drawing insights from our peers, we can enhance our comprehension and technological proficiency, formulate scientific research and development plans, and expedite the design process for intelligent electrical systems in new nuclear power R&D projects. In the nuclear power field, there has been a surge in intelligence-related work, with several institutions dedicated to the research and development of infrastructure platforms and information resources [8]. For instance, the real-time monitoring of primary systems and crucial equipment in operating units has been implemented, and equipment fault data models have been established leveraging deep learning to provide early warnings for potential defects, yielding notable outcomes [9]. Additionally, some units have pioneered research in equipment health management, encompassing the wireless network-based video monitoring of key equipment and remote inspections, thereby reducing the need for on-site inspections [10,11,12].
At present, intelligent applications are primarily localized, leaving room for enhancements in both depth and precision. Additional research is imperative to boost the effectiveness of these applications and foster their broader adoption. As digital modeling and intelligent applications evolve further, nuclear power plants’ industrial systems and equipment control are poised to evolve from automation to comprehensive intelligent control [13].
The structure of this paper is as follows: Section 2 provides a detailed discussion on the intelligence requirements and challenges faced by the electrical systems of nuclear power plants, including the current state of intelligence levels and existing issues. Section 3 introduces smart electrical equipment and its application in nuclear power plants, covering the architecture of smart devices and the intelligentization of medium- and low-voltage electrical equipment. Section 4 explores the intelligent operation and maintenance technologies and application trends of the electrical systems in nuclear power plants, involving the transition from automation to digitization, and then to intelligent development. Section 5 presents the development trends, basic principles, intelligent architecture, and development planning of the intelligentization of the electrical systems in nuclear power plants. Finally, in Section 6, we summarize the entire paper and provide an outlook for the future, emphasizing the potential of intelligent technology in enhancing the operational efficiency and safety of nuclear power plants.

2. Nuclear Power Plant Electrical System Intelligence Demands and Challenges

2.1. Current Status of Electrical System Intelligence

  • The rapid progression of artificial intelligence and big data technologies is fostering novel applications in power grid companies. These include intelligent defect recognition leveraging image recognition, intelligence-assisted inspections, and intelligent production commands, all of which offer valuable insights for the intelligent operation and maintenance of nuclear power plant electrical systems [14]. Initial successes have been achieved in areas such as computer vision, natural language processing with knowledge graphs, intelligent voice and speech recognition, and data intelligence technologies. The swift development of AI technology is poised to become a pivotal force in the realm of intelligent electrical equipment operation and maintenance [15,16];
  • The research and application of intelligent equipment are currently in the nascent stages of exploration. There exists a solid foundation in the integrated design and manufacturing of conventional sensing components and equipment bodies, encompassing examples like transformer top-oil temperature monitoring and cable joint humidity monitoring [17]. However, the concept of comprehensive equipment integration design grounded in the self-awareness of states is still in its infancy, and research into sensing components and methods is in its early stages. While traditional designs for nuclear power plant construction have gained widespread adoption, there remains significant room for exploration in the standardization and modularization of equipment related to the manufacturing, operation, and maintenance professions [18];
  • By enhancing the intelligence level of distribution equipment, it gains the ability to precisely perceive its actual operational status. This enables users to detect equipment defects proactively, thereby significantly improving safety, reliability, and maintenance efficiency [19]. Given the stringent reliability requirements for medium- and low-voltage distribution equipment in nuclear power plants, the application of intelligent non-safety-grade distribution equipment holds immense significance in reducing the risk of equipment failures in these facilities [20];
  • Intelligent inspection technology enables the automatic transportation of inspection objects and real-time monitoring of detection data through predefined inspection methods. This facilitates the prompt resolution of issues [21]. Nuclear power plants can adopt a management model for intelligent inspection equipment, collecting data information on crucial equipment and facilities across varying environments, thus laying the groundwork for achieving their management goals [22];
  • The intelligent operation and maintenance of distribution systems leverage advanced management systems to enable the real-time monitoring of diverse distribution data [23]. Operation and maintenance companies can keep a watchful eye on distribution operational data and warning notifications via a web client, seamlessly managing and dispatching personnel using mobile app platforms. Furthermore, maintenance personnel can offer timely feedback to the company through multimedia channels on the app, thereby enhancing the efficiency of electrical system management, eliminating traditional manual maintenance blind spots, and significantly reducing personnel and maintenance costs [24,25,26].

2.2. Problems with the Intelligentization of Nuclear Power Plant Electrical Systems

  • The fragmented information repositories of nuclear power plant equipment constrain the degree of intelligence and impede the later-stage tracking, servicing, and design optimization of unit equipment [27];
  • The traditional manual inspection process at nuclear power plants suffers from low efficiency, inconsistent detection quality, incomplete data, and a lack of real-time monitoring and accessibility [28]. This necessitates the adoption of a more scientific, comprehensive, and real-time inspection methodology;
  • The absence of comprehensive intelligent design and evaluation standards for nuclear power plant electrical systems necessitates the development of reasonable designs and the establishment of robust evaluation criteria [29];
  • The lack of a top-level design for information security protection in nuclear power plant electrical systems demands the exploration and implementation of a comprehensive information security system [30];
  • The safety protection measures of the electrical systems in nuclear power plants need to be enhanced. The electrical systems of nuclear power plants lack effective safety strategies in “lateral isolation, vertical encryption, and comprehensive protection”, and are facing issues such as insufficient anti-virus systems, weak host security, and the absence of data encryption and authentication mechanisms, necessitating targeted measures to enhance safety protection.

3. Intelligent Electrical Equipment and Its Application in Nuclear Power Plants

3.1. Architecture of Intelligent Electrical Equipment Technology

The architecture of smart electrical equipment comprises three distinct layers, namely the smart electrical equipment layer, the smart control system layer, and the cloud platform layer, as depicted in Figure 1 [31,32,33].
Figure 1. Intelligent electrical equipment system architecture.

3.2. Smartification of Medium- and Low-Voltage Electrical Equipment

  • Medium-voltage intelligent circuit breaker: Incorporating embedded temperature sensors, it gathers temperature data and facilitates the online intelligent monitoring of the opening/closing coils and energy storage motor. This expedited, visual understanding of the circuit breaker’s health status enables the dynamic diagnosis of its health trends [34];
  • Medium-voltage smart switchgear: This system enables the continuous online monitoring of busbar, cable temperatures, and the coordination status between circuit breakers and switchgear [35]. It supports remote or local control of circuit breaker operations, monitors the remaining electrical life, and incorporates individual arc protection, integrated protection, and the online monitoring of leakage currents and discharge counts for surge arresters [36];
  • Low-voltage distribution system: Leveraging smart motor management controllers, it controls, protects, and monitors low-voltage motors. Smart feeder protection controllers oversee feeder circuit monitoring, protection, and alarms. Smart meters measure, display, and store incoming circuit electrical parameters [37];
  • Intelligent low-voltage circuit breaker: Embedded temperature sensors enable temperature collection and online monitoring of the circuit breaker, with a real-time local display, providing a quick, visual understanding of its health status [38,39,40];
  • Low-voltage intelligent switchgear: An integrated temperature control system within the cabinet continuously monitors the environment and temperatures of drawers and electrical equipment. It offers over-temperature warnings, the timely detection of potential fault points, and allows for drawer replacement without power interruption, enhancing operational continuity and reliability. Additionally, energy consumption data analysis aids in improving energy efficiency [41].

3.3. Intelligent Local Control System

An autonomous and intelligent localized control system is being formulated with the capability to:
  • Optimize energy efficiency: Through a visual platform, it analyzes energy usage based on various load types, metering zones, and operational modes, facilitating comprehension of the system’s energy flow and pinpointing avenues for enhanced efficiency [42];
  • Facilitate operation and maintenance: By showcasing system diagrams, cabinet configurations, and communication network blueprints via a maintenance management interface, it enables oversight of the electrical system’s operational standing, equipment status and parameters, and communication statuses [43]. Smart monitoring mitigates the need for manual periodic inspections, thus lessening operational manpower and time expenditure, and offering pre-emptive warnings for equipment failures, enabling proactive maintenance and minimizing unscheduled downtimes [44];
  • Manage power quality: It conducts real-time surveillance of power quality parameters, governance apparatus, and electronic devices, capturing and documenting event occurrences and types, and generating comprehensive reports. This enables the remote execution of effective power management strategies [45];
  • Oversee electrical equipment: It compiles and displays the operational health status, vital equipment information, and operational metrics of electrical equipment. It also assesses the condition and aging of medium-voltage switchgear, medium-voltage circuit breakers, low-voltage distribution cabinets, and low-voltage circuit breakers [46,47,48].

3.4. Cloud Platform

Given the paramount importance of nuclear power data security, a dedicated cloud platform is employed to consolidate data gathered from smart devices. This platform serves as a repository and analysis hub, with core capabilities encompassing monitoring, optimization, management, and prediction. It conducts a comprehensive, intelligent analysis and evaluation of electrical systems and equipment, accurately assessing their current status and intelligently forecasting future trends. Furthermore, it calculates and assesses potential risks, thereby establishing a dynamic risk management and early warning system to ensure the safe and efficient operation of nuclear power facilities [49,50].

3.5. Electrical Main Equipment Intelligent Integration of Primary and Secondary Fusion

Primary and secondary fusion technology enhances the intelligence of electrical systems by integrating intelligent components of secondary equipment into primary electrical equipment, such as column switches, ring main units, and transformers. This integration enables state visualization, grid networking, and automation control, laying the foundation for the overall intelligence of the electrical system [51].
To facilitate automatic identification and plug-and-play capabilities in electrical system equipment, integrated devices adhere to standardized interfaces, communication protocols, and data models [52]. Analyzing and adhering to these standardized components allows for software-defined implementation, enabling plug-and-play functionality in fusion terminals, intelligent ring main units, and distribution transformers. This ultimately leads to the visualization of feeder areas [53,54,55].
Employing electronic transformers, as opposed to traditional electromagnetic transformers, eliminates errors during voltage and current signal transmission and processing to secondary equipment. This results in a significant improvement in the accuracy of protection, measurement, and metering devices, bolstering the performance of the electric measurement subsystem. This approach fulfills the technical requirements of primary and secondary fusion [56].
The key to primary and secondary fusion lies in standardized connections between primary and secondary components, enabling comprehensive state perception, functional alignment, and efficient operation and maintenance. This significantly enhances the operational reliability of the entire electrical system [57]. The key challenges we face include the following [58,59,60,61,62]:
  • Enhancing the coherence and compatibility of primary and secondary components in terms of both fundamental and intelligent functionalities;
  • Establishing standardized connection protocols for primary and secondary interfaces on the grid system level to guarantee the seamless replacement and plug-and-play compatibility of equipment from diverse manufacturers;
  • Boosting functional integration by incorporating capabilities like line loss management, fault distance determination, and single-phase grounding fault mitigation into primary and secondary systems, aligning with the automation demands of modern electrical systems;
  • Elevating the reliability of electronic transformer and sensor devices by thoroughly investigating and addressing issues such as electromagnetic interference and life cycle matching in secondary equipment.

6. Summary and Outlook

In recent years, the evolving trends in intelligent electrical maintenance technology and the operational insights gained from smart substations provide valuable references for boosting the electrical intelligence of nuclear power plants in the near future.
We have outlined a roadmap for the intelligent evolution of nuclear power plant electrical systems, aiming to transform electrical equipment and systems from automation to digitization, ultimately attaining intelligent development milestones. Technically speaking, this journey entails a shift from perception to cognition in terms of intelligence.
In the immediate term, our priority is on deploying intelligent equipment in operational nuclear power plants. However, as we look towards the medium- to long-term, we will redirect our focus to enhancing the overall intelligence level from a systemic perspective for newly planned nuclear power plants.
We have also crafted implementation plans for the intelligentization of electrical equipment in nuclear power plants. A key component of these plans is primary and secondary fusion technology, which seamlessly integrates intelligent units into primary equipment, thereby enhancing its overall intelligence.
With the rapid development of intelligent technology, the intelligent development of the electrical systems in nuclear power plants will continue to deepen and is expected to achieve significant leaps in the next few years.

Author Contributions

Conceptualization, Y.S. and Z.W.; methodology, Y.H. and J.Z.; formal analysis, B.W., X.D. and C.W.; writing—original draft preparation, Y.S., Z.W., Y.H., J.Z., B.W., X.D. and C.W. All authors have read and agreed to the published version of the manuscript.

Funding

Research Project of China National Nuclear Corporation Limited (No. KY2008-1101.5.1).

Data Availability Statement

No new data were created during the study period.

Conflicts of Interest

Author Yao Sun, Zhijian Wang, Yao Huang were employed by China Nuclear Power Engineering Co., Ltd. Author Jie Zhao, Bo Wang, Xuzhu Dong and Chenhao Wang were employed by School of Electrical Engineering and Automation, Wuhan University. All of the authors declare that the research was conducted in the absence of any commercial or financial rela-tionships that could be construed as a potential conflict of interest.

References

  1. Lu, C.; Lyu, J.; Zhang, L.; Gong, A.; Fan, Y.; Yan, J.; Li, X. Nuclear Power Plants With Artificial Intelligence in Industry 4.0 Era: Top-Level Design and Current Applications—A Systemic Review. IEEE Access 2020, 8, 194315–194332. [Google Scholar] [CrossRef]
  2. Duan, Q.; Ping, J.; Xie, H.; Wang, C.; Li, S. Power and Temperature Control of Nuclear Power Plant Based on Transfer Function Matrix Method. IEEE Access 2021, 9, 33922–33928. [Google Scholar] [CrossRef]
  3. Dong, Z.; Song, M.; Huang, X.; Zhang, Z.; Wu, Z. Module Coordination Control of MHTGR-Based Multi-Modular Nuclear Plants. IEEE Trans. Nucl. Sci. 2016, 63, 1889–1900. [Google Scholar] [CrossRef]
  4. Leake, H.C.; Kozo, E.; Attarian, G.E. A Simplified Method to Predict Post-Trip Switchyard Voltage at Nuclear Generating Stations. IEEE Trans. Power Deliv. 2014, 29, 1964–1969. [Google Scholar] [CrossRef]
  5. Yang, X.; Xue, Y.; Cai, B. Pathway Planning of Nuclear Power Development Incorporating Assessment of Nuclear Event Risk. J. Mod. Power Syst. Clean Energy 2024, 12, 500–513. [Google Scholar] [CrossRef]
  6. Ramana, M.V. Small Modular and Advanced Nuclear Reactors: A Reality Check. IEEE Access 2021, 9, 42090–42099. [Google Scholar] [CrossRef]
  7. Yogita, D.; Toshniwal, P.K.; Gupta, V.; Khurana, P. ADQ—Anomaly Detection and Quantification from Delayed Neutron Monitoring Data of Nuclear Power Plants. IEEE Sens. J. 2023, 23, 7207–7216. [Google Scholar] [CrossRef]
  8. Choo, J.; Jeong, C.; Choo, J. Transverse Electric Scattering of Open Cabinet in Nuclear Power Plants. IEEE Antennas Wirel. Propag. Lett. 2016, 15, 1204–1207. [Google Scholar] [CrossRef]
  9. George-Williams, H.; Lee, M.; Patelli, E. Probabilistic Risk Assessment of Station Blackouts in Nuclear Power Plants. IEEE Trans. Reliab. 2018, 67, 494–512. [Google Scholar] [CrossRef]
  10. Boghdady, T.A.; Mahmoud, M.; Zahab, E.A.; Tag-Eldin, E.; Sayed, M. Power Level Control of Nuclear Power Plants During Load Following Operation Using Fractional Order Controller Based on a Modified Algorithm. IEEE Access 2023, 11, 134382–134403. [Google Scholar] [CrossRef]
  11. Duan, Q.; Lu, R.; Xie, H.; Ping, J.; Lu, C.; Zhou, X.; Gao, J.; Li, J. Fault Diagnosis of Air Compressor in Nuclear Power Plant Based on Vibration Observation Window. IEEE Access 2020, 8, 222274–222284. [Google Scholar] [CrossRef]
  12. Takeda, S.; Sakurai, S.; Kasada, R.; Konishi, S. Plasma Control Requirements for Commercial Fusion Power Plants: A Quantitative Scenario Analysis With a Dynamic Fusion Power Plant Model. IEEE Trans. Plasma Sci. 2018, 46, 1205–1210. [Google Scholar] [CrossRef]
  13. Agarwal, V.; Buttles, J.W.; Beaty, L.H.; Naser, J.; Hallbert, B.P. Wireless Online Position Monitoring of Manual Valve Types for Plant Configuration Management in Nuclear Power Plants. IEEE Sens. J. 2017, 17, 311–322. [Google Scholar] [CrossRef]
  14. Yang, L.; Li, H.; Zhang, H.; Wu, Q.; Cao, X. Stochastic-Distributionally Robust Frequency-Constrained Optimal Planning for an Isolated Microgrid. IEEE Trans. Sustain. Energy, 2024; early access. [Google Scholar] [CrossRef]
  15. Akkawi, M.; Jiang, J. An Inverse Control-Based Set-Point Function for Steam Generator Level Control in Nuclear Power Plants. IEEE Trans. Nucl. Sci. 2011, 58, 3291–3304. [Google Scholar] [CrossRef]
  16. Chen, J.; Klein, J.; Wu, Y.; Xing, S.; Flammang, R.; Heibel, M.; Zuo, L. A Thermoelectric Energy Harvesting System for Powering Wireless Sensors in Nuclear Power Plants. IEEE Trans. Nucl. Sci. 2016, 63, 2738–2746. [Google Scholar] [CrossRef]
  17. Johansson, L.; Filtz, J.R.; DeFelice, P.; Sadli, M.; Plompen, A.; Heyse, J.; Hay, B.; Dinsdale, A.; Pommé, S.; Cassette, P.; et al. Metrology for New Generation Nuclear Power Plants–MetroFission. IEEE Trans. Nucl. Sci. 2014, 61, 2017–2023. [Google Scholar] [CrossRef]
  18. Jung, D.; Shin, J.; Lee, C.; Kwon, K.; Seo, J.T. Cyber Security Controls in Nuclear Power Plant by Technical Assessment Methodology. IEEE Access 2023, 11, 15229–15241. [Google Scholar] [CrossRef]
  19. Fernandes, A.; Pereira, R.C.; Sousa, J.; Carvalho, P.F.; Correia, M.; Rodrigues, A.P.; Carvalho, B.B.; Correia, C.M.; Gonçalves, B. FPGA Remote Update for Nuclear Environments. IEEE Trans. Nucl. Sci. 2016, 63, 1645–1649. [Google Scholar] [CrossRef]
  20. Hao, L.; Chang, J.; Hu, L.; Wang, X.; Zong, W.; Gui, L. Analysis of the Interturn Short Circuits of Stator Field Windings in Multiphase Angular Brushless Exciter at Nuclear Power Plant. IEEE Trans. Energy Convers. 2019, 34, 2126–2136. [Google Scholar] [CrossRef]
  21. Poudel, B.; Joshi, K.; Gokaraju, R. A Dynamic Model of Small Modular Reactor Based Nuclear Plant for Power System Studies. IEEE Trans. Energy Convers. 2020, 35, 977–985. [Google Scholar] [CrossRef]
  22. Rafiei, M.; Ansarifar, G.R.; Hadad, K. Core Power Control of a Nuclear Research Reactor During Power Maneuvering Transients Using Optimized PID-Controller Based on the Fractional Neutron Point Kinetics Model With Reactivity Feedback Effects. IEEE Trans. Nucl. Sci. 2019, 66, 1804–1812. [Google Scholar] [CrossRef]
  23. Saeed, A.; Akhtar, N.; Rashid, T.; Ansari, S.A. Evaluation of Fast Neutron Fluence for Reactor Pressure Vessel Surveillance of Chashma Nuclear Power Plants Units 1 and 2. IEEE Trans. Nucl. Sci. 2017, 64, 2661–2668. [Google Scholar] [CrossRef]
  24. Jin, X.; Guo, Y.; Sarkar, S.; Ray, A.; Edwards, R.M. Anomaly Detection in Nuclear Power Plants via Symbolic Dynamic Filtering. IEEE Trans. Nucl. Sci. 2011, 58, 277–288. [Google Scholar] [CrossRef]
  25. Cano-Megias, P.; Hidalgo-Salaverri, J.; Chacartegui, R.; Ayllon-Guerola, J.; Becerra-Villanueva, J.A.; Viezzer, E. Boosting the Efficiency of Future Fusion Power Plants Combining Energy and Heat Production. IEEE Trans. Plasma Sci. 2022, 50, 4430–4439. [Google Scholar] [CrossRef]
  26. Lee, C.J.; Yun, J.H. Integrated Response Time Evaluation Methodology for the Nuclear Safety Instrumentation System. IEEE Trans. Nucl. Sci. 2017, 64, 1211–1218. [Google Scholar] [CrossRef]
  27. Son, J.; Choi, J.; Yoon, H. New Complementary Points of Cyber Security Schemes for Critical Digital Assets at Nuclear Power Plants. IEEE Access 2019, 7, 78379–78390. [Google Scholar] [CrossRef]
  28. Yang, Y.; Wang, Y.; Wu, W. Allocating Ex-post Deviation Cost of Virtual Power Plants in Distribution Networks. J. Mod. Power Syst. Clean Energy 2023, 11, 1014–1019. [Google Scholar] [CrossRef]
  29. Cho, C.-S.; Chung, W.-H.; Kuo, S.-Y. Cyberphysical Security and Dependability Analysis of Digital Control Systems in Nuclear Power Plants. IEEE Trans. Syst. Man Cybern. Syst. 2016, 46, 356–369. [Google Scholar] [CrossRef]
  30. Singh, L.K.; Vinod, G.; Tripathi, A.K. Design Verification of Instrumentation and Control Systems of Nuclear Power Plants. IEEE Trans. Nucl. Sci. 2014, 61, 921–930. [Google Scholar] [CrossRef]
  31. Lee, C.-H.; Chen, B.-K.; Chen, N.-M.; Liu, C.-W. Lessons Learned From the Blackout Accident at a Nuclear Power Plant in Taiwan. IEEE Trans. Power Deliv. 2010, 25, 2726–2733. [Google Scholar] [CrossRef]
  32. Dong, Z.; Song, M.; Huang, X.; Zhang, Z.; Wu, Z. Coordination Control of SMR-Based NSSS Modules Integrated by Feedwater Distribution. IEEE Trans. Nucl. Sci. 2016, 63, 2682–2690. [Google Scholar] [CrossRef]
  33. Cao, S.; Shen, S.; Fan, S.; Wang, X.; Zhu, Z.; Shi, Y.; Lou, J. A Novel Detection Method for Valve Damage of Nuclear Power Using Attention-Based U-Net (ABUNet). IEEE Sens. J. 2023, 23, 21562–21573. [Google Scholar] [CrossRef]
  34. Okada, H.; Imamura, K.; Hirota, N.; Ando, T.; Shibatani, S.; Mizuno, N.; Nakanishi, M.; Mishima, F.; Akiyama, Y.; Nishijima, S.; et al. Development of a Magnetic Separation System of Boiler Feedwater Scale in Thermal Power Plants. IEEE Trans. Appl. Supercond. 2016, 26, 3701505. [Google Scholar] [CrossRef]
  35. Singh, P.; Singh, L.K. Modeling and Measuring Common Cause Failures in Measurement of Reliability of Nuclear Power Plant Systems. IEEE Trans. Instrum. Meas. 2021, 70, 3001608. [Google Scholar] [CrossRef]
  36. Dong, Z. Physically-Based Power-Level Control for Modular High Temperature Gas-Cooled Reactors. IEEE Trans. Nucl. Sci. 2012, 59, 2531–2549. [Google Scholar] [CrossRef]
  37. Zhang, X.; Ding, T.; Zhang, H.; Zeng, Z.; Siano, P.; Shahidehpour, M. A Two-Stage Stochastic Unit Commitment With Mixed-Integer Recourses for Nuclear Power Plants to Accommodate Renewable Energy. IEEE Trans. Sustain. Energy 2024, 15, 859–870. [Google Scholar] [CrossRef]
  38. Yang, X.; Cai, B.; Xue, Y. Review on Optimization of Nuclear Power Development: A Cyber-Physical-Social System in Energy Perspective. J. Mod. Power Syst. Clean Energy 2022, 10, 547–561. [Google Scholar] [CrossRef]
  39. Nodari, C.J.; da Cruz Saladanha, P.L.; Fontes, G.S. Safety Aspects in Dry Storage of Spent Nuclear Fuel in Long Term Operation for Brazilian Nuclear Power Plants. IEEE Lat. Am. Trans. 2020, 18, 1807–1816. [Google Scholar] [CrossRef]
  40. Gu, H.; Liu, G.; Li, J.; Yang, J. A Reliability-Based Mapping Scheme for Assessing System Operational Performance With Erroneous Human Behavior at NPPs. IEEE Access 2019, 7, 123416–123429. [Google Scholar] [CrossRef]
  41. Park, J.; Jung, W. A Study on the Validity of a Task Complexity Measure for Emergency Operating Procedures of Nuclear Power Plants—Comparing With a Subjective Workload. IEEE Trans. Nucl. Sci. 2006, 53, 2962–2970. [Google Scholar] [CrossRef]
  42. Xiao, J.; Zhou, Z.; Jing, X. Safety implementation of hydrogen igniters and recombiners for nuclear power plant severe accident management. Tsinghua Sci. Technol. 2006, 11, 549–558. [Google Scholar] [CrossRef]
  43. Wang, Y.; Yin, X.; Xu, W.; Qiao, J.; Tan, L. Active Arc Suppression Algorithm for Generator Stator Winding Ground Fault in the Floating Nuclear Power Plant. IEEE Trans. Power Deliv. 2022, 37, 5356–5365. [Google Scholar] [CrossRef]
  44. Dong, Z. Nonlinear Coordinated Control for MHTGR-Based Nuclear Steam Supply Systems. IEEE Trans. Nucl. Sci. 2014, 61, 2643–2656. [Google Scholar] [CrossRef]
  45. Bruce, A.R.W.; Gibbins, J.; Harrison, G.P.; Chalmers, H. Operational Flexibility of Future Generation Portfolios Using High Spatial- and Temporal-Resolution Wind Data. IEEE Trans. Sustain. Energy 2016, 7, 697–707. [Google Scholar] [CrossRef]
  46. Perillo, S.R.P.; Upadhyaya, B.R.; Li, F. Control and Instrumentation Strategies for Multi-Modular Integral Nuclear Reactor Systems. IEEE Trans. Nucl. Sci. 2011, 58, 2442–2451. [Google Scholar] [CrossRef]
  47. Jeon, H.; Kwon, I.; Je, M. Radiation-Hardened Sensor Interface Circuit for Monitoring Severe Accidents in Nuclear Power Plants. IEEE Trans. Nucl. Sci. 2020, 67, 1738–1745. [Google Scholar] [CrossRef]
  48. Ansari, S.A.; Haroon, M.; Rashid, A.; Kazmi, Z. Measurement and Analysis of Structural Integrity of Reactor Core Support Structure in Pressurized Water Reactor (PWR) Plant. IEEE Trans. Nucl. Sci. 2017, 64, 844–851. [Google Scholar] [CrossRef]
  49. Fujii, K.; Neda, T.; Suto, O.; Kaneda, M.; Kawamura, A. Application of Hierarchical Computer Complex Concept for Nuclear Power Plants. IEEE Trans. Nucl. Sci. 1983, 30, 806–810. [Google Scholar] [CrossRef]
  50. Moshkbar-Bakhshayesh, K.; Ghofrani, M.B. Development of an Efficient Identifier for Nuclear Power Plant Transients Based on Latest Advances of Error Back-Propagation Learning Algorithm. IEEE Trans. Nucl. Sci. 2014, 61, 602–610. [Google Scholar] [CrossRef]
  51. Liu, X.; Jiang, D.; Lee, K.Y. Decentralized Fuzzy MPC on Spatial Power Control of a Large PHWR. IEEE Trans. Nucl. Sci. 2016, 63, 2343–2351. [Google Scholar] [CrossRef]
  52. Kim, G.G.; Hyun, J.H.; Choi, J.H.; Bhang, B.G.; Ahn, H.-K. Quality Analysis of Photovoltaic System Using Descriptive Statistics of Power Performance Index. IEEE Access 2023, 11, 28427–28438. [Google Scholar] [CrossRef]
  53. Magne, S.; Nehr, S.; Buet, X.; Bentaïb, A.; Porcheron, E.; Grosseuvres, R.; Studer, E.; Scarpa, R.; Abdo, D.; Widloecher, J.L.; et al. In Situ Gas Monitoring by Fiber-Coupled Raman Spectrometry for H₂-Risk Management in Nuclear Containment During a Severe Nuclear Accident. IEEE Trans. Nucl. Sci. 2020, 67, 617–624. [Google Scholar] [CrossRef]
  54. Moshkbar-Bakhshayesh, K.; Ghofrani, M.B. Development of a New Method for Forecasting Future States of NPPs Parameters in Transients. IEEE Trans. Nucl. Sci. 2014, 61, 2636–2642. [Google Scholar] [CrossRef]
  55. Manjunatha, K.A.; Agarwal, V.; Mack, A.L.; Koester, D.; Adams, D.E. Total Unwrapped Phase-Based Diagnosis of Wall Thinning in Nuclear Power Plants Secondary Piping Structures. IEEE Access 2022, 10, 113726–113740. [Google Scholar] [CrossRef]
  56. Hao, L.; Chen, J.; Li, J.; Chen, J.; He, P.; Xiong, G.; Wei, Y.; Yang, D.; Wang, X.; Duan, X.; et al. Diagnosis of Rotor Winding Short-Circuit Fault in Multi-Phase Annular Brushless Exciter Through Stator Field Current Harmonics. IEEE Trans. Energy Convers. 2021, 36, 1808–1817. [Google Scholar] [CrossRef]
  57. Kim, D.S.; Lee, S.W.; Na, M.G. Prediction of Axial DNBR Distribution in a Hot Fuel Rod Using Support Vector Regression Models. IEEE Trans. Nucl. Sci. 2011, 58, 2084–2090. [Google Scholar] [CrossRef]
  58. Nie, P.; Wu, H.; Xu, J.; Wei, L.; Zhu, H.; Ni, L. Thermal Pollution Monitoring of Tianwan Nuclear Power Plant for the Past 20 Years Based on Landsat Remote Sensed Data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021, 14, 6146–6155. [Google Scholar] [CrossRef]
  59. Spangler, R.M.; Agarwal, V.; Cole, D.G. A Hybrid Reliability Model Using Generalized Renewal Processes for Predictive Maintenance in Nuclear Power Plant Circulating Water Systems. IEEE Access 2023, 11, 136726–136740. [Google Scholar] [CrossRef]
  60. Dong, Z.; Zhang, Z.; Dong, Y.; Huang, X. Cascaded HTGR Power-Level Control Only by Regulating Primary Helium Flow Rate. IEEE Trans. Nucl. Sci. 2020, 67, 1780–1790. [Google Scholar] [CrossRef]
  61. Kuroze, Y.; Yamada, M.; Shintani, H.; Nunoko, A.; Murakami, R. Continuous measurement of iodine concentration in the primary coolant of a nuclear power plant. IEEE Trans. Nucl. Sci. 1997, 44, 760–763. [Google Scholar] [CrossRef]
  62. Park, J.-E.; Choo, J.; Choo, H. Electromagnetic Scattering of Periodic Cabinets in Nuclear Power Plants: Parallel Polarization. IEEE Access 2019, 7, 16487–16493. [Google Scholar] [CrossRef]
  63. Li, J.; Tong, J.; Mao, D. Influence of DC supply systems on unplanned reactor trips in nuclear power plants. Tsinghua Sci. Technol. 2001, 6, 84–88. [Google Scholar]
  64. Giuliani, U.; Agostini, M.; Bustreo, C.; Zollino, G. The Fusion to Hydrogen Option in a Carbon Free Energy System. IEEE Access 2023, 11, 131178–131190. [Google Scholar] [CrossRef]
  65. Lee, C.-H.; Hsu, S.-C.; Hsi, P.-H.; Chen, S.-L. Transferring of VFTO From an EHV to MV System as Observed in Taiwan’s No. 3 Nuclear Power Plant. IEEE Trans. Power Deliv. 2011, 26, 1008–1016. [Google Scholar]
  66. Mizuno, N.; Mishima, F.; Akiyama, Y.; Okada, H.; Hirota, N.; Matsuura, H.; Maeda, T.; Shigemoto, N.; Nishijima, S. Removal of Iron Oxide With Superconducting Magnet High Gradient Magnetic Separation From Feed-Water in Thermal Plant. IEEE Trans. Appl. Supercond. 2015, 25, 3700804. [Google Scholar] [CrossRef]
  67. Liu, G.; Gu, H.; Shen, X.; You, D. Bayesian Long Short-Term Memory Model for Fault Early Warning of Nuclear Power Turbine. IEEE Access 2020, 8, 50801–50813. [Google Scholar] [CrossRef]
  68. Čerňan, M.; Halaška, J.; Müller, Z.; Tlustý, J. The Impact of Distributed Autonomous PV Installations on Critical Infrastructure in Crisis Situations. IEEE Access 2022, 10, 97520–97530. [Google Scholar] [CrossRef]
  69. Fabiani, D.; Mazzanti, G.; Suraci, S.V.; Diban, B. Innovative Development and Application of a Stress-Strength Model for Reliability Estimation of Aged LV Cables for Nuclear Power Plants. IEEE Trans. Dielectr. Electr. Insul. 2021, 28, 2083–2090. [Google Scholar] [CrossRef]
  70. Díaz, M.; Soler, E.; Llopis, L.; Trillo, J. Integrating Blockchain in Safety-Critical Systems: An Application to the Nuclear Industry. IEEE Access 2020, 8, 190605–190619. [Google Scholar] [CrossRef]
  71. Nguyen, C.T.; Almási, I.; Hlavathy, Z.; Zsigrai, J.; Lakosi, L.; Nagy, P.; Parkó, T.; Pós, I. Monitoring Burn-Up of Spent Fuel Assemblies by Gamma Spectrometry. IEEE Trans. Nucl. Sci. 2013, 60, 1107–1110. [Google Scholar] [CrossRef]
  72. Preble, D.W. Collapsing Power Grid Reliability and Economics. IEEE J. Radio Freq. Identif. 2022, 6, 500–504. [Google Scholar] [CrossRef]
  73. Torabi, K.; Safarzadeh, O.; Rahimi-Moghaddam, A. Robust Control of the PWR Core Power Using Quantitative Feedback Theory. IEEE Trans. Nucl. Sci. 2011, 58, 258–266. [Google Scholar] [CrossRef]
  74. Kulkarni, R.D.; Srivastava, G.D.; Rautela, P. Design Implementation and Parallel Operation of High-Current High-Power Multipulse Converters Feeding Nuclear Fuel Simulators. IEEE Trans. Ind. Appl. 2021, 57, 1598–1608. [Google Scholar] [CrossRef]
  75. Vajpayee, V.; Becerra, V.; Bausch, N.; Deng, J.; Shimjith, S.R.; Arul, A.J. L₁-Adaptive Robust Control Design for a Pressurized Water-Type Nuclear Power Plant. IEEE Trans. Nucl. Sci. 2021, 68, 1381–1398. [Google Scholar] [CrossRef]
  76. Matveev, I.B. Plasma or Retirement. Alternatives to the Coal-Fired Power Plants. IEEE Trans. Plasma Sci. 2011, 39, 3259–3262. [Google Scholar] [CrossRef]
  77. Mishra, A.K.; Shimjith, S.R.; Tiwari, A.P. Adaptive Unscented Kalman Filtering for Reactivity Estimation in Nuclear Power Plants. IEEE Trans. Nucl. Sci. 2019, 66, 2388–2397. [Google Scholar] [CrossRef]
  78. Choi, B.H.; Thai, V.X.; Lee, E.S.; Kim, J.H.; Rim, C.T. Dipole-Coil-Based Wide-Range Inductive Power Transfer Systems for Wireless Sensors. IEEE Trans. Ind. Electron. 2016, 63, 3158–3167. [Google Scholar] [CrossRef]
  79. Sasaki, S.; Tanaka, K.; Maki, K.-I. Microwave Power Transmission Technologies for Solar Power Satellites. Proc. IEEE 2013, 101, 1438–1447. [Google Scholar] [CrossRef]
  80. Fang, F.; Xiong, Y. Event-Driven-Based Water Level Control for Nuclear Steam Generators. IEEE Trans. Ind. Electron. 2014, 61, 5480–5489. [Google Scholar] [CrossRef]
  81. Chang, Y.; Huang, X.; Hao, Y.; Li, C.-W. Linear Representation and Sparse Solution for Transient Identification in Nuclear Power Plants. IEEE Trans. Nucl. Sci. 2013, 60, 319–327. [Google Scholar] [CrossRef]
  82. Jang, D.; Youn, S.; Park, J.-E.; Choo, J.; Choo, H. Electromagnetic Field Propagation and Indoor Exclusion Zone Analysis in a Nuclear Power Plant. IEEE Trans. Electromagn. Compat. 2020, 62, 2386–2393. [Google Scholar] [CrossRef]
  83. Kang, C.M.; Lee, H.J.; Ji, Y.H.; Kim, T.H.; Ryu, S.Y.; Kim, S.R.; Jo, S.K.; Kim, J.C.; Kim, S.H. A Cytogenetic Study of Korean Native Goat Bred in the Nuclear Power Plant using the Micronucleus Assay. J. Radiat. Res. 2005, 46, 283–287. [Google Scholar] [CrossRef] [PubMed][Green Version]
  84. Dong, Z. Model-Free Power-Level Control of MHTGRs Against Input Saturation and Dead-Zone. IEEE Trans. Nucl. Sci. 2015, 62, 3297–3310. [Google Scholar] [CrossRef]
  85. Pearson, A. Nuclear Power Plant Control beyond the 1980s. IEEE Trans. Nucl. Sci. 1980, 27, 17–22. [Google Scholar] [CrossRef]
  86. Guo, Z.; Uhrig, R.E. Nuclear power plant performance study by using neural networks. IEEE Trans. Nucl. Sci. 1992, 39, 915–918. [Google Scholar] [CrossRef]
  87. Rodrigues, A.P.; Correia, M.; Batista, A.; Sousa, J.; Goncalves, B.; Correia, C.M.; Varandas, C.A. Intelligent Platform Management Controller for Nuclear Fusion Fast Plant System Controllers. IEEE Trans. Nucl. Sci. 2011, 58, 1733–1737. [Google Scholar] [CrossRef]
  88. Guo, S.-X.; Liu, L.-G.; Pirjola, R.J.; Wang, K.-R.; Dong, B. Impact of the EHV Power System on Geomagnetically Induced Currents in the UHV Power System. IEEE Trans. Power Deliv. 2015, 30, 2163–2170. [Google Scholar] [CrossRef]
  89. Berger, W.; Furse, C. Spread Spectrum Techniques for Measurement of Dielectric Aging on Low Voltage Cables for Nuclear Power Plants. IEEE Trans. Dielectr. Electr. Insul. 2021, 28, 1028–1033. [Google Scholar] [CrossRef]
  90. Lee, D.; Arigi, A.M.; Kim, J. Algorithm for Autonomous Power-Increase Operation Using Deep Reinforcement Learning and a Rule-Based System. IEEE Access 2020, 8, 196727–196746. [Google Scholar] [CrossRef]
  91. Kim, H.; Lee, D.; Lee, C.W.; Kim, H.R.; Lee, S.J. Safety Assessment Framework for Nuclear Power Plant Decommissioning Workers. IEEE Access 2019, 7, 76305–76316. [Google Scholar] [CrossRef]
  92. Kim, K.; Shin, J.; Kim, B.-S.; Nah, W.; Lim, C.; Chai, J. Electrical and mechanical diagnosis of aging 600 V rated STP cables in a nuclear power plant. IEEE Trans. Dielectr. Electr. Insul. 2017, 24, 1574–1581. [Google Scholar] [CrossRef]
  93. Shen, J.-J.; Shen, Q.-Q.; Wang, S.; Lu, J.-Y.; Meng, Q.-X. Generation Scheduling of a Hydrothermal System Considering Multiple Provincial Peak-Shaving Demands. IEEE Access 2019, 7, 46225–46239. [Google Scholar] [CrossRef]
  94. Poudel, B.; Gokaraju, R. Optimal Operation of SMR-RES Hybrid Energy System for Electricity & District Heating. IEEE Trans. Energy Convers. 2021, 36, 3146–3155. [Google Scholar]
  95. Dasgupta, S.; Murphy, J.J. Degraded or Loss of Voltage Protection of Class 1E Auxiliary Power Systems in a Nuclear Power Plant. IEEE Trans. Nucl. Sci. 1979, 26, 888–894. [Google Scholar] [CrossRef]
  96. Dong, Z. Nonlinear Adaptive Power-Level Control for Modular High Temperature Gas-Cooled Reactors. IEEE Trans. Nucl. Sci. 2013, 60, 1332–1345. [Google Scholar] [CrossRef]
  97. Pelletier, C.A.; Cline, J.E.; Keller, J.H. Measurement of Sources of Iodine-131 Releases to the Atmosphere from Nuclear Power Plants. IEEE Trans. Nucl. Sci. 1974, 21, 478–483. [Google Scholar] [CrossRef]
  98. Liu, X.; Jiang, D.; Lee, K.Y. Quasi-min-max Fuzzy MPC of UTSG Water Level Based on Off-Line Invariant Set. IEEE Trans. Nucl. Sci. 2015, 62, 2266–2272. [Google Scholar] [CrossRef]
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