LAPSE:2024.1734
Published Article
LAPSE:2024.1734
The Evolving Technological Framework and Emerging Trends in Electrical Intelligence within Nuclear Power Facilities
Yao Sun, Zhijian Wang, Yao Huang, Jie Zhao, Bo Wang, Xuzhu Dong, Chenhao Wang
August 23, 2024
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.
Keywords
electrical system, intelligence, intelligent electrical equipment, intelligent inspection, nuclear power plant
Suggested Citation
Sun Y, Wang Z, Huang Y, Zhao J, Wang B, Dong X, Wang C. The Evolving Technological Framework and Emerging Trends in Electrical Intelligence within Nuclear Power Facilities. (2024). LAPSE:2024.1734
Author Affiliations
Sun Y: China Nuclear Power Engineering Co., Ltd., Beijing 100840, China
Wang Z: China Nuclear Power Engineering Co., Ltd., Beijing 100840, China
Huang Y: China Nuclear Power Engineering Co., Ltd., Beijing 100840, China
Zhao J: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China [ORCID]
Wang B: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Dong X: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China [ORCID]
Wang C: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Journal Name
Processes
Volume
12
Issue
7
First Page
1374
Year
2024
Publication Date
2024-07-01
ISSN
2227-9717
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Original Submission
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PII: pr12071374, Publication Type: Review
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LAPSE:2024.1734
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https://doi.org/10.3390/pr12071374
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Aug 23, 2024
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