Data Value-Added Service Comprehensive Evaluation Method on the Performance of Power System Big Data

Energies, Journal Year: 2025, Volume and Issue: 18(3), P. 700 - 700

Published: Feb. 3, 2025

With the development of digital economy, integration and secure sharing energy big data have become pivotal in driving innovation across production, distribution, consumption sectors. For power enterprises, leveraging to enhance operational efficiency drive business will play a crucial role value added. Firstly, based on value-added service framework system grid this paper explores basic technologies for applications designs technical roadmap services. Secondly, proposed methodology incorporates analytic hierarchy process (AHP) gray comprehensive evaluation method (GCE) determine weights key factors affecting Empirical research is conducted validate feasibility typical Additionally, proposes methods evaluating benefits services identifies mining management, customer discovery, asset utilization, providing theoretical support practical pathways transformation enterprises.

Language: Английский

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Environmental and public health risk management, remediation and rehabilitation options for impacts of radionuclide mining

Discover Sustainability, Journal Year: 2025, Volume and Issue: 6(1)

Published: March 26, 2025

Language: Английский

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Deep learning applications on satellite imagery datasets for nuclear nonproliferation and counter-proliferation

Annals of Nuclear Energy, Journal Year: 2025, Volume and Issue: 219, P. 111443 - 111443

Published: April 11, 2025

Language: Английский

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A review on nuclear emergency preparedness and response management

Annals of Nuclear Energy, Journal Year: 2025, Volume and Issue: 219, P. 111492 - 111492

Published: April 24, 2025

Language: Английский

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Design and implementation of the digital twin system for the negative ion based neutral beam injection

Nuclear Engineering and Technology, Journal Year: 2025, Volume and Issue: unknown, P. 103540 - 103540

Published: Feb. 1, 2025

Language: Английский

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A Survey of Artificial Intelligence Applications in Nuclear Power Plants

IoT, Journal Year: 2024, Volume and Issue: 5(4), P. 666 - 691

Published: Oct. 29, 2024

Nuclear power plants (NPPs) rely on critical, complex systems that require continuous monitoring to ensure safe operation under both normal and abnormal conditions. Despite the potential of artificial intelligence (AI) enhance predictive capabilities in these systems, limited research has been conducted application AI algorithms within NPPs. This presents a knowledge gap integration for improving safety, reliability, decision making NPP. In this study, we explore use methods, including machine learning real-time data analytics, applied NPP components address nonlinearity dynamic behavior inherent reactor operations. Through implementation Internet Things (IoT) devices, propose system enables early warning transmission regulatory authorities decision-makers, ensuring better coordination during incidents. Lessons from past nuclear accidents, such as Chernobyl, emphasize importance timely information dissemination mitigate risks. However, also challenges, cybersecurity risks need updated regulations safety-critical environments. The results study highlight urgent further NPPs, with particular focus addressing challenges implementation.

Language: Английский

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A Survey of Artificial Intelligence Applications in Nuclear Power Plants

Published: July 29, 2024

The different systems in the nuclear power plants (NPP) are critical and complex which requires a continuous rigorous monitoring for both normal abnormal conditions. However, due to nonlinearity of dynamic behavior these systems, implementation artificial intelligence within NPP components is crucial enhance predictability key operating parameters trend. On other hand, lessons learned from large accident proves that remote real-time coordination between stakeholders involved safety reactor needed. This feature can be implemented existing by embedding mobile computing networks plant components. network will send early warning transmit data on authorities such as regulatory authority decision-making committees, interpretability event deliver collaborative decision return mitigate risk associated with events increase overall plant. integration AI would most interest countries where reactors challenged reach during an accident. For instance, meltdown Chernobyl, Soviet government covered news attempt contain consequences but there was spread radioactive contamination some Europe countries. Therefore, case scenario result outcomes European notified through networking forecasted algorithms. In this paper, we examine modern technologies, potential application, features challenges, future work direction.

Language: Английский

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Investigation on the Thermal Characteristics of Electronic System and Prediction of Chip Temperature by Machine Learning

Nuclear Engineering and Technology, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 1, 2024

In this work, the thermal characteristics and steady-state temperatures (SST) of CPU FPGA electronic system in nuclear power plant are explored. Finite element analysis is performed to simulate test process. Furthermore, three machine learning algorithms used predict chips at different operating conditions. It found that when ambient temperature 20 °C all fans power-off, SST reaches 75 72 °C, respectively. While power-on, drops 37.5 33 °C. When increases 55 72.3 68.2 The finite model verified generate data. Three models by predicting under M-SVR has better prediction ability than DT ANN. findings can be for chip reliability evaluation other devices, provide a new method possible service

Language: Английский

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Enhancing radioactive waste management with cutting-edge digital technologies: a review

Academia Engineering, Journal Year: 2024, Volume and Issue: 3(4)

Published: Oct. 28, 2024

Language: Английский

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