Research on Intelligent Early-Warning System of Main Pipeline in Nuclear Power Plants Based on Hierarchical and Multidimensional Fault Identification Method

Recent tsunami generated on December 26, 2004 due to Sumatra earthquake of magnitude 9.3 resulted in inundation at the various coastal sites of India. The site selection and design of Indian nuclear power plants demand the evaluation of run up and the structural barriers for the coastal plants: Besides it is also desirable to evaluate the early warning system for tsunamigenic earthquakes. The tsunamis originate from submarine faults, underwater volcanic activities, sub-aerial landslides impinging on the sea and submarine landslides. In case of a submarine earthquake-induced tsunami the wave is generated in the fluid domain due to displacement of the seabed. There are three phases of tsunami: generation, propagation, and run-up. Reactor Safety Division (RSD) of Bhabha Atomic Research Centre (BARC), Trombay has initiated computational simulation for all the three phases of tsunami source generation, its propagation and finally run up evaluation for the protection of public life, property and various industrial infrastructures located on the coastal regions of India. These studies could be effectively utilized for design and implementation of early warning system for coastal region of the country apart from catering to the needs of Indian nuclear installations. This paper presents some results of tsunami waves based on different analytical/numerical approaches with shallow water wave theory.

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The concept of an early warning system for the use of nuclear technology in Indonesia

E3S Web of Conferences ◽

10.1051/e3sconf/202133104003 ◽

2021 ◽

Vol 331 ◽

pp. 04003

Author(s):

Dewi Prima Meiliasari ◽

Berton Suar Panjaitan ◽

Toto Heryanto ◽

Wilopo

Keyword(s):

Early Warning ◽

Early Warning System ◽

Nuclear Power ◽

Power Plants ◽

Information Dissemination ◽

Warning System ◽

Secondary Data ◽

Potential Threat ◽

Nuclear Disaster ◽

Warning Services

The application of nuclear power plants in the world is increasing and has the potential for accidents, including nuclear weapons tests from outside the Nuclear Non-Proliferation Treaty, thus allowing radioactive releases to fall into Indonesian territory, which is a potential threat. Indonesia also utilizes nuclear power in many fields, thus also potentially becoming a national threat. To prevent the threat of radiation potential requires a nuclear disaster Early Warning System (EWS) that can be accessed by the community. This research analysed the EWS that Indonesia needed, using qualitative methods by describing the results of research obtained from interviews and secondary data. Results and research discussions starting from observation and monitoring of radiation exposure, warning services, information dissemination, and supported by countermeasures for the effectiveness of the EWS. The results showed that the development of an integrated nuclear disaster EWS that is easily accessible to the community quickly and continuously under the development of an emergency is a strategy as a decision to reduce the risk of nuclear disaster. The development of the strategy requires a legal basis that regulates coordination between ministries/institutions, from planning to information dissemination to ensure the safety of the public and Indonesian people's security.

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ARTIFICIAL NEURAL NETWORKS IN PROCESS MONITORING A PRACTICAL EXPERIENCE WITH A PROTOTYPE TO PERFORM EARLY WARNING IN NUCLEAR POWER PLANTS

Industrial Applications of Neural Networks ◽

10.1142/9789812816955_0015 ◽

1998 ◽

pp. 121-132

Author(s):

F. de VIRON ◽

M. DE VLAMINCK ◽

J. M. RENDERS

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Process Monitoring ◽

Early Warning ◽

Nuclear Power ◽

Power Plants ◽

Nuclear Power Plants ◽

Practical Experience ◽

Artificial Neural

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Early-Warning System for Power Coal Storage and Supply Margin of Henan Province

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.291-294.2366 ◽

2013 ◽

Vol 291-294 ◽

pp. 2366-2374

Author(s):

Ye Fei Liu ◽

Huan Qi ◽

Su Qin Sun

Keyword(s):

Early Warning ◽

Early Warning System ◽

Power Plants ◽

Thermal Power ◽

Warning System ◽

Electrical Energy ◽

Henan Province ◽

Electricity Production ◽

Policy Makers ◽

Fuel Storage

China's needs of energy increased dramatically in these years. In China, Electrical energy are mainly generated by thermal power plants that use coal as fuel, thus electricity supply are linked to the power fuel (coals) storage of power plants. Henan has been changed form an energy exporter province to an energy importer province. Therefore, the fuel storage and supply of power plants are keys to the security of the province's social development, economics and energy supply. Research the margin of power fuel storage and supply can help the policy makers to learn the security conditions and trends of electricity production microscopically, reducing the risks in the power production process, and improving the efficiency of production and the efficiency of energy. Environmental and economic issues brought by the excessive storage can be reduced. This article describes the ideas and development of early warning system for power fuel storage and supply margin of Henan province.

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Application of Data Stream Outlier Mining Techniques in Steam Generator Safety Early Warning System of Nuclear Power Plant

2013 Fifth International Conference on Measuring Technology and Mechatronics Automation ◽

10.1109/icmtma.2013.74 ◽

2013 ◽

Author(s):

Liu Dingping ◽

Zheng Kaitao ◽

Yan Qiqi

Keyword(s):

Power Plant ◽

Nuclear Power Plant ◽

Steam Generator ◽

Early Warning ◽

Early Warning System ◽

Data Stream ◽

Nuclear Power ◽

Warning System ◽

Outlier Mining

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Signal Fault Identification in Nuclear Power Plants based on Deep Neural Networks

DAAAM Proceedings - Proceedings of the 30th International DAAAM Symposium 2019 ◽

10.2507/30th.daaam.proceedings.117 ◽

2019 ◽

pp. 0846-0852

Author(s):

Kim Seung Geun ◽

Chae Young Ho ◽

Poong Hyun Seong

Keyword(s):

Neural Networks ◽

Nuclear Power ◽

Power Plants ◽

Nuclear Power Plants ◽

Deep Neural Networks ◽

Fault Identification

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Research on the detection and early warning technology of harmful marine organisms in the water intake of nuclear power plant by 3D image sonar

E3S Web of Conferences ◽

10.1051/e3sconf/202129003013 ◽

2021 ◽

Vol 290 ◽

pp. 03013

Author(s):

Jianfei Zhang ◽

Zhongpeng Wu ◽

Chong An

Keyword(s):

Water Intake ◽

Early Warning ◽

Nuclear Power ◽

Power Plants ◽

Nuclear Power Plants ◽

Marine Organisms ◽

Industrial Safety ◽

Biological Information ◽

Marine Biological ◽

Cold Source

In view of the frequent water intake blockage caused by marine biological invasion in many nuclear power plants, which affects the safety of cold source in nuclear power plants, a defense system in depth including marine biological identification, detection, early warning and hierarchical response is established through investigation and analysis of the causes of the event. Underwater acoustic high-resolution multi beam detection method is used to detect jellyfish, hairy shrimp and other marine organisms, The echo characteristics are obtained, the distribution density in sensitive sea area is analyzed, and the effective early warning judgment is given. Among them, detection and early warning is the front-end disposal measure, which can provide accurate and timely marine biological information for the subsequent hierarchical response. At the same time, it can replace the underwater inspection work of divers, reduce the risk of divers, reduce the risk of industrial safety, and improve the reliability of cold source.

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