NEURAL NETWORK MODEL FOR ON-LINE THERMAL MARGIN ESTIMATION OF A NUCLEAR POWER PLANT

1992 ◽  
Author(s):  
Hyun-Koon Kim ◽  
Seung-Hyuk Lee ◽  
Soon-Heung Chang
Keyword(s):  
Neural Network ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Network Model ◽  
Neural Network Model ◽  
Nuclear Power ◽  
On Line

On-line restoration for turbulence degraded video in nuclear power plant reactors

2013 ◽  
Vol 9 (3) ◽  
pp. 601-610 ◽  
Author(s):  
Nicolas Paul ◽  
Antoine de Chillaz ◽  
Jean-Luc Collette
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
On Line

Study on Sensor with Mechanical Properties in Nuclear Power Plant with Application of BP Neural Network to Fault Tolerant Control

2013 ◽  
Vol 644 ◽  
pp. 56-59
Author(s):  
Jin Yang Li ◽  
Hong Xia ◽  
Shou Yu Cheng
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Bp Neural Network ◽  
Nuclear Power ◽  
Control Method ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Level Sensor

All kinds of sensor with mechanical properties often can go wrong in nuclear power plant. In this kind of situation, it puts forward a kind of active fault tolerant control method based on the improved BP neural network. Firstly, the method will train sensor by BP neural network. Secondly, it will be established dynamic model bank in all kinds of running state. The system will be detected by using BP neural network real time. When the sensor goes wrong, it will be controled by reconstruction. Taking pressurizer water-level sensor as the case, a simulation experiment was performed on the nuclear power plant simulator. The results showed that the proposed method is valid for the fault tolerant control of sensor in nuclear power plant.

On-line neuro-expert monitoring system for Borssele Nuclear Power Plant

2003 ◽  
Vol 43 (1-4) ◽  
pp. 397-404 ◽  
Author(s):  
K. Nabeshima ◽  
T. Suzudo ◽  
S. Seker ◽  
E. Ayaz ◽  
B. Barutcu ◽  
...  
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Monitoring System ◽  
Nuclear Power ◽  
On Line

Artificial Neural Network Model for Diagnosing the Performance and the Conditions of Air-Operated Valves

2020 ◽  
Author(s):  
Taeyun Kim ◽  
Jangbom Chai ◽  
Chanwoo Lim ◽  
Ilyoung Han
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Physical Models ◽  
Artificial Neural

Abstract Air-operated valves (AOVs) are used to control or shut off the flow in the nuclear power plants. In particular, the failure of safety-related AOV could have significant impacts on the safety of the nuclear power plants and therefore, their performances have been tested and evaluated periodically. However, the current method to evaluate the performance needs to be revised to enhance the accuracy and to identify defects of AOV independently of personal skills. This paper introduce the ANN (Artificial Neural Network) model to diagnose the performance and the condition altogether. Test facilities were designed and configured to measure the signals such as supply pressure, control pressure, actuator pressure, stem displacement and stem thrust. Tests were carried out in various conditions which simulate defects with leak/clogged pipes, the bent stem and so on. First, the physical models of an AOV are developed to describe its behavior and to parameterize the characteristics of each component for evaluating the performance. Secondly, CNN (Convolutional Neural Network) architectures are designed considering the developed physical models to make a lead to the optimal performance of ANN. To train the ANN effectively, the measured signals were divided into several regions, from each of which the features are extracted and the extracted features are combined for classifying the defects. In addition, the model can provide the parameters of maximum available thrust, which is the key factor in periodic verification of AOV with the required accuracy and classify more than 10 different kinds of defects with high accuracy.

Deep rectifier neural network applied to the accident identification problem in a PWR nuclear power plant

2019 ◽  
Vol 133 ◽  
pp. 400-408 ◽  
Author(s):  
Marcelo Carvalho dos Santos ◽  
Victor Henrique Cabral Pinheiro ◽  
Filipe Santana Moreira do Desterro ◽  
Renato Koga de Avellar ◽  
Roberto Schirru ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Identification Problem
Sign in / Sign up

Export Citation Format

Share Document