Nuclear power plant fault diagnosis based on genetic-RBF neural network

2006 ◽  
Vol 5 (3) ◽  
pp. 57-62 ◽  
Author(s):  
Xiao-cheng Shi ◽  
Chun-ling Xie ◽  
Yuan-hui Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Rbf Neural Network

Fault diagnosis of nuclear power plant based on genetic-RBF neural network

2010 ◽  
Vol 39 (1/2/3) ◽  
pp. 159 ◽  
Author(s):  
Chun Ling Xie ◽  
Jen Yuan Chang ◽  
Xiao Cheng Shi ◽  
Jing Min Dai
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Rbf Neural Network

Application of Fuzzy Neural Network to Fault Diagnosis of Sensor with Mechanical Properties in Nuclear Power Plant

2013 ◽  
Vol 644 ◽  
pp. 68-71 ◽  
Author(s):  
Jin Yang Li ◽  
Hong Xia
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Fault Diagnosis ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Fuzzy Neural Network ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Fuzzy Neural

In view of the sensor fault in nuclear power plant, it puts forward a method to fault diagnosis of sensor with mechanical properties based on fuzzy neural network. The method would be fuzzy logic control combined with neural network. It adjusted and corrected membership function parameters and network weights with back propagation algorithm. After the completion of fuzzy neural network training, it could get the credibility of sensor with mechanical properties real time. Taking pressurizer water-level sensor as the case, the simulation experiment results showed that the method is valid for the fault diagnosis of sensor with mechanical properties in nuclear power plant.

The Research of PWR Steam Generator with Water Level Control Based on RBF Neural Network

2014 ◽  
Vol 1014 ◽  
pp. 344-350
Author(s):  
Xu Hong Yang ◽  
Yang Jian ◽  
Cheng Chen Feng ◽  
Yang Xue
Keyword(s):  
Neural Network ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Water Level ◽  
Steam Generator ◽  
Nuclear Power ◽  
Rbf Neural Network ◽  
Stable Operation ◽  
Level Control ◽  
Non Linear System

In the steam generator with water level control system of nuclear power plant, there are various uncertainties in the controlled devices. Any actual system had certain nonlinear. Because of the steam generator is very important equipment in nuclear power plants, water level control plays a decisive role for the safe operation of nuclear power plant and it required stable operation and fast response of the whole system. For the highly complex, non-linear system , the traditional cascade PID control had been used cannot obtain satisfactory control effect, this paper try to use RBF neural network to optimize the PID parameters. The simulation experiments show that: the rbf neural network optimized controller made the control system’s robustness and control quality superior than the traditional PID controller, and described the method can be applied more widely.

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