Water Level Control of Steam Generator in Nuclear Power Plant Based on Intelligent MFAC-PID

2021 ◽  
Author(s):  
Yonglu Du ◽  
Haotian Li ◽  
Minrui Fei ◽  
Ling Wang ◽  
Pinggai Zhang ◽  
...  
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Water Level ◽  
Steam Generator ◽  
Nuclear Power ◽  
Level Control

Steam Generator Water Level Intelligent Control of PWR Nuclear Power Plant Based on Feed Water Regulation

2014 ◽  
Vol 644-650 ◽  
pp. 3616-3619
Author(s):  
Xu Hong Yang ◽  
Jian Yang ◽  
Ya Nan Wang ◽  
Yang Xue
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Water Level ◽  
Steam Generator ◽  
Intelligent Control ◽  
Nuclear Power ◽  
Feed Water ◽  
Level Control ◽  
Control Effect ◽  
Important Indicator

Water level control of the steam generator is an important indicator of the safe operation of nuclear power plant. The traditional PID controller system has the disadvantages of large amount of overshoot, long adjusting time, etc. Steam generator has complex, nonlinear and time-varying characteristics. This article proposes the adopting the BP neural network intelligent control algorithm. The simulation experiments results indicated that: Comparing with traditional PID control it has smaller overshoot and shorter adjustment time, more ideal control effect c.

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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