ICONE11-36225 FUZZY LOGIC CONTROLLER ARCHITECTURE FOR WATER LEVEL CONTROL IN NUCLEAR POWER PLANT STEAM GENERATOR USING ANFIS TRAINING METHOD

2003 ◽  
Vol 2003 (0) ◽  
pp. 408
Author(s):  
Vosoughi Naser ◽  
Amir Hasan Ekrami ◽  
Naseri Zahra
Keyword(s):  
Fuzzy Logic ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Water Level ◽  
Steam Generator ◽  
Nuclear Power ◽  
Fuzzy Logic Controller ◽  
Training Method ◽  
Level Control

Fuzzy Logic Controller Architecture for Water Level Control in Nuclear Power Plant Steam Generator (SG) Using ANFIS Training Method

2002 ◽  
Author(s):  
Naser Vosoughi ◽  
Zahra Naseri
Keyword(s):  
Fuzzy Logic ◽  
Power Plant ◽  
Water Level ◽  
Steam Generator ◽  
Nuclear Power ◽  
Fuzzy Logic Controller ◽  
Training Method ◽  
Input Output ◽  
Level Control ◽  
Single Output

Since suitable control of water level can greatly enhance the operation of a power station, a Fuzzy logic controller architecture is applied to show desired control of the water level in a Nuclear steam generator. With regard to the physics of the system, it is shown that two inputs, a single output and the least number of rules ( 9 rules ) are considered for a controller, and the ANFIS training method is employed to model functions in a controlled system. By using ANFIS training method, initial member functions will be trained and appropriate functions are generated to control water level inside the steam generators while using the stated rules. The proposed architecture can construct an input – output mapping based on both human knowledge (in from of Fuzzy if – then rules) and stipulated input – output data. In this paper with a simple test it has been shown that the architecture fuzzy logic controller has a reasonable response to one step input at a constant power. Through computer simulation, it is found that Fuzzy logic controller is suitable, especially for the water level deviation and abrupt steam flow disturbances that are typical in the existing power plant. /3/, /6/

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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