A Novel RBF-PID Control Strategy for Main Steam Temperature Based on QPSO Algorithm

2017 ◽  
Author(s):  
Yue-Chao Wang ◽  
Feng-Ping Pan ◽  
Ling-Ling Shi ◽  
Zhi-Qiang Pang ◽  
Juan-Juan Ren ◽  
...  
Keyword(s):  
Control Strategy ◽  
Pid Control ◽  
Steam Temperature ◽  
Main Steam

Review of Control Strategies Employing Neural Network for Main Steam Temperature Control in Thermal Power Plant

2014 ◽  
Vol 66 (2) ◽  
Author(s):  
N. A. Mazalan ◽  
A. A. Malek ◽  
Mazlan A. Wahid ◽  
M. Mailah
Keyword(s):  
Neural Network ◽  
Power Plant ◽  
Temperature Control ◽  
Pid Control ◽  
Thermal Power Plant ◽  
Thermal Power ◽  
Heat Rate ◽  
Steam Temperature ◽  
Multiple Variables ◽  
Main Steam

Main steam temperature control in thermal power plant has been a popular research subject for the past 10 years. The complexity of main steam temperature behavior which depends on multiple variables makes it one of the most challenging variables to control in thermal power plant. Furthermore, the successful control of main steam temperature ensures stable plant operation. Several studies found that excessive main steam temperature resulted overheating of boiler tubes and low main steam temperature reduce the plant heat rate and causes disturbance in other parameters. Most of the studies agrees that main steam temperature should be controlled within ±5 Deg C. Major factors that influenced the main steam temperature are load demand, main steam flow and combustion air flow. Most of the proposed solution embedded to the existing cascade PID control in order not to disturb the plant control too much. Neural network controls remains to be one of the most popular algorithm used to control main steam temperature to replace ever reliable but not so intelligent conventional PID control. Self-learning nature of neural network mean the load on the control engineer re-tuning work will be reduced. However the challenges remain for the researchers to prove that the algorithm can be practically implemented in industrial boiler control.

Generalized Predictive PID Control for Main Steam Temperature Based on Improved PSO Algorithm

2017 ◽  
Vol 21 (3) ◽  
pp. 507-517 ◽  
Author(s):  
Zhongda Tian ◽  
◽  
Shujiang Li ◽  
Yanhong Wang
Keyword(s):  
Pid Control ◽  
Pid Controller ◽  
Particle Swarm Optimization Algorithm ◽  
System Control ◽  
Pd Controller ◽  
Control Performance ◽  
Control Loop ◽  
Steam Temperature ◽  
Swarm Optimization ◽  
Main Steam

The large inertia and long delay characteristics of main steam temperature control system in thermal power plants will reduce the system control performance. In order to improve the system control performance, a generalized predictive PID control for main steam temperature strategy based on improved particle swarm optimization algorithm is proposed. The performance index of incremental PID controller of main control loop and PD controller of auxiliary control loop based on generalized predictive control algorithm is established. An improved particle swarm optimization algorithm with better fitness and faster convergence speed is proposed for online parameters optimization of performance index. The optimal control value of PID controller and PD controller can be obtained. The simulation experiment compared with fuzzy PID and fuzzy neural network is carried out. Simulation results show that proposed control method has faster response speed, smaller overshoot and control error, better tracking performance, and reduces the lag effect of the control system.

Based on the BP Neural Network-PID Series Control Boiler Main-Steam Temperature System Research

2011 ◽  
Vol 354-355 ◽  
pp. 968-973 ◽  
Author(s):  
Wen Zhu ◽  
Jian Ping Sun
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Cascade Control ◽  
Steam Temperature ◽  
Control Effect ◽  
Real Time System ◽  
Main Steam ◽  
Neural Network Pid

Due to the boiler main-steam temperature system exists more serious characteristics,such as much capacitive,nonlinear,time-varying and lag, so adopt cascade control strategy. This paper design a control algorithm which is based on BP neural network, it can accelerate the regulating time, and combined with the conventional PID controller, constitute the BP neural network - PID cascade control strategy. This control strategy not only contain the BP neural network control in real time system strong anti-interference ability characteristic, but also fully utilize the PID controller response speed characteristic. The simulation results show that based on the BP neural network - PID series control boiler main-steam temperature system can achieve satisfactory control effect.

Adaptive Fuzzy PID Control Design and Simulate of Main Steam Temperature of Power Plant

2011 ◽  
Vol 58-60 ◽  
pp. 2472-2476
Author(s):  
Qi Hua Cheng ◽  
Nai Jun Xie
Keyword(s):  
Pid Control ◽  
Large Scale ◽  
Control Method ◽  
Thermal Power ◽  
Human Mind ◽  
Fuzzy Pid ◽  
Steam Temperature ◽  
Fuzzy Pid Control ◽  
Knowledge Reasoning ◽  
Main Steam

Large-scale thermal power units in the main steam temperature as the controlled object with a large delay, large inertia, nonlinear and time-varying characteristics, leads to its control difficult, and make traditional PID control method is difficult to achieved good. Fuzzy systems articulate knowledge, reasoning similar to the human mind the advantages of fuzzy PID control strategy is proposed and used as a powerful mathematical ability of fuzzy PID control system simulation, the result indicate the fuzzy PID algorithm has the ability of fast, immunity, and robust.

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