Neural Network Modeling For Main Steam Temperature System

2014 ◽  
Vol 69 (3) ◽  
Author(s):  
N. A. Mazalan ◽  
A. A. Malek ◽  
Mazlan A. Wahid ◽  
M. Mailah
Keyword(s):  
Neural Network ◽  
Dead Time ◽  
Steam Pressure ◽  
Neural Network Modeling ◽  
Steam Temperature ◽  
Power Plant Boiler ◽  
Plant Data ◽  
Main Steam ◽  
Generator Output ◽  
Plant Boiler

Main Steam Temperature (MST) is non-linear, large inertia, long dead time and load dependant parameters. The paper present MST modeling method using actual plant data by utilizing MATLAB's Neural Network toolbox. The result of the simulation showed the MST model based on actual plant data is possible but the raw data need to be pre-processed for better output. Generator output, total main steam flow, main steam pressure and total spray flow are four main parameters affected the behavior of MST in coal fired power plant boiler.

Main Steam Temperature Modeling Based on Levenberg-Marquardt Learning Algorithm

2013 ◽  
Vol 388 ◽  
pp. 307-311 ◽  
Author(s):  
Nor Azizi Mazalan ◽  
A.A. Malek ◽  
Mazlan A. Wahid ◽  
Musa Mailah ◽  
Aminuddin Saat ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Plant ◽  
Learning Algorithm ◽  
Steam Pressure ◽  
Steam Temperature ◽  
Plant Behavior ◽  
Temperature Modeling ◽  
Levenberg Marquardt ◽  
Main Steam ◽  
Generator Output

Main steam temperature is one of the most important parameters in coal fired power plant. Main steam temperature is often describe as non-linear and large inertia with long dead time parameters. This paper present main steam temperature modeling method using neural network with Levenberg-Marquardt learning algorithm. The result of the simulation showed that the main steam temperature modeling based on neural network with Levenberg-Marqurdt learning algorithm is able to replicate closely the actual plant behavior. Generator output, main steam flow, main steam pressure and total spraywater flow are proven to be the main parameters affected the behavior of main steam temperature in coal fired power plant.

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.

Research of Intelligent Control Policy on Main Steam Temperature of Thermal Power Plant

2011 ◽  
Vol 143-144 ◽  
pp. 307-311 ◽  
Author(s):  
Yu Feng Luo ◽  
Lu Lu Liu
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Control Strategies ◽  
Thermal Power ◽  
Network Control ◽  
Neural Network Control ◽  
Steam Temperature ◽  
Boiler Load ◽  
Fuzzy Neural ◽  
Main Steam

In view of the nonlinear, time-variable, long delay, large inertia character of main steam temperature system, the difficult point of control is summarized. The status quo of application study on main steam temperature by fuzzy control, neural network and fuzzy neural network control, genetic algorithms is introduced. And taking "the 600 MW concurrent boiler load in 100%" as an example, carry on the main steam temperature control simulation by means of Matlab/Simulink software. In this simulation, four control strategies are taking for the simulation experiment, and comparing with the traditional PID control algorithm. The simulation results prove that fuzzy neural network control strategies have good robustness, fast response, short setting time, and great potential for the control of main steam temperature.

Economic Analysis of a 660MW Supercritical Turbine for Steam Initial Parameters

2014 ◽  
Vol 960-961 ◽  
pp. 1550-1553 ◽  
Author(s):  
Yu Lin Tang ◽  
Shan Tu ◽  
Yang Du ◽  
Chao Wang ◽  
Hong Juan Wang
Keyword(s):  
Power Plant ◽  
Thermal Power ◽  
Steam Pressure ◽  
Heat Consumption ◽  
Operating Parameters ◽  
Steam Temperature ◽  
Operating Modes ◽  
Endothermic Process ◽  
Steam Parameters ◽  
Main Steam

Economic diagnosis of thermal power units is to determine the economy of its operating parameters and operating modes by quantitative and qualitative analysis, which is significant to economic operation and energy saving of power plant. On the basis of equivalent enthalpy drop method and the theory of variable conditions, the economic diagnosis model of operating parameters was established. As main steam temperature and main steam pressure for example, economic diagnosis of a 660MW supercritical steam turbine unit was performed. The result demonstrates that improving the main steam temperature or main steam pressure can reduce heat consumption of the unit. The essence of improving the initial steam parameters is to improve the average temperature of the steam cycle endothermic process, thus improving the circulation efficiency and reducing heat consumption. The economic impact of main steam temperature is up to 0.61g/(kW·h), while which of main steam pressure is little. Therefore, by increasing the initial steam parameters, especially the main steam temperature, to improve the economy of the entire power plant is the main way to enhance the efficiency of power plant in the current.

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