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.

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.

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.

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.

scholarly journals Simulation of 25 Mwe Steam Power Plants Using Gate Cycle

2019 ◽  
Vol 8 (6) ◽  
pp. 2326-2630
Keyword(s):  
Power Plant ◽  
Thermal Efficiency ◽  
Input Data ◽  
Power Plants ◽  
Electrical Power ◽  
Steam Pressure ◽  
Steam Temperature ◽  
Steam Power ◽  
Main Input ◽  
Main Steam

Power plants using steam are a very popular system today. To develop a construction of power plant system requires an accurate analysis in determining operating parameters as expected. Designing with manual calculations certainly requires a very long time. One of faster method use a thermodynamic simulation system such as a Gate Cycle. The goal of this research was to simulate a steam power plant to produce 25 MW net electric power and to investigate the effect of an increasing of main steam temperature, main steam pressure and condenser pressure on electrical power and thermal efficiency. The simulation was done using the main input data of simulation were tempe rature of 535 0C, pressure of 89 bar, condenser pressure of 0.084 bar and heating value of low rank coal of 3800 kcal/kg. The main steam temperature was varied of 515; 535; 555 and 575 0C. The main steam pressure was varied of 79; 89; 99 and 120 bar, The condenser pressure was varied at 0.064; 0.074; 0.084 and 0.094 bar. The simulation results showed the net electric power produced of 25.8 MW on the main input data. An increasing of the main steam temperature and the main steam pressure would increase the net electrical power and the thermal efficiency but an increasing of condenser pressure would decrease the net electrical power and the thermal efficiency

Multivariable Predictive Control of the Main Steam Temperature in Power Plant

2013 ◽  
Vol 11 (4) ◽  
Author(s):  
Wang Ting ◽  
Feng Xiaolu ◽  
Chen Jiangtao ◽  
Zhang Rong ◽  
Xu Wei
Keyword(s):  
Power Plant ◽  
Predictive Control ◽  
Steam Temperature ◽  
Main Steam

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.

Material Selection of the Four Main Pipelines for 1000mW Ultra Supercritical Unit

2011 ◽  
Vol 148-149 ◽  
pp. 1399-1403
Author(s):  
Zhong Min Li ◽  
Zhi Li ◽  
Jun Guo
Keyword(s):  
Power Plant ◽  
Mechanical Performance ◽  
Material Selection ◽  
Steam Pressure ◽  
Feed Water ◽  
Main Pipelines ◽  
Stress Coefficient ◽  
Heat Expansion ◽  
Main Steam ◽  
Selection Of

The power plant boiler mainly constitutes of pipelines for the main steam, hot sections of the reheated steam, cold sections of the reheated steam and the high pressure feed water. Material selection of the four main pipelines will have a marked effect on the security and economy of the power plant operation. The main steam pressure of the 1000MW ultra supercritical unit in Tianjin SDIC Jinneng Electric Power CO.LTD is 27.56MPa, and the main steam temperature is 605°C. It puts forward the high mechanical performance and high temperature performance of the four main pipelines. The thermal stress, coefficient of the heat expansion, operation reliability and the economy, and the compositions of the four main pipelines material selection should be taken into account when demonstration is processed.

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