Main Steam Temperature Research Articles

Delay Separated Neural Network Inverse Control in Main-Steam Temperature System

Delay Separated Neural Network Inverse Control in Main-Steam Temperature System

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

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.

Power output estimation of a steam turbine model using ridge regression

ABSTRACTThis paper presents a ridge regression model for the prediction of output power in a steam turbine. The operating data above the 95% load level from the plant's past one year records were collected and validated to serve as a baseline performance data set. The threshold range determined from the baseline data set was used to detect signal errors in new operating data. Finally, the variables most strongly influence turbine-generator output were selected as inputs for the ridge regression based turbine model. After training and validation of key parameters, including main steam pressure, main steam temperature and main steam flow were used to estimate turbine-generator output. The effectiveness of the proposed ridge regression based turbine model was demonstrated using plant operating data obtained from a 40 MW thermal power plant. Results show that the ridge regression based turbine model is more reliable with the good estimation efficiency and the trend. This paper investigates the ridge regression problem in multi-collinear data. Regular and standardised ridge coefficients are compared with regular and standardised least squares coefficients. Ridge standard error is compared with least squares standard error. The eigen-vectors associated with each eigen value, incremental percentage, cumulative percentage, condition number of the regressors and variance inflation factors associated with the variables are discussed. Methods to choose biasing parameter k are also presented.

Research and Application of Intelligent Control Technology for Main Steam Temperature Regulation in Ultra-Supercritical Thermal Power Plants

Main steam temperature regulation is one of the most demanded control loop in modulating control system of thermal power plant. According to the characteristics of main steam temperature, an intelligent control technology has been proposed. To eliminate lag and inertia in main steam temperature regulation process, the intelligent control technology integrates several advanced algorithms. The application effects in several ultra-supercritical thermal power plants prove that the control technology has outstanding robustness and excellent adaptability in both variable load and steady state condition.

Main Steam Temperature Modeling Based on Levenberg-Marquardt Learning Algorithm

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.

Simulation of Main Steam Temperature Control System Based on Fuzzy PID and Improved Smith Predictor

The main steam temperature process has the characteristics of large inertia, large time delay and time-variation , to solve this problem, the fuzzy PID cascade control system with Smith predictor is presented .The improved Smith predictor with the effect of filtering was used in the main loop of the cascade control system, and fuzzy PID compound controller was used as the main controller based on the advantage of fuzzy control and PID. Through establishing the mathematical model and simulating the main steam temperature control system, either the parameter changed or invariable, the results show that the method can improve the control qualities and enhance the robustness and stability of the control system.

Research on Control Method for Intermediate Point Temperature of Supercritical Boiler Based on Active Disturbance Rejection Cascade Control

As the important signal of once-through boiler's water-fuel ratio control, intermediate point temperature of supercritical boiler plays a key role in the decision of steam and water’s boundary, the match of fuel and water, the control of main steam temperature. The condition of its control directly impacts the once-through boiler’s safety and economy running. For an uncertainty model, a good control quality can not be gained using the general PID controller when the unit load has great changes, so that the main steam temperature fluctuates greatly. According to this problem, a control system about intermediate point temperature of supercritical boiler based on active-disturbances-rejection multiple model was designed. Corresponding active disturbances rejection controller (ADRC) about different model in different operation points was designed. It can choose corresponding controller according to the operation condition so as to achieve an adaptive control of a wide range. The simulation results show that the system has a strong robustness and sufficient capability for control . DOI: http://dx.doi.org/10.11591/telkomnika.v11i7.2846 Full Text: PDF

Multivariable Predictive Control of the Main Steam Temperature in Power Plant

This paper aims at the problems existing in the cascade Proportional Integration Differential (PID) control of the main steam temperature system in power plants nowadays, such as large dynamic deviation, poor load adaptability. etc. The two multivariable model predictive controllers are designed and applied to control the second stage desuperheater inlet temperature and the main steam temperature, respectively, which is aimed by the simulation platform for 200 MW unit. The controlled object of the main steam temperature is simulated, and both predictive control and cascade PID control strategies are designed to control the object. The results of the stimulation experiments have shown that the predictive controller is superior to the traditional cascade PID controller in dynamic deviation and load adaptability of main steam temperature in power plant. DOI: http://dx.doi.org/10.11591/telkomnika.v11i4.2376

A Study of Fixed Steam Pressure Control of Ultra-Supercritical Unit

Ultra-supercritical (USC) unit is more and more popular these years for its advantages. In this paper, a model predictive control (MPC) method is introduced for coordinated control of USC unit running in fixed pressure mode. Three inputs (i.e. valve opening, coal flow and feedwater flow) are employed to control three outputs (i.e. load, main steam temperature and main steam pressure). Piecewise models of the USC unit are obtained using the three inputs and the three outputs. In simulation, the output power follows load demand quickly and main steam temperature can be controlled around the setpoint closely in load tracking control. The simulation results show the effeteness of the proposed methods.

Multi-model Switching Predictive Functional Control of Boiler Main Steam Temperature

Multi-model Switching Predictive Functional Control of Boiler Main Steam Temperature

Exergy and exergo-economic evaluation of Isfahan steam power plant

The objective of this paper is to perform the exergy and exergo–economic analysis of Isfahan steam power plant. A simulation program is used for modelling the Isfahan steam power plant in Iran. The exergy balance is used to estimate the exergy destruction in each component of the plant. Moreover, the cost balance equations are solved to determine the cost of exergy destruction in each part of the Isfahan STPP. The highest exergy destruction has mainly occurred in boiler and preheater1&condenser by almost 360.65 MW and 38.81 MW, respectively. In addition, the cost destruction for boiler is about 15090.8 US$/h and for preheater1&condenser is 6056.57 US$/h. Furthermore, the effect of key parameters such as the main steam temperature, reheated steam temperature, condenser pressure and number of the feed water heaters on the cycle performance as well as the final cost of electricity is determined. In particular, by decreasing the condenser, pressure from 0.7 to 0.05 bar the final cost of electricity increases from 7.81 US$/GJ to 9.87 US$/GJ. Accordingly, by increasing the number of the feed water heaters from none to 6, the electricity price will decrease from 19.53 US$/GJ to 8.89 US$/GJ.

The Main Steam Temperature Cascade PID Controller Parameters Tuning Optimization Based on Ant Colony Algorithm

The main steam temperature of the boiler adopted usually a cascade PID control. Traditional PID parameter engineering tuning method has a very high demand for the control personnel. And the control loop of inner and outer ring interacted. Adjusting wasted the time and labor, and the control effects were difficult to achieve the optimal. Adopting the ant colony algorithm can optimize Cascade PID controller parameters. A combination of multiple parameters was the ant colony's foraging path, and the moment integral of absolute error was the optimization objective function, through the simulation for an ant foraging process, the optimal combination of parameters was identified. The simulation results showed that the main steam temperature cascade PID controller based on ant colony algorithm has good control effect.

Multimodel Parameters Identification for Main Steam Temperature of Ultra-Supercritical Units Using an Improved Genetic Algorithm

AbstractA parameter identification method based on genetic algorithm (GA) is presented to solve the multimodel parameters identification problem for the main steam temperature of ultra-supercritical (USC) units. Linear ranking selection, nonuniform linear crossover, and Gaussian mutation are employed in the algorithm design to enhance the convergence speed and the accuracy of the identification. Besides, the uniform design method is executed to initialize the population and the sigmoid function with adaptation is applied to adjust the probabilities of crossover and mutation. Simulations carried out with the field operation data from Haimen USC units, including two processes—parameters identification and model verification. The simulation results show that the improved genetic algorithm performs well in global parameters searching and the proposed identification methodology offers good results for the multimodel parameters identification.

PID Control of Main Steam Temperature Used Quantum Particle Swarm Optimization

The main steam temperature is always an important indicator of the boiler operation quality, high or low will affect the quality of boiler operation. At first, introduce a algorithm PSO, which can used to optimize the PID parameters of a main steam temperature control system. Then, improved the PSO, and studied a kind of improved particle swarm algorithm—quantum apply quantum-behaved particle swarm optimization (QPSO). And this algorithm is used to optimize the PID parameters of a main steam temperature control system, got the best parameters. In the end, simulation result shows that, compared with basic particle swarm optimization (PSO),QPSO can make main steam temperature control system has a better control of quality, and improves the system of static and dynamic characteristics.

Transient Analysis of Supercritical Water-Cooled Reactor with Main Feed-Water Parameter Changing

Based on a transient analysis code that developed with coupled neutron and thermal hydraulic calculation model for supercritical water reactor, the transient characteristics under feed-water parameter changing is detailed analyzed, including temperature decreasing, flow-rate decreasing and pressure increasing. The results show that, if no control system action given, flow rate or temperature decreasing would lead to the increase of main steam temperature and decrease of core power, but pressure increasing shows a little influence. If control systems are put into operation, transient characteristics will be obviously changed but finally recovered to normal level. As the same changing percentage 3% is selected, main steam temperature during pressure decreasing transient can reach its highest level in 510.9°C, and return to normal at the time of 90s. But for feed-water temperature decreasing transient, main steam temperature shows an obvious fluctuation during its adjustment and returns to normal until 170s from 503°C. In addition, compared with the results calculated by non-coupling calculation model, main steam temperature calculated by above coupling model shows a smaller deviation but a little longer time needed for returning to its normal value.

Applied Research on Main Steam Temperature Control Based on Internal Model Control

Principles and characteristics of internal model control (IMC) are illustrated. It has a simple and general structure such as that of a PID controller. Applications of IMC based on single loop PID controller tuning in main steam temperature of boiler in power plant were investigated. It can also become easier to achieve in a distributed control system (DCS) via control module configuration. It was applied in boiler of power plant to tune the main steam temperature PID parameters. The application results demonstrate the validity of the proposed method.

The application of immune genetic algorithm in main steam temperature of PID control of BP network

In order to overcome the uncertainties, large delay, large inertia and nonlinear property of the main steam temperature controlled object in the power plant, a neural network intelligent PID control system based on immune genetic algorithm and BP neural network is designed. Using the immune genetic algorithm global search optimization ability and good convergence, optimize the weights of the neural network, meanwhile adjusting PID parameters using BP network. The simulation result shows that the system is superior to conventional PID control system in the control of quality and robustness.

Dynamic Model Identification of the Main Steam Temperature for Supercritical Once-through Boiler

In this paper the dynamic model of single-input and single-output (SISO) is established, which take the coal feed as the input and the main steam temperature as the output. Recursive extended least squares (RELS) was used to identify the system model, and the ideal identification results were achieved. The results showed that the output of identification and actual output in good agreement by usage of the recorded data to verify the accuracy of model further.

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

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

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.