Steam Temperature In Power Plant Research Articles

The control of superheater steam temperature in power plants using model predictive controller

The temperature of the steam produced by the superheater has been regarded as one of the most crucial parameters for steam power plant regulation. It must be precisely regulated within a restricted temperature range; overheating would result in the degradation of the substance. The recommended hybrid technique employed the Model Predictive Controller and Improved Fruit Fly Optimization Algorithm to regulate temperature levels and guarantee stability. Using the proposed method, model-based predictive control aims to enhance control quality and identify the optimal value. By employing this approach, the superheater can attain the optimal configurations for the steam temperature plant, thereby simultaneously enhancing the stability of the plant. The principal aim of this manuscript is to attain a reduction in the Integral Absolute Error to 40.6%. The simulation was conducted using the MATLAB/Simulink platform, and the outcomes demonstrate that the proposed method exhibits superior performance compared to those that have been documented in the literature.

A hybrid technique to control superheater steam temperature in power plants using multi modeling and predictive sliding mode control

One of the most critical variables to control in a steam power plant is superheater steam temperature. It must be carefully managed within a narrow temperature range; too high temperatures will cause material degradation. Temperature variation impacts the plant's efficiency, causing unplanned disturbances, because to frequent and intense load fluctuations and stringent requirement for performance and safety. To control the temperature level and maintain stability, the proposed hybrid approach is applied with MM-PSMC in this research. Fuzzy Logic Controller (FLC) and Golden Eagle Optimization [GEO] are combined in the suggested hybrid approach. Using the GEO technique, the FLC aims to increase control quality and identify the optimum value. This technique enables the superheater to attain the best-fit settings for the steam temperature plant while also increasing the plant's stability. The primary goal of this paper is to lower the IAE index at the superheater temperature plant. Finally, the MATLAB/Simulink platform provides steady-state values of the control levels of the superheater steam temperature under various scenarios. The suggested technique is compared to existing methods such as RNN with EHO, RNN with PSO, and ANN. Simulation results shows the proposed method gives good results Maximum 160 MW of power is generated and when the set value intelligent optimization compensation method is employed, the SST's control quality is dramatically improved—by 90 % when compared to the initial control—in terms of overshoot, control error, and relation to risk management. The proposed method controls the temperature at 535°C compared to existing methods.

Stochastic configuration network based cascade generalized predictive control of main steam temperature in power plants

The main steam temperature (MST) in power plants suffers from nonlinearity and large time delay, which cause large overshoot and long settling time under widely used cascade proportion integration differentiation (PID) controller. In order to cope with the negative effects, we propose a stochastic configuration network (SCN) based cascade generalized predictive control (GPC) scheme to improve the performance of the MST. A three-layer SCN is employed to model the MST process. The SCN is constructed by two phases, i.e., initial phase and real-time phase. The initial phase determines the structure and primary parameters of the learner model using the stochastic configuration algorithm. The real-time phase employs weighted recursive least squares (WRLS) for building the real-time MST process model for GPC design. Taking into account some constraints of the MST process, Karush–Kuhn–Tucker (KKT) conditions are applied for solving the constrained receding-horizon optimization problem. The derived explicit solutions of GPC avoid the implicit form which usually has to be solved iteratively. Comparative simulations demonstrate the superiority of the proposed SCN based cascade GPC (SCN-CGPC). Finally, the proposed SCN-CGPC is implemented via a standalone external MST control system in a power plant. The effectiveness and practicability are validated with the real-world application.

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

Automatic T-S fuzzy model with application to designing predictive controller

A novel methodology is proposed to automatically extract T-S fuzzy model with enhanced performance using VABC-FCM algorithm, a novel Variable string length Artificial Bee Colony algorithm (VABC) based Fuzzy C-Mean clustering technique. Such automatic methodology not requires a priori specification of the rule number and has low approximation error and high prediction accuracy with appreciate rule number. Afterward, a new predictive controller is then proposed by using the automatic T-S fuzzy model as the dynamic predictive model and VABC as the rolling optimizer. Some experiments were conducted on the superheated steam temperature in power plant to validate the performance of the proposed predictive controller. It suggests that the proposed controller has powerful performance and outperforms some other popular controllers.

Convenient T–S fuzzy model with enhanced performance using a novel swarm intelligent fuzzy clustering technique

To automatically extract T–S fuzzy models with enhanced performance from data is an interesting and important issue for fuzzy system modeling. In this paper, a novel methodology is proposed for this issue based on a three-step procedure. Firstly, the idea of variable length genotypes is introduced to the artificial bee colony (ABC) algorithm to derive a so-called Variable string length Artificial Bee Colony (VABC) algorithm. The VABC algorithm can be used to solve a kind of optimization problems where the length of the optimal solutions is not known as a priori. Secondly, fuzzy clustering without knowing cluster number as a priori is viewed as such kind of optimization problem. Thus, a novel version of Fuzzy C-Means clustering technique (VABC-FCM), holding powerful global search ability, is proposed based on the VABC algorithm. Use of VABC allows the encoding of variable cluster number. This makes VABC-FCM not require a priori specification of the cluster number. Finally, the proposed VABC-FCM algorithm is used to extract T–S fuzzy model from data. Such VABC-FCM based convenient T–S fuzzy model extraction methodology does not require a specification of rule number as a priori. Some artificial data sets are applied to validate the performance of the convenient T–S fuzzy model. The experimental results show that the proposed convenient T–S fuzzy model has low approximation error and high prediction accuracy with appreciate rule number. Moreover, the convenient T–S fuzzy model is used to model the characteristics of superheated steam temperature in power plant, and the results suggest the powerful performance of the proposed method.

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

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.

Boiler Modelling and Optimal Control of Steam Temperature in Power Plants

Achieving accurate control of main steam temperature is a very difficult task in Thermal power plants due to the large process lag (8 to 10 minutes) associated with the superheater system. A control oriented boiler model and an appropriate optimal control strategy are the essential tools for improving the accuracy of this control system. This paper offers a comprehensive integrated 8 th order mathematical model for the boiler and a Kalman Filter based state predictive controller for effectively controlling the main steam temperature and to enhance the efficiency of the boiler. In order to demonstrate the effectiveness of the control system, three more advanced control methods are experimented with the boiler model - Pole placement controller, Optimal controller with state observer and Optimal controller with Kalman filter. Simulation results have illustrated that the Predictive controller method with Kalman filter state estimator and predictor is the most appropriate one for the optimization of main steam temperature control. At present, we are in the process of implementing this control strategy in running Thermal power plants.

Autotuning PID Controller for Superheated Steam Temperature in Power Plants

An autotuning PID controller is applied to control the superheated steam temperature in a heat recovery steam generator (HRSG) of a combined cycle power plant. The autotuning PID controller has been evaluated using a HRSG non-lineal model. The result obtained in the simulation indicated that the autotuning PID controller exhibits a better performance than to the conventional PI controller well tuned. The autotuning PID controller consists of the following main modules: parameters estimation, tuning strategy, gain-scheduling control, supervision level and user interface

Advancing Boiler Steam Conditions

Factors preventing the advancement of steam temperature in power plant are discussed. The construction of a coal-fired pilot plant designed to superheat steam to 760 deg C (1400 deg F) is described and the results of corrosion trials analyzed. Conclusions are drawn from the experiments relating to possible advances in design.