Main Steam Temperature Control Research Articles

A novel community development algorithm and its application to optimize main steam temperature of supercritical units

This paper summarizes the community where people live development laws and proposes a novel meta-heuristic called community development algorithm (CDA). To enhance the optimization process, CDA innovatively designed two unique attraction effects based on the degree of people’s direct contribution to society and abstracted four novel inter-particle learning ways based on people’s four social activities: Marriage, Entrepreneurship, Working, and University. Theoretically, it is demonstrated that CDA can satisfactorily balance the exploration and the exploitation stages, and necessary and sufficient conditions for CDA convergence are derived. The 59 functions of Congress on Evolutionary Computation 2014 and 2017 are introduced as the benchmark. Under the pressure of other 32 well-known meta-heuristics, statistical results, including averages, standard deviations, rank-sum tests, and sign-rank tests, as well as graphical results, including convergence curves and box plots, confirm that CDA is competent for solving numerical optimization problems with efficiency and reliability. Significantly, the quantitative analysis demonstrated that CDA’s average ranking is ahead of the emperor penguin optimizer by 73.5% while ahead of the fully informed search algorithm by 62.1%, respectively. Additionally, the scalability and fastness of CDA are validated by scalability and wall clock time analysis, and the classical application of Congress on Evolutionary Computation 2011 demonstrates the outstanding accuracy and stability of CDA. Finally, CDA is utilized to optimize the controller parameter in the main steam temperature control system of the supercritical unit. The control response curves show that CDA generally provides better parameters than compared algorithms, demonstrating the merits and potential to solve complex real-world engineering problems.

Design of Multi-Input Multi-Output Non-linear Model Predictive Control for Main Steam Temperature of Super Critical Boiler

Flexible operation of coal-fired power plants is becoming increasingly necessary for successful integration of large-scale renewable power generation into the power grid. The maximum ramp rate and the number of load cycles are generally limited by the thermal stress experienced by the boiler pressure parts, turbine metallurgy and creep and fatigue of critical thick-walled components Main steam temperature is a critical operating parameter that must be controlled within acceptable limits for safe operation. Main steam temperature deviation beyond acceptable limit has impact on boiler pressure parts and turbine material of construction due to creep and fatigue effect. Base load operating units do not require steep ramp rate and hence recommended ramping rates are kept low within the safe operating zone in comparison to the flexible operation of the units with wide range load change width. Thermal stresses are caused by the temperature changes inside the thick-walled components and turbine steam admission parameters. Hence, the quality of main steam temperature control plays a vital role in flexible operation of the coal fired units. Conventional cascaded PID temperature control loop architecture performs well at steady state condition within a limited variation of load change at low ramp rate but it acts slowly and performs poorly at transient operating conditions of flexible operation of the boiler turbine with wide range load variation and load cycle with high ramp rate and remains far from rated conditions. In this paper, a Multi-Input Multi-Output (MIMO) Non-linear Model Predictive Control (MPC) design for regulation of the main steam temperature of a Once-Through supercritical Boiler is proposed. The controller is based on a non-linear dynamic model which incorporates dynamics of the variables of interest. It has the capability to operate effectively across a wide load range while maintaining main steam temperature within acceptable limits. A notable advancement in this design of MPC is the incorporation of coal flow demand and feedwater flow demand as additional control inputs alongside primary and secondary spray flows. In simulation test cases, the MPC controller demonstrates satisfactory performance and computational efficiency.

Short-Term Prediction of the Intermediate Point Temperature of a Supercritical Unit Based on the EEMD–LSTM Method

The quality of the intermediate point temperature control of a supercritical unit is directly related to the quality of the coal–water ratio and main steam temperature control of the supercritical unit, which is also related to the economy and safety of the unit. In order to improve the accuracy of short-term predictions of the intermediate point temperature, a short-term prediction model of the intermediate point temperature based on the EEMD (Ensemble Empirical Mode Decomposition)-LSTM (Long Short-Term Memory) model is proposed. This model uses the data of a 600 MW thermal power station in 2022 as a sample. The EEMD method is used to decompose the historical data into IMF components and residual components, and the correlation between each component and the original data is calculated. The relevant components are sent to the LSTM neural network, and all the sub-components are superimposed to obtain the final intermediate point temperature prediction results. At the same time, the BP and LSTM models are built to compare the errors with the proposed model. The results show that the single model will produce large errors when predicting the factors of large data fluctuations. The EEMD–LSTM coupling model can fully extract the detailed features and the prediction effect is obvious. The prediction accuracy of the EEMD–LSTM prediction model built in this paper is significantly better than that of the other two models. It has certain application value in the research field of intermediate point temperature prediction and can meet the requirements of short-term predictions of the intermediate point temperature.

An LSTM-PSO-Based Predictive Strategy and Its Application in the Main Steam Temperature Control in Power Plants

We proposed an LSTM-PSO predictive controller strategy to overcome the large delay and strong disturbance during main steam temperature control. First, the long short-term memory (LSTM) network model was developed to forecast the future trend of mainstream temperature, and the deviation between the model output and the expected value after feedback correction was minimized by the particle swarm optimization (PSO) algorithm. Then, the LSTM-PSO predictive controller was taken as the primary controller to maintain the steam temperature of the coal-fired boiler. A comparative simulation with conventional PID cascade control and dynamic matrix controller strategies was given, and the test calculations indicate that the adjustment time was shortened and the overshoot was reduced. Finally, the LSTM – PSO predictive control strategy was deployed into the actual boiler operation process of a 300 MW coal-fired power plant, within 0.5 °C steady-state error and 2 °C dynamic error.

Research on active disturbance rejection control of the main steam temperature of supercritical thermal power unit based on improved particle swarm optimization

The stability of the boiler’s main steam temperature is very important for the safe operation of the unit. At present, the mainstream PID control scheme is simple to use and convenient to set, but it cannot accomplish a acceptable ascendancy effect in the face of the main steam temperature control structure; with a long time delay, large inertia. In view of this situation, first of all, this paper introduces the linear active disturbance rejection control (LADRC) technology in a steam temperature control structure. The main loop adopts the linear active disturbance rejection controller and the subloop adopts the P-controller. Then, the particle swarm optimization algorithm is improved and used to modify the parameter of the LADRC, so that the LADRC system adjusted according to the theory can better complete the control task. Finally, a simulation to control the temperature is given, and the results show that the LADRC control system can track quickly and stably; in addition, as against with this traditional PID control strategy, it can achieve strong anti-interference performance.

Main Steam Temperature Control Based on Variable Universe Fuzzy Dynamic Matrix Control

Main Steam Temperature Control Based on Variable Universe Fuzzy Dynamic Matrix Control

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.

A novel linear active disturbance rejection controller for main steam temperature control based on the simultaneous heat transfer search

The main steam temperature of boiler outlet has been deemed as a significant parameter of the safety and economic performances in the thermal power plant operation. The complex working status of the thermal generation endures highly uncertain factors and remarkable disturbance, which call for effective controlling approaches in the corresponding temperature management. The linear active disturbance rejection controller (LADRC) is a conducive and powerful controlling method, whereas strong correlation between LADRC parameters leads to difficulties in optimally determining the controller parameters. Aiming at eliminating the negative effects on main steam temperature control caused by uncertainties factors and disturbances, a high performance LADRC based on a novel parameters optimization strategy, the simultaneous heat transfer search (SHTS) algorithm, is designed to deliver a stability, rapidity, and precision of control process. In the presented SHTS algorithm, all the three phases of heat transfer are randomly and parallel operated, providing a significant improvement towards the optimization performance. The proposed algorithm is first verified on various benchmark functions contrasted to state-of-the-art counterparts in performance validating, and then adopted in the parameter selection of LADRC in the main steam temperature control system. The excellent control performance, strong robustness and disturbance rejection ability of the designed approach are illustrated through the simulation results on main steam temperature control system.

Study on Temperature Control Strategy of Boiler Main Steam for Frequent Action of AGC Command in Power Grid

Traditional PID is widely used in the main steam temperature control system of thermal power unit Cascade control. Although this control scheme is easy to be debugged on site, it is impossible to solve the problem of large fluctuation and over temperature of steam temperature only by adjusting PID parameters due to the characteristics of variable parameters, large inertia and large time delay of main steam temperature control system. Especially when the AGC command of power grid fluctuates frequently, the conventional control strategy is difficult to deal with, resulting in the change of main steam temperature regulation quality Poor, affecting unit economy and safety. In this paper, the sub PID set value adaptive control logic and desuperheating water injection logic to prevent the main steam temperature overshoot are used to solve the problem of the quality deterioration of the main steam temperature regulation under the frequent fluctuation of AGC instructions.

Multi Model Robust PID Control of Main Steam Temperature based on Gap Metric

In order to adapt to the current construction of smart power plants, thermal power plants put forward higher requirements for the main steam temperature control. The traditional cascade PID control scheme is difficult to achieve satisfactory control quality. In this paper, several models of the main steam temperature plant are classified according to the gap metric, and then the corresponding robust PID controller is designed for different models. The simulation results show that the multi model robust PID control based on gap metric can effectively solve the large delay and nonlinearity of the main steam temperature plant, and the control performance is better than the cascade PID control, which is worth popularizing in engineering application.

An Efficient Robust Predictive Control of Main Steam Temperature of Coal-Fired Power Plant

Regulating performance of the main steam temperature (MST) system concerns the economy and safety of the coal-fired power plant (CFPP). This paper develops an offset-free offline robust model predictive control (RMPC) strategy for the MST system of CFPP. Zonotope-type uncertain model is utilized as the prediction model in the proposed RMPC design owing to its features of higher accuracy, compactness of representation and less complexity. An offline RMPC aiming at the system robustness and computational efficiency is then developed to maintain the desired steam temperature in case of wide operating condition change. The proposed RMPC is realized by two stages: in the first stage, the RMPC law set, which is the piecewise affine (PWA) of the MST system state is designed offline; then in the second stage, the explicit control law is selected online according to the current state. To achieve an offset-free tracking performance, a manipulated variable target observer is employed to update the chosen RMPC law. The control simulations using on-site operating data of a 1000 MW ultra-supercritical power plant show that the proposed approach can achieve satisfactory control performance and online computation efficiency even under complicated operating conditions.

The Control System of Boiler Main Steam Temperature Based on Heat Transfer Calculation

The cascade PID control system used in the main steam temperature control of traditional thermal power plants has large hysteresis and large inertia characteristics. In order to solve the above problems, the program of heat transfer process of main steam and boiler flue gas is built based on Matlab. The amount of desuperheated water obtained by the program ensures that the main steam temperature of the boiler is the target temperature. Then the heat transfer calculation program is imported into the multi-model switching control system simulation platform based on heat transfer calculation built by Simulink. The results of simulation show that the multi-model switching control system based on heat transfer calculation solves the problem of large inertia and large delay of the main steam temperature successfully, and the control system can adapt to changes in boiler load.

Analysis of boiler main steam temperature control system with multi-model switching

The paper analyzes the main steam temperature of the boiler with multi-model switching control based on the PID control system. According to the calculation result of the multi-model switching index, when the boiler load changes, the switcher will switch the controlled object of the boiler to the closest corresponding control model under the typical load. Moreover, the function of the leading zone and the function of the inertia zone of the PID control system are integrated into an equivalent first-order inertia plus pure lag function. Comparing these two methods, it is concluded that the control system with the first-order inertia plus pure lag function has a better control effect.

Fuzzy-PID controller based on variable universe for main steam temperature system

ABSTRACTThe high inertia and long delay characteristics of main steam temperature control system in the thermal power plants will reduce the system control performance. In order to improve the control performance, a Fuzzy-PID control strategy based on variable universe for main steam temperature system is proposed. Variable universe method is adopted to solve the local parameters self-regulation optimisation problem of conventional PID controller. Variable universe guarantees that the parameters of the system are global optimal. It solves the problem that the fuzzy controller cannot guarantee the high accuracy under the given rules. The scale factor is chosen according to fuzzy rules. The advantages of variable universe and Fuzzy-PID are combined. Fuzzy-PID controller based on variable universe is established. Compared with other control methods, the simulation experiments are carried out. Simulation results show that Fuzzy-PID controller based on variable universe has faster response speed, smaller overshoot and error, better tracking performance and reduces the lag effect of the control system.

Simulation of Main Steam Temperature Control System Based on Neural Network

Simulation of Main Steam Temperature Control System Based on Neural Network

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

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.

Main steam temperature control based on GA-BP optimised fuzzy neural network

The high inertia and long time-delay characteristics of main steam temperature control system in a thermal power plant will reduce the system control performance. In order to solve this problem, a genetic algorithm-back propagation (GA-BP) optimised fuzzy neural network control strategy is proposed in this paper. Gauss function is chosen as membership function and fuzzy neural network is designed. GA combined with BP algorithm is chosen for the offline parameters optimisation of fuzzy neural network, and then BP algorithm is used for online parameters optimisation. GA-BP optimisation algorithm overcomes the shortcomings of GA algorithm or BP algorithm which is used to adjust the parameters of fuzzy neural network controller. The simulation experiment compared with cascade PID and fuzzy neural network is carried out. Simulation results show that the controller based on GA-BP optimised fuzzy neural network has faster response speed, smaller overshoot and error, better tracking performance, and reduces the lag effect of the control system under different load, working conditions and membership functions.

Main steam temperature control based on GA-BP optimised fuzzy neural network

Main steam temperature control based on GA-BP optimised fuzzy neural network

Study on an improved algorithm for optimization of PID parameters

The improved ant colony algorithm is the hybrid algorithm consisting of the genetic algorithm and ant colony algorithm convergence. Through the introduction of the gauss mutation, we achieve the goal of improving ant colony algorithm. Using coal-fired power plant unit as main steam temperature controlled object, we design the PID controller based on improved ant colony algorithm. And setting of PID parameters by Z - N method has carried on the comparative analysis of the main steam temperature control system. Simulation results show that PID optimization based on improved ant colony algorithm can greatly improve the dynamic performance of the control system. So we verify the sophistication and effectiveness of the algorithm.

Performance Assessment of Power Plant Main Steam Temperature Control System based on ADRC Control

This paper makes an assessment of the typical main steam temperature system in thermal power plant. Due to main steam temperature system is a cascade control system. So, it should be assessed with the performance assessment method of cascade control systems. So firstly, this paper deduces the theoretical process of performance assessment of cascade control system based on minimum variance. Then with this method assesses the performance of four typical loads in thermal power plants when using active disturbance rejection-proportion (ADRC-P) cascade control and smith active disturbance rejection-proportion (SADRC-P) cascade control respectively. Finally, makes comparison of the results. Results show that, when using the same control strategy, different loads has different performance. Load with lager inertia has better performance; When using different control strategies, performance under SADRC-P control is better than that under ADRC-P control.