Boiler-turbine Unit Research Articles

Robust adaptive fault-tolerant tracking for uncertain output-constrained nonlinear systems with unknown time-varying powers and application to the reduced-order dynamical model of a boiler-turbine unit

Robust adaptive fault-tolerant tracking for uncertain output-constrained nonlinear systems with unknown time-varying powers and application to the reduced-order dynamical model of a boiler-turbine unit

A dynamic hybrid model of supercritical once-through boiler-turbine unit including recirculation mode and once-through mode

Supercritical once-through boiler-turbine units play a paramount role in maintaining grid stability owing to their exceptional efficiency and flexibility. Nevertheless, achieving precise modeling of this device under various operational conditions remains a significant challenge. This study conducts modeling and simulation of an actual supercritical once-through coal-fired power plant. The findings signify that the hybrid model proposed herein adeptly encompasses all operational modes of supercritical once-through units, including start-up recirculation mode and once-through mode. To bolster model precision, an optimization algorithm grounded on weighted mean of vectors (INFO) has been employed for parameter identification, while the INFO-XGBoost was utilized to fashion a machine learning model for static parameters. It has been validated that the developed model accurately captures the dynamic characteristics of the unit. Furthermore, the utilization of the INFO-XGboost to model static parameters has been proved to enable the errors in principal outputs of coordinated control system to be within 0.35 % of MAPE across the entire operational process, thus facilitating the design of more refined and intelligent control systems.

A dynamic mathematical model used for controller design of a supercritical once-through boiler-turbine unit in all load conditions

Recently, widespread adoption of renewable energy significantly impacts the security and stability of power grid. Utilizing coal-fired units in real-time load figuration remains a primary method to ensure grid stability. Therefore, supercritical Once-Through Boiler-Turbine (OTBT) units continuously enhance flexibility to meet the evolving needs of the grid. To broaden the load boundary and achieve fully automated control from unit start-up to rated load, establishing a dynamic mathematical model used for controller design of OTBT units in all load conditions is a necessary prerequisite. This model should accurately predict model outputs in both once-through mode (OTM) and recirculation mode (RCM) while also reflecting the dynamic transition processes between modes. In this study, we first conducted a detailed analysis of the characteristics of recirculation system. Mass and energy conservation equations are established separately for economizer-waterwall system and superheater system, including a level model for storage tank in RCM. Subsequently, through reasonable assumptions and analysis of the transition process, the modified steam dryness is identified as a crucial indicator to distinguish between RCM and OTM. This approach unified the model structures in RCM and OTM, avoiding issues related to model switching during mode transitions. Following this, model parameters were identified based on an improved dynamic search fireworks algorithm. Finally, multiple sets of comparative simulation experiments demonstrated that the established model exhibits high accuracy throughout the unit operation. Results from open-loop simulations indicated that this model can serve as a foundation for controller design of coordinated control system in all load conditions.

Distributed Control for Uncertain Multiagent Systems With the Powers of Positive-Odd Numbers: A Low-Complexity Design Approach

This work shows that low-complexity prescribed performance control (PPC) can be used to realize leader-following consensus for uncertain second-order multi-agent systems (MASs) with the powers of positive odd numbers, i.e., agents whose dynamics are a chain of integrators with positive odd powers. Low-complexity PPC is a control methodology whose strongest feature is its adaptation-free structural simplicity: uncertainty can be handled without estimation of unknown parameters nor approximation structures (neural networks, fuzzy logic systems, etc.) being involved in the control design. While the state of the art has focused on strict- and pure-feedback MASs, i.e., dynamics including a chain of integrators with powers equal to one: in this note, we show that the same low-complexity can be retained for more general integrator dynamics (with positive-odd-integer powers). To this purpose, several new technical tools are proposed to handle the challenges caused by the presence of high powers in the integrators both in leader and follower agents. A dynamical boiler-turbine units system is used to validate the effectiveness of the theoretical findings.

Fixed-Time Control of Asymmetric Output-Constrained Nonlinear Systems and Its Application to Boiler–Turbine Unit

Fixed-Time Control of Asymmetric Output-Constrained Nonlinear Systems and Its Application to Boiler–Turbine Unit

Linear Quadratic Optimal Robust Multivariable Proportional-Integral Control Design for a Boiler-Turbine Unit

Abstract Boiler-turbine unit is a highly nonlinear, coupled, and ill-conditioned system. Moreover, the presence of physical constraints, such as actuator magnitude and rate limits, makes the system difficult to control. This paper employs a fixed multivariable proportional-integral (PI) controller of the I–P structure for robust linear quadratic (LQ) compensation of a nonlinear boiler-turbine benchmark. In order to ensure that a single PI controller works for the whole operating region of this nonlinear system, the linearized model of the system is represented as a norm-bounded, time-varying uncertain system. The ranges of the uncertain parameters of this linearized model are determined from different operating points of the nonlinear system. To design the PI controller for the uncertain system, first, it is transformed into a state feedback design for an augmented uncertain system and then the state feedback gains satisfying some LQ performance limit are computed by solving a linear matrix inequality (LMI) problem. As the uncertainty in the feedforward matrix of the linearized model cannot be considered in the above design process, an LMI-based method is developed to check if the designed PI controller performance in H∞ sense is close to the one if the neglected uncertainty is included. The performance of the controller is tested on the nonlinear boiler-turbine unit under several operating conditions and physical constraints. Comparisons are also made with some existing PI controllers, to show the superiority of the proposed robust PI controller.

Robust & nonlinear control of an ultra-supercritical coal fired once-through boiler-turbine unit in order to optimize the uncertain problem

The ultra-supercritical once-through boiler (OTB) unit is an advanced power generation technology with high plant efficiency and low emissions. However, it is difficult to realize a coordinate control for the ultra-supercritical OTB unit to achieve the fast and stable dynamic response during the load tracking and grid frequency disturbances and in the presence of unavoidable uncertainties. In this paper, an accurate grey box multivariable coupled nonlinear model of an ultra-supercritical boiler-turbine unit is considered. Steam pressure at throttle valve, specific enthalpy in separator and active power are adjusted at desired values by manipulation of the fuel rate command, feedwater rate command and throttle valve opening. In addition to the mentioned accurate model, limitations of operational parameters and uncertainty sources are considered in the controller design. This makes this research unique from previous works. According to affine nonlinear control theory, a nonlinear robust sliding mode controller is developed. Then using MATLAB μ - DK iteration syntax, a linear H-infinity (H∞) robust control is synthesized with selecting proper weight functions. This method is suitable for all types of power plants with a similar mathematical modeling approach. Simulations in MATLAB Simulink toolbox reveals that both designed controllers achieve appropriate performance in the presence of uncertainties and parameters’ limitations, but nonlinear controller has better performance. Finally, deficiencies of a linear controller with respect to the proposed designed robust controllers are denoted.

Receding Galerkin Optimal Control with High-Order Sliding Mode Disturbance Observer for a Boiler-Turbine Unit

The control of the boiler-turbine unit is important for its sustainable and robust operation in power plants, which faces great challenges due to the control unit’s serious nonlinearity, unmeasurable states, variable constraints, and unknown time-varying lumped disturbances. To address the above issues, this paper proposes a receding Galerkin optimal controller with a high-order sliding mode disturbance observer in a composite scheme, in which a high-order sliding mode disturbance observer is first employed to estimate the lumped disturbances based on a deviation form of the mathematical model of the boiler-turbine unit. Subsequently, under the hypothesis of state constraint, a receding Galerkin optimal controller is designed to compensate the lumped disturbances by embedding their estimates into the mathematically based predictive model at each sampling time instant. With the help of an interpolation polynomial, Gauss integration, and nonlinear solvers, an optimal control law is then obtained based on a Galerkin optimization algorithm. Consequently, disturbance rejection, target tracking, and constraint handling performance of a controlled closed-loop system are improved. Some simulation cases are conducted on a mathematical boiler-turbine unit model to demonstrate the effectiveness of the proposed method, which is supported by the quantitative result analysis, such as tracking and disturbance rejection performance indexes.

Stable feedback linearization-based economic MPC scheme for thermal power plant

The major concern of modern power plants is changing from tracking control to environmental and economic issues. The economic model predictive control (EMPC) scheme, which incorporates the boiler-turbine unit's dynamic tracking and economic optimization into one online framework, can well enhance the dynamic economic performance. Considering the strong nonlinearity that existed in the boiler-turbine system, this paper presents an advanced EMPC scheme based on the input/output feedback linearization (IOFL) approach. The boiler-turbine dynamics are converted via the IOFL method to a linear form, which can be readily used to constitute the standard EMPC scheme. A dual-mode method is adopted in this paper to guarantee the stability of the IOFL EMPC strategy for the boiler-turbine system, in which the first-mode optimizes the economic objective function while preserving the states of the system within a feasible region, and the second-mode moves the system state to the optimum operating point using an auxiliary controller. The simulations under the MATLAB environment demonstrate that the application of the IOFL-based EMPC scheme enhances the economic and dynamic output performance under load demand changes in comparison with fuzzy hierarchical MPC and fuzzy economic MPC schemes.

Generalized predictive control using improved recurrent fuzzy neural network for a boiler-turbine unit

The ultra supercritical (USC) for the boiler-turbine unit has become an advanced power generation technology due to its high combustion efficiency and low-carbon emission. Considering the complex nonlinearity and uncertainty in USC boiler-turbine units, a novel recurrent fuzzy neural network (RFNN) is produced to model dynamic responses of nonlinear systems and improve the control performance of the outputs in the boiler-turbine unit. However, the number of fuzzy rules in previous networks is mostly predefined with experts’ experience, which contributes to the decline in the generalization and efficiency of system modeling. This challenge is tackled in this paper by a subtractive clustering (SC) algorithm, which can determine the optimal number of fuzzy sets in the proposed RFNN. Additionally, aiming at minimizing the tracking errors of the outputs in the boiler-turbine unit, a global generalized predictive control (GPC) strategy is further designed to control the fuel flow, the feedwater flow, and the steam governor valve in the boiler-turbine unit. With the real-time data generated in a 1000MW USC boiler-turbine unit, the proposed SC-RFNN-based GPC can achieve a testing root mean-square-error (RMSE) of 0.0411 as well as the lower integral absolute error (IAE) values of system outputs.

Fractional-Order PID Controller (FOPID)-Based Iterative Learning Control for a Nonlinear Boiler System

For the boiler-turbine unit in power systems, a coordinated control structure plays a crucial role in maintaining the balance in supply and demand of energy, reducing pollutant emissions and optimizing energy efficiency. The matching requirements of the turbine and boiler, the wide range of load changes, and the cooperative operation of many devices in the power system pose many challenges to designing a coordinated control system for the boiler turbine system, thus making the control design a difficult task. In this paper, iterative learning control (ILC) is used to maintain the throttle pressure and megawatt output power of a boiler turbine at the desired set points by controlling the hybrid pattern design structure. Simulation results show that the proposed approach can maintain the desired set points, and the desired response can also be obtained faster by using the proposed approach compared to the ones available in the literature.

Dynamic model for subcritical circulating fluidized bed boiler-turbine units operated in a wide-load range

As the proportion of renewable energy generation increases in power grid, most of circulating fluidized bed (CFB) boiler-turbine units are required to operate in a wide-load range to maintain the stability of power grid. However, few studies focus on modeling of CFB units operated in a wide-load range, especially in ultra-low load. This study develops a dynamic model for subcritical CFB boiler-turbine units operated in a wide-load range, which can provide foundation for designing flexible operation mode. In this work, different characteristics of subcritical CFB units in ultra-low load were analyzed. Then, the model structure with balance between complexity and accuracy was derives from mass and energy conservation laws as well as thermodynamic principles. The parameters and functions in model were identified based on running data from a 330 MW subcritical CFB unit. Validation results show that the of model has satisfactory accuracy in wide-load range, from ultra-low load to full load. Besides, the model can reflect essential dynamic characteristics of the unit. Therefore, the model can be feasible for performance analysis and testing control strategy.

MLD–MPC for Ultra-Supercritical Circulating Fluidized Bed Boiler Unit Using Subspace Identification

Before carbon capture and storage technologies can truly be promoted and applied, and nuclear or renewable energy power generation can become predominant, it is important to further develop more efficient and ultra-low emission USC units on the basis of leveraging the strengths of CFB technology. In view of this complex system with strong nonlinearity such as the boiler-turbine unit of a thermal power unit, the establishment of a model that is suitable for control is indispensable for the operation and the economics of the process. In this study the form of the nonlinear model after linearization at the steady-state point has been fully considered and an improved subspace identification method, which is based on the steady-state point deviations data, was proposed in order to identify a piecewise affine model. In addition, the construction of the excitation signal in practical applications has been fully considered. The identification results demonstrate that this method has a better adaptability to strong nonlinear systems. The identification normalized root mean square errors of each working condition were almost all less than 10%. On this basis, a framework that is widely applicable to complex system control has been established by combining with the mixed logic dynamic (MLD) model. The canonical form realization was performed in order to transfer the local models into the same state basis. The predictive control was carried out on the boiler-turbine system of a 660-MW ultra-supercritical circulating fluidized bed unit that was based on the above framework. The results indicate that the predictive control performance is closely related to the setting value of the ramp rate and, therefore, prove the effectiveness of the framework.

A dynamic nonlinear model used for controller design of a 600 MW supercritical circulating fluidized bed boiler-turbine unit

• Energy storage characteristic and variable coal quality are considered in modeling. • Parameters in model change with different load condition to improve model accuracy. • The model is validated by a 600 MW supercritical once-through CFB unit. • Open-loop step responses show the different inertia and nonlinearity of CCS. • Transfer function matrix of CCS is derived from the dynamic nonlinear model. Large-scale circulating fluidized bed (CFB) power generation units with high steam pressure and temperature have been put into operation in order to obtain high cycle efficiency and less emissions. To ensure the flexible operation of such CFB units, it is necessary to build a model for the design of coordinated control system (CCS). However, few studies focus on modeling of such CFB units. Thus, this work develops a mechanistic dynamic nonlinear model of a large-scale supercritical CFB unit, which is suitable for CCS design because of its relatively low complexity. Firstly, model structure is derived from mass and energy conservation laws, combined with analysis of operational characteristics of a 600 MW supercritical CFB unit. Then, unknown parameters and nonlinear functions in model are identified based on running data by using regression analysis and optimization algorithm. Validation results show that the model has satisfactory accuracy and it can capture essential dynamic characteristics of the unit and nonlinearity of CCS. More importantly, the model accuracy is further improved by proposed variable dynamic parameters and correction of coal quality. After this, transfer function matrix of CCS is derived from dynamic nonlinear model, which can be applied to controller design and simulation analysis.

Nonlinear model predictive control of USC boiler-turbine power units in flexible operations via input convex neural network

Coordinated control of an ultra-supercritical (USC) boiler-turbine unit in the flexible-operation mode is challenging because of internal nonlinearity, various unknown disturbances, and the strong-coupled multivariable behavior of the unit. A novel nonlinear model predictive control (NMPC) approach is proposed to improve the flexibility of the USC unit, which introduces an input convex neural network (ICNN) to model the dynamics of the USC unit, and then computes the optimal control decisions by solving a convex model predictive control problem. Simulations are conducted on a 1000 MW USC boiler-turbine unit. The results demonstrate that the ICNN exhibits equivalent performance to a conventional neural network in learning the system dynamics. The root mean square errors of the throttle steam pressure, the separator steam enthalpy, and the power are 0.003 MPa, 0.162 kJ/kg, and 0.513 MW, respectively. In load tracking and disturbance rejection simulations, the proposed ICNN-based NMPC outperforms the linear model predictive control and conventional neural network-based NMPC. The ICNN-based NMPC provides a fast and stable load tracking capacity at a ramp rate of 10.8% MCR/min. Moreover, the superiority of the proposed approach is further confirmed by comparison with the internal-model robust adaptive control method, which is a state-of-the-art method. The proposed approach has good potential to improve the operational flexibility of USC power units.

Model predictive control based control strategy for battery energy storage system integrated power plant meeting deep load peak shaving demand

Due to China's power supply structure, the conventional power units are responsible for the deep load shaving regulation to meet the high penetration challenge of renewable energy. To improve the capability of the peaking load shaving and the power regulation quality, battery energy storage systems (BESS) can be used to cooperate power units to satisfy the multi-objective regulation needs. This paper proposes a novel unified control scheme to smooth the power output of the power plant and meet the strict power load demands distributed from the automatic generation centre (AGC). The proposed coordination control strategy consists of unit load demand scheduler, multi-objective reference governor, fuzzy logic based model predictive control (FMPC) for the boiler-turbine unit, and one-step model predictive control for battery energy storage system. Based on the control scheme, we can achieve: 1) The operation of the boiler-turbine unit is more energy-saving and reliable while the service life of the valves is extended; 2) With the participation of battery energy storage system, the power output of the boiler-turbine unit is smooth and the tracking performances of the unified generation unit, including tracking accuracy and transient behaviour, are improved; 3) Various constraints can be explicitly coped with under the framework of model predictive control.

A dynamic model for supercritical once-through circulating fluidized bed boiler-turbine units

It is urgent to establish a dynamic model of once-through supercritical circulating fluidized bed (SCFB) power generation units in order to explore the flexible operation mode. This study aims to establish such a dynamic model with the balance between model complexity and accuracy. Within the analysis on operation characteristics of SCFB units, some reasonable assumptions and simplifications were introduced. Then, a dynamic model structure was derived from mass, energy balance laws as well as thermodynamic principles. And the unknown parameters in the model were identified by regression analysis and quantum genetic algorithm, combined with running data from a 350 MW SCFB unit. After this, the open-loop step responses and closed-loop experiments were developed to further validate the established model. The simulation results show that all model outputs can track the dynamic trends of running data in different load conditions with the satisfactory accuracy. More importantly, the model captures the special dynamic characteristics of the SCFB unit. Therefore, the model can be feasible and applicable for advanced control algorithms test and flexible operation control system design of SCFB units.

Design and implementation of virtual experiment for complex control system: A case study of thermal control process

Online experimental systems that include remote and virtual experiments based on the Internet has become a new form of experiment in engineering education. This paper proposes a virtual experiment for the complex process control system. It is based on the networked control system laboratory (NCSLab), which provides a reliable framework for virtual thermal experiment. Taking the combustion system of 1000 MW ultra-supercritical coal-fired once-through boiler-turbine units as an example, the framework and the implementation process of the online system are given in this paper. A new presentation form for complex processes control experiment is provided, which could be a solution for simulating physical experimental equipment in virtual environments. The potential danger in practical experiments could be avoided. Additionally, the real-time operation for complex control systems could be presented in a 3D scene. Meanwhile, the flexibility and efficiency could be increased in engineering experimental education. Moreover, using the experiment evaluation method, the references and feedback for teaching and assessment are provided in this paper.

Nonlinear dynamic investigation and anti-bifurcation control of a boiler-turbine unit via dual-mode fuzzy model predictive control strategy

The increase in renewable energy sources connected to the grid has increased flexibility requirements in the operation of thermal power plants. Because of severe nonlinearity and various disturbances, the dynamic behavior of the boiler-turbine unit will change significantly at different operating conditions. Further, modeling uncertainties cofound definitive knowledge about the changes, exacerbating efforts to control. In this paper, a general disturbed model of the boiler-turbine unit for control design is determined through the nonlinear dynamic analysis on the concepts of bifurcation and limit cycles behavior rather than given by an artificial chosen. Then, a dual-mode fuzzy predictive control strategy is proposed on discrete-time Takagi-Sugeno fuzzy model. Online optimal problem of the approach is solved to accomplish the fast-tracking task; to achieve the anti-bifurcation control objective, a novel minimal robust positively invariant set corresponding with a local controller is offline obtained in order to change the undesired nonlinear behavior into stable limit cycles. With the input-to-state stability theory, the proposed control strategy is theoretically proved and given in terms of linear matrix inequalities. Simulation carried out on the boiler-turbine unit demonstrates that the proposed dual-mode control strategy achieves the disturbance suppression ability and the improvement of the nonlinear dynamic behavior.

A dynamic nonlinear model for a wide-load range operation of ultra-supercritical once-through boiler-turbine units

As the penetration of renewable energy generation increases in power grid, ultra-supercritical (USC) once-through boiler-turbine (OTBT) units are required to operate in a wide load range to maintain the grid stability. Coordinated control system (CCS) coordinates boiler and turbine systems to track load demand and ensure safe and economical operation simultaneously. However, few studies focus on modeling the USC-OTBT units for CCS design under the current stringent requirement. This study develops a dynamic nonlinear model of a USC-OTBT unit operating in a wide load range, which is a first step toward designing an effective CCS. This work first analyzes and captures the nonlinear characteristics of the USC-OTBT unit in low load and extends to model the dynamic process of the unit in a wider load range, from low load to full load. Besides, two energy functions are presented by analyzing open-loop responses to improve the model structure and dynamic modeling accuracy. Model structure is derived from mass and energy conservation laws, and model parameters and functions are identified based on running data. Validation results demonstrate the effectiveness of the model for a wide load range operation of the USC-OTBT unit, providing a basis for an effective CCS design.