Modeling and sliding mode predictive control of the ultra-supercritical boiler-turbine system with uncertainties and input constraints

Tian, Zhen; Yuan, Jingqi; Zhang, Xiang; Kong, Lei; Wang, Jingcheng

doi:10.1016/j.isatra.2018.03.004

Cited by 40 publications

(13 citation statements)

References 24 publications

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“…The aforementioned gray‐box modeling has simultaneously made use of prior knowledge of the working mechanism and information in the historical operation data 20 ; therefore, gray‐box modeling has received significant attentions. In previous studies, the control‐oriented models of subcritical units 21‐24 and once‐through units 6,25‐27 can be found. These are all conducted through gray‐box modeling.…”

Section: Introductionmentioning

confidence: 99%

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The control‐oriented model of coordinated control system based on stochastic differential equations

Niu

et al. 2020

Energy Science & Engineering

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Considering the large‐scale development of renewable energy resources globally, control optimization of coal‐fired power plants is becoming increasingly crucial. The dynamics of the coordinated control system (CCS) must be studied prior to designing a controller. These dynamics of the subcritical units have been expressed through various modeling methods. However, in previous studies, ordinary differential equations (ODEs) have been primarily employed, which cannot reflect the uncertainties in the system. Therefore, a model with a greater accuracy should be observed to comprise uncorrelated residuals. In this study, the uncertainties in the calorific value of fired coal and combustion process were analyzed first. A normal distribution of disturbance was assumed in this process. The dynamics of the CCS were then described with stochastic differential equations (SDEs). Furthermore, a parameter estimation procedure was designed. The residual evaluation is employed to improve the model evaluation. After simulations, the SDE‐based model‐3 in this study elucidates the dynamics of the subcritical boiler‐turbine system better than the ODE‐based model and other SDE‐based models. The measured values can be regarded as a possible result of this model, rendering it a potential platform to employ stochastic model predictive control in CCS.

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Section: Introductionmentioning

confidence: 99%

“…The same inputs would lead to unique and same outputs. However, besides measurement errors, there are still several undetermined factors working within the coordinated control system operation 21,27 . These factors would lead to the undetermined outputs with the same inputs.…”

Section: Introductionmentioning

confidence: 99%

The control‐oriented model of coordinated control system based on stochastic differential equations

Niu

et al. 2020

Energy Science & Engineering

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“…In [6], the dynamic model of a 1000 MW power plant was established by combining the experimental modeling approach and the first-principle modeling approach, which can be feasible and applicable for simulation analysis and testing control algorithms. Based on this model, a sliding mode predictive controller was proposed in [19] to achieve excellent load tracking ability under wide-range operation. In [20], this model was further improved with added closed-loop validations and more reasonable structure.…”

Section: Introductionmentioning

confidence: 99%

Stacked Auto-Encoder Modeling of an Ultra-Supercritical Boiler-Turbine System

et al. 2019

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The ultra-supercritical (USC) coal-fired boiler-turbine unit has been widely used in modern power plants due to its high efficiency and low emissions. Since it is a typical multivariable system with large inertia, severe nonlinearity, and strong coupling, building an accurate model of the system using traditional identification methods are almost impossible. In this paper, a deep neural network framework using stacked auto-encoders (SAEs) is presented as an effective way to model the USC unit. In the training process of SAE, maximum correntropy is chosen as the loss function, since it can effectively alleviate the influence of the outliers existing in USC unit data. The SAE model is trained and validated using the real-time measurement data generated in the USC unit, and then compared with the traditional multilayer perceptron network. The results show that SAE has superiority both in forecasting the dynamic behavior as well as eliminating the influence of outliers. Therefore, it can be applicable for the simulation analysis of a 1000 MW USC unit.

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“…In recent years, much attention has also been paid to the improvement of the robustness against unknown uncertainties and external disturbances for the B-T unit control, such as L1 adaptive state feedback control [22], sliding mode control [23][24][25], adaptive variable structure, and H∞ robust optimal control [26]. The idea of improving robustness of energy systems is further explored by active disturbance rejection control (ADRC) approaches [27,28], such as nonlinear disturbance rejection control with the combination of a stable feedback controller and a sliding mode observer [29], ADRC based on direct energy balance [30], and robust mode predictive control [31,32]. However, most of these approaches, except two enhanced MPCs in [31] and [32], cannot effectively deal with the constraints, which may deteriorate the control performance of CCS in practice.…”

Section: Introductionmentioning

confidence: 99%

“…The idea of improving robustness of energy systems is further explored by active disturbance rejection control (ADRC) approaches [27,28], such as nonlinear disturbance rejection control with the combination of a stable feedback controller and a sliding mode observer [29], ADRC based on direct energy balance [30], and robust mode predictive control [31,32]. However, most of these approaches, except two enhanced MPCs in [31] and [32], cannot effectively deal with the constraints, which may deteriorate the control performance of CCS in practice. Moreover, except for [30], the models, on which these control strategies are based, are derived from the provided nonlinear mathematical model, which is actually not easily obtained for real power plants.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Predictive Control for a Boiler–Turbine Unit Based on a Local Model Network and Immune Genetic Algorithm

et al. 2019

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This paper proposes a nonlinear model predictive control (NMPC) strategy based on a local model network (LMN) and a heuristic optimization method to solve the control problem for a nonlinear boiler–turbine unit. First, the LMN model of the boiler–turbine unit is identified by using a data-driven modeling method and converted into a time-varying global predictor. Then, the nonlinear constrained optimization problem for the predictive control is solved online by a specially designed immune genetic algorithm (IGA), which calculates the optimal control law at each sampling instant. By introducing an adaptive terminal cost in the objective function and utilizing local fictitious controllers to improve the initial population of IGA, the proposed NMPC can guarantee the system stability while the computational complexity is reduced since a shorter prediction horizon can be adopted. The effectiveness of the proposed NMPC is validated by simulations on a 500 MW coal-fired boiler–turbine unit.

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Modeling and sliding mode predictive control of the ultra-supercritical boiler-turbine system with uncertainties and input constraints

Cited by 40 publications

References 24 publications

The control‐oriented model of coordinated control system based on stochastic differential equations

The control‐oriented model of coordinated control system based on stochastic differential equations

Stacked Auto-Encoder Modeling of an Ultra-Supercritical Boiler-Turbine System

Nonlinear Predictive Control for a Boiler–Turbine Unit Based on a Local Model Network and Immune Genetic Algorithm

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