Drum-type Boiler-turbine System Research Articles

Modeling and Finite-Horizon MPC for a Boiler-Turbine System Using Minimal Realization State-Space Model

This paper aims to address a finite-horizon model predictive control (MPC) for non-linear drum-type boiler-turbine system using a system-identification method. Considering that the strong state coupling of a non-linear mechanism model, the subspace identification method is first utilized to obtain a linear state-space model, and transformed into an input–output model. By taking the inputs and outputs of the input–output model as system states, an augmented non-minimal state-space (NMSS) model of state measurable is constructed. In order to reduce the computation burden, the augmented NMSS model is further transformed into a canonical formulation by adopting a Kalman decomposition. Based on the minimal realization state-space model, the MPC controller is parameterized as a finite-horizon optimization problem. Finally, simulations are performed and evaluated the performance of the proposed method, and the simulation results show that: the linear model approximate the non-linear system accurately; the proposed MPC method can achieve a satisfactory stable control performance; and the computation time 18.388 s for the overall optimization problem also illustrates the real-time performance effectively.

Application of Single- and Multi-Objective Evolutionary Algorithms for Optimal Nonlinear Controller Design in Boiler–Turbine System

This paper presents an application of evolutionary algorithm techniques for optimal nonlinear controller design in drum-type boiler–turbine system. Designing of controller for third-order boiler–turbine system is always a complicated task due to the presence of highly interactive nonlinearities. The present work is the first one which attempts to design and implement recently developed finite time convergent controller in third-order boiler–turbine dynamics, and it is described as multi-input–multi-output (MIMO) nonlinear system. The present work explores the possibility of application of single-objective and multi-objective evolutionary algorithm techniques toward optimal tuning of finite time convergent controller to achieve the desired performance for third-order boiler–turbine system. The single-objective evolutionary algorithm techniques such as real-coded genetic algorithm (RGA), modified particle swarm optimization (MPSO), differential evolution (DE), and self-adaptive differential evolution (SADE) are implemented with the minimization of integral square error (ISE) as an objective to obtain optimal tuning parameters. Also, the present paper explores the possibility of simultaneous minimization of conflicting objectives such as ISE and computational cost of the proposed controller using multi-objective evolutionary algorithms such as non-dominated sorting genetic algorithm (NSGA) and modified non-dominated sorting genetic algorithm-II (MNSGA-II). The performance of the proposed optimal finite time convergent controller is validated by simulating different kinds of set point changes, and the obtained results are presented as various case studies. The adaptability of the proposed controller during parameter variations is also examined. The performance of the single-objective and multi-objective evolutionary algorithms has been statistically analyzed, and the results are reported. The results reveal that among the four single-objective EA techniques, SADE offers better performance due to its inherit self-adaptive capability. Also, during multi-objective optimization, MNSGA-II has provided better solution due the presence of dynamic crowding distance (DCD) and control elitism (CE) strategies.

Nonlinear fuzzy model predictive iterative learning control for drum-type boiler–turbine system

Advanced control strategy is necessary to ensure high efficiency and high load-following capability in the operation of modern power plant. Model predictive control (MPC) has been widely used for controlling power plant. Nevertheless, MPC needs to further improve its learning ability especially as power plants are nonlinear under load-cycling operation. Iterative learning control (ILC) and MPC are both popular approaches in industrial process control and optimization. The integration of model-based ILC with a real-time feedback MPC constitutes the model predictive iterative learning control (MPILC). Considering power plant, this paper presents a nonlinear model predictive controller based on iterative learning control (NMPILC). The nonlinear power plant dynamic is described by a fuzzy model which contains local liner models. The resulting NMPILC is constituted based on this fuzzy model. Optimal performance is realized within both the time index and the iterative index. Convergence property has been proven under the fuzzy model. Deep analysis and simulations on a drum-type boiler–turbine system show the effectiveness of the fuzzy-model-based NMPILC

An Intelligent Multi-multivariable Dynamic Matrix Control Scheme for a 160 MW Drum-type Boiler-Turbine System

A 160 MW drum-type boiler-turbine system is developed in the present research through a multi-multivariable dynamic matrix control (DMC) scheme and a multi-multivariable model approach. A novel intelligence-based decision mechanism (IBDM) is realized to support both model approach and control scheme. In such case, the responsibility of the proposed IBDM is to identify the best multivariable model of the system and the corresponding multivariable DMC scheme to cope with the system at each instant of time in an appropriate manner.

A genuine nonlinear approach for controller design of a boiler–turbine system

This paper proposes a genuine nonlinear approach for controller design of a drum-type boiler–turbine system. Based on a second order nonlinear model, a finite-time convergent controller is first designed to drive the states to their setpoints in a finite time. In the case when the state variables are unmeasurable, the system will be regulated using a constant controller or an output feedback controller. An adaptive controller is also designed to stabilize the system since the model parameters may vary under different operating points. The novelty of the proposed controller design approach lies in fully utilizing the system nonlinearities instead of linearizing or canceling them. In addition, the newly developed techniques for finite-time convergent controller are used to guarantee fast convergence of the system. Simulations are conducted under different cases and the results are presented to illustrate the performance of the proposed controllers.

On the practice of artificial intelligence based predictive control scheme: a case study

This paper describes a novel artificial intelligence based predictive control scheme for the purpose of dealing with so many complicated systems. In the control scheme proposed here, the system has to be first represented through a multi-Takagi-Sugeno-Kang (TSK) fuzzy-based model approach to make an appropriate prediction of the system behavior. Subsequently, a multi-generalized predictive control (GPC) scheme, which is organized based on a number of GPC schemes, is realized in line with the investigated model outcomes, at chosen operating points of the system. In case of the proposed control strategy realization, the investigated multi-GPC scheme is instantly updated to handle the system by activating the best control scheme through a new GPC identifier, while the system output is suddenly varied with respect to time. To present the applicability of the proposed control scheme, an industrial tubular heat exchanger system and also a drum-type boiler-turbine system have been chosen to drive through the proposed strategy. In such a case, the simulations are carried out and the corresponding results are compared with those obtained using traditional GPC scheme in addition to nonlinear GPC (NLGPC) scheme, as benchmark approaches, where the acquired results of the proposed control scheme are desirably verified.

Step-Response Model Development for Dynamic Matrix Control of a Drum-Type Boiler–Turbine System

This paper presents the application of dynamic matrix control (DMC) to a drum-type boiler-turbine system. Two types of step-response models for DMC are investigated in designing the DMC; one is developed with the linearization of the nonlinear plant model and the other is developed with the process step-response data. Then, the control performances of the DMC based on both types of models are evaluated. Because of severe nonlinearity of drum water-level dynamics, it is observed that the simulation with the step-response model based on the test data shows satisfactory results, while the linearized model is not suitable for controller design of the drum-type boiler-turbine system.

화력발전 보일러-터빈 시스템을 위한 Dynamic Matrix Control(DMC)의 계단응답모델 선정에 관한 연구

동역학행렬제어(Dynamic Matrix Control) 기법은 각종 산업 현장에서 가장 활발하게 적용되고 있는 고급 제어 기법으로, 최근에는 공정제어의 표준 기법으로 인식되고 있다. 일반적으로 동역학행렬제어에서는 대상 플랜트의 거동을 묘사하기 위하여 계단 응답 모델을 이용한다. 본 논문에서는 화력발전의 보일러-터빈 시스템에 동역학행렬제어 기법을 적용하기 위하여, 두 가지 방식으로 선정된 계단응답모델의 제어 성능을 비교하였다. 먼저 주어진 대상 플랜트의 정확한 비선형 모델을 알고 있다고 가정하여 이의 선형화를 통한 계단응답 모델과 대상시스템의 계단응답 실험을 통하여 얻어진 두 가지 계단응답 모델의 유효성을 비교 분석하였다. 분석 결과 실험적인 계단 응답 모델을 사용한 경우가 우월한 제어 성능을 나타냄을 확인하였다.

Modelling and Identification of a 500MW Drum-Type Boiler-Turbine System

This paper is concerned both with the development of a mathematical model of a 500MW boiler-turbine system, using statistical identification techniques, and with the more general problem of developing a methodology for the statistical identification of a large scale MIMO industrial plant from input/ output data. The work described is carried out using a MIMO modelling and identification program (SIPAC) developed in-house and consisting of data conditioning routines, correlation analysis routines and systems fitting procedure which is an extension of the Zellner and Theil 3SLS estimator. Validation of the resulting model is carried out using residual analysis and model simulation. The C.A.D. package will be described and the boiler-turbine modelling discussed in detail.