Adaptive control for the steam temperature of thermal power plants

Matsumura, Shiro; Ogata, Kenichi; Fujii, Seizo; Shioya, H.; Nakamura, Hideo

doi:10.1016/0967-0661(94)90001-9

Cited by 27 publications

(10 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies in the application of advanced control, predictive control, and adaptive control to the regulation of superheater steam temperatures in large power plants are presented in [SSLG00, KC07b,LLT03,Moe99,MOF+94,FJG93,Maf97,KC07a]. Also a previous implementation of adaptive predictive control (APC) at the Pasajes de San Juan power station, belonging to the company Iberdrola, is presented in [PPCC97].…”

Section: Introductionmentioning

confidence: 98%

ADEX Optimized Adaptive Control System for Superheated Steam Temperature in a Coal-Fired Power Plant

Martín‐Sánchez

Rodellar

2014

ADEX Optimized Adaptive Controllers and Systems

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 98%

ADEX Optimized Adaptive Control System for Superheated Steam Temperature in a Coal-Fired Power Plant

Martín‐Sánchez

Rodellar

2014

ADEX Optimized Adaptive Controllers and Systems

View full text Add to dashboard Cite

“…Other studies in the application of advanced control, predictive control, and /or adaptive control to the regulation of superheater steam temperatures in large power plants are presented in and as well as a previous implementation of adaptive predictive control (APC) at the Pasajes de San Juan power station , belonging to the company Iberdrola.…”

Section: Introductionmentioning

confidence: 99%

Adaptive predictive expert control of superheated steam temperature in a coal‐fired power plant

Pérez

Geddes

Clegg

2012

Adaptive Control & Signal

View full text Add to dashboard Cite

SUMMARY This paper describes the application of an adaptive predictive expert (ADEX) control solution to the attemperation process for superheated steam temperature control in the ScottishPower coal‐fired power station at Cockenzie near Edinburgh in Scotland. The project comprised two phases, the first of which was a trial to establish the feasibility and potential benefits of ADEX compared with the existing control system by using a software platform on PC connected to the plant Programmable Logic Controller (PLC) via Object linking and embedding for Process Control (OPC), and the second phase was to verify and install a permanent, robust solution using an ADEX controller module with a digital link to the plant PLC. The overall objective of the project was to reduce steam temperature variations caused by load changes and other perturbations in order to protect the steam turbine (Unit 3) and to minimize thermal stresses in the attemperation pipework and boiler and so minimize premature failures along with associated cost risk. In the first phase, ADEX demonstrated a 50% improvement in reducing steam temperature variation overall compared with the existing method, and the subsequent installation of the ADEX controller module provided the necessary robustness and backups for a final industrial solution. The results of both phases are displayed graphically. Copyright © 2012 John Wiley & Sons, Ltd.

show abstract

“…Over the last 10 years, several advanced control methods for the control of thermal power plants have been reported. Examples are the generalized predictive control (GPC) method based on local model networks [5], the self-tuning minimum variance control method for steam temperature control [3], the self-tuning multi-loop generalized predictive control (GPC) method [2], and the multivariable dynamic matrix control (DMC) method based on linear models [6]. Of these, the local model Toyoda [7] introduced an algorithm based on an amplitude-dependent auto-regressive model [1], which uses an off-line method to identify the load-dependent non-linearity of thermal power plants.…”

Section: Introductionmentioning

confidence: 99%

A nonlinear exponential ARX model-based multivariable generalized predictive control strategy for thermal power plants

Peng

Ozaki

Haggan-Ozaki

et al. 2002

IEEE Trans. Contr. Syst. Technol.

View full text Add to dashboard Cite

This paper presents a modeling and control method for thermal power plants having nonlinear dynamics varying with load. First a load-dependent exponential ARX (Exp-ARX) model that can effectively describe the plant nonlinear properties and requires only off-line identification is presented. The model is then used to establish a constrained multivariate multi-step predictive control (ExpMPC) strategy whose effectiveness is illustrated by a simulation study of a 600 megawatt (MW) thermal power plant. Although the predictive control algorithm may be used without resorting to online parameter estimation, it is much more reliable, and displays much better control performance than the usual GPC algorithm.

show abstract

Adaptive control for the steam temperature of thermal power plants

Cited by 27 publications

References 10 publications

ADEX Optimized Adaptive Control System for Superheated Steam Temperature in a Coal-Fired Power Plant

ADEX Optimized Adaptive Control System for Superheated Steam Temperature in a Coal-Fired Power Plant

Adaptive predictive expert control of superheated steam temperature in a coal‐fired power plant

A nonlinear exponential ARX model-based multivariable generalized predictive control strategy for thermal power plants

Contact Info

Product

Resources

About