Nonlinear multivariable hierarchical model predictive control for boiler-turbine system

Xiaobing Kong, Xiangjie Liu and Kwang Y. Lee

Energy, 2015, vol. 93, issue P1, 309-322

Abstract: Economic optimal control has been a major concern in modern power plant. The HMPC (hierarchical model predictive control) incorporates both the plant-wide economic process optimization and regulatory process control into a hierarchical control structure, in which the model predictive control technology has been an effective tool for solving the higher-layer economic optimization problems. Since the power plants are typically nonlinear multivariable large-scale processes, applications of the HMPC can be computationally extensive and resulting in nonlinear and non-convex optimization problems. Since the power plant dynamics changes with load, fuzzy model representing the local input–output relations of the nonlinear power plant system is incorporated to facilitate the convex QP (quadratic program) routine, and thus realize the HMPC. Detailed analysis on power plant steam-boiler generation system has been made to demonstrate the effectiveness of the proposed nonlinear HMPC.

Keywords: Hierarchical model predictive control; Convex optimization; Boiler-turbine system; Fuzzy model (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544215012402
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:93:y:2015:i:p1:p:309-322

DOI: 10.1016/j.energy.2015.09.030

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().