Fuzzy modeling and stable model predictive tracking control of large-scale power plants

被引:68
作者
Wu, Xiao [1 ]
Shen, Jiong [1 ]
Li, Yiguo [1 ]
Lee, Kwang Y. [2 ]
机构
[1] Southeast Univ, Dept Energy Informat & Automat, Nanjing 210096, Jiangsu, Peoples R China
[2] Baylor Univ, Dept Elect & Comp Engn, Waco, TX 76798 USA
基金
中国国家自然科学基金;
关键词
Power plant; Stable model predictive control; Subspace identification; Fuzzy clustering; TS-fuzzy model; BOILER-TURBINE SYSTEM; MIMO LPV SYSTEMS; SUBSPACE IDENTIFICATION; DESIGN;
D O I
10.1016/j.jprocont.2014.08.007
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper develops a stable model predictive tracking controller (SMPTC) for coordinated control of a large-scale power plant. First, a Takagi-Sugeno (TS) fuzzy model is established to approximate the behavior of the boiler-turbine coordinated control system (CCS) using fuzzy clustering and subspace identification (SID). Then, an SMPTC is designed based on the fuzzy model to track the power and pressure set-points while guaranteeing the input-to-state stability and the input constraints of the system. An output-based objective function is adopted for the proposed SMPTC so that the controller could be directly applicable for the data-driven model. Moreover, the effect of modeling mismatches and unknown plant variations has been overcome by the use of a disturbance term and steady-state target calculator (SSTC). Simulation results for a 600 MW power plant show that an off-set free tracking performance can be achieved over a wide range load variation. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1609 / 1626
页数:18
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