An optimized nonlinear generalized predictive control for steam temperature in an ultra supercritical unit

Chuanliang Cheng; Chen Peng; Tengfei Zhang; Deliang Zeng

doi:10.1016/j.isatra.2023.03.033

An optimized nonlinear generalized predictive control for steam temperature in an ultra supercritical unit

ISA Trans. 2023 Aug:139:13-23. doi: 10.1016/j.isatra.2023.03.033. Epub 2023 Mar 30.

Authors

Chuanliang Cheng¹, Chen Peng², Tengfei Zhang³, Deliang Zeng⁴

Affiliations

¹ School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 201900, China.
² School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 201900, China. Electronic address: c.peng@shu.edu.cn.
³ College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
⁴ State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China.

PMID: 37019703
DOI: 10.1016/j.isatra.2023.03.033

Abstract

The optimize control of the ultra supercritical (USC) unit has been a major concern in power industry. The intermediate point temperature process is a multi-variable system with strong nonlinearity, large scale and great delay, which greatly affects the safety and economy of the USC unit. Generally, it is difficult to realize effective control by using conventional methods. This paper presents a nonlinear generalized predictive control based on a composite weighted human learning optimization network (CWHLO-GPC) to improve the control performance of intermediate point temperature. Based on the characteristics of the onsite measurement data, the heuristic information is incorporated into the CWHLO network, and expressed by different local linear models. Then, global controller is elaborately constituted based on a scheduling program inferred from the network. Compared with classical generalized predictive control (GPC), the non-convex problem is effectively solved by introducing CWHLO models into the convex quadratic program (QP) routine of local linear GPC. Finally, detailed analysis on set point tracking and interference resisting via simulation is addressed to illustrate the efficiency of the proposed strategy.

Keywords: A composite weighted human learning optimization network; Convex optimization; Generalized predictive control; Ultra supercritical unit.