The optimization of the start-up scheduling for a 320 MW steam turbine

Dong-Mei Ji, Jia-Qi Sun, Yue Dui and Jian-Xing Ren

Energy, 2017, vol. 125, issue C, 345-355

Abstract: It is the aim of thermal power plants start-up scheduling to minimize the start-up time and limit the turbine rotor stresses, which is a highly nonlinear problem and has multiple local optima. A novel mathematical model of start-up schedule was proposed in this study. Based on the operation regulations of 320 MW subcritical condensing steam turbine at a power plant, different cold start-up optimization schedules were made according to the mathematical model of start-up schedule. The finite element method (FEM) was applied to calculate the temperature field and stress field of the steam turbine rotor under different schedules. Taking the calculation results as the sample data, support vector machines (SVM) was employed to establish the regression model of start-up schedules and the dangerous point stress under the start-up schedule. Combining the regression model, the optimal start-up parameters could be obtained by the particle swarm optimization (PSO) algorithm. The results show that limiting the maximum Von Mises stress, the start-up time of the optimized schedule was reduced by almost 150 min compared with the original schedule. At the same time the optimal start-up schedule was verified by FEM, and the maximum Von Mises stresses at the dangerous points satisfy the stress requirements.

Keywords: Steam turbine rotor; Support vector machine (SVM); Particle swarm optimization (PSO) algorithm; Optimized start-up (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544217303225
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:125:y:2017:i:c:p:345-355

DOI: 10.1016/j.energy.2017.02.139

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 ().