Parameter Identification of Pump Turbine Governing System Using an Improved Backtracking Search Algorithm

Jianzhong Zhou, Chu Zhang, Tian Peng and Yanhe Xu
Additional contact information
Jianzhong Zhou: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Chu Zhang: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Tian Peng: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Yanhe Xu: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2018, vol. 11, issue 7, 1-18

Abstract: Accurate parameter identification of pump turbine governing system (PTGS) is of great importance to the precise modeling of pumped storage unit. As PTGS is characterized by uncertainties and strong nonlinear characteristics, it is difficult to identify its parameters. To solve the parameter identification problem for PTGS, an improved backtracking search algorithm (IBSA) is proposed by combining the original BSA with the orthogonal initialization technique, the chaotic local search operator, the elastic boundary processing strategy and the adaptive mutation scale factor. The proposed IBSA algorithm for parameter identification of PTGS was applied on an illustrative example to demonstrate its accuracy and efficiency. The simulation results have shown that IBSA performed better compared with the particle swarm optimization, the gravitational search algorithm and the original BSA in regard to solution quality and parameter identification accuracy.

Keywords: pump turbine governing system; parameter identification; improved backtracking search algorithm; orthogonal initialization; chaotic local search; elastic boundary processing; adaptive mutation scale factor (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/11/7/1668/pdf (application/pdf)
https://www.mdpi.com/1996-1073/11/7/1668/ (text/html)

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:gam:jeners:v:11:y:2018:i:7:p:1668-:d:154648

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().