Gas Turbine Model Identification Based on Online Sequential Regularization Extreme Learning Machine with a Forgetting Factor

Rui Yang, Yongbao Liu (), Xing He () and Zhimeng Liu
Additional contact information
Rui Yang: College of Power Engineering, Naval University of Engineering, Wuhan 430033, China
Yongbao Liu: College of Power Engineering, Naval University of Engineering, Wuhan 430033, China
Xing He: College of Power Engineering, Naval University of Engineering, Wuhan 430033, China
Zhimeng Liu: College of Power Engineering, Naval University of Engineering, Wuhan 430033, China

Energies, 2022, vol. 16, issue 1, 1-19

Abstract: Due to the advantages of high convergence accuracy, fast training speed, and good generalization performance, the extreme learning machine is widely used in model identification. However, a gas turbine is a complex nonlinear system, and its sampling data are often time-sensitive and have measurement noise. This article proposes an online sequential regularization extreme learning machine algorithm based on the forgetting factor (FOS_RELM) to improve gas turbine identification performance. The proposed FOS_RELM not only retains the advantages of the extreme learning machine algorithm but also enhances the learning effect by rapidly discarding obsolete data during the learning process and improves the anti-interference performance by using the regularization principle. A detailed performance comparison of the FOS_RELM with the extreme learning machine algorithm and regularized extreme learning machine algorithm is carried out in the model identification of a gas turbine. The results show that the FOS_RELM has higher accuracy and better robustness than the extreme learning machine algorithm and regularized extreme learning machine algorithm. All in all, the proposed algorithm provides a candidate technique for modeling actual gas turbine units.

Keywords: gas turbine; model identification; machine learning; forgetting 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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/1/304/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/1/304/ (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:16:y:2022:i:1:p:304-:d:1016906

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 ().