Parameter identification of interior permanent magnet synchronous based on local search-based hybrid genetic algorithm

Yilin Zhu, Qi Chen, Kun Li, Wei Yang and Yun Huang

Journal of Electromagnetic Waves and Applications, 2022, vol. 36, issue 9, 1311-1322

Abstract: A hybrid genetic algorithm (Is-hGA) parameter identification method based on local search was proposed to solve the anti-salient characteristics of interior permanent magnet synchronous motor (IPMSM) and the defects of traditional genetic algorithm (GA) parameter identification method. In this hybrid optimization method, genetic algorithm is used for global search and hill-climbing algorithm is used for local search. This can not only improve the poor local search ability of genetic algorithm, but also greatly save calculation time. This method can identify four parameters of stator resistance, d-q axis inductance and permanent magnet flux linkage simultaneously. The performance of traditional GA and proposed Is-hGA in IPMSM parameter identification is compared by constructing an experimental platform. As a result, the proposed method can have more accurate identification precise.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/09205071.2021.2022004 (text/html)
Access to full text is restricted to subscribers.

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:taf:tewaxx:v:36:y:2022:i:9:p:1311-1322

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tewa20

DOI: 10.1080/09205071.2021.2022004

Access Statistics for this article

Journal of Electromagnetic Waves and Applications is currently edited by Mohamad Abou El-Nasr and Pankaj Kumar Choudhury

More articles in Journal of Electromagnetic Waves and Applications from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().