Self-commissioning of permanent magnet synchronous machine drives using hybrid approach

M. Sertug Basar, Michael Møller Bech, Torben Ole Andersen, P. S. Andersen

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Abstract

Self-commissioning of permanent-magnet (PM) synchronous machines (PMSMs) is of prime importance in an industrial drive system because control performance and system stability depend heavily on the accurate machine parameter information. This article focuses on a combination of offline and online parameter estimation for a non-salient pole PMSM which eliminates the need for any prior knowledge on machine parameters. Stator resistance and inductance are first identified at standstill utilising fundamental and high-frequency excitation signals, respectively. A novel method has been developed and employed for inductance estimation. Then, stator resistance, inductance and PM flux are updated online using a recursive least-squares (RLS) algorithm. The proposed controllers are designed using MATLAB/Simulink® and implemented on d-Space® real-time system incorporating a commercially available PMSM drive.
Original languageEnglish
Title of host publicationPower Electronics, Machines and Drives (PEMD 2014), 7th IET International Conference on
Number of pages5
PublisherInstitution of Engineering and Technology
Publication date2014
Pages1-5
ISBN (Electronic)978-1-84919-815-8
DOIs
Publication statusPublished - 2014
Event7th IET International Conference on Power Electronics, Machines and Drives, PEMD 2014 - Manchester, United Kingdom
Duration: 8 Apr 201410 Apr 2014

Conference

Conference7th IET International Conference on Power Electronics, Machines and Drives, PEMD 2014
Country/TerritoryUnited Kingdom
CityManchester
Period08/04/201410/04/2014
SponsorALTERA, Cummins Generator Technologies, Emerson Electric Co., et al., Knowledge Transfer Network KTN, OMICRON
SeriesI E T Conference Publication Series
ISSN0537-9989

Keywords

  • High-frequency
  • Online
  • Parameter estimation
  • PMSM
  • RLS

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