Automated Maximum Torque per Ampere Identification for Synchronous Reluctance Machines with Limited Flux Linkage Information

Shuo Wang, Vasyl Varvolik, Yuli Bao, Ahmed Aboelhassan, Michele Degano, Giampaolo Buticchi, He Zhang

Research output: Journal PublicationArticlepeer-review

2 Citations (Scopus)

Abstract

The synchronous reluctance machine is well-known for its highly nonlinear magnetic saturation and cross-saturation characteristics. For high performance and high-efficiency control, the flux-linkage maps and maximum torque per ampere table are of paramount importance. This study proposes a novel automated online searching method for obtaining accurate flux-linkage and maximum torque per ampere Identification. A limited 6 × 2 dq-axis flux-linkage look-up table is acquired by applying symmetric triangle pulses during the self-commissioning stage. Then, three three-dimensional modified linear cubic spline interpolation methods are applied to extend the flux-linkage map. The proposed golden section searching method can be easily implemented to realize higher maximum torque per ampere accuracy after 11 iterations with a standard drive, which is a proven faster solution with reduced memory sources occupied. The proposed algorithm is verified and tested on a 15-kW SynRM drive. Furthermore, the iterative and execution times are evaluated.

Original languageEnglish
Article number96
JournalMachines
Volume12
Issue number2
DOIs
Publication statusPublished - Feb 2024

Keywords

  • flux-linkage map
  • golden section searching method
  • magnetic saturation
  • maximum torque per ampere
  • synchronous reluctance motors
  • three-dimensional modified linear cubic spline interpolation method

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science (miscellaneous)
  • Mechanical Engineering
  • Control and Optimization
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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