Interest and Applicability of Meta-Heuristic Algorithms in the Electrical Parameter Identification of Multiphase Machines

Daniel Gutierrez-Reina, Federico Barrero, Jose Riveros, Ignacio Gonzalez-Prieto, Sergio L. Toral and Mario J. Duran
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Daniel Gutierrez-Reina: Department of Engineering, Loyola University Andalusia, 41014 Seville, Spain
Federico Barrero: Electronic Engineering Department, University of Seville, 41092 Sevilla, Spain
Jose Riveros: Faculty of Engineering, University of Talca, Curicó 3340000, Chile
Ignacio Gonzalez-Prieto: Thermal and Electrical Engineering Department, University of Huelva, 21007 Huelva, Spain
Sergio L. Toral: Electronic Engineering Department, University of Seville, 41092 Sevilla, Spain
Mario J. Duran: Department of Electrical Engineering, University of Malaga, 29071 Malaga, Spain

Energies, 2019, vol. 12, issue 2, 1-15

Abstract: Multiphase machines are complex multi-variable electro-mechanical systems that are receiving special attention from industry due to their better fault tolerance and power-per-phase splitting characteristics compared with conventional three-phase machines. Their utility and interest are restricted to the definition of high-performance controllers, which strongly depends on the knowledge of the electrical parameters used in the multiphase machine model. This work presents the proof-of-concept of a new method based on particle swarm optimization and standstill time-domain tests. This proposed method is tested to estimate the electrical parameters of a five-phase induction machine. A reduction of the estimation error higher than 2.5% is obtained compared with gradient-based approaches.

Keywords: multiphase drives; off-line identification methods; meta-heuristic algorithms (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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