Open AccessAlienor method applied to induction machine parameters identification
Author(s) -
Latifa Khemici,
M. Bounekhla,
Elghalia Boudissa
Publication year - 2020
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v10i1.pp223-232
Subject(s) - identification (biology) , transient (computer programming) , computer science , transformation (genetics) , minification , multivariable calculus , voltage , genetic algorithm , algorithm , control theory (sociology) , artificial intelligence , control engineering , engineering , machine learning , biochemistry , chemistry , botany , electrical engineering , control (management) , biology , gene , programming language , operating system
This paper presents an identification method to estimate simultaneously the electrical and mechanical induction machine (IM) parameters by using only the measured current and the corresponding phase voltage. This identification method is based on the output error and uses the multidimensional Alienor global optimization method as a minimization technique. Alienor method is essentially based on converting multivariable problem to monovariable one. To improve the Alienor method performance, the reducing transformation is proposed and compared with the genetic algorithm (GA). Firstly, the identification method is verified using the simulated data. Secondly, the validation is then confirmed by measured data from one machine. The corresponding computed transient and steady state currents agree well with the measured data. The results obtained show the superiority of the proposed Alienor method versus GA in terms of computing time.