Data-driven-based vector space decomposition modeling of multiphase induction machines

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Abu-Seif, Mohamed A. and Ahmed, Mohamed and Metwly, Mohamed Y. and Abdel-Khalik, Ayman S. and Hamad, Mostafa S. and Ahmed, Shehab and Elmalhy, Noha (2023) Data-driven-based vector space decomposition modeling of multiphase induction machines. IEEE Transactions on Energy Conversion, 38 (3). pp. 2061-2074. ISSN 0885-8969 (https://doi.org/10.1109/TEC.2023.3255792)

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Abstract

For contemporary variable-speed electric drives, the accuracy of the machine's mathematical model is critical for optimal control performance. Basically, phase variables of multiphase machines are preferably decomposed into multiple orthogonal subspaces based on vector space decomposition (VSD). In the available literature, identifying the correlation between states governed by the dynamic equations and the parameter estimate of different subspaces of multiphase IM remains scarce, especially under unbalanced conditions, where the effect of secondary subspaces sounds influential. Most available literature has relied on simple RL circuit representation to model these secondary subspaces. To this end, this paper presents an effective data-driven-based space harmonic model for n-phase IMs using sparsity-promoting techniques and machine learning with nonlinear dynamical systems to discover the IM governing equations. Moreover, the proposed approach is computationally efficient, and it precisely identifies both the electrical and mechanical dynamics of all subspaces of an IM using a single transient startup run. Additionally, the derived model can be reformulated into the standard canonical form of the induction machine model to easily extract the parameters of all subspaces based on online measurements. Eventually, the proposed modeling approach is experimentally validated using a 1.5 Hp asymmetrical six-phase induction machine.

ORCID iDs

Abu-Seif, Mohamed A., Ahmed, Mohamed ORCID logoORCID: https://orcid.org/0000-0002-8569-4763, Metwly, Mohamed Y., Abdel-Khalik, Ayman S., Hamad, Mostafa S., Ahmed, Shehab and Elmalhy, Noha;
  • Item type:Article
    ID code:87636
    Dates:
    Date
    Event
    30 September 2023
    Published
    22 August 2023
    Published Online
    5 March 2023
    Accepted
    3 October 2022
    Submitted
    Notes:Copyright © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering > Production of electric energy or power
    Technology > Electrical engineering. Electronics Nuclear engineering
    Department:?? 13050 ??
    Depositing user: Pure Administrator
    Date deposited:14 Dec 2023 15:38
    Last modified:14 Nov 2024 01:19
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/87636
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