Online incremental inductance identification for reluctance synchronous motors

Matteo Berto; Luigi Alberti; Floran Martin; Marko Hinkkanen

doi:10.1109/IECON48115.2021.9589537

Online incremental inductance identification for reluctance synchronous motors

Matteo Berto^*, Luigi Alberti, Floran Martin, Marko Hinkkanen

^*Corresponding author for this work

University of Padova

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

2 Citations (Scopus)

242 Downloads (Pure)

Abstract

The paper deals with the online incremental inductances estimation of a synchronous motor at low speeds using a high-frequency voltage injection. The control scheme is analogous to that used in position estimation algorithms, with the difference that the current control and the rotating voltage injection operate on the real dq axes. Thus a position sensor is required to apply this method. The corresponding current response is measured, filtered, and processed with an ellipse fitting technique. The estimated ellipse coefficients are then used to retrieve the incremental inductances online without the need of any post processing. A novel formulation to express the estimation error valid for other conventional signal injection techniques is presented. The method has been validated experimentally on a reluctance synchronous motor at locked rotor and during load and speed transients.

Original language	English
Title of host publication	IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society
Publisher	IEEE
Number of pages	6
ISBN (Electronic)	978-1-6654-3554-3
ISBN (Print)	978-1-6654-0256-9
DOIs	https://doi.org/10.1109/IECON48115.2021.9589537
Publication status	Published - 13 Nov 2021
MoE publication type	A4 Conference publication
Event	Annual Conference of the IEEE Industrial Electronics Society - Virtual, online, Toronto, Canada Duration: 13 Oct 2021 → 16 Oct 2021 Conference number: 47 https://ieeeiecon.org/

Publication series

Name	Proceedings of the Annual Conference of the IEEE Industrial Electronics Society
ISSN (Electronic)	2577-1647

Conference

Conference	Annual Conference of the IEEE Industrial Electronics Society
Abbreviated title	IECON
Country/Territory	Canada
City	Toronto
Period	13/10/2021 → 16/10/2021
Internet address	https://ieeeiecon.org/

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10.1109/IECON48115.2021.9589537

ELEC_Berto_etal_Online_Incremental_Inductance_Identification_IEEE_IECON_2021Accepted author manuscript, 1.78 MB

Cite this

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title = "Online incremental inductance identification for reluctance synchronous motors",

abstract = "The paper deals with the online incremental inductances estimation of a synchronous motor at low speeds using a high-frequency voltage injection. The control scheme is analogous to that used in position estimation algorithms, with the difference that the current control and the rotating voltage injection operate on the real dq axes. Thus a position sensor is required to apply this method. The corresponding current response is measured, filtered, and processed with an ellipse fitting technique. The estimated ellipse coefficients are then used to retrieve the incremental inductances online without the need of any post processing. A novel formulation to express the estimation error valid for other conventional signal injection techniques is presented. The method has been validated experimentally on a reluctance synchronous motor at locked rotor and during load and speed transients.",

author = "Matteo Berto and Luigi Alberti and Floran Martin and Marko Hinkkanen",

year = "2021",

month = nov,

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language = "English",

isbn = "978-1-6654-0256-9",

series = "Proceedings of the Annual Conference of the IEEE Industrial Electronics Society",

publisher = "IEEE",

booktitle = "IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society",

address = "United States",

note = "Annual Conference of the IEEE Industrial Electronics Society, IECON ; Conference date: 13-10-2021 Through 16-10-2021",

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Berto, M, Alberti, L, Martin, F & Hinkkanen, M 2021, Online incremental inductance identification for reluctance synchronous motors. in IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society. Proceedings of the Annual Conference of the IEEE Industrial Electronics Society, IEEE, Annual Conference of the IEEE Industrial Electronics Society, Toronto, Ontario, Canada, 13/10/2021. https://doi.org/10.1109/IECON48115.2021.9589537

Online incremental inductance identification for reluctance synchronous motors. / Berto, Matteo; Alberti, Luigi; Martin, Floran et al.
IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2021. (Proceedings of the Annual Conference of the IEEE Industrial Electronics Society).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Online incremental inductance identification for reluctance synchronous motors

AU - Berto, Matteo

AU - Alberti, Luigi

AU - Martin, Floran

AU - Hinkkanen, Marko

N1 - Conference code: 47

PY - 2021/11/13

Y1 - 2021/11/13

N2 - The paper deals with the online incremental inductances estimation of a synchronous motor at low speeds using a high-frequency voltage injection. The control scheme is analogous to that used in position estimation algorithms, with the difference that the current control and the rotating voltage injection operate on the real dq axes. Thus a position sensor is required to apply this method. The corresponding current response is measured, filtered, and processed with an ellipse fitting technique. The estimated ellipse coefficients are then used to retrieve the incremental inductances online without the need of any post processing. A novel formulation to express the estimation error valid for other conventional signal injection techniques is presented. The method has been validated experimentally on a reluctance synchronous motor at locked rotor and during load and speed transients.

AB - The paper deals with the online incremental inductances estimation of a synchronous motor at low speeds using a high-frequency voltage injection. The control scheme is analogous to that used in position estimation algorithms, with the difference that the current control and the rotating voltage injection operate on the real dq axes. Thus a position sensor is required to apply this method. The corresponding current response is measured, filtered, and processed with an ellipse fitting technique. The estimated ellipse coefficients are then used to retrieve the incremental inductances online without the need of any post processing. A novel formulation to express the estimation error valid for other conventional signal injection techniques is presented. The method has been validated experimentally on a reluctance synchronous motor at locked rotor and during load and speed transients.

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U2 - 10.1109/IECON48115.2021.9589537

DO - 10.1109/IECON48115.2021.9589537

M3 - Conference article in proceedings

SN - 978-1-6654-0256-9

T3 - Proceedings of the Annual Conference of the IEEE Industrial Electronics Society

BT - IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society

PB - IEEE

T2 - Annual Conference of the IEEE Industrial Electronics Society

Y2 - 13 October 2021 through 16 October 2021

ER -

Online incremental inductance identification for reluctance synchronous motors

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Matteo Berto

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Online incremental inductance identification for reluctance synchronous motors

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Matteo Berto

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