Permanent magnet synchronous machines inter-turn short circuit fault detection by means of model-based residual analysis

Fernando Alvarez-Gonzalez; Antonio Griffo; Bo Wang

doi:10.1109/IECON.2018.8591661

Permanent magnet synchronous machines inter-turn short circuit fault detection by means of model-based residual analysis

Fernando Alvarez-Gonzalez
, Antonio Griffo
, Bo Wang

University of Sheffield

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

18 Citations (Scopus)

Abstract

Short circuit faults in the stator winding are a common failure mode in Permanent Magnet Synchronous Machines (PMSM). Model-based fault detection has a potential benefit of being less sensitive to transient conditions compared to frequency-domain based methods for fault detection. However, accurate models of the machine being monitored are required in order to produce accurate fault detection whilst avoiding false alarms. This paper presents a model-based fault detection method based on the analysis of the residual between the measured currents and those predicted by an accurate real-time model of the machine. The model is nonlinear and based in pre-calculated flux linkages look up tables obtained through magnetostatic finite element analysis (FEA), accounting for magnetic saturation and spatial harmonics. The use of a residual provides a mean to amplify frequency components caused by a fault. Computer simulations, real-time Hardware-in-the-Loop (HIL) methods and experimental validations are provided.

Original language	English
Title of host publication	Proceedings
Subtitle of host publication	IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	647-652
Number of pages	6
ISBN (Electronic)	9781509066841
DOIs	https://doi.org/10.1109/IECON.2018.8591661
Publication status	Published - 26 Dec 2018
Externally published	Yes
Event	44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018 - Washington, United States Duration: 20 Oct 2018 → 23 Oct 2018

Publication series

Name	Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society

Conference

Conference	44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018
Country/Territory	United States
City	Washington
Period	20/10/18 → 23/10/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Fault detection
Hardware-in-the-Loop
Inter-turn fault
Modelling
Permanent Magnet Synchronous Machines

Access to Document

10.1109/IECON.2018.8591661

Cite this

Alvarez-Gonzalez, F., Griffo, A., & Wang, B. (2018). Permanent magnet synchronous machines inter-turn short circuit fault detection by means of model-based residual analysis. In Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society (pp. 647-652). Article 8591661 (Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IECON.2018.8591661

Alvarez-Gonzalez, Fernando ; Griffo, Antonio ; Wang, Bo. / Permanent magnet synchronous machines inter-turn short circuit fault detection by means of model-based residual analysis. Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 647-652 (Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society).

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title = "Permanent magnet synchronous machines inter-turn short circuit fault detection by means of model-based residual analysis",

abstract = "Short circuit faults in the stator winding are a common failure mode in Permanent Magnet Synchronous Machines (PMSM). Model-based fault detection has a potential benefit of being less sensitive to transient conditions compared to frequency-domain based methods for fault detection. However, accurate models of the machine being monitored are required in order to produce accurate fault detection whilst avoiding false alarms. This paper presents a model-based fault detection method based on the analysis of the residual between the measured currents and those predicted by an accurate real-time model of the machine. The model is nonlinear and based in pre-calculated flux linkages look up tables obtained through magnetostatic finite element analysis (FEA), accounting for magnetic saturation and spatial harmonics. The use of a residual provides a mean to amplify frequency components caused by a fault. Computer simulations, real-time Hardware-in-the-Loop (HIL) methods and experimental validations are provided.",

keywords = "Fault detection, Hardware-in-the-Loop, Inter-turn fault, Modelling, Permanent Magnet Synchronous Machines",

author = "Fernando Alvarez-Gonzalez and Antonio Griffo and Bo Wang",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018 ; Conference date: 20-10-2018 Through 23-10-2018",

year = "2018",

month = dec,

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doi = "10.1109/IECON.2018.8591661",

language = "English",

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Alvarez-Gonzalez, F, Griffo, A & Wang, B 2018, Permanent magnet synchronous machines inter-turn short circuit fault detection by means of model-based residual analysis. in Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society., 8591661, Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, Institute of Electrical and Electronics Engineers Inc., pp. 647-652, 44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018, Washington, United States, 20/10/18. https://doi.org/10.1109/IECON.2018.8591661

Permanent magnet synchronous machines inter-turn short circuit fault detection by means of model-based residual analysis. / Alvarez-Gonzalez, Fernando; Griffo, Antonio; Wang, Bo.
Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society. Institute of Electrical and Electronics Engineers Inc., 2018. p. 647-652 8591661 (Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Permanent magnet synchronous machines inter-turn short circuit fault detection by means of model-based residual analysis

AU - Alvarez-Gonzalez, Fernando

AU - Griffo, Antonio

AU - Wang, Bo

PY - 2018/12/26

Y1 - 2018/12/26

N2 - Short circuit faults in the stator winding are a common failure mode in Permanent Magnet Synchronous Machines (PMSM). Model-based fault detection has a potential benefit of being less sensitive to transient conditions compared to frequency-domain based methods for fault detection. However, accurate models of the machine being monitored are required in order to produce accurate fault detection whilst avoiding false alarms. This paper presents a model-based fault detection method based on the analysis of the residual between the measured currents and those predicted by an accurate real-time model of the machine. The model is nonlinear and based in pre-calculated flux linkages look up tables obtained through magnetostatic finite element analysis (FEA), accounting for magnetic saturation and spatial harmonics. The use of a residual provides a mean to amplify frequency components caused by a fault. Computer simulations, real-time Hardware-in-the-Loop (HIL) methods and experimental validations are provided.

AB - Short circuit faults in the stator winding are a common failure mode in Permanent Magnet Synchronous Machines (PMSM). Model-based fault detection has a potential benefit of being less sensitive to transient conditions compared to frequency-domain based methods for fault detection. However, accurate models of the machine being monitored are required in order to produce accurate fault detection whilst avoiding false alarms. This paper presents a model-based fault detection method based on the analysis of the residual between the measured currents and those predicted by an accurate real-time model of the machine. The model is nonlinear and based in pre-calculated flux linkages look up tables obtained through magnetostatic finite element analysis (FEA), accounting for magnetic saturation and spatial harmonics. The use of a residual provides a mean to amplify frequency components caused by a fault. Computer simulations, real-time Hardware-in-the-Loop (HIL) methods and experimental validations are provided.

KW - Fault detection

KW - Hardware-in-the-Loop

KW - Inter-turn fault

KW - Modelling

KW - Permanent Magnet Synchronous Machines

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U2 - 10.1109/IECON.2018.8591661

DO - 10.1109/IECON.2018.8591661

M3 - Conference contribution

AN - SCOPUS:85061540938

T3 - Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society

SP - 647

EP - 652

BT - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018

Y2 - 20 October 2018 through 23 October 2018

ER -

Alvarez-Gonzalez F, Griffo A, Wang B. Permanent magnet synchronous machines inter-turn short circuit fault detection by means of model-based residual analysis. In Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society. Institute of Electrical and Electronics Engineers Inc. 2018. p. 647-652. 8591661. (Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society). doi: 10.1109/IECON.2018.8591661

Permanent magnet synchronous machines inter-turn short circuit fault detection by means of model-based residual analysis

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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