Modelling and Detecting Faults of Permanent Magnet Synchronous Motors in Dynamic Operations
The thesis contributes by simplifying modelling and diagnosis of faults in permanent magnet synchronous motors (PMSMs), working with dynamic load- and speed profiles.
Sveinung Attestog
PhD Candidate
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Sveinung Attestog of the Faculty of Engineering and Science at the University of Agder has submitted his thesis entitled «Modelling and Detecting Faults of Permanent Magnet Synchronous Motors in Dynamic Operations» and will defend the thesis for the PhD-degree Monday 14 November 2022.
He has followed the PhD programme in Engineering and Science at UiA, with Specialisation in Engineering Sciences, scientific field Mechatronics.
Summary of the thesis by Sveinung Attestog:
Modelling and Detecting Faults of Permanent Magnet Synchronous Motors in Dynamic Operations
Permanent magnet synchronous motors (PMSMs) have played a key role in commercial and industrial applications, i.e. electric vehicles and wind turbines.
They are popular due to their high efficiency, control simplification and large torque-to-size ratio although they are expensive.
Faults occur
A fault will eventually occur in an operating PMSM, either by improper maintenance or wear from thermal and mechanical stresses. The most frequent PMSM faults are bearing faults, short-circuit and eccentricity.
PMSM may also suffer from demagnetisation, which is unique in permanent magnet machines.
Condition monitoring or fault diagnosis schemes are necessary for detecting and identifying these faults early in their incipient state, e.g. partial demagnetisation and inter-turn short circuit.
Successful fault classification will ensure safe operations, speed up the maintenance process and decrease unexpected downtime and cost.
Recent research
The research in recent years is drawn towards fault analysis under dynamic operating conditions, i.e. variable load and speed.
Most of these techniques have focused on the use of voltage, current and torque, while magnetic flux density in the air-gap or the proximity of the motor has not yet been fully capitalised.
Two main research topics
This dissertation focuses on two main research topics in modelling and diagnosis of faulty PMSM in dynamic operations.
The first problem is to decrease the computational burden of modelling and analysis techniques.
The first contributions are new and faster methods for computing the permeance network model and quadratic time-frequency distributions. Reducing their computational burden makes them more attractive in analysis or fault diagnosis.
The second contribution is to expand the model description of a simpler model. This can be achieved through a field reconstruction model with a magnet library and a description of both magnet defects and inter-turn short circuits.
The second research topic is to simplify the installation and complexity of fault diagnosis schemes in PMSM. The aim is to reduce required sensors of fault diagnosis schemes, regardless of operation profiles.
Conventional methods often rely on either steady-state or predefined operation profiles, e.g. start-up. A fault diagnosis scheme robust to any speed changes is desirable since a fault can be detected regardless of operations.
The final contribution is the implementation of reinforcement learning in an active learning scheme to address the imbalance dataset problem. Samples from a faulty PMSM are often initially unavailable and expensive to acquire. Reinforcement learning with a weighted reward function might balance the dataset to enhance the trained fault classifier’s performance.
Disputation facts:
The trial lecture and the public defence will take place on campus in Auditorium C2 040, Campus Grimstad, and online via the Zoom conferencing app - registration link below.
Dean, professor Michael Rygaard Hansen, Department of Engineering Sciences, Faculty of Engineering and Science, University of Agder, will chair the disputation.
The trial lecture Monday 14 November at 10:15 hours
Public defense Monday 14 November at 12:15 hours
Given topic for trial lecture: «Offline testing of synchronous machines for diagnostic purposes»
Thesis Title: «Modelling and Detecting Faults of Permanent Magnet Synchronous Motors in Dynamic Operations»
Search for the thesis in AURA - Agder University Research Archive, a digital archive of scientific papers, theses and dissertations from the academic staff and students at the University of Agder.
The thesis is available here:
https://uia.brage.unit.no/uia-xmlui/handle/11250/3025635
Paper VI is excluded from the dissertation until the article will be published. A full version of the thesis is available at the faculty - please contact Kristine Evensen Reinfjord (see left column).
The Candidate: Sveinung Attestog (1994, Bygland) Bachelor degree in Renewable Energy, UiA (2016), Masters degree in Renewable Energy, UiA (2018). Master thesis: «Electromagnetic and thermal modelling for prognosis of distribution transformer». Present position: Trainee at Trainee Sør, at present deployed at Agder Energy Net.
Opponents:
First opponent: Professor Mohamed Benbouzid, University of Brest, Brest, France
Second opponent: Associate Professor Sergio Manuel Ângelo da Cruz, Department of Electrical and Computer Engineering, University of Coimbra - Pole II (UC), Portugal
Professor Henrik Kofoed Nielsen, Faculty of Engineering and Science, University of Agder, is appointed as the administrator for the assessment committee.
Supervisors in the doctoral work were Professor Kjell Gunnar Robbersmyr, University of Agder (main supervisor) and Professor van Khang Huynh, University of Agder (co-supervisor)
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Opponent ex auditorio:
The chair invites members of the public to pose questions ex auditorio in the introduction to the public defense. Deadline is during the break between the two opponents. The person asking questions should have read the thesis. For online audience the Contact Persons e-mail are available in the chat function during the Public Defense, and questions ex auditorio can be submitted to Kristine Evensen Reinfjord at e-mail kristine.reinfjord@uia.no