Multi-Objective Bayesian Optimization of Squirrel-Cage Induction Machine
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F24%3APU151567" target="_blank" >RIV/00216305:26220/24:PU151567 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10700205" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10700205</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICEM60801.2024.10700205" target="_blank" >10.1109/ICEM60801.2024.10700205</a>
Alternative languages
Result language
angličtina
Original language name
Multi-Objective Bayesian Optimization of Squirrel-Cage Induction Machine
Original language description
In electrical engineering, a design of electrical machine using numerical methods, such as Finite element method, is a common practice. Electrical machines are complex multi-physical systems where for finding the optimal sets of designs solutions, called Pareto fronts, a very effective approach is to use multi-objective optimization. The most popular method for multi-objective optimization of machine design is the use of numerical optimization algorithms such as NSGA-II. However, due to the time-consuming nature of induction machines simulations, this approach is not very effective. This paper addresses this issue by proposing machine learning as a solution, specifically utilizing Multi-objective Bayesian optimization. This optimization method has been used in many industries as an efficient global optimization of the modeled system. By using the right acquisition function, the search space can be efficiently navigated to find the optimal candidates. Moreover, the optimization requires only a limited number of samples. The main aim of this paper is to present this method, which is demonstrated on the optimization of a 1.5 kW induction machine with time-consuming calculations. The machine optimization approach is not the main focus here, as this method can be effectively applied to any machine design or even any optimization approach. Furthermore, two possible approaches of machine optimization using this method are presented here.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2024 International Conference on Electrical Machines (ICEM)
ISBN
979-8-3503-7060-7
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
„“-„“
Publisher name
IEEE
Place of publication
Turín, Itálie
Event location
Torino
Event date
Sep 1, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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