Improved sampling algorithm for stochastic modelling of random-wound electrical machines

Lehikoinen, Antti; Chiodetto, Nicola; Arkkio, Antero; Belahcen, Anouar

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Improved sampling algorithm for stochastic modelling of random-wound electrical machines

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Title:	Improved sampling algorithm for stochastic modelling of random-wound electrical machines
Author(s):	Lehikoinen, Antti ; Chiodetto, Nicola ; Arkkio, Antero ; Belahcen, Anouar
Date:	2019-06-17
Language:	en
Pages:	5 3976-3980
Department:	Department of Electrical Engineering and Automation Newcastle University Computational Electromechanics
Series:	The Journal of Engineering, issue 17
ISSN:	2051-3305
DOI-number:	10.1049/joe.2018.8093
Keywords:	stochastic processes, electric machines, Monte Carlo methods, machine windings, sampling methods, improved sampling algorithm, stochastic modelling, random-wound electrical machines, high circulating current losses, circuit model, Monte Carlo analysis
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Citation: Lehikoinen , A , Chiodetto , N , Arkkio , A & Belahcen , A 2019 , ' Improved sampling algorithm for stochastic modelling of random-wound electrical machines ' , The Journal of Engineering , no. 17 , pp. 3976-3980 . https://doi.org/10.1049/joe.2018.8093
Abstract: Random-wound electrical machines often suffer from high circulating current losses. These losses vary from machine to machine in a stochastic fashion. This study proposes an improved sampling algorithm for quantifying the uncertainty inherent in random windings. The algorithm is then combined with a circuit model to perform Monte Carlo analysis on the losses. The results are compared to measurements, and a good agreement is observed.

Description: \| openaire: EC/H2020/339380/EU//ALEM
Permanent link to this item: http://urn.fi/URN:NBN:fi:aalto-202001021212 Email this Export to RefWorks QR Code Print BibTex Tweet