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

Antti Lehikoinen; Nicola Chiodetto; Antero Arkkio; Anouar Belahcen

doi:10.1049/joe.2018.8093

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

Antti Lehikoinen^*, Nicola Chiodetto, Antero Arkkio, Anouar Belahcen

^*Corresponding author for this work

Newcastle University

Research output: Contribution to journal › Article › Scientific › peer-review

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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.

Original language	English
Pages (from-to)	3976-3980
Number of pages	5
Journal	The Journal of Engineering
Issue number	17
DOIs	https://doi.org/10.1049/joe.2018.8093
Publication status	Published - 17 Jun 2019
MoE publication type	A1 Journal article-refereed

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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eLEC_Lehikoinen_et_al_Improved_sampling_algorithm_The_Journal_of_EngineeringFinal published version, 2.91 MBLicence: CC BY

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title = "Improved sampling algorithm for stochastic modelling of random-wound electrical machines",

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.",

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",

author = "Antti Lehikoinen and Nicola Chiodetto and Antero Arkkio and Anouar Belahcen",

note = "| openaire: EC/H2020/339380/EU//ALEM",

year = "2019",

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doi = "10.1049/joe.2018.8093",

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AU - Lehikoinen, Antti

AU - Chiodetto, Nicola

AU - Arkkio, Antero

AU - Belahcen, Anouar

N1 - | openaire: EC/H2020/339380/EU//ALEM

PY - 2019/6/17

Y1 - 2019/6/17

N2 - 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.

AB - 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.

KW - stochastic processes

KW - electric machines

KW - Monte Carlo methods

KW - machine windings

KW - sampling methods

KW - improved sampling algorithm

KW - stochastic modelling

KW - random-wound electrical machines

KW - high circulating current losses

KW - circuit model

KW - Monte Carlo analysis

U2 - 10.1049/joe.2018.8093

DO - 10.1049/joe.2018.8093

M3 - Article

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SP - 3976

EP - 3980

JO - The Journal of Engineering

JF - The Journal of Engineering

IS - 17

ER -

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

Abstract

Keywords

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