Novel optimisation algorithm of electrical machines

Zheng Tan; N.J. Baker; Wenping Cao

doi:10.1049/cp.2016.0323

Novel optimisation algorithm of electrical machines

Zheng Tan, N.J. Baker, Wenping Cao

College of Engineering and Physical Sciences

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

This paper presents a case study for multi-variable and multi-modal design optimisation of a doubly fed induction generator (DFIG) based on surrogate-model optimisation algorithm. The DFIG's winding of stator and rotor are optimised to obtain higher efficiency for rewinding purposes. First, a Latin hypercube design is selected as the design of experiments to obtain sampling points. Then, the surrogate model is constructed using Kriging Model (KRG) method based on the Latin hypercube design. Finally, the particle swarm optimisation algorithm is applied in conjunction with the finite element method to achieve the machine design optimisation.

Original language	English
Title of host publication	8th IET International Conference on Power Electronics, Machines and Drives (PEMD 2016), 2016
Publisher	IET
ISBN (Print)	978-1-78561-188-9
DOIs	https://doi.org/10.1049/cp.2016.0323
Publication status	Published - 10 Nov 2016
Event	8th IET International Conference on Power Electronics, Machines and Drives - Glasgow, United Kingdom Duration: 19 Apr 2016 → 21 Apr 2016

Conference

Conference	8th IET International Conference on Power Electronics, Machines and Drives
Abbreviated title	PEMD 2016
Country/Territory	United Kingdom
City	Glasgow
Period	19/04/16 → 21/04/16

Bibliographical note

-

Keywords

DFIG
Kriging model
particle swarm optimisation

Access to Document

10.1049/cp.2016.0323

Cite this

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title = "Novel optimisation algorithm of electrical machines",

abstract = "This paper presents a case study for multi-variable and multi-modal design optimisation of a doubly fed induction generator (DFIG) based on surrogate-model optimisation algorithm. The DFIG's winding of stator and rotor are optimised to obtain higher efficiency for rewinding purposes. First, a Latin hypercube design is selected as the design of experiments to obtain sampling points. Then, the surrogate model is constructed using Kriging Model (KRG) method based on the Latin hypercube design. Finally, the particle swarm optimisation algorithm is applied in conjunction with the finite element method to achieve the machine design optimisation.",

keywords = "DFIG, Kriging model, particle swarm optimisation",

author = "Zheng Tan and N.J. Baker and Wenping Cao",

note = "-; 8th IET International Conference on Power Electronics, Machines and Drives, PEMD 2016 ; Conference date: 19-04-2016 Through 21-04-2016",

year = "2016",

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doi = "10.1049/cp.2016.0323",

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TY - GEN

T1 - Novel optimisation algorithm of electrical machines

AU - Tan, Zheng

AU - Baker, N.J.

AU - Cao, Wenping

N1 - -

PY - 2016/11/10

Y1 - 2016/11/10

N2 - This paper presents a case study for multi-variable and multi-modal design optimisation of a doubly fed induction generator (DFIG) based on surrogate-model optimisation algorithm. The DFIG's winding of stator and rotor are optimised to obtain higher efficiency for rewinding purposes. First, a Latin hypercube design is selected as the design of experiments to obtain sampling points. Then, the surrogate model is constructed using Kriging Model (KRG) method based on the Latin hypercube design. Finally, the particle swarm optimisation algorithm is applied in conjunction with the finite element method to achieve the machine design optimisation.

AB - This paper presents a case study for multi-variable and multi-modal design optimisation of a doubly fed induction generator (DFIG) based on surrogate-model optimisation algorithm. The DFIG's winding of stator and rotor are optimised to obtain higher efficiency for rewinding purposes. First, a Latin hypercube design is selected as the design of experiments to obtain sampling points. Then, the surrogate model is constructed using Kriging Model (KRG) method based on the Latin hypercube design. Finally, the particle swarm optimisation algorithm is applied in conjunction with the finite element method to achieve the machine design optimisation.

KW - DFIG

KW - Kriging model

KW - particle swarm optimisation

UR - http://www.scopus.com/inward/record.url?scp=85009100538&partnerID=8YFLogxK

UR - http://digital-library.theiet.org/content/conferences/10.1049/cp.2016.0323

U2 - 10.1049/cp.2016.0323

DO - 10.1049/cp.2016.0323

M3 - Conference publication

AN - SCOPUS:85009100538

SN - 978-1-78561-188-9

BT - 8th IET International Conference on Power Electronics, Machines and Drives (PEMD 2016), 2016

PB - IET

T2 - 8th IET International Conference on Power Electronics, Machines and Drives

Y2 - 19 April 2016 through 21 April 2016

ER -

Novel optimisation algorithm of electrical machines

Abstract

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

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