LAPSE:2023.25326
Published Article
LAPSE:2023.25326
Intelligent Optimization of Switched Reluctance Motor Using Genetic Aggregation Response Surface and Multi-Objective Genetic Algorithm for Improved Performance
Chiweta Emmanuel Abunike, Ogbonnaya Inya Okoro, Sumeet S. Aphale
March 28, 2023
In this paper, a thorough framework for multiobjective design optimization of switched reluctance motor (SRM) is proposed. Selection of stator and rotor pole embrace coefficients is an essential step in the SRM design process since it influences torque output and torque ripple in SRM. The problem of determining optimal pole embrace is formulated as a multi-objective optimization problem with the objective of optimizing average torque, efficiency and torque ripple, and response surface models were obtained based on the genetic aggregation method. The results obtained by genetic aggregation response surface (GARS) and the non-dominated genetic algorithm (NSGA-II) were validated with the finite element method (FEM) model of the initial SRM. The optimized model displayed better efficiency profile over a wide speed range. The initial and optimized models recorded maximum efficiencies of 85% and 94.05%, respectively, at 2000 rpm. The efficiency values of 93.97−94.05% were achieved for the three pareto optimal candidates. The findings indicate the viability of the suggested strategy and support the use of GARS and NSGA-II as useful methods for addressing SRM key challenges.
Keywords
efficiency, genetic aggregation response surface, Genetic Algorithm, pole embrace coefficients, switched reluctance motor, torque ripple
Suggested Citation
Abunike CE, Okoro OI, Aphale SS. Intelligent Optimization of Switched Reluctance Motor Using Genetic Aggregation Response Surface and Multi-Objective Genetic Algorithm for Improved Performance. (2023). LAPSE:2023.25326
Author Affiliations
Abunike CE: School of Engineering, University of Aberdeen, Aberdeen AB24 3UE, UK; Department of Electrical/Electronic Engineering, Michael Okpara University of Agriculture, Umudike 440101, Abia State, Nigeria
Okoro OI: Department of Electrical/Electronic Engineering, Michael Okpara University of Agriculture, Umudike 440101, Abia State, Nigeria [ORCID]
Aphale SS: School of Engineering, University of Aberdeen, Aberdeen AB24 3UE, UK [ORCID]
Journal Name
Energies
Volume
15
Issue
16
First Page
6086
Year
2022
Publication Date
2022-08-22
Published Version
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en15166086, Publication Type: Journal Article
Record Map
Published Article

LAPSE:2023.25326
This Record
External Link

doi:10.3390/en15166086
Publisher Version
Download
Files
[Download 1v1.pdf] (4.1 MB)
Mar 28, 2023
Main Article
License
CC BY 4.0
Meta
Record Statistics
Record Views
79
Version History
[v1] (Original Submission)
Mar 28, 2023
 
Verified by curator on
Mar 28, 2023
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2023.25326
 
Original Submitter
Auto Uploader for LAPSE
Links to Related Works
Directly Related to This Work
Publisher Version