Affiliation:
1. Centre of Excellent on Applied Electromagnetic Systems, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
Abstract
This paper presents surrogate-model based optimization for synchronous
reluctance motor (SynRm) with transversally laminated rotor. A radial basis
function (RBF) model with 12 input variables and three outputs is first
trained. A dataset is obtained using finite element method to estimate
parameters of RBF model. By building RBF model, the RBF network can predicts
the outputs of the SynRm with good accuracy Using non-dominated sorting
genetic algorithm (NSGA II), pareto front is obtained. The SynRm is designed
to maximize the maximum developed torque and power factor of the motor with
constrained torque ripple.
Publisher
National Library of Serbia
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Mechanical Engineering,Energy Engineering and Power Technology,Control and Systems Engineering
Cited by
2 articles.
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1. Research on Financial Risk Prediction Method of Listed Companies Based on GA-RBF Neural Network;2022 6th International Symposium on Computer Science and Intelligent Control (ISCSIC);2022-11
2. Optimization of a High-Speed Synchronous Reluctance Machine's Rotor Topology;2021 International Conference on Electrotechnical Complexes and Systems (ICOECS);2021-11-16