基于径向基网络的同步磁阻电机优化

Q3 Engineering

Serbian Journal of Electrical Engineering Pub Date : 2020-01-01 DOI:10.2298/sjee2002223n

Amir Erfani, J. Faiz

{"title":"基于径向基网络的同步磁阻电机优化","authors":"Amir Erfani, J. Faiz","doi":"10.2298/sjee2002223n","DOIUrl":null,"url":null,"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.","PeriodicalId":37704,"journal":{"name":"Serbian Journal of Electrical Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Optimization of synchronous reluctance motor based on radial basis network\",\"authors\":\"Amir Erfani, J. Faiz\",\"doi\":\"10.2298/sjee2002223n\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":37704,\"journal\":{\"name\":\"Serbian Journal of Electrical Engineering\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Serbian Journal of Electrical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2298/sjee2002223n\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Serbian Journal of Electrical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2298/sjee2002223n","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}

引用次数: 2

摘要

提出了一种基于代理模型的横向层合转子同步磁阻电机优化方法。首先训练一个具有12个输入变量和3个输出的径向基函数(RBF)模型。利用有限元法对RBF模型参数进行估计，得到数据集。通过建立RBF模型，RBF网络能够较好地预测SynRm的输出，利用非支配排序遗传算法(NSGA II)得到pareto front。SynRm的设计目的是在约束转矩脉动的情况下，最大限度地提高电机的最大开发扭矩和功率因数。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Optimization of synchronous reluctance motor based on radial basis network

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Serbian Journal of Electrical Engineering Energy-Energy Engineering and Power Technology

CiteScore

1.30

自引率

0.00%

发文量

审稿时长

25 weeks

期刊介绍： The main aims of the Journal are to publish peer review papers giving results of the fundamental and applied research in the field of electrical engineering. The Journal covers a wide scope of problems in the following scientific fields: Applied and Theoretical Electromagnetics, Instrumentation and Measurement, Power Engineering, Power Systems, Electrical Machines, Electrical Drives, Electronics, Telecommunications, Computer Engineering, Automatic Control and Systems, Mechatronics, Electrical Materials, Information Technologies, Engineering Mathematics, etc.