Design optimization of Quasi-flat linear PM generator for wave energy converter in south coast of Java using flower pollination algorithm

Budi Azhari, F. Danang Wijaya, Muhammad Rifa'i Putra Sugita, Kukuh Daud Pribadi
{"title":"Design optimization of Quasi-flat linear PM generator for wave energy converter in south coast of Java using flower pollination algorithm","authors":"Budi Azhari, F. Danang Wijaya, Muhammad Rifa'i Putra Sugita, Kukuh Daud Pribadi","doi":"10.1109/INAES.2017.8068569","DOIUrl":null,"url":null,"abstract":"South coast of Java has been highly preferred as the placement location of wave energy converter, due to its relatively huge energy potential. Furthermore, linear permanent magnet generator (LPMG) has became one of the popular methods to convert wave energy to electrical energy. Based on the shape of stator core, there are several models of LPMG. Quasi-flat LPMG is one of them, which has rectangular stator core shape. In this paper, the quasi-flat LPMG would be designed and optimized. The design considered wave characteristics in the south coast of Java. Meanwhile, the optimization was performed to minimize resulted copper loss for equal output power. To achieve the optimization goal, flower pollination algorithm (FPA) was used. This algorithm continuously adjusts several geometrical parameter of the LPMG by certain mechanism until the objective function is met. The results showed that the optimization could reduce the resulted copper loss of about 71.95%. It also increased the electrical efficiency from 83.2% to 95.28%.","PeriodicalId":382919,"journal":{"name":"2017 7th International Annual Engineering Seminar (InAES)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 7th International Annual Engineering Seminar (InAES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INAES.2017.8068569","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

South coast of Java has been highly preferred as the placement location of wave energy converter, due to its relatively huge energy potential. Furthermore, linear permanent magnet generator (LPMG) has became one of the popular methods to convert wave energy to electrical energy. Based on the shape of stator core, there are several models of LPMG. Quasi-flat LPMG is one of them, which has rectangular stator core shape. In this paper, the quasi-flat LPMG would be designed and optimized. The design considered wave characteristics in the south coast of Java. Meanwhile, the optimization was performed to minimize resulted copper loss for equal output power. To achieve the optimization goal, flower pollination algorithm (FPA) was used. This algorithm continuously adjusts several geometrical parameter of the LPMG by certain mechanism until the objective function is met. The results showed that the optimization could reduce the resulted copper loss of about 71.95%. It also increased the electrical efficiency from 83.2% to 95.28%.
基于花授粉算法的爪哇南海岸波浪能转换器准平面线性PM发电机设计优化
爪哇南海岸由于其相对巨大的能源潜力,一直是波浪能转换器的首选放置地点。此外,线性永磁发电机(LPMG)已成为波浪能转换为电能的常用方法之一。根据定子铁心的形状,有几种不同的模型。准平板型LPMG就是其中一种,定子铁芯形状为矩形。本文将对准平面型LPMG进行设计和优化。设计考虑了爪哇南海岸的波浪特性。同时,在输出功率相等的情况下,进行了铜损耗最小化的优化。为了实现优化目标,采用了花授粉算法(FPA)。该算法通过一定的机制不断调整LPMG的多个几何参数,直到满足目标函数。结果表明,优化后铜的损失可降低约71.95%。它还将电气效率从83.2%提高到95.28%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信