Soft computing applications in the electric power industry

H. Vanlandingham, F. Azam
{"title":"Soft computing applications in the electric power industry","authors":"H. Vanlandingham, F. Azam","doi":"10.1109/SMCIA.1999.782698","DOIUrl":null,"url":null,"abstract":"This paper focuses on two distinct types of problems; namely, sensor redundancy and set-point control. For the latter problem the workhorse of the industry is the 3-element proportional-integral-derivative (PID) controller which can be tuned to provide reasonable performance over a relatively wide range of operation and control problems. PID controllers can, however, become detuned over time as operators continually make minor adjustments. Solutions to the problems of sensor redundancy and self-tuning controllers are discussed using artificial neural networks (ANNs), which can learn in a static mode in the case of sensor redundancy, or dynamically (on-line) in the case of self-tuning adaptation.","PeriodicalId":222278,"journal":{"name":"SMCia/99 Proceedings of the 1999 IEEE Midnight - Sun Workshop on Soft Computing Methods in Industrial Applications (Cat. No.99EX269)","volume":"159 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SMCia/99 Proceedings of the 1999 IEEE Midnight - Sun Workshop on Soft Computing Methods in Industrial Applications (Cat. No.99EX269)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SMCIA.1999.782698","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

This paper focuses on two distinct types of problems; namely, sensor redundancy and set-point control. For the latter problem the workhorse of the industry is the 3-element proportional-integral-derivative (PID) controller which can be tuned to provide reasonable performance over a relatively wide range of operation and control problems. PID controllers can, however, become detuned over time as operators continually make minor adjustments. Solutions to the problems of sensor redundancy and self-tuning controllers are discussed using artificial neural networks (ANNs), which can learn in a static mode in the case of sensor redundancy, or dynamically (on-line) in the case of self-tuning adaptation.
软计算在电力工业中的应用
本文主要关注两种不同类型的问题;即传感器冗余和设定点控制。对于后一个问题,工业界的主要工作是3元比例-积分-导数(PID)控制器,它可以在相对广泛的操作和控制问题上进行调整,以提供合理的性能。然而,随着时间的推移,随着操作员不断地进行微小的调整,PID控制器可能会变得失谐。利用人工神经网络(ANNs)讨论了传感器冗余和自整定控制器问题的解决方案,在传感器冗余的情况下,它可以静态模式学习,在自整定适应的情况下,它可以动态(在线)学习。
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:604180095
Book学术官方微信