住宅小区光伏识别的非侵入式负荷监测算法

Moreno Jaramillo, A. M. Jaramillo, D. Laverty, Jesús Martínez, del Rincón, P. Brogan, D. Morrow
{"title":"住宅小区光伏识别的非侵入式负荷监测算法","authors":"Moreno Jaramillo, A. M. Jaramillo, D. Laverty, Jesús Martínez, del Rincón, P. Brogan, D. Morrow","doi":"10.1109/ISSC49989.2020.9180192","DOIUrl":null,"url":null,"abstract":"This paper presents a novel approach for identification of photovoltaic systems in the residential sector. This is needed in response to increasing embedded generation on distribution networks. To date non-intrusive load monitoring techniques have focused mostly on identifying conventional loads on the customer side. This paper demonstrates the application of non-intrusive load monitoring to identify residential distributed generation, thereby enabling techniques to improve system flexibility and stability. The demonstrated methodology combines basic statistics with the Support Vector Machine technique, to identify PV load signatures. PMU measurements from the residential sector are used to aggregate measurements based largely on electric current records. The methods presented have applications for network operators, both in real time control and generation scheduling.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Non-Intrusive Load Monitoring Algorithm for PV Identification in the Residential Sector\",\"authors\":\"Moreno Jaramillo, A. M. Jaramillo, D. Laverty, Jesús Martínez, del Rincón, P. Brogan, D. Morrow\",\"doi\":\"10.1109/ISSC49989.2020.9180192\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a novel approach for identification of photovoltaic systems in the residential sector. This is needed in response to increasing embedded generation on distribution networks. To date non-intrusive load monitoring techniques have focused mostly on identifying conventional loads on the customer side. This paper demonstrates the application of non-intrusive load monitoring to identify residential distributed generation, thereby enabling techniques to improve system flexibility and stability. The demonstrated methodology combines basic statistics with the Support Vector Machine technique, to identify PV load signatures. PMU measurements from the residential sector are used to aggregate measurements based largely on electric current records. The methods presented have applications for network operators, both in real time control and generation scheduling.\",\"PeriodicalId\":351013,\"journal\":{\"name\":\"2020 31st Irish Signals and Systems Conference (ISSC)\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-05-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 31st Irish Signals and Systems Conference (ISSC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISSC49989.2020.9180192\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 31st Irish Signals and Systems Conference (ISSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSC49989.2020.9180192","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10

摘要

本文提出了一种识别住宅光伏系统的新方法。这是为了应对配电网络中嵌入式发电的增加而需要的。迄今为止,非侵入式负载监测技术主要集中在识别客户端的常规负载上。本文演示了非侵入式负荷监测在识别住宅分布式发电中的应用,从而使技术能够提高系统的灵活性和稳定性。演示的方法将基本统计与支持向量机技术相结合,以识别光伏负载特征。来自住宅部门的PMU测量主要用于基于电流记录的汇总测量。所提出的方法在网络运营商的实时控制和发电调度中都有应用。
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
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Non-Intrusive Load Monitoring Algorithm for PV Identification in the Residential Sector
This paper presents a novel approach for identification of photovoltaic systems in the residential sector. This is needed in response to increasing embedded generation on distribution networks. To date non-intrusive load monitoring techniques have focused mostly on identifying conventional loads on the customer side. This paper demonstrates the application of non-intrusive load monitoring to identify residential distributed generation, thereby enabling techniques to improve system flexibility and stability. The demonstrated methodology combines basic statistics with the Support Vector Machine technique, to identify PV load signatures. PMU measurements from the residential sector are used to aggregate measurements based largely on electric current records. The methods presented have applications for network operators, both in real time control and generation scheduling.
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
自引率
0.00%
发文量
0
期刊最新文献
Effects of Intra-Subject Variation in Gait Analysis on ASD Classification Performance in Machine Learning Models Practical Implementation of APTs on PTP Time Synchronisation Networks Not Everything You Read Is True! Fake News Detection using Machine learning Algorithms Semi-Supervised Learning with Generative Adversarial Networks for Pathological Speech Classification Reduced Complexity Approach for Uplink Rate Trajectory Prediction in Mobile Networks
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1