发布求助
文献互助 智能选刊 最新文献

2022 IEEE Green Energy and Smart System Systems(IGESSC)最新文献

筛选
英文 中文
RU-Net: Solar Panel Detection From Remote Sensing Image RU-Net:基于遥感图像的太阳能板检测
2022 IEEE Green Energy and Smart System Systems(IGESSC) Pub Date : 2022-11-07 DOI: 10.1109/IGESSC55810.2022.9955325
Linyuan Li, Ethan Lau
{"title":"RU-Net: Solar Panel Detection From Remote Sensing Image","authors":"Linyuan Li, Ethan Lau","doi":"10.1109/IGESSC55810.2022.9955325","DOIUrl":"https://doi.org/10.1109/IGESSC55810.2022.9955325","url":null,"abstract":"With increasing impact of global climate change, huge efforts are needed to reduce greenhouse gas emissions. The rooftop solar panels installation is one of the mechanism. In this paper, we focus on distribution and deployment degree of rooftop solar panels, and identify locations and total surface area of solar panels within a given geographic area in tackling the climate change. A comprehensive database of the location of solar panels on rooftops is important to assist analysts and policymakers in defining strategies for further expansion of solar energy. The deep learning method was used for the detection of solar panel location and their surface using the aerial imagery. While focusing on light weight image segmentation and low-resolution images, we proposed a two-branch solar panel detection framework consisting of classifier and segmentation branch, which was trained using the public data set of remote sensing images. This work provided an efficient and scalable method to detect solar panels, achieving an area under the curve (AUC) of 0.97 for classification and intersection over union (IOU) score of 0.84 for segmentation performance.","PeriodicalId":166147,"journal":{"name":"2022 IEEE Green Energy and Smart System Systems(IGESSC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116929036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Detection of High Impedance Faults in Microgrids using Machine Learning 基于机器学习的微电网高阻抗故障检测
2022 IEEE Green Energy and Smart System Systems(IGESSC) Pub Date : 2022-11-04 DOI: 10.1109/IGESSC55810.2022.9955330
Pallav Kumar Bera, Vajendra Kumar, Samita Rani Pani, Vivek Bargate
{"title":"Detection of High Impedance Faults in Microgrids using Machine Learning","authors":"Pallav Kumar Bera, Vajendra Kumar, Samita Rani Pani, Vivek Bargate","doi":"10.1109/IGESSC55810.2022.9955330","DOIUrl":"https://doi.org/10.1109/IGESSC55810.2022.9955330","url":null,"abstract":"This article presents differential protection of the distribution line connecting a wind farm in a microgrid. Machine Learning (ML) based models are built using differential features extracted from currents at both ends of the line to assist in relaying decisions. Wavelet coefficients obtained after feature selection from an extensive list of features are used to train the classifiers. Internal faults are distinguished from external faults with CT saturation. The internal faults include the high impedance faults (HIFs) which have very low currents and test the dependability of the conventional relays. The faults are simulated in a 5-bus system in PSCAD/EMTDC. The results show that ML-based models can effectively distinguish faults and other transients and help maintain security and dependability of the microgrid operation.","PeriodicalId":166147,"journal":{"name":"2022 IEEE Green Energy and Smart System Systems(IGESSC)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124065229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
首页 上一页
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
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
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