Pub Date : 2022-11-07DOI: 10.1109/IGESSC55810.2022.9955325
Linyuan Li, Ethan Lau
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.
{"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}
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.
{"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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：