A novel deep learning architecture for distribution system topology identification with missing PMU measurements-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

A novel deep learning architecture for distribution system topology identification with missing PMU measurements

Published:2022-09 Issue: Volume:15 Page:100543
ISSN:2590-1230
Container-title:Results in Engineering
language:en
Short-container-title:Results in Engineering

Author:

Raghuvamsi Y,Teeparthi Kiran^ORCID,Kosana Vishalteja

Publisher

Elsevier BV

Subject

General Engineering

Reference42 articles.

1. A deep neural network approach for online topology identification in state estimation;Gotti;IEEE Trans. Power Syst.,2021

2. Simultaneous estimation of state variables and network topology for power system real-time modeling;Da Silva;Elec. Power Syst. Res.,2016

3. Distribution grid state estimation from compressed measurements;Alam;IEEE Trans. Smart Grid,2014

4. Urban mv and lv distribution grid topology estimation via group lasso;Liao;IEEE Trans. Power Syst.,2018

5. A recursive bayesian approach for identification of network configuration changes in distribution system state estimation;Singh;IEEE Trans. Power Syst.,2010

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A review on topology identification methods and applications in distribution networks;Electric Power Systems Research;2024-09

2. Abnormal line loss identification and category classification of distribution networks based on semi-supervised learning and hierarchical classification;Frontiers in Energy Research;2024-03-20

3. Data Imputation Using Self Attention Based Model for Enhancing Distribution Grid Monitoring and Protection Systems;IEEE Transactions on Instrumentation and Measurement;2024

4. Deep learning models for improved accuracy of a multiphase flowmeter;Measurement;2023-01

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号京ICP备18003416号-3