A generalizable and sensor-independent deep learning method for fault detection and location in low-voltage distribution grids-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

A generalizable and sensor-independent deep learning method for fault detection and location in low-voltage distribution grids

Published:2020-10 Issue: Volume:276 Page:115299
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Sapountzoglou Nikolaos,Lago Jesus,De Schutter Bart,Raison Bertrand

Funder

H2020 Marie Skłodowska-Curie Actions

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference80 articles.

1. The costs of electricity interruptions in Spain. Are we sending the right signals?;Linares;Energy Policy,2013

2. Using data from connected thermostats to track large power outages in the United States;Meier;Appl Energy,2019

3. State-of-the-art review on power grid resilience to extreme weather events: Definitions, frameworks, quantitative assessment methodologies, and enhancement strategies;Jufri;Appl Energy,2019

4. Economic trends and comparisons for optimizing grid-outage resilient photovoltaic and battery systems;Tsianikas;Appl Energy,2019

5. Gönen T. Electric power distribution engineering. Boca Raton: Taylor & Francis; 2014.

Cited by 44 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 the Application of Artificial Intelligence in Anomaly Analysis Detection and Fault Location in Grid Indicator Calculation Data;Energies;2024-07-29

2. Intelligent fault detection strategy for knowledge entities in fault semantic networks of distribution network based on siamese networks;PLOS ONE;2024-05-16

3. Fault classification and location of a PMU-equipped active distribution network using deep convolution neural network (CNN);Electric Power Systems Research;2024-04

4. Artificial Intelligence Based Fault Detection and Classification in Power Systems: An Automated Machine Learning Approach;2024 23rd International Symposium INFOTEH-JAHORINA (INFOTEH);2024-03-20

5. Effect of device coupling mode on output characteristics in piezoelectric grating voltage sensor;Measurement;2024-01

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