Real-Time DC Series Arc Fault Detection Based on Noise Pattern Analysis in Photovoltaic System
Author:
Affiliation:
1. Department of Energy Systems Engineering, Chung-Ang University, Seoul, South Korea
2. School of Energy Systems Engineering, Chung-Ang University, Seoul, South Korea
Funder
National Research Foundation of Korea
Development of dc Arc Interruption Technology and Performance Evaluation Facility for Medium and Large PV System Development
Korea Institute of Energy Technology Evaluation and Planning
Ministry of Trade, Industry and Energy
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/41/10103730/09950518.pdf?arnumber=9950518
Reference22 articles.
1. Series DC Arc Fault Detection Based on Ensemble Machine Learning
2. Diagnosis of Series DC Arc Faults—A Machine Learning Approach
3. Detection of DC series arc in more electric aircraft power system based on optical spectrometry
4. Series DC Arc Fault Detection Method for PV Systems Employing Differential Power Processing Structure
5. Recognition of electric arcing in the DC-wiring of photovoltaic systems
Cited by 9 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. Advanced impedance mismatch technique for detecting faults in photovoltaic systems;Energy Science & Engineering;2024-06-17
2. Aircraft DC Arc Fault Detection Approach Based on Sliding Time Window with Peak-to-Peak Value and Spectrum Integration;2024 IEEE 7th International Electrical and Energy Conference (CIEEC);2024-05-10
3. Various Feature-Based Series Direct Current Arc Fault Detection Methods Using Intelligence Learning Models and Diverse Domain Exclusion Techniques;Machines;2024-04-03
4. Fast Arc Detection Technology Based on Fractal Dimension for SADA Slip Ring in Satellite Power System;Energies;2024-02-25
5. DC Series Arc Fault Diagnosis Scheme Based on Hybrid Time and Frequency Features Using Artificial Learning Models;Machines;2024-02-01
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