Fault location in distribution networks based on SVM and impedance-based method using online databank generation
Author:
Funder
Energistyrelsen
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-021-06541-2.pdf
Reference51 articles.
1. Morales JA, Orduña E, Rehtanz C, Cabral RJ, Bretas AS (2016) Ultra high speed deterministic algorithm for transmission lines disturbance identification based on principal component analysis and Euclidean norm. Int J Electr Power Energy Syst 80:312–324
2. Krishnananda KR, Dasha PK, Naeemb MH (2015) Detection, classification, and location of faults in power transmission lines. Int J Electr Power Energy Syst 67(1):76–86
3. Zidan A, El-Saadany EF (2015) Incorporating customers’ reliability requirements and interruption characteristics in service restoration plans for distribution systems. Energy 87:192–200
4. Esmaeeli M, Kazemi A, Shayanfar HA, Haghifam MR (2015) Multistage distribution substations planning considering reliability and growth of energy demand. Energy 84:357–364
5. Dashti R, Sadeh J (2014) Accuracy improvement of impedance based fault location method for power distribution network using distributed-parameter line model. Int Trans Electr Energy Syst 24(3):318–334
Cited by 17 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. Fault current constrained impedance-based method for high resistance ground fault location in distribution grid;Electric Power Systems Research;2024-02
2. Artificial Neural Networks-Based Fault Localization in Distributed Generation Integrated Networks Considering Fault Impedance;IEEE Access;2024
3. A Fault Branch Location Approach for Partially Observable Distribution Network;2023 IEEE 7th Conference on Energy Internet and Energy System Integration (EI2);2023-12-15
4. Quantum annealing algorithm for fault section location in distribution networks;Applied Soft Computing;2023-12
5. Deep learning-based fault location framework in power distribution grids employing convolutional neural network based on capsule network;Electric Power Systems Research;2023-10
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