A Classification Method for Power-Quality Disturbances Using Hilbert–Huang Transform and LSTM Recurrent Neural Networks
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering
Link
http://link.springer.com/content/pdf/10.1007/s42835-020-00612-5.pdf
Reference39 articles.
1. IEEE recommended practice for monitoring electric power quality (2009) IEEE Std 1159-2009 (Revision of IEEE Std 1159-1995) c1–81. https://doi.org/10.1109/IEEESTD.2009.5154067
2. Shukla S, Mishra S, Singh B (2014) Power quality event classification under noisy conditions using EMD-based de-noising techniques. IEEE Trans Ind Inform 10(2):1044–1054. https://doi.org/10.1109/TII.2013.2289392
3. Uyar M, Yildirim S, Gencoglu MT (2009) An expert system based on s-transform and neural network for automatic classification of power quality disturbances. Expert Syst Appl 36(3):5962–5975
4. Gaing Z-L (2004) Wavelet-based neural network for power disturbance recognition and classification. IEEE Trans Power Deliv 19(4):1560–1568
5. He H, Starzyk JA (2005) A self-organizing learning array system for power quality classification based on wavelet transform. IEEE Trans Power Deliv 21(1):286–295
Cited by 25 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. Classification of complex power quality disturbances under noisy environment using Root-Music and least square techniques;Electric Power Systems Research;2025-01
2. CB-HDM: ECG signal based heart disease classification using convolutional block attention assisted hybrid deep Maxout network;Biomedical Signal Processing and Control;2024-09
3. A Comparative Analysis between FFT, EMD, and EEMD for Epilepsy Detection;INGENIERIA-BOGOTA;2024
4. Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue;Energies;2024-01-20
5. A Novel Approach for Evaluating Power Quality in Distributed Power Distribution Networks Using AHP and S-Transform;Energies;2024-01-14
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