Power quality event classification using optimized Bayesian convolutional neural networks
Author:
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Electrical and Electronic Engineering
Link
https://link.springer.com/content/pdf/10.1007/s00202-020-01066-8.pdf
Reference32 articles.
1. Wang S, Chen H (2019) A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network. Appl Energy 235:1126–1140. https://doi.org/10.1016/j.apenergy.2018.09.160
2. Bagheri A, Gu IYH, Bollen MHJ, Balouji E (2018) A robust transform-domain deep convolutional network for voltage dip classification. IEEE Trans Power Deliv 33:2794–2802. https://doi.org/10.1109/tpwrd.2018.2854677
3. Prasad CD, Nayak PK (2018) Performance assessment of swarm-assisted mean error estimation-based fault detection technique for transmission line protection. Comput Electr Eng 71:115–128. https://doi.org/10.1016/j.compeleceng.2018.07.030
4. Wu N, Wang H (2018) Deep learning adaptive dynamic programming for real time energy management and control strategy of micro-grid. J Clean Prod 204:1169–1177. https://doi.org/10.1016/j.jclepro.2018.09.052
5. Bajaj M, Singh AK (2020) An analytic hierarchy process-based novel approach for benchmarking the power quality performance of grid-integrated renewable energy systems. Electr Eng. https://doi.org/10.1007/s00202-020-00938-3
Cited by 24 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. Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles;Electronics;2024-05-17
2. Power quality monitoring in electric grid integrating offshore wind energy: A review;Renewable and Sustainable Energy Reviews;2024-03
3. Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue;Energies;2024-01-20
4. Optimal Electricity Load Interruption Based on Time Series Classification With Super Learner and Feature Filtering;IEEE Access;2024
5. Time–Frequency Convolution Neural Network for Classification of Single and Combined Power Quality Disturbances;Lecture Notes in Networks and Systems;2024
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