Visual analytics and prediction system based on deep belief networks for icing monitoring data of overhead power transmission lines

被引:0
|
作者
Chi Zhang
Qing-wu Gong
Koji Koyamada
机构
[1] Wuhan University,School of Electrical Engineering and Automation
[2] Kyoto University,Graduate School of Engineering
[3] Kyoto University,Academic Center for Computing and Media Studies
来源
Journal of Visualization | 2020年 / 23卷
关键词
Icing thickness; Visualization; Deep belief network; Power transmission line;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
页码:1087 / 1100
页数:13
相关论文
共 50 条
  • [31] Deep Belief Network-Based Hammerstein Nonlinear System for Wind Power Prediction
    Li, Feng
    Zhang, Mingguang
    Yu, Yang
    Li, Shengquan
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2024, 73
  • [32] Water Pollution Prediction Based on Deep Belief Network in Big Data of Water Environment Monitoring
    Liang, Li
    SCIENTIFIC PROGRAMMING, 2021, 2021
  • [33] Big Data Analytics and Deep Learning Based Sentiment Analysis System for Sales Prediction
    Khatiwada, Aamod
    Kadariya, Pradeep
    Agrahari, Sandip
    Dhakal, Rabin
    2019 IEEE PUNE SECTION INTERNATIONAL CONFERENCE (PUNECON), 2019,
  • [34] Prediction of Power Supply Induced Jitter via Deep Belief and Knowledge-based Neural Networks
    Javaid, Ahsan
    Achar, Ramachandra
    Tripathi, Jai Narayan
    2024 IEEE 28TH WORKSHOP ON SIGNAL AND POWER INTEGRITY, SPI 2024, 2024,
  • [35] Photovoltaic Power Prediction Based on Hybrid Deep Learning Networks and Meteorological Data
    Guo, Wei
    Xu, Li
    Wang, Tian
    Zhao, Danyang
    Tang, Xujing
    SENSORS, 2024, 24 (05)
  • [36] Power System Load Node Classification Based on Deep Belief Networks and Support Vector Machines
    Sun, Xiaoxiang
    Li, Tong
    Hu, Yunxu
    Mi, Ning
    Zhong, Hailiang
    Lu, Wenan
    2022 4TH INTERNATIONAL CONFERENCE ON SMART POWER & INTERNET ENERGY SYSTEMS, SPIES, 2022, : 1708 - 1713
  • [37] Real-time sag monitoring system for high-voltage overhead transmission lines based on power-line carrier signal behavior
    de Villiers, Wernich
    Cloete, Johannes Hendrik
    Wedepohl, L. Martin
    Burger, Arthur
    IEEE TRANSACTIONS ON POWER DELIVERY, 2008, 23 (01) : 389 - 395
  • [38] A data-driven method based on deep belief networks for backlash error prediction in machining centers
    Zhe Li
    Yi Wang
    Kesheng Wang
    Journal of Intelligent Manufacturing, 2020, 31 : 1693 - 1705
  • [39] A data-driven method based on deep belief networks for backlash error prediction in machining centers
    Li, Zhe
    Wang, Yi
    Wang, Kesheng
    JOURNAL OF INTELLIGENT MANUFACTURING, 2020, 31 (07) : 1693 - 1705
  • [40] Transformer-based deep learning networks for fault detection, classification, and location prediction in transmission lines
    Baadji, Bousaadia
    Belagoune, Soufiane
    Boudjellal, Sif Eddine
    NETWORK-COMPUTATION IN NEURAL SYSTEMS, 2024,