Infrared and Laser Engineering, Volume. 51, Issue 12, 20220097(2022)

Image data compression technology of smart grid operation based on deep learning

Xin Xia1, Chuanliang He1, Yingjie Lv2, Shouzhi Wang2, Bo Zhang2, Chen Chen3, Haipeng Chen4、*, and Meixuan Li5、*
Author Affiliations
  • 1State Grid Laboratory of Power Line Communication Application Technology, Beijing Smart-Chip Microelectronics Techno1ogy Co., Ltd, Beijing 102200, China
  • 2Beijing Electric Power Science & Smart Chip Technology Company Limited, Beijing 100192, China
  • 3College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130026, China
  • 4Department of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
  • 5Institute for Interdisciplinary Quantum Information Technology, Jilin Engineering Normal University, Changchun 130052, China
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    References(15)

    [1] Chen W J, Zhao G L. Key technologies and equipment of new power system with new energy as the main body[J]. Global Energy Internet, 5, 1(2022).

    [2] [2] Jia Y Q. Research on sequence spatial data compression technology based on deep learning[D]. Harbin: Harbin Institute of Technology, 2021. (in Chinese)

    [3] Chen S W, Gao C Y, Hu C. Adaptive waveform data compression based on similarity segmentation and resampling[J]. Journal of Electronic Measurement and Instrumentation, 33, 178-185(2019).

    [4] Wang Y Z, Sun L Q. Application of data compression technology in ship power monitoring system[J]. Journal of Shanghai Institute of Ship Transportation Science, 43, 55-60(2020).

    [5] Unterweger A, Engel D. Resumable load data compression in smart grids[J]. IEEE Transactions on Smart Grid, 6, 919-929(2015).

    [6] Chen Y, Wang Y L. Lossless data compression scheme of intelligent distribution network monitoring system[J]. Guangdong Electric Power, 34, 90-98(2021).

    [7] Zhao H S, Feng J H, Ma L B. Data compression of distribution network infrared image monitoring based on tensor Tucker decomposition[J]. Power System Technology, 45, 1632-1639(2021).

    [8] [8] Ye J X. Research on image acquisition reconstruction of power system based on compressed sensing[D]. Wuhan: Hubei University of Technology, 2020. (in Chinese)

    [9] [9] Zhao H S, Liu B C, Wang L J, et al. Blind super resolution method f infrared images of power equipment based on compressed sensing[JOL]. Power System Technology, (20220112) [20220124]. (in Chinese)

    [10] Zhao H H, Jiang Y, Lin R, et al. Research on acceleration and compression of transmission line inspection image detection model[J]. Guangdong Electric Power, 33, 123-128(2020).

    [11] [11] Wang Z H. Research on compressed sensing reconstruction of electrical equipment images under the framewk of deep learning[D]. Wuhan: Hubei University of Technology, 2021. (in Chinese)

    [12] Peng J S, Sun L X, Wang L, et al. ED-YOLO electric power inspection UAV obstacle avoidance target detection algorithm based on model compression[J]. Journal of Instrumentation, 42, 161-170(2021).

    [13] Tang N Y, Cai L, Zhu T, et al. Construction of image recognition model for power equipment based on deep learning[J]. Automation and Instrumentation, 54-57(2020).

    [14] [14] Wu Y F, Li F S, Yu T, et al. Power data compression highprecision reconstruction based on residual dual attention mechanism wk[JOL]. Power System Technology, (2022115)[20220124]. (in Chinese)

    [15] Zhang S Q, Yang F B, Wang X X. Ghost imaging optimization method based on autoencoding neural network[J]. Electronic Measurement Technology, 44, 77-83(2021).

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    Xin Xia, Chuanliang He, Yingjie Lv, Shouzhi Wang, Bo Zhang, Chen Chen, Haipeng Chen, Meixuan Li. Image data compression technology of smart grid operation based on deep learning[J]. Infrared and Laser Engineering, 2022, 51(12): 20220097

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    Paper Information

    Category: Image processing

    Received: Jan. 21, 2022

    Accepted: --

    Published Online: Jan. 10, 2023

    The Author Email: Haipeng Chen (haipeng0704@126.com), Meixuan Li (limx@jlenu.edu.cn)

    DOI:10.3788/IRLA20220097

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