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Power distribution network inspection vision system based on bionic vision image processing

Author

Listed:
  • Fangzhou Hao

    (Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd.)

  • Jieran Ma

    (Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd.)

  • Linhuan Luo

    (Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd.)

  • Weijun Dang

    (Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd.)

  • Yiwei Xue

    (Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd.)

Abstract

In order to improve the effect of power distribution network inspection and reduce the hidden dangers and operating costs of the power distribution network inspection, this paper combines the bionic vision image processing technology to construct an intelligent power distribution network inspection vision system, and proposes a bionic model based on the principle of biological visual distance that takes the Kinect Depth information value as a parameter. Moreover, this paper uses the model in the vision system to improve its real-time performance. In addition, with the support of intelligent algorithms, this paper constructs the structure model of the power distribution network inspection vision system, and proposes a new type of intelligent inspection system design for power distribution network to achieve a good effect of improving the efficiency of power distribution network inspection and management level in production practice. Finally, this paper combines experiments to prove the reliability of this system.

Suggested Citation

  • Fangzhou Hao & Jieran Ma & Linhuan Luo & Weijun Dang & Yiwei Xue, 2023. "Power distribution network inspection vision system based on bionic vision image processing," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(2), pages 568-577, April.
    • Handle: RePEc:spr:ijsaem:v:14:y:2023:i:2:d:10.1007_s13198-021-01268-8
    DOI: 10.1007/s13198-021-01268-8
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    References listed on IDEAS

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    1. Sergei Koulayev, 2014. "Search for differentiated products: identification and estimation," RAND Journal of Economics, RAND Corporation, vol. 45(3), pages 553-575, September.
    2. Brehm, Margaret & Imberman, Scott A. & Lovenheim, Michael F., 2017. "Achievement effects of individual performance incentives in a teacher merit pay tournament," Labour Economics, Elsevier, vol. 44(C), pages 133-150.
    3. Zhen-Lin Chen & Jia-Ming Meng & Yong Cao & Ji-Li Yin & Run-Qian Fang & Sheng-Bo Fan & Chao Liu & Wen-Feng Zeng & Yue-He Ding & Dan Tan & Long Wu & Wen-Jing Zhou & Hao Chi & Rui-Xiang Sun & Meng-Qiu Do, 2019. "A high-speed search engine pLink 2 with systematic evaluation for proteome-scale identification of cross-linked peptides," Nature Communications, Nature, vol. 10(1), pages 1-12, December.
    4. Hultmann Ayala, Helon Vicente & Coelho, Leandro dos Santos & Mariani, Viviana Cocco & Askarzadeh, Alireza, 2015. "An improved free search differential evolution algorithm: A case study on parameters identification of one diode equivalent circuit of a solar cell module," Energy, Elsevier, vol. 93(P2), pages 1515-1522.
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