Research on intelligent substation monitoring by image recognition method-Reference-Cited by-同舟云学术

Research on intelligent substation monitoring by image recognition method

Published:2020-11-13 Issue:1 Volume:22 Page:1-7
ISSN:1553-779X
Container-title:International Journal of Emerging Electric Power Systems
language:en
Short-container-title:

Author:

Tang Weijie¹^ORCID,Chen Honggang²

Affiliation:

1. State Grid Shanghai Municipal Electric Power Company , Shanghai 200122 , China

2. State Grid Electric Power Research Institute, SMEPC , Shanghai 200437 , China

Abstract

Abstract This study mainly analyzed the improved three-frame difference algorithm for the identification of active targets in the intelligent substation. The improved three-frame difference algorithm introduced the Gaussian mixture background algorithm on the basis of the traditional three-frame difference method. The Gaussian mixture background algorithm, traditional three-frame difference method, and improved three-frame difference method were tested in the actual substation. The results showed that the improved difference method eliminated the non-target background more thoroughly when recognizing the moving target in the image; in the tested video, the improved algorithm had the highest precision and recall ratios for the active target in the video. To sum up, the improved three-frame difference method can more accurately and effectively identify the active targets in the monitoring video, so as to provide an effective support for the unmanned monitoring of intelligent substation.

Publisher

Walter de Gruyter GmbH

Subject

Energy Engineering and Power Technology

Link

https://www.degruyter.com/document/doi/10.1515/ijeeps-2020-0189/pdf

Reference15 articles.

1. Zhang, MT, Zhang, WM, Hu, JY, Wang, Y, Yang, G, Li, J, . Target image detection algorithm for substation equipment based on HOG feature. In: International conference on mechatronics and intelligent robotics. Cham: Springer; 2017, vol. 691:500–6 pp. https://doi.org/10.1007/978-3-319-70990-1_72.

2. Wang, TZ, Yu, H, Lu, ZM. Research and application toward the atlas variation detection technology of soft sensor in substation’s state intelligence analysis system. Eng Technol Res; 2016. https://doi.org/10.12783/dtetr/iceta2016/7051.

3. Zhang, X, Li, H. Detection and recognition of a ground moving target under random dynamic conditions. Microw Opt Technol Lett 2020;62:2463–72. https://doi.org/10.1002/mop.32345.

4. Liu, Y, Cai, Z, Si, Y. Moving object detection algorithm in rural substation based on time-space-frequency-domain. Trans Chin Soc Agric Eng 2018;34:207–14. https://doi.org/10.11975/j.issn.1002-6819.2018.15.026.

5. Yang, J, Wang, X, Zang, X, Dai, ZY. Cloud computing and visual attention based object detection for power substation surveillance robots. In: Canadian conference on electrical and computer engineering 2015; 2015:337–42 pp. https://doi.org/10.1109/CCECE.2015.7129299.

Cited by 6 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. Multi-objective Defect Detection of Substation Equipment Based on SA-YOLOv7 Algorithm;2023 3rd International Conference on Robotics, Automation and Intelligent Control (ICRAIC);2023-11-24

2. Substation Personnel Fall Detection Based on Improved YOLOX;Electronics;2023-10-18

3. Panoramic Assessment Method of Substation Equipment Health Status Based on Multisource Monitoring and Deep Convolution Neural Network under Edge Computing Architecture;Journal of Electrical and Computer Engineering;2023-04-20

4. Martial Arts Moves Recognition Method Based on Visual Image;J INF PROCESS SYST;2022

5. Electronic Communication Fault Signal Recognition Based on Data Mining Algorithm;Wireless Communications and Mobile Computing;2022-07-13