Share Email Print
cover

Proceedings Paper

Research on installation quality inspection system of high voltage customer metering device based on image recognition
Author(s): Bei HE; Fu-li YANG; Xue-dan TAO; Shi-liang CHANG; KANG WU
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

With the rapid development of the scale of the power grid, the site construction and the operations environment is more widespread and more complex. The installation work of the high-voltage customer metering device is heavy, which is not standardized. In addition, managers supervise the site construction progress only through the person in charge of each work phrase. It is inefficient and difficult to control the multi-team and multi-unit cross work. Therefore, it is necessary to establish a scientific system to detect the quality of installation and management practices to standardize installation work of the metering device. Based on the research of image recognition and target detection system, this paper presents a high-voltage customer metering device installation quality inspection system based on digital image processing, image feature extraction and SVM classification decision. The experimental results show that the proposed scheme is feasible. And it can be used to accurately extract the metering components in the image, which can be also accurately and quickly classified. Our method is of great significance for the implementation and monitoring of the power system in installation and specification

Paper Details

Date Published:
PDF
Proc. SPIE LID100, LIDAR Imaging Detection and Target Recognition 2017, ; doi: 10.1117/12.2292655
Show Author Affiliations
Bei HE, State Grid Chongqing Electric Power Co. (China)
Fu-li YANG, State Grid Chongqing Electric Power Co. Electric Power Research Institute (China)
Xue-dan TAO, State Grid Chongqing Electric Power Co. Electric Power Research Institute (China)
Shi-liang CHANG, State Grid Chongqing Electric Power Co. Electric Power Research Institute (China)
KANG WU, Beijing Normal University (China)


Published in SPIE Proceedings Vol. LID100:
LIDAR Imaging Detection and Target Recognition 2017

© SPIE. Terms of Use
Back to Top