Intelligent recognition algorithm of connection relation of substation secondary wiring drawing based on D-LLE algorithm

The accurate recognition of the connection relationship of the substation secondary wiring drawings is a key link in the operation and maintenance of the power system, but the traditional manual recognition method has the problems of time-consuming, labor-intensive and error-prone. Therefore, this paper proposes an intelligent recognition algorithm for substation secondary wiring drawings based on D-LLE algorithm. The composition of the substation secondary layout diagram is analyzed, and the connection relationship feature image is preprocessed, including smoothing and contrast enhancement, to improve the image quality. D-LLE algorithm (Denoised Locally Linear Embedding) is used to effectively extract the connection features in the drawings, which improves the feature expression ability and recognition accuracy. The improved Canny operator is used to extract the feature edge of the connection relationship, and the feature recognition algorithm is used to realize the intelligent recognition of the connection relationship. The experimental results show that the completeness of connection information extraction of substation secondary wiring drawings obtained by the proposed algorithm is between 98.56% and 99.98%, and the mean absolute error of intelligent recognition drawings connection relationship is always lower than 0.01. It shows that the proposed algorithm can accurately and quickly identify the connection relationship in the substation secondary wiring drawings, significantly improve the recognition efficiency, and has strong robustness and versatility, which can provide strong support for the intelligent operation and maintenance of the power system.

Authors

Publication Details

Journal
Discover Applied Sciences
Published
2024-12-21
DOI
https://doi.org/10.1007/s42452-024-06443-7
Citations
2
Primary Topic
Digital Media Forensic Detection
Type
article
Field-Weighted Citation Impact
0.50

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article

Intelligent recognition algorithm of connection relation of substation secondary wiring drawing based on D-LLE algorithm

Fang Kang, Bojie Yang, Qian Li, Hongzhi Liu et al.
2 citations
Discover Applied Sciences
Digital Media Forensic Detection
0.50
article

Intelligent recognition algorithm of connection relation of substation secondary wiring drawing based on D-LLE algorithm

Fang Kang, Bojie Yang, Qian Li, Hongzhi Liu, Hongyang Zhang
article en
2 citations

Abstract

The accurate recognition of the connection relationship of the substation secondary wiring drawings is a key link in the operation and maintenance of the power system, but the traditional manual recognition method has the problems of time-consuming, labor-intensive and error-prone. Therefore, this paper proposes an intelligent recognition algorithm for substation secondary wiring drawings based on D-LLE algorithm. The composition of the substation secondary layout diagram is analyzed, and the connection relationship feature image is preprocessed, including smoothing and contrast enhancement, to improve the image quality. D-LLE algorithm (Denoised Locally Linear Embedding) is used to effectively extract the connection features in the drawings, which improves the feature expression ability and recognition accuracy. The improved Canny operator is used to extract the feature edge of the connection relationship, and the feature recognition algorithm is used to realize the intelligent recognition of the connection relationship. The experimental results show that the completeness of connection information extraction of substation secondary wiring drawings obtained by the proposed algorithm is between 98.56% and 99.98%, and the mean absolute error of intelligent recognition drawings connection relationship is always lower than 0.01. It shows that the proposed algorithm can accurately and quickly identify the connection relationship in the substation secondary wiring drawings, significantly improve the recognition efficiency, and has strong robustness and versatility, which can provide strong support for the intelligent operation and maintenance of the power system.

Discover Applied SciencesVol. 7(1)
Science and Technology Project of State Grid, State Grid Shandong Electric Power Company
Openalex Percentile: Top 33%
Digital Media Forensic Detection
0.50
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