Research on deep learning of meteorological sample data associated with power grid

Xiangzhou Chen; Lingchao Gao

doi:10.1117/12.2685701

15 August 2023 Research on deep learning of meteorological sample data associated with power grid

Xiangzhou Chen, Lingchao Gao

Proceedings Volume 12719, Second International Conference on Electronic Information Technology (EIT 2023); 127190G (2023) https://doi.org/10.1117/12.2685701
Event: Second International Conference on Electronic Information Technology (EIT 2023), 2023, Wuhan, China

Abstract

With the increasing number of energy internet users year by year, manual inspections are gradually being replaced by unmanned inspections. The target case algorithm based on mixed convolutional neural analysis has been widely applied in grid related snow, rain, ice, and wind intelligent cases. However, in practical applications, it has been found that due to the small size of the collected targets to be located, the accuracy of the mixed convolutional neural analysis model will decrease when the shooting angle is tilted and the lighting conditions are poor. This is because the hybrid convolutional neural analysis of this algorithm is relatively low. When there is a significant difference in angle or illumination from the case, the positioning and collection ability of the model will be severely affected. On this basis, the feature fusion part of ONNX algorithm and the selection of loss function and feature vector frame size are improved, and the improved CNN fusion method is used to classify various data in grid related snow, rain, ice and wind. Actual measurements and repeated experiments have shown that this method can be effectively applied to the recognition of various grid related snow, rain, ice, and wind data, optimizing mixed convolutional neural analysis, and greatly improving the recognition efficiency of grid related snow, rain, ice, and wind data.

Citation Download Citation

Xiangzhou Chen and Lingchao Gao "Research on deep learning of meteorological sample data associated with power grid", Proc. SPIE 12719, Second International Conference on Electronic Information Technology (EIT 2023), 127190G (15 August 2023); https://doi.org/10.1117/12.2685701

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Power grids

Rain

Ice

Feature fusion

Sand

Detection and tracking algorithms

Deep learning

Show All Keywords

Keywords/Phrases

Search In:

Publication Years