Open AccessResearch on Power Network Data Management Based on Convolutional Neural Network
Author(s) -
Ruifeng Zhao,
Bo Li,
Weisi Guo,
Jiangang Lu,
Shiming Li
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1748/3/032061
Subject(s) - computer science , offset (computer science) , interpolation (computer graphics) , grid , data mapping , data mining , convolutional neural network , artificial neural network , real time computing , algorithm , artificial intelligence , database , mathematics , motion (physics) , geometry , programming language
Faced with huge business data, collecting and statistics of these data can be easily achieved through the system. However, during data analysis and result output, there is a large amount of data that cannot be intuitively accepted at a glance, resulting in disorder and inefficient work. Therefore, the grid load thermal map is scaled with cubic convolution interpolation in the paper, which requires the offset distance determination of the floating point in the horizontal and vertical directions. Moreover, based on the operating data of transformer and line, the analysis and verification of the load thermal map in power grid is realized. Besides, problem equipment can be clearly displayed on the map, which is convenient for staffs to identify and analyze.