Open AccessDevelopment and Application of Convolutional Neural Network for the Recognition of Objects in the Scheme of Electric Grid
Author(s) -
Maria Rezaeva,
R Y Semendyaev
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 - 10.1088/1742-6596/2096/1/012020
Subject(s) - schematic , computer science , convolutional neural network , artificial neural network , scheme (mathematics) , electric power system , power (physics) , electric power , circuit diagram , grid , architecture , artificial intelligence , pattern recognition (psychology) , electronic engineering , engineering , mathematical analysis , physics , geometry , mathematics , quantum mechanics , art , visual arts
One of the current design problems in the electric power industry is the labor intensity of synthesizing mathematical models to calculate the electrical modes of the network. It takes a lot of time to compose a detailed model of a large power system based on circuit diagram data. To simplify, speed up, and automate the data input, a convolutional neural network is proposed. In this paper the definition of convolutional neural network is given, its elements are described, the architecture of neural network is developed, the accuracy of its work on the schematic diagrams of various power systems is 0.84.