Open AccessApplication of neural network technologies in power engineering
Author(s) -
A. V. Andryushin,
É. K. Arakelyan,
Ivan Shcherbatov,
A. A. Kosoy,
N.S. Dolbikova
Publication year - 2019
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 - 10.1088/1742-6596/1370/1/012054
Subject(s) - artificial neural network , computer science , key (lock) , closeness , reliability engineering , control (management) , energy management , state (computer science) , energy (signal processing) , energy consumption , industrial engineering , systems engineering , risk analysis (engineering) , artificial intelligence , control engineering , engineering , electrical engineering , computer security , mathematical analysis , statistics , mathematics , algorithm , medicine
In this work, key areas of artificial neural networks using in the energy sector are highlighted. The application of neural network technologies to assess the current technical condition of energy equipment, systems and tools for various purposes is implemented on classification algorithms, allowing to establish the degree of closeness of the current technical state to the “normal” state through the use of key technological (diagnostic) parameters. The prediction of operating modes (normal, emergency, etc.), the occurrence of defects, failures and accidents is carried out by establishing functional dependencies using historical data on the operation of power equipment. The data accumulated for different periods are used to predict the consumption of various types of energy and loads during these periods. On the basis of neural network algorithms, operational intelligent control of the operating modes of energy facilities is implemented, and systems for dispatch control, decision support, repair management, etc. are developed.