Issue |
E3S Web Conf.
Volume 498, 2024
III International Conference on Actual Problems of the Energy Complex: Mining, Production, Transmission, Processing and Environmental Protection (ICAPE2024)
|
|
---|---|---|
Article Number | 01003 | |
Number of page(s) | 8 | |
Section | Problems of the Energy Complex | |
DOI | https://doi.org/10.1051/e3sconf/202449801003 | |
Published online | 06 March 2024 |
Development and research of an intelligent diagnostic system for equipment of electric power complexes
Ufa State Petroleum Technological University, 1, Kosmonavtov st., Ufa, 450064, Russia
* Corresponding author: eapp@yandex.ru
Intelligent systems represent a new direction in the electrical power industry, and training students in this area requires appropriate updating of curricula and laboratory equipment. In this regard, it is necessary to create educational and research complexes in special disciplines to train specialists in intelligent electric power systems. This article presents an educational and research laboratory complex with elements of artificial intelligence for diagnosing the technical condition of equipment in electric power complexes. A free version of the IDE was used as an integrated development environment, which provides the basic functions and tools necessary for developing and debugging Python projects. The software part of the complex has been developed, including a digital twin of the laboratory installation, an executive part and a neural network model.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.