Issue |
E3S Web Conf.
Volume 304, 2021
2nd International Conference on Energetics, Civil and Agricultural Engineering (ICECAE 2021)
|
|
---|---|---|
Article Number | 01001 | |
Number of page(s) | 7 | |
Section | Energetics | |
DOI | https://doi.org/10.1051/e3sconf/202130401001 | |
Published online | 21 September 2021 |
Neural network model of decision making in electric power facilities under conditions of uncertainty
Department of Information Processing and Management, Tashkent State Technical University named after Islam Karimov, 100000 Tashkent, Uzbekistan
* Corresponding author: oksanaporubay@gmail.com
The article is devoted to the issue of creating a mathematical model of the problem of making management decisions in electric power facilities based on modern intelligent technologies, which makes it possible to take into account the influence of various factors on the operating modes of the power system. A systematic approach to describing processes in the mathematical language of the theory of fuzzy sets is proposed. To solve the problem of controlling the operating modes of the power system, a neurofuzzy network has been developed that combines the algorithms of Takagi-Sugeno fuzzy inference, as well as a recurrent neural network. An adaptive learning algorithm based on the Frechet method is proposed for training a neural network. The analysis of the efficiency of the fuzzy control model under the conditions of various modes of functioning of the local power system is carried out.
© The Authors, published by EDP Sciences, 2021
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.