Using Neural Networks in Controlling Low- and Medium-Capacity Gas-Turbine Plants
- Authors: Kavalerov B.V.1, Bakhirev I.V.1, Kilin G.A.1
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Affiliations:
- Department of Electrical Engineering and Mechanics, Perm National Research Polytechnic University
- Issue: Vol 90, No 11 (2019)
- Pages: 737-740
- Section: Article
- URL: https://journals.rcsi.science/1068-3712/article/view/231627
- DOI: https://doi.org/10.3103/S106837121911004X
- ID: 231627
Cite item
Abstract
The possibilities of using neural network technologies for synthesizing new and improving existent gas-turbine plant (GTP) control systems are considered. Modern gas-turbine plant control systems are often developed on the basis of aviation automatic control systems, without taking into account the peculiarities of load changes in electricity generation. As a result, frequency-related quality indicators of electricity, such as maximum deviation and recovery time, do not always meet requirements that have been set out. This study is aimed at improving the quality of generated electricity. A list of different disturbances that can arise in an electric power system is provided, as well as the results of using the neural network model of a GTP to optimize the parameters of the gas-turbine unit adjuster.
About the authors
B. V. Kavalerov
Department of Electrical Engineering and Mechanics, Perm National Research Polytechnic University
Author for correspondence.
Email: journal-elektrotechnika@mail.ru
Russian Federation, Perm, 614990
I. V. Bakhirev
Department of Electrical Engineering and Mechanics, Perm National Research Polytechnic University
Email: journal-elektrotechnika@mail.ru
Russian Federation, Perm, 614990
G. A. Kilin
Department of Electrical Engineering and Mechanics, Perm National Research Polytechnic University
Email: journal-elektrotechnika@mail.ru
Russian Federation, Perm, 614990
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