Using Neural Networks in Controlling Low- and Medium-Capacity Gas-Turbine Plants


Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

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

Supplementary files

Supplementary Files
Action
1. JATS XML

Copyright (c) 2019 Allerton Press, Inc.