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

被引：1
作者：
Kavalerov B.V. [1 ]
Bakhirev I.V. [1 ]
Kilin G.A. [1 ]
机构：
[1] Department of Electrical Engineering and Mechanics, Perm National Research Polytechnic University, Perm
来源：
Russian Electrical Engineering | 2019年 / 90卷 / 11期
基金：
俄罗斯基础研究基金会;
关键词：
gas-turbine plant; gas-turbine unit; neural network; neural network model;
D O I：
10.3103/S106837121911004X
中图分类号：
学科分类号：
摘要：
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. © 2019, Allerton Press, Inc.
引用
收藏
页码：737 / 740
页数：3
相关论文
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