Abstract
Gas turbine units are widely used as a drive for a synchronous generator in a gas turbine power plant. The main problem here lies in the fact that the control systems of such gas turbine plants are transferred practically unchanged from their aviation counterparts. This situation leads to inefficient operation of the gas turbine power plant, which affects the quality of electricity generation. To solve this problem, it is necessary to improve the control algorithms for the automatic control systems of gas turbine plants. When solving this problem, gas turbine plants should be considered in interaction with other subsystems and units; for gas turbine power plants, this is, first of all, an electric generator and the electric power system as a whole. Setting up a control system is one of the most costly stages of their production, both in terms of finance and time. Especially time-consuming operations are non-automated manual configuration management system for developmental and operational testing. Therefore, it is proposed to use a software-modeling complex, on the basis of which it is possible to obtain a neural network mathematical model of a gas turbine power plant and conduct its tests.
Highlights
The design and development of the electric power industry requires the introduction of modern control algorithms for gas turbine units (GTU) as part of gas turbine power plants (GTPP)
Two power supply schemes with different modes were considered as a source of experimental data: 1. GTPP operates on a dedicated load, power dropping mode of active-inductive load from 6000 kW to 1000 kW; 2
GTPP operates on a dedicated load in parallel with a infinite power network, power surge mode of active-inductive load from 1000 kW to 2000 kW; In article [12], in which a multi-mode model of a GTPP and a power system was obtained, the architecture of a neural network with 2 hidden layers and 30 neurons in each hidden layer was used
Summary
The design and development of the electric power industry requires the introduction of modern control algorithms for gas turbine units (GTU) as part of gas turbine power plants (GTPP). For the synthesis of such algorithms, it is necessary to carry out iterative procedures for testing the GTPP as a control object [1,2,3,4]. In the presence of a mathematical model (MM), the procedure for setting up and testing the control system is greatly simplified, since the direct operation of a real object is excluded. Using the theory of neural networks [5 - 9] allows to obtain such mathematical models [10, 11]. Neural networks make it possible to diagnose GTU as part of GTES [12, 13]
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