Turbine-generator system with a load state observer

Objective: To generate a mathematical model of a gas turbine engine state observer for a GT1h series gas turbine locomotive. Methods: Calculations and modeling of processes were performed using software packages for mathematical modeling of complex electromechanical systems with implementation in Matlab, while data processing and graph plotting were performed using Microsoft Excel. Results: It has been shown that the use of the mathematical model of the state observer in the automatic control system allows providing conditions for the formation of an optimal power load trajectory of the Gas Turbine Engine — Traction Generator system when regulating the power, taking into account the limitations associated with the physical processes occurring in the gas turbine engine. Practical importance: The use of the mathematical model of the state observer makes it possible to generate rational gas turbine load trajectories in the entire range of its use.

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Identification of the mathematical model of the automatic control system of a gas turbine engine

Bulletin of Perm National Research Polytechnic University. Electrotechnics, informational technologies, control systems ◽

10.15593/2224-9397/2019.3.08 ◽

2019 ◽

pp. 121-135

Author(s):

Nadezhda Lamanova ◽

Arthur Pleshivykh ◽

Igor Gribkov ◽

Almir Fatykov

Keyword(s):

Mathematical Model ◽

Control System ◽

Automatic Control ◽

Gas Turbine ◽

Automatic Control System ◽

Gas Turbine Engine ◽

Turbine Engine ◽

The Mathematical Model

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Identification of the Mathematical Model of a Gas Turbine Engine Taking Into Account the Uncertainty of the Initial Data

10.1115/gtindia2021-76018 ◽

2021 ◽

Author(s):

Oleg Baturin ◽

Grigorii Popov ◽

Paúl Nicolalde ◽

Anastasia Korneeva

Keyword(s):

Mathematical Model ◽

Probability Distribution ◽

Initial Data ◽

Mathematical Models ◽

Gas Turbine ◽

Gas Turbine Engine ◽

Bivariate Distribution ◽

Calculation Error ◽

Turbine Engine ◽

The Mathematical Model

Abstract The article describes the method developed by the authors and tested on the example of the AI-25 engine. The study was focused on determining the probability distribution of the output parameters of a gas turbine engine mathematical model. The distribution was obtained considering the uncertainty of the initial data. The paper describes the identified problems and possible ways to solve them. In particular, it was found that it is not possible to study the influence of more than 7..8 input parameters on the probability distribution of output parameters with the current level of development of computer technology even using simple mathematical models. For this reason, a method has been developed to obtain reliable results while reducing the number of considered input data based on sensitivity analysis. The paper also proposed a way of comparing stochastic experimental and computational data with each other using a bivariate distribution. This method allows a precise characterisation of the calculation error using 4 numerical values. The experience obtained in the work has shown that taking into account the uncertainty of the initial data dramatically changes the process of interpreting the results. It should be noted that the obtained results are universal and can be used with other mathematical models in various industries although they were developed on the example of the mathematical model of a gas turbine engine.

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Optimal State-Space Control of a Gas Turbine Engine

Journal of Engineering for Gas Turbines and Power ◽

10.1115/1.2906654 ◽

1992 ◽

Vol 114 (4) ◽

pp. 763-767 ◽

Cited By ~ 12

Author(s):

J. W. Watts ◽

T. E. Dwan ◽

C. G. Brockus

Keyword(s):

State Space ◽

Gas Turbine ◽

State Space Model ◽

Gas Turbine Engine ◽

The State ◽

Operating Conditions ◽

Turbine Engine ◽

Analog Control ◽

Linear State ◽

High Level

An analog fuel control for a gas turbine engine was compared with several state-space derived fuel controls. A single-spool, simple cycle gas turbine engine was modeled using ACSL (high level simulation language based on FORTRAN). The model included an analog fuel control representative of existing commercial fuel controls. The ACSL model was stripped of nonessential states to produce an eight-state linear state-space model of the engine. The A, B, and C matrices, derived from rated operating conditions, were used to obtain feedback control gains by the following methods: (1) state feedback; (2) LQR theory; (3) Bellman method; and (4) polygonal search. An off-load transient followed by an on-load transient was run for each of these fuel controls. The transient curves obtained were used to compare the state-space fuel controls with the analog fuel control. The state-space fuel controls did better than the analog control.

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