SYSTEM AND METHOD FOR CONTROLLING A GAS TURBINE ENGINE

• US 20160208639A1
• Filed: 01/19/2015
• Published: 07/21/2016
• Est. Priority Date: 01/19/2015
• Status: Active Grant

• Current Assignee: Rtx Corporation
• Original Assignee: United Technologies Corporation (Rtx Corporation)
• Inventors: Cai, Chaohong
• Granted Patent: US 9,732,625 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: F01D 17/06 responsive to speed
• CPC Class Codes: F01D 17/105 by passing part of the fluid
• CPC Class Codes: F01D 21/003 Arrangements for testing or measuring for measuring vibrations G01H
• CPC Class Codes: F05D 2220/32 in gas turbines
• CPC Class Codes: F05D 2240/12 Fluid guiding means, e.g. vanes
• CPC Class Codes: F05D 2270/304 Spool rotational speed
• CPC Class Codes: F05D 2270/335 Output power or torque
• CPC Class Codes: F05D 2270/52 by electrical means, e.g. relays or switches
• CPC Class Codes: F05D 2270/62 Electrical actuators
• CPC Class Codes: G05B 13/048 using a predictor

Abstract

A control system for a gas turbine engine including a power turbine is disclosed. The control system may include a control module to receive engine operating goals and an estimated current engine state, wherein the estimated current engine state is produced by a model-based estimation module using a bandwidth signal produced by an adaptation logic module. The control module is operative to determine fuel flow, inlet guide vane schedules and stability bleed schedules based at least in part on the received engine operating goals and the estimated current engine state, and to send signals to a gas generator of the gas turbine engine in order to control the gas generator according to the determined fuel flow, inlet guide vane schedules and stability bleed schedules.

Claims

1. A control system for a gas turbine engine, the control system comprising:

a first sensor configured to generate a thrust lever angle signal indicative of a thrust lever angle commanded of the gas turbine engine;

a second sensor in communication with the gas turbine engine and configured to generate at least one operating parameter signal indicative of at least one operating parameter of the gas turbine engine;

a power rating module configured to receive the thrust lever angle signal and generate at least one goal signal indicative of an operating point goal for the gas turbine engine;

an adaptation logic module configured to receive the at least one goal signal and the at least one operating parameter signal and generate a bandwidth signal indicative of an error between the at least one goal signal and the at least one operating parameter signal;

a model-based estimation module configured to receive the bandwidth signal and generate an engine estimate signal indicative of estimated current engine parameters of the gas turbine engine based at least in part on the bandwidth signal; and

a model-based control module configured to receive the at least one operating parameter signal, the at least one goal signal, and the engine estimate signal and to determine at least one of fuel flow, inlet guide vane schedules and stability bleed schedules based at least in part on the received at least one operating parameter signal, the at least one goal signal and the engine estimate signal, and to send effector signals to a gas generator of the gas turbine engine in order to control the gas generator according to at least one of the determined fuel flow, inlet guide vane schedules and stability bleed schedules.

2. The control system of claim 1, wherein the model-based control module is configured to use a model-based constrained dynamic inversion methodology to determine at least one of the fuel flow, the inlet guide vane schedules and the stability bleed schedules.

3. The control system of claim 1, wherein the at least one operating parameter signal comprises at least one of real-time speed, power, torque and thrust of the gas turbine engine.

4. The control system of claim 1, wherein the power rating module utilizes predetermined tables to generate the at least one goal signal.

5. The control system of claim 1, wherein the model-based control module is further configured to receive signals indicative of operation limits of the gas turbine engine, real-time inlet guide vane schedules and real-time stability bleed schedules.

6. The control system of claim 1, wherein the at least one operating parameter signal comprises current engine speed, power, torque and/or thrust of the gas turbine engine.

7. The control system of claim 1, wherein the adaptation logic module comprises:

a comparator configured to determine an error signal comprising a difference between the at least one goal signal and the at least one operating parameter signal;

a low pass filter configured to receive the error signal and produce a filtered error signal;

an integrator configured to receive the filtered error signal and produce an integrated error signal; and

a look-up table configured to produce the bandwidth signal based at least in part on at least one of the filtered error signal and the integrated error signal.

8. The control system of claim 1, wherein the model-based estimation module comprises:

a comparator configured to determine an error signal comprising a difference between the at least at least one operating parameter signal and a measurement of the engine estimate signal;

a bias estimator configured to receive the error signal and the bandwidth signal and to produce a bias signal;

a state variable model configured to produce a prediction signal indicative of a current state of the gas turbine engine;

a prediction signal biasing device configured to receive the bias signal and the prediction signal and produce a predicted parameter signal indicative of the current state of the gas turbine engine; and

a data storage device configured to store the predicted parameter signal and to produce the engine estimate signal.

9. The control system of claim 8, wherein the bias estimator comprises one of a low pass filter, a Kalman filter, a neural network, optimal estimation, recursive system identification, asymptotic observer and an adaptive logic based on L1 adaptive control theory.

10. The control system of claim 1, wherein the model-based estimation module comprises:

a state variable model configured to produce a prediction signal indicative of a current state of the gas turbine engine;

a comparator configured to determine an error signal comprising a difference between the prediction signal and a feedback of the engine estimate signal;

a low pass filter configured to receive the error signal and the bandwidth signal and to produce a bias signal;

a prediction signal biasing device configured to receive the bias signal and the prediction signal and produce the engine estimate signal.

11. A control system for a gas turbine engine including a power turbine, the control system comprising:

a sensor in communication with the gas turbine engine and configured to generate at least one operating parameter signal indicative of at least one operating parameter of the gas turbine engine;

a load control module configured to receive a desired power turbine speed signal and to output a power turbine torque request (Qpt_req); and

an adaptation logic module configured to receive the desired power turbine speed signal and the at least one operating parameter signal and generate a bandwidth signal indicative of an error between the desired power turbine speed signal and the at least one operating parameter signal;

a model-based estimation module configured to receive the bandwidth signal and generate an engine estimate signal indicative of estimated current engine parameters of the gas turbine engine based at least in part on the bandwidth signal; and

a gas generator control module configured to receive the power turbine torque request (Qpt_req) from the load control module and the estimated current engine parameters from the model-based estimation module, to determine at least one of fuel flow, inlet guide vane schedules, and stability bleed schedules based at least in part on the received power turbine torque request (Qpt_req) and the estimated current engine parameters, and to send signals to a gas generator of the gas turbine engine in order to control the gas generator according to at least one of the determined fuel flow, inlet guide vane schedules and stability bleed schedules.

12. The control system of claim 11, wherein the gas generator control module is configured to use a model-based constrained dynamic inversion methodology to determine at least one of the fuel flow, the inlet guide vane schedules and the stability bleed schedules.

13. The control system of claim 12, wherein the gas generator control module comprises:

a gas generator model configured to determine system data based at least in part on the at least one operating parameter;

an optimization formulation module configured to determine optimization data based at least in part on the system data; and

a constrained optimization solver configured to use the model-based constrained dynamic inversion of the optimization data to determine at least one of the fuel flow, the inlet guide vane schedules and the stability bleed schedules.

14. The control system of claim 11, wherein the load control module comprises:

a rotor model configured to receive a real-time collective lever angle command signal and to determine model system data;

an optimization formulator configured to receive the model system data and to produce optimization data; and

a constrained optimization solver configured to receive the optimization data and to generate the power turbine torque request (Qpt_req).

15. The control system of claim 11, wherein a load is produced by a rotor coupled to the power turbine.

16. The control system of claim 11, wherein the at least one operating parameter signal comprises current engine speed, power and/or thrust of the gas turbine engine.

17. The control system of claim 11, wherein the adaptation logic module comprises:

a comparator configured to determine an error signal comprising a difference between the desired power turbine speed signal and the at least one operating parameter signal;

a low pass filter configured to receive the error signal and produce a filtered error signal;

an integrator configured to receive the filtered error signal and produce an integrated error signal; and

a look-up table configured to produce the bandwidth signal based at least in part on at least one of the filtered error signal and the integrated error signal.

18. The control system of claim 11, wherein the model-based estimation module comprises:

a comparator configured to determine an error signal comprising a difference between the at least at least one operating parameter signal and a feedback of the engine estimate signal;

a bias estimator configured to receive the error signal and the bandwidth signal and to produce a bias signal;

a state variable model configured to produce a prediction signal indicative of a current state of the gas turbine engine;

a prediction signal biasing device configured to receive the bias signal and the prediction signal and produce a predicted parameter signal indicative of the current state of the gas turbine engine; and

a data storage device configured to store the predicted parameter signal and to produce the engine estimate signal.

19. The control system of claim 18, wherein the bias estimator comprises one of a low pass filter, a Kalman filter, a neural network, optimal estimation, recursive system identification, asymptotic observer and an adaptive logic based on L1 adaptive control theory.

20. The control system of claim 11, wherein the model-based estimation module comprises:

a state variable model configured to produce a prediction signal indicative of a current state of the gas turbine engine;

a comparator configured to determine an error signal comprising a difference between the prediction signal and a feedback of the engine estimate signal;

a low pass filter configured to receive the error signal and the bandwidth signal and to produce a bias signal;

a prediction signal biasing device configured to receive the bias signal and the prediction signal and produce the engine estimate signal.