Neural network for combustion system flame detection

Patent metadata

Field	Value
Publication number	US-2018016992-A1
Application number	US-201615208263-A
Country	US
Kind code	A1
Filing date	Jul 12, 2016
Priority date	Jul 12, 2016
Publication date	Jan 18, 2018
Grant date	—

Abstract

A system includes a processor configured to execute an artificial neural network (ANN). The processor is configured to receive one or more operational parameters associated with an operation of a turbine system. The turbine system includes one or more combustors. The processor is further configured to analyze, via the ANN, the one or more operational parameters to determine a characteristic pattern, and to generate, via the ANN, an output based at least in part on the determined characteristic pattern. The output includes an indication of an intensity of a flame of the one or more combustors to determine a presence or an absence of the flame.

First claim

1 . A system, comprising:
a processor configured to execute an artificial neural network (ANN), and configured to:
receive one or more operational parameters associated with an operation of a turbine system, wherein the turbine system comprises one or more combustors;
analyze, via the ANN, the one or more operational parameters to determine a characteristic pattern; and
generate, via the ANN, an output based at least in part on the determined characteristic pattern, wherein the output comprises an indication of an intensity of a flame of the one or more combustors to determine a presence or an absence of the flame.

2 . The system of claim 1 , wherein the processor is configured to receive a compressor discharge pressure (CPD) input as the one or more operational parameters.

3 . The system of claim 1 , wherein the processor is configured to receive a turbine shaft speed as the one or more operational parameters.

4 . The system of claim 1 , wherein the processor is configured to receive an exhaust temperature, a mechanical energy input, an electrical energy input, a differential pressure, or a combination thereof, as the one or more operational parameters.

5 . The system of claim 1 , wherein the ANN comprises a feedforward ANN comprising at least three layers.

6 . The system of claim 1 , wherein the processor is configured to learn the characteristic pattern over a plurality of operating conditions of the turbine system.

7 . The system of claim 1 , wherein the processor is configured to analyze the one or more operational characteristics to determine a rate of increase or a rate of decrease of the one or more operational parameters as the determined characteristic pattern.

8 . The system of claim 1 , wherein the processor is configured to generate the output comprising an indication of a flame blowout of the one or more combustors.

9 . The system of claim 1 , comprising a controller configured to receive the output and to execute a control action for controlling at least one component coupled to the turbine system.

10 . The system of claim 9 , wherein the controller is configured to execute the control action comprising actuating an actuator, and wherein the actuator is configured to control a flow of fuel into the one or more combustors.

11 . The system of claim 9 , wherein the controller is configured to execute the control action comprising actuating an actuator, and wherein the actuator is configured to control a flow of air into the one or more combustors.

12 . The system of claim 1 , wherein the processor is configured to be programmably retrofitted with instructions to:
analyze, via the ANN, the one or more operational parameters to determine the characteristic pattern; and generate, via the ANN, the output based at least in part on the determined characteristic pattern.

13 . A non-transitory computer-readable medium having computer executable code stored thereon, the code comprising instructions to:
cause a processor to receive one or more operational parameters associated with an operation of a turbine system, wherein the turbine system comprises one or more combustors; cause the processor to execute an artificial neural network (ANN) to analyze the one or more operational parameters to determine a characteristic pattern; and cause the processor to utilize the ANN to generate an output based at least in part on the determined characteristic pattern, wherein the output comprises an indication of an intensity of a flame of the one or more combustors.

14 . The non-transitory computer-readable medium of claim 13 , wherein the code comprises instructions to cause the processor to receive a compressor discharge pressure (CPD) input as the one or more operational parameters.

15 . The non-transitory computer-readable medium of claim 13 , wherein the code comprises instructions to cause the processor to receive a turbine shaft speed as the one or more operational parameters.

16 . The non-transitory computer-readable medium of claim 13 , wherein the code comprises instructions to cause the processor to receive an exhaust temperature, a mechanical energy input, an electrical energy input, a differential pressure, or a combination thereof, as the one or more operational parameters.

17 . The non-transitory computer-readable medium of claim 13 , wherein the code comprises instructions to cause the processor to learn the characteristic pattern over a plurality of operating conditions of the turbine system.

18 . The non-transitory computer-readable medium of claim 13 , wherein the code comprises instructions to cause the processor to determine a rate of increase or a rate of decrease of the one or more operational parameters as the determined characteristic pattern.

19 . A system, comprising:
a data analytics system comprising an artificial neural network (ANN) configured to:
receive a first operational parameter, a second operational parameter, and a third operational parameter associated with an operation of a gas turbine system, wherein the gas turbine system comprises a plurality of combustors;
analyze, via the ANN, at least one of the first operational parameter, the second operational parameter, and the third operational parameter to determine a characteristic pattern of the at least one of the first operational parameter, the second operational parameter, and the third operational parameter; and
generate, via the ANN, an output based at least in part on the determined characteristic pattern, wherein the output comprises an indication of an intensity of a flame of the one or more combustors to determine the presence or absence of the combustor flame; and
a controller configured to receive the output and to generate a control command based thereon.

20 . The system of claim 19 , wherein the controller is configured to generate the control command to adjust an inlet airflow, an exit airflow, an exit pressure, an inlet fuel flow, or a combination thereof, of the plurality of combustors.

Assignees

• Gen Electric

Inventors

• Kalya Prabhanjana
• Kulkarni Abhijit
• Handa Vikas
• Vavilala Pradeep Kumar
• Kandula Vamshi

Classifications

• F05B2270/20

  to optimise the performance of a machine · CPC title

• F05B2270/504

  electronic means, e.g. electronic tubes, transistors or IC's within an electronic circuit · CPC title

• F05B2270/709

  with neural networks · CPC title

• F05D2270/44

  active, predictive, or anticipative · CPC title

• F02C9/28 Primary

  Regulating systems responsive to plant or ambient parameters, e.g. temperature, pressure, rotor speed (F02C9/30 - F02C9/38, F02C9/44 take precedence) · CPC title