TURBINE OPERATIONAL FLEXIBILITY

• US 20150176498A1
• Filed: 12/20/2013
• Published: 06/25/2015
• Est. Priority Date: 12/20/2013
• Status: Active Grant

• Current Assignee: GE Infrastructure Technology, LLC (GE Vernova, Inc.)
• Original Assignee: General Electric Company
• Inventors: Tiwari, Awadesh Kumar, Muthuralingam, Mahendra, Gali, Praveen Kumar
• Granted Patent: US 9,581,086 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: F02C 9/00 Controlling gas-turbine plants; Controlling fuel supply in air- breathing jet-propulsion plants controlling air intakes F02C7/057; controlling turbines F01D; controlling compressors F04D27/00; controlling in general G05
• CPC Class Codes: G05B 13/024 in which a parameter or coefficient is automatically adjusted to optimise the performance
• CPC Class Codes: G05B 23/0283 Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL] preventive maintenance, i.e. planning maintenance according to the available resources without monitoring the system G06Q10/06
• CPC Class Codes: G05B 23/0286 Modifications to the monitored process, e.g. stopping operation or adapting control

Abstract

A device receives sensed operation parameters related to the operation of a machine. The device utilizes the sensed operation parameters in a first analyzer that generates first characteristics related to degradation of the machine and utilizes the sensed operation parameters in a second analyzer configured to generate second characteristics related to thermal characteristics of the machine, wherein the thermal characteristics include thermal stresses present in the machine. The device also utilizes the first and second characteristics and a third characteristic related to operator behaviors related to the current operation of the machine to generate remaining useful life characteristics of components of the machine and generates operational set points for control of the machine based in part on the remaining useful life characteristics, desired machine performance characteristics based on the operator behaviors, and maintenance characteristics of the machine.

Claims

1. A device configured to:

receive sensed operation parameters related to the operation of a machine;

utilize the sensed operation parameters in a first analyzer configured to generate first characteristics related to degradation of the machine;

utilize the sensed operation parameters in a second analyzer configured to generate second characteristics related to thermal characteristics of the machine, wherein the thermal characteristics include thermal stresses present in the machine;

utilize the first characteristic related to degradation of the machine, the second characteristics related to thermal characteristics of the machine, and a third characteristic related to operator behaviors related to the current operation of the machine to generate remaining useful life characteristics of components of the machine; and

generate operational set points for control of the machine based in part on the remaining useful life characteristics, desired machine performance characteristics based on the operator behaviors, and maintenance characteristics of the machine.

2. The device of claim 1, wherein the device is configured to utilize a neural network to generate the remaining useful life characteristics of components of the machine.

3. The device of claim 1, wherein the device is configured to receive the sensed operation parameters in real time.

4. The device of claim 1, wherein the device is configured to analyze an operational behavior of the machine based upon user settings indicating the operator behaviors and generate an indication of the operational behavior of the machine.

5. The device of claim 4, wherein the device is configured to analyze a cost of operation of the machine and generate an indication of the cost of operation of the machine.

6. The device of claim 5, wherein the device is configured to generate the operational set points for control of the machine based in part on the indication of the cost of operation of the machine.

7. The device of claim 6, wherein the device is configured to generate the operational set points for control of the machine based in part on design margins related to the capacity and capabilities of the machine.

8. The device of claim 1, wherein the device is configured to utilize a neural network to generate the operational set points for control of the machine.

9. The device of claim 1, wherein the device is configured to transmit the operational set points for control of the machine to an actuator of the machine.

10. The device of claim 1, wherein the machine comprises a power generation unit.

11. An article of manufacture, comprising:

a tangible non-transitory machine-readable media having encoded thereon processor-executable instructions comprising;

instructions to receive sensed operation parameters related to the operation of a machine;

instructions to apply the sensed operation parameters in a first analyzer configured to generate first characteristics related to degradation of the machine;

instructions to apply the sensed operation parameters in a second analyzer configured to generate second characteristics related to thermal characteristics of the machine, wherein the thermal characteristics include thermal stresses present in the machine;

instructions to apply the characteristic related to degradation of the machine, the second characteristics related to thermal characteristics of the machine, and a third characteristic related to operator behaviors related to the current operation of the machine to generate remaining useful life characteristics of components of the machine; and

instructions to generate operational set points for control of the machine based in part on the remaining useful life characteristics, desired machine performance characteristics based on the operator behaviors, and maintenance characteristics of the machine.

12. The article of manufacture of claim 11, comprising instructions to analyze an operational behavior of the machine based upon user settings and generate an indication of the operational behavior of the machine.

13. The article of manufacture of claim 12, comprising instructions to analyze a cost of operation of the machine and generate an indication of the cost of operation of the machine.

14. The article of manufacture of claim 13, comprising instructions to generate the operational set points for control of the machine based in part on the indication of the cost of operation of the machine.

15. The article of manufacture of claim 14, comprising instructions to generate the operational set points for control of the machine based in part on design margins related to the capacity and capabilities of the machine.

16. The article of manufacture of claim 11, comprising instructions to transmit an indication of the operational set points for control of the machine.

17. A tangible non-transitory machine-readable media comprising code configured to:

determine total remaining useful life of components of a machine based at least upon thermal stresses in the machine;

determine a first degradation factor of the machine due to operator behaviors related to the current operation of the machine;

determine a second degradation factor related to the duration of time that the machine is active;

determine operating costs of the machine related to behaviors related to the operation of the machine;

determine system losses at transients and steady state operation based on the operation of the machine; and

utilize the total remaining useful life, the first degradation factor, the second degradation factor, the operating costs, and the system losses to generate operational set points for control of the machine.

18. The tangible non-transitory machine-readable media of claim 17 comprising a neural network to generate the total remaining useful life components of the machine.

19. The tangible non-transitory machine-readable media of claim 17 comprising a neural network to generate the operational set points for control of the machine.

20. The tangible non-transitory machine-readable media of claim 17, wherein the code is configures to transmit an indication of the operational set points for control of the machine.