Methods and systems for generating wind turbine control schedules

• US 10,907,611 B2
• Filed: 06/21/2016
• Issued: 02/02/2021
• Est. Priority Date: 06/30/2015
• Status: Active Grant

• Current Assignee: Vestas Wind Systems A/S
• Original Assignee: Vestas Wind Systems A/S
• Inventors: Spruce, Chris, Byreddy, Chakradhar
• Primary Examiner(s): Ortiz Rodriguez, Carlos R
• Application Number:
  US15/736,720
• Publication Number:
  US 20180187649A1
• Time in Patent Office: 1,687 Days
• CPC Class Codes: F03D 7/0292 to reduce fatigue
• CPC Class Codes: F03D 7/046 with learning or adaptive control, e.g. self-tuning, fuzzy logic or neural network
• CPC Class Codes: F03D 7/048 controlling wind farms
• CPC Class Codes: F03D 80/50 Maintenance or repair
• CPC Class Codes: F03D 9/257 the wind motor being part of a wind farm
• CPC Class Codes: F05B 2220/706 an electrical generator
• CPC Class Codes: F05B 2260/821 Parameter estimation or prediction
• CPC Class Codes: F05B 2270/1033 Power (if explicitly mentioned)
• CPC Class Codes: F05B 2270/20 to optimise the performance of a machine
• CPC Class Codes: F05B 2270/332 Maximum loads or fatigue criteria
• CPC Class Codes: F05B 2270/335 Output power or torque
• CPC Class Codes: F05B 2270/404 active, predictive, or anticipative
• CPC Class Codes: F05B 2270/808 Strain gauges; Load cells
• CPC Class Codes: G05B 19/048 Monitoring; Safety
• CPC Class Codes: G05B 2219/2619 Wind turbines
• 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: Y02E 10/72 Wind turbines with rotation axis in wind direction

Abstract

Aspects described herein include a method of generating a control schedule for a wind turbine. The control schedule specifies a maximum power level that varies over time. The method comprises determining a value indicative of a current remaining fatigue lifetime of the wind turbine or of one or more components. The method further comprises applying an optimization function to perform an optimization to determine an optimized control schedule. The optimization comprises varying a plurality of parameters of an initial control schedule to produce a varied control schedule, estimating a future fatigue lifetime to be consumed by the wind turbine or the one or more components over the duration of the varied control schedule, and constraining the optimization according to one or more input constraints.

Claims

1. A method of generating a control schedule for a wind turbine, the control schedule specifying a maximum power level of the wind turbine that varies over time, the method comprising:

determining, based on one or both of measured site data and operating data for the wind turbine, a value indicative of a current remaining fatigue lifetime of the wind turbine or of one or more components of the wind turbine;

applying an optimization function to perform an optimization to determine an optimized control schedule, the optimization comprising;

varying an initial control schedule to produce a varied control schedule, wherein varying the initial control schedule comprises varying a plurality of parameters comprising a target lifetime of the wind turbine and a number of component replacements for the one or more components;

estimating, based on the current remaining fatigue lifetime, a future fatigue lifetime to be consumed by the wind turbine or the one or more components over the duration of the varied control schedule; and

constraining the optimization according to one or more input constraints;

and

providing the optimized control schedule as an input to a wind turbine controller or to a wind power plant controller, wherein the wind turbine controller or the wind power plant controller is configured to generate one or more control signals to control a power output of the wind turbine according to the optimized control schedule,

wherein the optimized control schedule reflects a selected combination of the number of component replacements and the target lifetime of the wind turbine, and

wherein, for the selected combination, the future fatigue lifetime at least meets the target lifetime.

2. The method according to claim 1, wherein the initial control schedule specifies a relative variation over time of the maximum power level up to which the wind turbine may operate.

3. The method according to claim 1, wherein varying the plurality of parameters comprises:

varying the target lifetime and varying the number of component replacements up to a maximum number.

4. The method according to claim 1, wherein the one or more components include one or more of:

a blade, a pitch bearing, a pitch actuation system, a hub, a main shaft, a main bearing, a gearbox, a generator, a converter, a yaw drive, a yaw bearing, and a transformer.

5. The method according to claim 1, wherein the one or more input constraints comprise one of both of:

an upper maximum power output of the wind turbine allowed by the turbine design and a minimum power output of the wind turbine.

6. The method according to claim 1, wherein determining the value indicative of the current remaining fatigue lifetime comprises:

applying sensor data from one or more sensors of the wind turbine to one or more lifetime usage estimation (LUE) algorithms.

7. The method according to claim 1, wherein determining the value indicative of the current remaining fatigue lifetime comprises:

using data from a condition monitoring system.

8. The method according to claim 1, wherein determining the value indicative of the current remaining fatigue lifetime comprises:

using a site check program that determines loads acting on the one or more components based upon data obtained from sensors of a wind park, parameters relating to the wind park, and a design of the wind turbine.

9. The method according to claim 1, wherein varying the initial control schedule comprises:

varying the initial control schedule to minimize a levelized cost of energy (LCoE).

10. The method according to claim 9, wherein a LCoE model is used to determine the LCoE, the LCoE model including parameters for one or more of:

a capacity factor of the wind turbine, indicative of an energy generated by the wind turbine over a period divided by an energy that could have been generated by the wind turbine during the period had the wind turbine operated continuously at rated power;

an availability of the wind turbine, indicative of the time the wind turbine will be available to generate electricity; and

a park efficiency, indicative of an energy generated by the wind turbine over a period divided by the energy that could have been generated by the wind turbine during the period had the wind turbine operated in wind that was wholly undisturbed by upstream wind turbines.

11. The method according to claim 10, wherein the LCoE model further includes parameters for one or more of:

costs associated with replacing the one or more components, the costs including downtime of the wind turbine, labor and equipment for component replacement, manufacture or refurbishment costs of replacement components, and transportation costs of the replacement components to a site of the wind turbine; and

service costs associated with replacement of wear components.

12. The method according to claim 1, wherein the optimized control schedule is a schedule of maximum power levels up to which the wind turbine can be operated.

13. The method according to claim 1, wherein the optimized control schedule is indicative of an amount of fatigue damage that should be incurred over time, the method further comprising:

operating the wind turbine, based on one or more lifetime usage estimations (LUEs), to incur fatigue damage at the rate indicated by the optimized control schedule.

14. The method according to claim 1, wherein the optimized control schedule specifies maximum power levels above a rated power of the wind turbine.

15. The method according to claim 1, wherein the optimized control schedule indicates how the maximum power level varies over the future fatigue lifetime of the wind turbine.

16. The method according to claim 1, wherein determining the value indicative of the current remaining fatigue lifetime, applying the optimization function, and providing the optimized control schedule are repeated periodically.

17. The method according to claim 16, wherein determining the value indicative of the current remaining fatigue lifetime, applying the optimization function, and providing the optimized control schedule are repeated daily, monthly, or annually.

18. A controller for a wind turbine or a wind power plant, the controller configured to perform the method of claim 1.

19. A computer program product comprising:

a non-transitory computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code executable by one or more processors to perform an operation for generating a control schedule for a wind turbine, the control schedule specifying a maximum power level of a wind turbine that varies over time, the operation comprising;

receiving initial values for a set of variables, the set of variables comprising operating variables of the wind turbine, an initial control schedule, one or more constraints, and data indicative of a current remaining fatigue lifetime of the wind turbine or of one or more components of the wind turbine;

optimizing the initial control schedule by minimizing or maximizing an operation parameter that is dependent upon the set of variables, wherein optimizing the initial control schedule comprises varying a plurality of variables of the set of variables from the initial values in accordance with the current remaining fatigue lifetime and the one or more constraints, wherein the plurality of variables comprise a target lifetime of the wind turbine and a number of component replacements for the one or more components; and

providing the optimized control schedule as an input to a wind turbine controller or to a wind power plant controller, wherein the wind turbine controller or the wind power plant controller is configured to generate one or more control signals to control a power output of the wind turbine according to the optimized control schedule,

wherein the optimized control schedule reflects a selected combination of the number of component replacements and the target lifetime of the wind turbine.

20. The computer program product according to claim 19, wherein the initial control schedule specifies a relative variation over time of the maximum power level up to which the wind turbine may operate.

21. The computer program product according to claim 19, the operation further comprising:

receiving sensor data; and

calculating an initial value for the operation parameter.

22. The computer program product according to claim 19, wherein the one or more components are one or more of:

a blade, a pitch bearing, a pitch actuation system, a hub, a main shaft, a main bearing, a gearbox, a generator, a converter, a yaw drive, a yaw bearing, and a transformer.

23. The computer program product according to claim 19,

wherein the operation parameter is a levelized cost of energy (LCoE) for the turbine, and

wherein optimizing the initial control schedule comprises minimizing the LCoE.

24. The computer program product according to claim 23, wherein a LCoE model is used to determine the LCoE, the LCoE model including parameters for one or more of:

a capacity factor of the wind turbine, indicative of an energy generated by the wind turbine over a period divided by an energy that could have been generated by the wind turbine during the period had the wind turbine operated continuously at rated power;

an availability of the wind turbine, indicative of the time the wind turbine will be available to generate electricity; and

a park efficiency, indicative of an energy generated by the wind turbine over a period divided by the energy that could have been generated by the wind turbine during the period had the wind turbine operated in wind that was wholly undisturbed by upstream wind turbines.

25. The computer program product according to claim 24, wherein the LCoE model further includes parameters for one or more of:

costs associated with replacing the one or more components, the costs including downtime of the wind turbine, labor and equipment for component replacement, manufacture or refurbishment costs of replacement components, and transportation costs of the replacement components to a site of the wind turbine; and

service costs associated with replacement of wear components.

26. A method of generating a control schedule for a wind power plant comprising a plurality of wind turbines, the control schedule indicating, for each wind turbine, how a maximum power level of the wind turbine varies over time, the method comprising:

determining, based on one or both of measured site data and operating data for the plurality of wind turbines, a value indicative of a current remaining fatigue lifetime of each wind turbine of the plurality of wind turbines or of one or more components of each wind turbine;

applying an optimization function to perform an optimization to determine an optimized control schedule, the optimization comprising;

varying an initial control schedule to produce a varied control schedule, wherein varying the initial control schedule comprises varying a plurality of parameters for each wind turbine, the plurality of parameters comprising a target lifetime of the wind turbine and a number of component replacements for the one or more components;

estimating a future fatigue lifetime to be consumed by the plurality of wind turbines or the one or more components of each wind turbine over the duration of the varied control schedule based on the current remaining fatigue lifetime of each wind turbine, wherein estimating the future fatigue lifetime is performed using a site check program that determines loads acting on the one or more components of each wind turbine based upon;

data obtained from wind power plant sensors, parameters relating to the wind power plant, design of the plurality of wind turbines, and interactions between different turbines of the wind power plant; and

constraining the optimization according to one or more input constraints;

and

providing the optimized control schedule as an input to a wind power plant controller, wherein the wind power plant controller is configured to generate one or more control signals to control a power output of each of the plurality of wind turbines according to the optimized control schedule,

wherein the optimized control schedule reflects a selected combination of the number of component replacements and the target lifetime of the wind turbine, and

wherein, for the selected combination, the future fatigue lifetime at least meets the target lifetime.

27. The method according to claim 26, wherein the initial control schedule specifies, for each wind turbine, a relative variation over time of the maximum power level up to which the wind turbine may operate.

28. The method according to claim 26, wherein the one or both of the measured site data and the operating data includes sensor data collected prior to one of:

commissioning and construction of the wind turbine or the wind power plant.

29. The method according to claim 26, wherein the optimization function varies, for at least one component of at least one wind turbine, a number of times that the component may be replaced over the target lifetime of the wind turbine.

30. The method according to claim 29, wherein the optimization function varies, for at least one component of at least one wind turbine, when the component may be replaced during the target lifetime of the wind turbine.

31. The method according to claim 26, wherein for any given time period within the optimized control schedule, a summed power of the plurality of wind turbines does not exceed an amount of power that can be carried in a connection from the wind power plant to a grid.