Methods and systems for generating wind turbine control schedules

• US 10,928,816 B2
• Filed: 06/21/2016
• Issued: 02/23/2021
• 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): Dalbo, Michael J
• Application Number:
  US15/738,208
• Publication Number:
  US 20180173215A1
• Time in Patent Office: 1,708 Days
• CPC Class Codes: F03D 7/0292 to reduce fatigue
• CPC Class Codes: F03D 7/045 with model-based controls
• CPC Class Codes: F03D 7/046 with learning or adaptive control, e.g. self-tuning, fuzzy logic or neural network
• CPC Class Codes: F03D 7/047 characterised by the controller architecture, e.g. multiple processors or data communications
• CPC Class Codes: F03D 7/048 controlling wind farms
• CPC Class Codes: F03D 80/50 Maintenance or repair
• 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: 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: G06F 2111/04 Constraint-based CAD
• CPC Class Codes: G06F 2113/06 Wind turbines or wind farms
• CPC Class Codes: G06F 2119/12 Timing analysis or timing optimisation
• CPC Class Codes: G06F 30/17 Mechanical parametric or variational design
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: Y02E 10/72 Wind turbines with rotation axis in wind direction

Abstract

A control schedule for a wind turbine indicates how the turbine maximum power level varies over time. Generating the schedule includes determining a value indicative of the current remaining fatigue lifetime of the turbine, or one or more turbine components, based on measured wind turbine site and/or operating data; applying an optimisation function that varies an initial control schedule to determine an optimised control schedule by varying the trade-off between energy capture and fatigue life consumed by the turbine or the one or more turbine components until an optimised control schedule is determined.

Claims

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

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

applying an optimisation function that varies an initial control schedule to determine an optimised control schedule by varying the trade off between energy capture and fatigue life consumed by the turbine or the one or more turbine components until an optimised control schedule is determined, the optimisation including;

estimating future fatigue lifetime consumed by the turbine or turbine component over the duration of the varied control schedule based on the current remaining fatigue lifetime and the varied control schedule; and

constraining the optimisation of the control schedule according to one or more input constraints, wherein the input constraints include a maximum number of permitted component replacements for one or more turbine components and the optimisation further includes varying an initial value for a wind turbine lifetime to determine a target wind turbine lifetime; and

operating the turbine according to the optimised control schedule.

2. The method according to claim 1, further comprising:

optimising the control schedule by varying the timing and the number of component replacements up to the maximum number.

3. The method according to claim 1 wherein the one or more turbine components that can be replaced include one or more of:

the blades, pitch bearing, pitch actuation system, hub, main shaft, main bearing, gearbox, generator, converter, yaw drive, yaw bearing or transformer.

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

5. The method according to claim 1 wherein the input constraints further comprise the upper maximum power output of the turbine allowed by the turbine design and/or the minimum power output of the turbine.

6. The method according to claim 1 wherein determining a value indicative of the current remaining fatigue lifetime of the turbine or one or more turbine components comprises applying sensor data from one or more turbine sensors to one or more lifetime usage estimation algorithms.

7. The method according to claim 1 wherein determining a value indicative of the current remaining fatigue lifetime of the turbine or one or more turbine components comprises using data from a condition monitoring system.

8. The method according to claim 1 wherein determining a value indicative of the current remaining fatigue lifetime of the turbine or one or more turbine components comprises using data obtained from wind park sensors in combination with a site check program that determines loads acting on turbine components based upon the wind park sensors and parameters relating to the wind park and the wind turbine design.

9. The method according to claim 1 wherein optimisation of the control schedule comprises:

varying the control schedule to minimise the levelised cost of energy (LCoE).

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

capacity factor, indicative of the energy generated over a period divided by the energy that could have been generated if the turbine were to have operated continuously at rated power for that period;

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

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

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

costs associated with replacing one or more components, including turbine downtime, labour and equipment for component replacement, manufacture or refurbishment costs of the replacement components, and transportation costs of the refurbished or replacement components to the power plant; and

service costs associated with replacement of wear parts.

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

13. The method according to claim 1, wherein the control schedule is indicative of the amount of fatigue damage that should be incurred over time, the method further comprising operating the wind turbine, based on one or more LUEs, to incur fatigue damage at the rate indicated by the control schedule.

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

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

16. The method according to claim 1 further comprising providing the optimised control schedule to a wind turbine controller or wind power plant controller to control the power output of a wind turbine.

17. The method according to claim 1 wherein the method is repeated periodically.

18. The method according to claim 17 wherein the method is repeated daily, monthly or annually.

19. A controller, comprising:

an input/output (I/O) interface;

a memory; and

a processor communicatively coupled to the I/O interface and the memory;

wherein the processor is configured to perform an operation generating a control schedule for a wind turbine, the control schedule indicating how the turbine maximum power level varies over time, the operation, comprising;

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

applying an optimisation function that varies an initial control schedule to determine an optimised control schedule by varying the trade off between energy capture and fatigue life consumed by the turbine or the one or more turbine components until an optimised control schedule is determined, the optimisation including;

estimating future fatigue lifetime consumed by the turbine or turbine component over the duration of the varied control schedule based on the current remaining fatigue lifetime and the varied control schedule; and

constraining the optimisation of the control schedule according to one or more input constraints, wherein the input constraints include a maximum number of permitted component replacements for one or more turbine components and the optimisation further includes varying an initial value for a wind turbine lifetime to determine a target wind turbine lifetime; and

operating the turbine according to the optimised control schedule.

20. A computing system for generating a control schedule for a wind turbine, the control schedule indicating how the turbine maximum power level varies over time, the computing system comprising a processor and a memory storing a program, wherein, when executed by the processor, the program performs an operation, the operation comprising:

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

optimising the control schedule by minimising or maximising an operation parameter received at the optimisation module that is dependent upon the set of variables by varying one or more of the variables from its initial value in accordance with the remaining fatigue lifetime of the turbine or the one or more turbine components and the one or more constraints; and

operating the turbine according to the optimised control schedule;

wherein the constraints include a maximum number of permitted component replacements for one or more turbine components and the optimisation module is further configured to vary an initial value for a wind turbine lifetime to determine a target wind turbine lifetime.

21. The computing system according to claim 20, the operation further comprising receiving the initial values for the set of variables and the sensor data and calculating an initial value for the operation parameter.

22. The computing system according to claim 20 wherein the one or more turbine components are one or more of:

the blades, pitch bearing, pitch actuation system, hub, main shaft, main bearing, gearbox, generator, converter, yaw drive, yaw bearing or transformer.

23. The computing system according to claim 20 wherein the operation parameter is the levelised cost of energy (LCoE) for the turbine, and optimising the control scheduled comprises minimising the levelised cost of energy (LCoE).

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

capacity factor, indicative of the energy generated over a period divided by the energy that could have been generated if the turbine were to have operated continuously at rated power for that period;

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

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

25. The computing system according to claim 24 wherein the model further includes parameters for one or more of:

costs associated with replacing one or more components, including turbine downtime, labour and equipment for component replacement, manufacture or refurbishment costs of the replacement components, and transportation costs of the refurbished or replacement components to the power plant; and

service costs associated with replacement of wear parts.

26. A controller, comprising:

an input/output (I/O) interface configured to receive;

initial values for a set of variables, being operating variables of a wind turbine and including an initial control schedule;

one or more constraints; and

data indicative of the current remaining fatigue lifetime of the turbine or one or more turbine components;

a memory; and

an optimizer module configured to be executed by a hardware processor communicatively coupled to the I/O interface and the memory, wherein the optimizer module, when executed by the hardware processor, performs an operation generating a control schedule for the wind turbine, the control schedule indicating how the turbine maximum power level varies over time, the operation, comprising;

optimizing the control schedule by minimising or maximising an operation parameter received at the optimisation module that is dependent upon the set of variables by varying one or more of the variables from its initial value in accordance with the remaining fatigue lifetime of the turbine or the one or more turbine components and the one or more constraints; and

outputting the optimised control schedule; and

the hardware processor further configured to operate the turbine according to the optimised control schedule;

wherein the constraints include a maximum number of permitted component replacements for one or more turbine components and the optimisation module is further configured to vary an initial value for a wind turbine lifetime to determine a target wind turbine lifetime.