Systems and methods to correct induction for LIDAR-assisted wind turbine control

Patent metadata

Field	Value
Publication number	US-10539116-B2
Application number	US-201615208723-A
Country	US
Kind code	B2
Filing date	Jul 13, 2016
Priority date	Jul 13, 2016
Publication date	Jan 21, 2020
Grant date	Jan 21, 2020

Abstract

Methods, apparatus, systems and articles of manufacture are disclosed to provide wind turbine control and compensate for wind induction effects. An example method includes receiving wind speed data from a Light Detecting and Ranging (LIDAR) sensor. The example method includes receiving operating data indicative of wind turbine operation. The example method includes determining an apriori induction correction for wind turbine operating conditions with respect to the LIDAR wind speed data based on the operating data. The example method includes estimating a wind signal from the LIDAR sensor that is adjusted by the correction. The example method includes generating a control signal for a wind turbine based on the adjusted LIDAR estimated wind signal.

First claim

What is claimed is:

1. A method of controlling a wind turbine, the method comprising:
receiving wind speed data from a Light Detecting and Ranging (LIDAR) sensor;
receiving operating data indicative of wind turbine operation;
determining an apriori induction correction for wind turbine operating conditions with respect to the LIDAR wind speed data based on the operating data, wherein the apriori induction correction includes determining a mean induction model and a delayed dynamic induction model based on the operating data indicative of wind turbine operation;
estimating a wind signal from the LIDAR sensor that is adjusted by the apriori induction correction; and
generating a control signal for the wind turbine based on the adjusted LIDAR estimated wind signal.

2. The method of claim 1 , wherein the operating data indicative of wind turbine operation includes a measurement or a model-based estimate of at least one of turbine thrust, turbine speed, turbine torque, turbine yaw, position, or turbine blade pitch.

3. The method of claim 1 , wherein a wind field model quantifying at least one of a mean induction effect and a dynamic induction effect is used to estimate the wind signal from the LIDAR sensor.

4. The method of claim 3 , wherein the mean induction model includes determining a mean induction factor using a low pass filter on the operating data indicative of wind turbine operation to generate a wind field model quantifying the mean induction effect.

5. The method of claim 3 , wherein the dynamic induction model includes determining a dynamic induction factor using the operating data indicative of wind turbine operation to generate a wind field model quantifying the dynamic induction effect.

6. The method of claim 1 , wherein the control signal is configured to control the wind turbine based on at least one of: a) a rotor effective wind speed corrected for turbine operation effects, b) a rotor effective wind direction corrected for turbine operation effects, c) and a rotor effective shear corrected for turbine operation effects.

7. The method of claim 6 , wherein the control signal is configured to continuously adjust operation of the wind turbine including at least one of blade pitch, yaw, torque, turbine set-point, or turbine operating constraint via the control signal.

8. A tangible, computer-readable storage medium including instructions which, when executed by a processor, cause the processor to at least:
receive wind speed data from a Light Detecting and Ranging (LIDAR) sensor;
receive operating data indicative of wind turbine operation;
determine an apriori induction correction for wind turbine operating conditions with respect to the LIDAR wind speed data based on the operating data, wherein the apriori induction correction includes determining a mean induction model and a delayed dynamic induction model based on the operating data indicative of wind turbine operation;
estimate a wind signal from the LIDAR sensor that is adjusted by the apriori induction correction; and
generate a control signal for a wind turbine based on the adjusted LIDAR estimated wind signal.

9. The computer-readable storage medium of claim 8 , wherein the operating data indicative of wind turbine operation includes a measurement or a model-based estimate of at least one of turbine thrust, turbine speed, turbine torque, turbine yaw, position, or turbine blade pitch.

10. The computer-readable storage medium of claim 8 , wherein a wind field model quantifying at least one of a mean induction effect and a dynamic induction effect is used to estimate the wind signal from the LIDAR sensor.

11. The computer-readable storage medium of claim 10 , wherein the mean induction model includes determining a mean induction factor using a low pass filter on the operating data indicative of wind turbine operation to generate a wind field model quantifying the mean induction effect.

12. The computer-readable storage medium of claim 8 , wherein the dynamic induction model includes determining a dynamic induction factor using the operating data indicative of wind turbine operation to generate a wind field model quantifying the dynamic induction effect.

13. The computer-readable storage medium of claim 8 , wherein the control signal is configured to control the wind turbine based on at least one of: a) a rotor effective wind speed corrected for turbine operation effects, b) a rotor effective wind direction corrected for turbine operation effects, c) and a rotor effective shear corrected for turbine operation effects.

14. The computer-readable storage medium of claim 13 , wherein the control signal is configured to continuously adjust operation of the wind turbine including at least one of blade pitch, yaw, torque, turbine set-point, or turbine operating constraint via the control signal.

15. A wind turbine control apparatus, the apparatus comprising:
a wind estimation processor particularly configured to:
receive wind speed data from a Light Detecting and Ranging (LIDAR) sensor;
receive operating data indicative of wind turbine operation;
determine an apriori induction correction for wind turbine operating conditions with respect to the LIDAR wind speed data based on the operating data, wherein the apriori induction correction includes determining a mean induction model and a delayed dynamic induction model based on the operating data indicative of wind turbine operation;
estimate a wind signal from the LIDAR sensor that is adjusted by the apriori induction correction; and
generate a control signal for a wind turbine controller based on the adjusted LIDAR estimated wind signal.

16. The apparatus of claim 15 , wherein the operating data indicative of wind turbine operation includes a measurement or a model-based estimate of at least one of turbine thrust, turbine speed, turbine torque, turbine yaw, position, or turbine blade pitch.

17. The apparatus of claim 15 , wherein the control signal is configured to control the wind turbine based on at least one of: a) a rotor effective wind speed corrected for turbine operation effects, b) a rotor effective wind direction corrected for turbine operation effects, c) and a rotor effective shear corrected for turbine operation effects.

Assignees

• Gen Electric

Inventors

• Davoust Samuel
• Shane Conner B
• Daher Adegas Fabiano
• Markham Thomas Stephen
• Mashtare Dale Robert

Classifications

• G01S17/58

  Velocity or trajectory determination systems; Sense-of-movement determination systems · CPC title

• F03D7/04

  Automatic control; Regulation · CPC title

• F05B2270/8042

  Lidar systems · CPC title

• F05B2220/30

  in turbines · CPC title

• F05B2270/32

  Wind speeds · CPC title