System and method for estimating wind coherence and controlling wind turbine based on same

Patent metadata

Field	Value
Publication number	US-9926912-B2
Application number	US-201615251515-A
Country	US
Kind code	B2
Filing date	Aug 30, 2016
Priority date	Aug 30, 2016
Publication date	Mar 27, 2018
Grant date	Mar 27, 2018

Abstract

The present disclosure is directed to a system and method for estimating an overall wind coherence acting on a wind turbine and using same to dynamically adapt the gain or bandwidth of pitch or torque or yaw control logic within a wind turbine. The method includes generating, via sensors, a plurality of sensor signals reflective of wind conditions near the wind turbine. The method also includes filtering, via at least one filter, the sensor signals at a predetermined frequency range considered damaging for turbine sub-system loading. Thus, the method also includes estimating an overall damaging wind coherence acting on the wind turbine as a function of distance-normalized wind coherences, which themselves are derived from auto and cross-covariances of pairs of filtered signals. The distance normalization uses a model of natural coherence dissipation with distance.

First claim

What is claimed is:

1. A method for estimating an overall wind coherence acting on a wind turbine, the method comprising:
generating, via a plurality of sensors, a plurality of sensor signals reflective of wind conditions near the wind turbine;
filtering, via at least one filter, the sensor signals at a predetermined frequency range considered damaging for turbine subsystem loading;
determining a loads-relevant covariance of each pair of the filtered sensor signals, the covariance corresponding to a measure of how much each pair of the filtered sensor signals change together;
determining a plurality of distance-normalized wind coherences acting on the wind turbine as a function of the covariance of each pair of the filtered sensor signals; and,
estimating the overall wind coherence acting on the wind turbine as a function of the distance-normalized wind coherences.

2. The method of claim 1 , further comprising smoothing the covariance using a fading memory smoothing filter.

3. The method of claim 1 , wherein the plurality of sensors comprise at least one Light Detection and Ranging (LIDAR) sensor.

4. The method of claim 1 , wherein the at least one filter comprises a band-pass filter.

5. The method of claim 1 , further comprising determining a distance between the sensors that generated the pair of sensor signals.

6. The method of claim 5 , further comprising normalizing the distance-normalized wind coherence by the distance between the sensors when the distance is less than a predetermined threshold.

7. The method of claim 5 , further comprising amplifying the distance-normalized wind coherence when the distance is greater than a predetermined threshold.

8. The method of claim 1 , wherein determining the covariance of each pair of the sensor signals comprises multiplying each pair of sensor signals together.

9. The method of claim 1 , wherein estimating the overall wind coherence acting on the wind turbine as a function of the distance-normalized wind coherences further comprises averaging the distance-normalized wind coherences.

10. The method of claim 1 , further comprising estimating the overall wind coherence acting on the wind turbine in real-time online.

11. The method of claim 1 , wherein the wind conditions near the wind turbine comprise at least one of wind speeds, wind directions, wind gusts, wind turbulence.

12. A system for controlling a wind turbine, the system comprising:
a plurality of sensors configured to generate a plurality of sensor signals reflective of wind conditions near the wind turbine; and,
a controller communicatively coupled to the plurality of sensors and comprising at least one processor, the processor configured to perform one or more operations, the one or more operations comprising:
filtering, via at least one filter, the sensor signals at a predetermined frequency range considered damaging for turbine loading;
determining a covariance of each pair of the filtered sensor signals, the covariance corresponding to a measure of how much each pair of the filtered sensor signals change together;
determining a plurality of distance-normalized wind coherences acting on the wind turbine as a function of the covariance of each pair of the filtered sensor signals;
estimating an overall wind coherence acting on the wind turbine as a function of the distance-normalized wind coherences; and,
controlling the wind turbine based on the overall wind coherence.

13. A method for controlling a wind turbine, the method comprising:
generating, via a plurality of sensors, a plurality of sensor signals reflective of wind conditions near the wind turbine;
filtering, via at least one filter, the sensor signals at a predetermined frequency range considered damaging for turbine loading;
determining a covariance of each pair of the filtered sensor signals, the covariance corresponding to a measure of how much each pair of the filtered sensor signals change together;
determining a plurality of distance-normalized wind coherences acting on the wind turbine as a function of the covariance of each pair of the filtered sensor signals; and,
estimating an overall wind coherence acting on the wind turbine as a function of the distance-normalized wind coherences; and,
controlling the wind turbine based on the overall wind coherence.

14. The method of claim 13 , further comprising smoothing the covariance using a fading memory smoothing filter.

15. The method of claim 13 , wherein the plurality of sensors comprise at least one Light Detection and Ranging (LIDAR) sensor.

16. The method of claim 13 , further comprising:
determining a distance between the sensors that generated the pair of sensor signals;
normalizing the distance-normalized wind coherence by the distance between the sensors when the distance is less than a predetermined threshold; and,
amplifying the distance-normalized wind coherence when the distance is greater than a predetermined threshold.

17. The method of claim 14 , wherein determining the covariance of each pair of the sensor signals comprises multiplying each pair of sensor signals together.

18. The method of claim 14 , wherein estimating the overall wind coherence acting on the wind turbine as a function of the distance-normalized wind coherences further comprises averaging the distance-normalized wind coherences.

19. The method of claim 14 , further comprising estimating the overall wind coherence acting on the wind turbine in real-time online so as to prevent damage to the wind turbine.

20. The method of claim 14 , wherein the wind conditions near the wind turbine comprise at least one of wind speeds, wind directions, wind gusts, wind turbulence.

Assignees

• Gen Electric

Inventors

• Deshpande Ameet Shridhar
• Mccoy Timothy J
• Gupta Sandeep

Classifications

• F03D7/048

  controlling wind farms · CPC title

• F03D7/0248

  by mechanical means acting on the power train · CPC title

• F03D9/25

  the apparatus being an electrical generator (F03D9/22 takes precedence) · CPC title

• F03D9/005

  Mechanical Engineering · mapped topic

• F03D7/046 Primary

  with learning or adaptive control, e.g. self-tuning, fuzzy logic or neural network · CPC title