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