Low and Middle Wind Speed Underestimation.
Case study: Low and Middle Wind Speed Underestimation.
1) Yaw misalignment (YM) detection
2) Quick turbulence intensity (TI) profile
3) Quick power curve (PC) verification
Key benefits of a LiDAR-based performance verification:
A 4-beam LiDAR is temporarily mounted on top of the nacelle, together with a number of calibrated instruments, and a data collection and communication unit in the nacelle. Every second, the LiDAR measures the horizontal wind speed and directionat hub height in front of the turbine at 10 simulatenous measurement ranges, between 50m to 400m.
Compared to met mast-mounted cup anemometers, sufficient data to evaluate the wind turbine power performance can be collected much faster by the nacelle-based LiDAR.
The average relative wind direction and wind speed (at hub height) are computed every 10 minutes from several measurement ranges in front of the turbine. These measurements are validated or discarded based on standard or more advanced criteria, such as cut-in & rated wind speed, low data quality, etc.
Turbulence intensity results are based on wind reconstructions calculated at a distance closest to 2.5 times the diameter of the rotor, which in this case is 280m. The blocked sectors are calculated according to the requirements from IEC 61400-12-1 2017, Annex A.
The power curve was determined using the LiDAR wind speed measurements (blue) at 280m (distance closest to 2.5 times the rotor diameter), and compared to the power curve based on SCADA wind speeds (orange). Both power curves use the power output from SCADA. Also, Fig. 6 shows the warranted power curve, as provided by the manufacturer.