LiDAR-based turbine performance verification

Key benefits of a LiDAR-based performance verification:

• increase energy production
• avoid premature wear due to excessive stress and loads
• prevent critical component failures and extend turbine lifetime
• assess faster the power performance

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.

Results:

• results show a static yaw misalignment of -1.59° 
• the yaw error variations over time, see Fig. 1, indicate a stable yaw control
• low wind speeds produce larger variability in the average yaw error, as the turbine is still trying to face the wind, but as the wind speed increases this difference becomes smaller, as seen in Fig. 2

Conclusion:

• the time of a LiDAR-based yawmis alignment campaign can be reduced to less than 7 days, if wind conditions are favorable (see cumulative yaw error evolution of time in Fig. 1)
• results indicate that the turbine has a good yaw behavior

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.

Results:

• there is high turbulence intensity in the south-west sector, see Fig.3
• the relative wind direction plotted against the 360° nacelle direction (Fig. 4) shows that the wind sector is disturbed by the nearby obstacles (Fig. 5)

 
Conclusion:

• increased turbulence intensity produces a lower wind speed which leads to a decreased energy production
• sectors affected by wakes affect should be excluded from the analysis

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.

Results:

• SCADA PC overestimates the actual PC, especially for the wind speed below the rated value.
• a considerably high underestimation by the SCADA wind speed is shown in the mean wind speed deviation, see red line in Fig. 7, i.e., more than 25% for the low wind speed

Conclusion:

• the differences observed at the SCADA and LiDAR PCs are caused by the wind speed measurement
• delayed WTG cut-in due to SCADA underestimation of the actual wind speed