LiDAR-based turbine performance verification

Case study: Wrong Transfer Function Detection at Small-large Generators Switch.

Campaign details
Alt Objective:
Assess the performance of a turbine using a 4-beam LiDAR
Alt Wind turbine:
Neg Micon 72
Rotor 72m
Alt Commissioning year:
Alt Campaign duration:
40 days
Alt Campaign outcome:
Yaw misalignment ≈ 6%, high TI in N, E, S-E sectors, misguided SCADA PC calculation due to wrong wind speed transfer function
Campaign objectives

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:

Measurement principle and set-up

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.

Yaw misalignment

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.



Energy prod
5 530 000 kWh/year
Elect. price
0.097 €/kWh
-5.62 °
Potential gain
53 641 kWh/year
5 203,18 €/year
Tab. 1: Potential power gain based on the cos² relationship
Fig. 1: Wind speed vs. Yaw error
Turbulence intensity and wake effect

TI results are based on wind reconstructions calculated at a distance closest to 2.5 times the diameter of the rotor (i.e., 160m). The blocked sectors in Fig. 2 are calculated according to the requirements from IEC 61400-12-1 2017, Annex A. All plots are based only on the data during the campaign.

Fig. 2: Farm layout
Fig. 4: Wind rose
Fig. 4: TI at 160m



Fig. 5: Relative wind direction
Power curve

The power curves were determined by using the LiDAR wind speed measurements at 160m, distance closest to 2.5 times the rotor diameter, or SCADA wind speeds, against the power output from SCADA (Fig. 7 & Fig. 6). Also, these figures show, in red, the warranted power curve, as provided by the manufacturer.



Fig. 6: Power curve SCADA
Fig. 7: Power curve Ventus
Fig. 8: Wind speed comparison