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 -5.62° 
• 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. 1
• the potential production gain of this turbine is 0.97%, which translates in >53 000 kWh/year or >5 000€, as seen in Tab. 1

Conclusion:

• a YM correction is recommended for this turbine to avoid production loss and additional stress loads on critical components of the turbine, which translates into premature wear and revenue loss

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.

Results:

• the wind sector is disturbed by the nearby turbines (see shaded areas in Fig.2 & 5), causing high TI in the N, E, S-E sectors (Fig.4)
• there is higher TI in the N, S, SW sectors, see Fig. 2
• even with less wind from S, SW sectors (see Fig. 4), there is indication of disturbed wind sector

 
Conclusion:

• increased TI produces a lower wind speed which leads to a decreased energy production
• sectors affected by wakes have been excluded from the PC analysis

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.

Results:

• total downtime period (turbine shows a status code) of ≈ 1 day => production loss of 0.37%
• warranted vs. measured production during campaign shows an overperformance of ≈ 7%
• SCADA wind speed transfer function around the switch between small-large generator seems to underestimate the wind speed (see gray areas in Fig. 6, 7 & 8)

Conclusion:

• the faulty generator switch captured by SCADA PC is not verified by LiDAR PC, but the latter suggests the wind transfer function as the root cause
• further investigation of the downtime period is also recommended