Power and renewables

Yaw optimization using high-frequency wind turbine data

Yaw optimization using high-frequency wind turbine data

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Thomas van Delft

Thomas van Delft

Senior Engineer, Renewable Energy Analytics

I regularly hear concerns from wind farm owners and operators regarding wind turbine yaw misalignment.

What drives much of their fear is that yaw misalignment is notoriously difficult to detect and so their wind farm may be losing revenue without them ever knowing.

When operating correctly, wind turbines constantly track the wind by pointing into the wind flow. Ideally, this yawing behaviour ensures that the turbine extracts the maximum amount of energy from the wind at any given moment. Unfortunately, in certain circumstances, a wind turbine might not be aligned with the wind flow and consequently is unable to extract as much energy.

The possible causes of yaw misalignment are often related to the incorrect measurement of the wind direction. This may be due to a miscalibration of the wind vane, an instrument used to show the direction of the wind. It may also be due to a distortion of the wind flow where the wind direction at the measurement point (normally behind the rotor) is not the same as the wind direction in front of the rotor.

One approach to the detection of yaw misalignment is to measure the wind direction in front of the rotor. This approach usually requires the installation of additional hardware and will often be more costly than a purely analytical approach. Another approach is to measure the performance of the turbine as a function of yaw offset angle (the relative wind direction). It can be inferred that the offset angle that corresponds with the greatest performance is the angle at which the turbine is truly facing into the wind.

The latter approach, whilst not requiring costly hardware installation, does present its own challenges. One challenge is that the data commonly available from a wind turbine is recorded at a 10-minute resolution. This resolution lacks the necessary precision for tracking changes in performance relating to yaw alignment, as the required granularity is lost in the 10-minute averaging. The use of 10-minute data would, at best, only reveal gross yaw misalignments. Therefore, the solution is to make use of higher-resolution data, preferably in the region of one-second to 30-second intervals.

As our customers are increasingly able to access high-frequency wind turbine data, we have been able to conduct a number of yaw optimization analyses. The results from these analyses have been compelling, in many cases showing misalignments that, when corrected, present energy gains of over 1%.

Yaw alignment is not the only concern. In one of our recent studies we identified that one turbine had a very different yawing behaviour than the rest in the wind farm. It appeared to be taking much longer to yaw into the wind every time the wind changed direction. As well as having a slow yawing response, the same turbine had three times as many yaw operations than the other turbines and was also reporting far more yaw-system-related alarms. All the indications pointed towards mechanical resistance in the yaw mechanism, possibly caused by a ‘sticky’ yaw brake. This particular issue was costing the owner 1.8% of that turbine’s energy production.

High-frequency wind turbine data is increasingly being used to identify structural integrity issues, such as rotor imbalance or compromised foundations. Through yaw optimization, this data can be employed to increase energy production. From the work we have conducted so far, yaw optimization analyses have turned hidden energy losses into quantifiable energy gains every time. Is this a hidden opportunity at your wind farm? Get in touch to find out more.

Contact us:

Thomas van Delft

Thomas van Delft

Senior Engineer, Renewable Energy Analytics