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Machine learning in the performance analyis of wind farms

Updated: Apr 26, 2021


After a technical improvement of a wind turbine, the question often arises which change the improvement has on the performance of the turbine. For example, one would like to know how much more power is produced after the calibration of a nacelle misalignment.

First of all, it is convenient to compare the power curve of the turbine in the periods before and after the measure.

In this article, we highlight inaccuracies in this approach and present a completely new approach that allows reliable power curve comparisons to be made with the help of machine learning.

Example: Correction of Yaw Misalignment

We would like to present our thoughts on behalf of the example of a nacelle misalignment correction.

One can assume, that the calibration of a systematic yaw error, e.g. by our Turbit Measurement System (TMS), is leading to an improvement of the overall performance of the wind turbine. This Article describes the theoretical and physical background of the effects of a yaw error on the performance of wind turbines.

Theoretical Increases in Total Power

If you take the annual yields of a wind turbine and add a theoretical additional output of 1-3%, you can quickly see the economic benefit of a nacelle misalignment calibration.

Evidenvce of an increase in yield?

The question arises, however, as to how this increase in performance can be demonstrated by real power measurements. The process of converting kinetic wind energy into electrical energy is very complex and highly dependent on meteorological parameters.

Problems of a normal power curve Comparison

If you make a change to a wind turbine, you want to know how much this change changes the performance of the turbine. The performance of a wind turbine does not only depend on the wind speed. This leads to a change in the power curve (power per wind speed) under different meteorological conditions.

A normal power curve comparison of two time periods is not sufficient to be able to recognize exactly which was the cause. E.g. the meteorological conditions can change over the time and thus affect the power curve.

Dependence of the power curve on the air density

The air density is, besides the wind speed, one of the most important factors on the power curve of wind turbines. At the same wind speed, the energy content of the wind changes as a function of the air density. The denser the air, the more energy the wind has and the more power the wind turbine can convert from the wind.

Air density, in turn, depends mainly on temperature, air pressure and humidity

The figure on the left shows a clear difference in the power curve of a plant between summer and winter. The main reason is the average temperature and the associated different air density.