The effect of intermittent and Gaussian inflow conditions on wind energy converters is studied experimentally. Two different flow situations were created in a wind tunnel using an active grid. Both flows exhibit nearly equal mean velocity values and turbulence intensities but strongly differ in their two point statistics, namely their distribution of velocity increments on a variety of timescales, one being Gaussian distributed, and the other one being strongly intermittent. A horizontal axis model wind turbine is exposed to both flows, isolating the effect on the turbine of the differences not captured by mean values and turbulence intensities. Thrust, torque and power data were recorded and analyzed, showing that the model turbine does not smooth out intermittency. Intermittent inflow is converted to similarly intermittent turbine data on all scales considered, reaching down to sub-rotor scales in space. This indicates that it is not correct to assume a smoothing of intermittent wind speed increments below the size of the rotor.

Wind energy converters (WECs) work in a turbulent environment and are
therefore turbulence-driven systems. The turbulent wind interacts with the

Generally, the characteristics of the output dynamics of a WEC need to be
understood in detail for multiple reasons. Power fluctuations have been
reported in numerous studies, causing challenges in grid stability

Wind dynamics in the atmospheric boundary layer have been investigated
extensively. Here, one has to differentiate between analyses concerning the
statistics of the wind speed

In the field of wind energy research, it is still unclear to what extent wind
dynamics transfer to the parameters of a WEC such as loads, power etc. This most
likely depends on the relevant timescales, which change with the
system dynamics. Therewith, the conversion from wind to power, loads etc.
vary with the turbine type. Consequently, it is important what scales in
time and space are relevant to quantify the impact of turbulent wind on WECs

Using wind tunnel experiments, we contribute to the ongoing discussion on the conversion process of non-Gaussian wind statistics to wind turbine data such as power, thrust and torque. A model wind turbine and an active grid for flow manipulation were used in order to examine to what extent Gaussian-distributed and highly intermittent wind speeds affect the model turbine dynamics differently.

This paper is organized as follows: Sect.

Since WECs work in turbulent wind conditions, a proper characterization of these
conditions becomes necessary

In this section, we give a brief overview of the methods used in the industry
standard and beyond, along with their mathematical background, without claims
of completeness. Furthermore, the methods of data analysis used in this study are
introduced. We refer to

A general first step in characterizing a time series of wind velocities,

Going one step further in the sense of two point quantities, we will consider
velocity changes during a time lag

In order to include all higher-order structure functions,

For design load calculations, different turbulence models are used. One,
which is suggested by the IEC standard, is the Kaimal model, which considers
power spectral densities and features merely Gaussian statistics. In this paper, we investigate to what
extent wind characteristics not captured by standard models impact wind
turbines. Furthermore, we consider a synthetic wind speed time series based
on the Kaimal turbulence model, created using the software TurbSim

First two statistical moments and turbulence intensities of a synthetic wind speed time series based on the Kaimal model and offshore data (FINO1). Values are rounded to two decimal places.

Schematic drawing of the experimental setup, side view. Scales do
not match,

The experiments were conducted in a wind tunnel of the University of
Oldenburg in open jet configuration. The outlet of 0.8 m

The excitation protocols of the motors were designed so that two different
flow situations with the same mean wind velocities and comparable turbulence
intensities were realized. At the same time, they strongly differ in their
distributions of increments: one flow (A) being Gaussian distributed, the
other one (B) being highly intermittent on a broad range of timescales, which
shows a distinctly heavy-tailed distribution of velocity increments. The
resulting time series are discussed in Sect.

The flows considered were characterized using three single-wire hot-wire
probes simultaneously in one plane normal to the main flow direction. The
probes were arranged so that one was located at the position of the model
wind turbine's hub and the other two in

The model wind turbine and the active grid installed in a wind tunnel of the University of Oldenburg.

A three-bladed horizontal-axis model wind turbine with a rotor diameter of

To measure the thrust force acting on the turbine, it was placed on a three-component force balance (ME-Meßsysteme K3D120-50 N). Only the thrust
force in main flow direction is considered; thus,

Original (black) and filtered (red) example time series of the wind
speed

For each experiment, data were recorded simultaneously. During flow
characterization the three hot-wire probes were synchronized and during
turbine data acquisition the thrust force, power, torque and hot-wire
signals were recorded synchronously. Generally, all data sets are
superimposed with some kind of measurement noise, which we generally want to
exclude from our analyses, while preserving the fluctuations of the turbine
signals resulting from the inflow. The data sets are filtered using a sixth-order Butterworth low-pass filter at a cutoff frequency of 15 Hz for the
thrust data and 45 Hz for the power and torque data. Further details about
the approach are shown in Appendix

Velocity time series as defined in Eq. (

Excerpts of both time series shown in Fig.

As previously described, we will consider increment PDF of different timescales,

Overview of scales considered in relation to certain characteristic
turbine lengths. The timescales

Throughout the following analyses, two different flow situations will be
considered and used as inflow conditions for the model wind turbine.
Figure

However, just by looking at the time series, a difference becomes obvious,
which will be investigated further. Therefore, Fig.

First two statistical moments of the time series shown in Fig.

Next, we investigate the performance data of the model wind turbine when
exposed to both A and B flows. To begin with, we consider the thrust force in
main flow direction,

So far, we have considered the thrust force of the turbine as an example,
showing a transfer of intermittency from

To what extent non-Gaussian wind statistics impact
WECs is an ongoing discussion
throughout the wind energy research community. Using an active grid to create
different turbulent inflow conditions allows experimental investigations of
the impact of turbulence on wind turbines. This study can therefore
supplement present approaches in the literature that investigate the impact
of non-Gaussianity based on numerical simulations

When processing the experimental data, signal fluctuations not resulting from
the inflow are excluded from the analysis by previously applied low-pass
filters. While noise is only a minor issue considering the power and torque,
the thrust data from the force balance are significantly superimposed by
signal fluctuations resulting from the setup itself; see
Fig.

There might also be aerodynamic effects that are of even higher frequency than the inflow fluctuations and are therefore not captured due to the filtering. Such effects at the rotor are possibly excluded by the low-frequency filtering. This study, however, focuses on dynamics caused by the inflow turbulence.

Considering Fig.

In this study, an experimental setup that allows the investigation of interactions between various turbulent flows with a model wind turbine was realized. Experiments were performed in order to elaborate on the impact of non-Gaussian wind statistics on WECs. Our results do not show any filtering of the intermittent features of wind fields found in the atmosphere by the turbine. Consequently, one should be aware that wind characteristics, which are not reflected in standard wind field descriptions, the IEC 61400-1 for example, have a significant impact on wind turbines. Intermittent inflow is converted to similarly intermittent turbine data on all scales considered, ranging down to sub-rotor scales. Thus, statistical properties of the inflow time series that are not captured by describing them using one-point statistics are of relevance and should be included in standards characterizing inflow conditions. If intermittent inflows lead to intermittent loading, including extreme loads that occur much more frequently than currently modeled in the standards, then this has implications for the use of the current standards in designing wind turbines to withstand the wind conditions experienced.

The offshore data analyzed in this paper was made available by the DEWI and
the Federal Maritime and Hydrographic Agency (German Bundesamt für
Seeschifffahrt und Hydrographie). Access can be requested through

Furthermore, the experimental datasets are archived by the University of Oldenburg. The data can be made available by contacting the corresponding author.

As described in Sect.

Power spectral density (PSD) of

Magnitude-squared coherence of filtered hot-wire data and
thrust

For completeness, the variances

Variances of each increment time series,

Variances of each increment time series for
the experimental data.

We thank the Reiner Lemoine Stiftung (RLS) for funding parts of this work and Stefan Ivanell for providing the rotor blade design. Furthermore, we thank Philip Rinn and Matthias Wächter for fruitful discussions as well as the German Bundesamt für Seeschifffahrt und Hydrographie (Federal Maritime and Hydrographic Agency) and the DEWI for providing the FINO1 data. Edited by: H. Hangan Reviewed by: two anonymous referees