Wind farm sites in complex terrain are subject to local wind phenomena, which have a relevant impact on a wind turbine's annual energy
production. To reduce investment risk, an extensive site evaluation is therefore mandatory. Stationary long-term measurements are supplemented by
computational fluid dynamics (CFD) simulations, which are a commonly used tool to analyse and understand the three-dimensional wind flow above complex terrain. Though under
intensive research, such simulations still show a high sensitivity to various input parameters like terrain, atmosphere and numerical setup. In
this paper, a different approach aims to
Complex and mountainous terrain gains importance for wind farm development due to land use conflicts and high wind potential caused by speed-up effects at escarpments and steep ridges. Nevertheless, such orographic features as well as obstacles, roughness differences and jet and tunnel effects result in a complex wind field. On these sites, the risk of annual energy production (AEP) overestimation is increased (Lange et al., 2017). Within a wind farm in complex terrain, which was analysed by Ayala et al. (2017), the AEP of single wind turbines varied by up to 25 %, although wake effects seem neglectable when taking into account the park layout and prevailing wind directions.
An increasing demand for renewable energy and high investment risks in the case of a false AEP prognosis make wind flows in complex terrain an intensively investigated research topic, concerning both measurement and simulation. Computational fluid dynamics (CFD) simulations are a common tool to investigate the spatially distributed wind speeds above complex terrain and are widely used in site assessment and research. Although huge advances in computational power have allowed even more detailed flow simulations in recent years, CFD simulations still show great sensitivity to assumptions and simplifications such as terrain details and surface roughness (Jancewicz and Szymanowski, 2017; Lange et al., 2017), atmospheric stability (Koblitz et al., 2014), and turbulence models (Tabas et al., 2019) in addition to various numerical parameters. Remaining uncertainties and long computation times make extensive measurements for sites in complex terrain mandatory for a bankable site assessment (International Electrotechnical Commission, 2009; Measnet, 2016; Fördergesellschaft Windenergie und andere Dezentrale Energien, 2017).
Nevertheless, guideline-compliant measurement equipment such as met masts and light detection and ranging (lidar) systems is operated stationarily with
a focus on maximum statistical coverage. Such systems are not applicable to investigating the spatial deviation of wind speeds within a certain area.
The state of the art to measure three-dimensional wind fields above complex terrain is multiple doppler lidar configurations. Depending on the number of
lidars, wind speeds in one, two or three directions can be measured remotely, even at a distance of kilometres. This has been successfully performed
in various field studies in complex terrain. For example in Kassel, Germany (Pauscher et al., 2016), triple doppler lidar measurements showed good
agreement concerning wind speeds in comparison to a sonic anemometer. In Perdigão, scanning lidars successfully measured wind speed distributions
between a double ridge (Vasiljević et al., 2017). Nevertheless, these measurement systems do have some limitations: as Stawiarski et al. (2013)
point out, the measurement error in a lidar depends, amongst other things, on the angle of the intersecting beams. This can lead to errors
“on the order of 0.3 to 0.4
A different approach to measuring meteorological variables at specific positions is the usage of an unmanned aerial vehicles (UAVs). Autonomous UAVs, especially fixed-wing systems with pitot-type wind sensors, have been used for atmospheric research for 20 years (Holland et al., 2001; Spiess et al., 2007; Reuder et al., 2009). In recent years, a fixed-wing system with a five-hole probe has been developed to analyse wind speed, the inclination angle and turbulence intensity at an escarpment in the Swabian Alps (Wildmann et al., 2017). In El Bahlouli et al. (2019), a measurement of a fixed-wing system was compared to CFD simulations at the WINSENT test site. Both systems showed plausible results, although the necessary minimum flight speed of fixed-wing systems in general only allows short time measurements for a specific position. Additionally, measurement values were also averaged for a certain flight distance, resulting in an increased probe volume size of several metres. Although both studies aimed to investigate the spatial distribution of wind speeds, temporal changes in the overall wind situation during a single measurement campaign were not taken into account.
Contrary to fixed-wing systems, rotary-wing aircraft can hold their position in mid-air for several minutes. This has three major benefits: first of
all, it allows for an easier system validation by just performing hovering flights close to a stationary sensor. This was done for example by Neumann and Bartholmai (2015), Palomaki et al. (2017), Nolan et al. (2018), and Vasiljević et al. (2020), already showing promising results. A further overview
is given by Abichandani et al. (2020), comparing the root mean square error (RMSE) of wind speed and direction measurements of several UAV sensor
combinations in the literature. So far, turbulence intensity measurements have not been compared. The second benefit is that a stationary, airborne
measurement also allows for a reduction in stochastic measurement errors by calculating averaged values for wind speed and direction. Furthermore,
rotary-wing UAVs offer greater flexibility concerning their measurement strategy. An exact number, position and duration of measurement points can be
chosen. Safe operation at low and high flight levels in complex terrain is also possible. Shimura et al. (2018) for example use a hexarotor UAV to
measure wind vector profiles up to 1000
Within our project we have equipped a multi-rotor UAV with a 3D ultrasonic anemometer. The combined system is called In this case, the air and it fluctuation is (i) measured variable, (ii) working medium and (iii) a disturbance for the carrier system. Movements and rotations of the UAV as well as rotor-induced flows have a significant impact on the measured wind speed, direction and turbulence intensity. Accuracy of a single measurement point has to be evaluated. In Sect. 2 of this paper, we are going to present the achieved measurement accuracy of the WindLocator UAV, not only for wind speed and direction but also for turbulence intensity. CFD simulations offer the possibility of investigating the 3D wind field at each point for every single time step. UAVs instead measure one point after another and, contrary to scanning lidars, take considerable time in doing so. The question arises, what kind of measurement strategy is suitable when it comes to merging individual measurement points into one single distribution of meteorological variables? In Sect. 3, the influence of diurnal wind speed variation is investigated during two test campaigns above complex terrain, utilising a simple measurement strategy. Results of the WindLocator are compared to a ground-level anemometer to decide to what extent such a system is suitable as a reference. In the future, those findings combined with a simulation campaign will be used to find a robust measurement strategy.
Measurement system WindLocator (unfolded) without battery packs.
The measurement system, which has been used for the measurement campaigns within this paper, has two main, independent components: a powerful carrier system and a sensor unit, which consists of a commercially available ultrasonic anemometer and a self-developed compensation and data acquisition unit.
The foldable, commercial carrier system is a battery-powered octocopter with a flight time of 25
Specifications of carrier system.
The Gill WindMaster 3D ultrasonic anemometer is placed on top of the compensation unit centred above the rotor plane. Mounting the sensor on top of the UAV has several advantages. First of all, the rotational symmetry of the system allows wind measurement independent from the yaw angle and wind direction. Additionally, this setup results in a horizontally centred mass during hovering and therefore leads to relatively small moments to be compensated for by the UAV. This improves flight performance and flight time. Besides, the downwash above the rotors is less turbulent than below.
The distance of the sensor's measurement volume to the rotor plane is 750
Specifications of the ultrasonic anemometer.
Except for the power supply, the self-developed compensation and data acquisition unit is completely independent from the UAV. If requirements
concerning the carrier system change, the compensation unit as a whole can be reapplied easily onto a new aircraft. It weighs 420
All following calculations and measurements have been evaluated based on data that have been processed by the compensation unit. The system validation
in general was conducted on several levels of detail, beginning with the
Fixed UAV.
Indoor flight in zero-wind conditions.
After several synthetic tests with a fixated UAV to evaluate rotor influences (Fig. 2), the WindLocator's compensation unit was tested during an indoor flight under zero-wind conditions (Fig. 3).
Utilising the internal barometer, an altitude of around 4
North–east–down wind speed components of indoor flight in zero wind conditions.
Figure 4 shows the data in all three measured directions at a resolution of 10
Measured wind speed components during indoor flight.
As expected, mean wind speeds during the indoor flight are close to zero. SDs of up to 0.23
WindLocator hovering close to stationary anemometer.
After proving that under zero-wind conditions, mean values are in good agreement with our expectations, a measurement setup was created to compare the
performance of the WindLocator with a stationary anemometer. In flat terrain 2
Resulting ground-level wind speed and regression plot of measurement 2.
Four 10 min measurements with
The diagram in Fig. 6 shows exemplarily the compensated for wind speeds of the WindLocator in comparison to the stationary reference as well as the corresponding regression plot.
Comparison of measurement points at ground level.
For all measurement points (see Table 4), a very good agreement of the 10 min mean wind speed between the WindLocator and reference has been achieved,
especially when taking into account the turbulent wind situation during such a low-altitude flight. Turbulence intensities (TIs) of up to 44 % have
been calculated for the stationary reference. Although there are absolute differences of
Comparison of measurements on 134
An analysis of wind directions during this experiment was not yet possible, because an accurate orientation of the stationary measurement system could
not be guaranteed. This was taken into account for the next experiment at a 134
Resulting wind speed at 134
The results during the met mast experiment show a slightly different picture compared to the ground-level measurements. The turbulence intensity is still reasonably well estimated. Additionally, measurements 2 and 4 show a good correlation of the WindLocator with the corresponding reference speed. However, mean wind speed deviations for the first and third measurement not only are higher than before but also vary a lot more compared to the other measurements of that day. Significant deviations mainly occur during the first half of the measurements (Fig. 7), e.g. seconds 180 to 270 for measurement 3.
Wind direction at 134
Those deviations are a result of the pilot still doing positional adjustments during the measurement point. Nevertheless, those adjustments seem not
to have a critical impact on the UAV's wind direction estimation, which shows very good correlation through all measurement points with a maximum mean
deviation between met mast and WindLocator of 2.4
The WindLocator performed very well throughout the tests, especially concerning the calculation of averaged measurement quantities like speed and
direction. When the system used its GPS-based hover mode without interference by a pilot, mean wind speed differences compared to a reference were
below 0.12
Test area (red square) and stationary measurement location (
The test site for this measurement strategy is a small hill in the south of North Rhine-Westphalia in the German Eifel and was chosen for the
following reasons:
With a yearly mean wind speed of 6.5–7 The terrain is considered to be complex. The slope around the hill in most parts is greater than 40 The region in general is easily accessible and was considered suitable for wind turbines.
All diagram coordinates within this chapter are referenced to the UTM coordinate 32U 308450 5604720.
Comparison of measurement campaigns.
Within this paper, two different measurement campaigns are presented. While the mean wind speeds are within a similar range, wind directions differ, resulting in different inflow conditions into the measuring area.
Terrain and measurement points (data source: Geobasis NRW).
The presented campaign aims to investigate the feasibility of using a simple measurement strategy for the identification of the spatial distributions
of meteorological variables (wind speed, turbulence intensity, inclination) above complex terrain. This information will be used in the further course
of the project for the development of the final measurement strategy. The measurement strategy can be described as follows:
The WindLocator automatically flies to one measuring point after another and measures at each position for a specified duration. This duration is chosen as 5 min in the framework of this feasibility study, which is considered to be a reasonable trade-off between limited
battery time and statistical coverage for each point. To reduce experimental complexity, measurement points are located within a two-dimensional plane. The surveyed plane is roughly
400 At each measurement point, relevant variables like averaged wind speed and direction, turbulence intensity, and the inclination angle are measured and
saved together with the position and a timestamp derived from the GPS. Additionally, a ground-level (3
The feasibility study within this paper addresses two basic questions on the postprocessing of the gathered measurement data. In the first step it will be discussed whether the temporal change in the wind speed during the measurement campaign has to be taken into account for the further investigation of the spatial distribution of the meteorological variables. A necessary condition for a constant spatial distribution is a constant wind direction, which will be verified at the beginning. Variations in averaged wind speeds at the stationary reference are used to estimate the impact of temporal variations within the airborne measurements in comparison to expected spatial variations. The result of this analysis is also valid for turbulence intensity, as it depends on the wind speed. Additionally, the spatial distribution of turbulence intensities is checked for plausibility. The influence of temporal changes on inclination angles is checked in a qualitative manner by comparing them to the terrain.
Assuming that the temporal change in the wind speed has a significant effect on the measurement, in a second step it will be investigated whether the
ground-level (3
Comparison of resulting wind speeds from WindLocator (compensated) and reference at 10
Figure 11 gives an overview of the measured resulting wind speeds
Measurement points, wind vector and inclination angle of M1
Figure 12 represents all measurement points for both campaigns, showing the results of the single points measured one after another. During both
measurements, wind directions are in good agreement with the mean wind direction. With mean absolute deviations of 9.9
Mean wind speeds of WindLocator and ground station for M1
Figure 13 shows the averaged wind speeds for measurement points one after another for WindLocator and ground station. Over all UAV measurement points,
an absolute variation in mean wind speeds of between 2 and 6
Normalised wind speeds for M1
Otherwise, the stationary reference (assuming it to be an indicator of the overall wind situation) would not have shown any significant differences
in wind speed over time. This is clearly not the case, especially when looking at the normalised reference wind speed, calculated by dividing the mean
wind speed value of each point by the maximum mean value of all points of that measurement (Fig. 14). Normalised variations in the stationary
reference and the WindLocator data are on a comparable order of magnitude (
Turbulence intensities of M1
Figure 15 shows turbulence intensities measured at each single point. The mean turbulence intensity over all measurement points of M2 is 18 % and
therefore slightly higher than during M1 with 15 %. This seems plausible due to the forested and steep escarpment in the upwind direction for
M2. However, single turbulence intensities within the measured field seem to vary rather strongly (between 10 % and 30 %) and without obvious
influences by terrain and surface. As the normal turbulence model of IEC 61400 predicts, turbulence intensity depends significantly on mean wind
speed. Very low average speeds of only 2–3
Although wind speeds vary significantly over time, inclination angles do show plausible results (Fig. 12). The flow and therefore the inclination
angles follow the terrain quite well for M1, varying mostly between
Wind speed increase from reference to WindLocator for M1.
All in all, temporal wind speed variations do have a significant impact while measuring wind speed distribution and therefore have to be
compensated for. A simple approach would be calculating a wind speed-up value compared to a representative stationary reference. Although the stationary
reference in this experiment is only 3
Measured mean wind speed distribution above complex terrain.
Figure 17 combines the measured results on the one hand and the UAV's GPS data on the other hand into a spatial distribution. The purple arrow indicates the mean wind direction. Each red arrow represents a measurement point, showing the measured horizontal wind direction and indicating with its length the wind speed increase compared to the stationary reference. The wind speed increase is then interpolated linearly between measurement points to create a contour plot. The background shows the digital-terrain-model data.
The calculated speed-up factor of around 2 seems plausible, when assuming a logarithmic wind profile with a roughness length of 0.2
As seen for M2, the presented measurement strategy obviously depends strongly on the stationary reference, its positioning and expected spatial
variations and therefore cannot be considered to be valid in general. A change in wind direction from 310
These findings are currently being evaluated with a simulative approach to find more robust measurement strategies, independent from the terrain, location, surface and prevailing wind situation. Once this has been obtained, several of such measurements (for example for different overall wind speeds and directions) might be combined to achieve an extensive insight into the location's wind situation. In future, this could allow bankable site assessment, similarly to how CFD simulations are used today.
Within this paper, a UAV-based measurement system called WindLocator, its validation and its experimental application above complex terrain were presented. The measurement system consists of an octocopter, a commercial ultrasonic anemometer centred above the rotor plane, and a self-developed compensation and data acquisition unit. The latter was the enabler to efficiently reduce wind measurement errors due to movements of the UAV and rotor influences. This has been shown in two test scenarios in different wind and turbulence conditions.
In both tests, very good agreement with reference data could be achieved. Mean wind speeds have been estimated with a maximum difference of
0.12
The biggest advantage of an airborne measurement system is its flexibility, allowing accurate measurements at any arbitrary point in a wind field above any kind of landscape. This could make the WindLocator a potential alternative to CFD simulations in complex terrain, delivering an analogue result for a specific weather situation without long computation times or modelling uncertainties.
During two measurements at a hilly and forested region in the German Eifel, diurnal wind variations were found to be relevant for measuring wind speed
distributions and turbulence intensity. Plausible wind direction and inclination were measured even without taking into account temporal
variations. Although more advanced measurement strategies are currently under development, for one specific campaign, a simple strategy was sufficient
to reduce the influence of diurnal wind speed variations: while the WindLocator was automatically flying from point to point, a stationary reference
at ground level was used to compensate the temporal wind speed variations between single measurement points. The result was a plane of 4 times 4
measurement points, including information on wind speed increase compared to the reference and three-dimensional wind directions. Spatial differences
of approximately
The underlying software code is not publicly available as it is considered to be property of the IME Aachen GmbH.
Data can be provided by contacting the corresponding author.
Validation, measurements and analysis were conducted by CI. LS chose the measurement site and was involved in developing the measurement strategy. GJ, RS and BJ contributed with comments and discussions about each section in the manuscript.
Christian Ingenhorst is a PhD student at the Institute for Machine Elements and Machine Design (IMSE) and an employee at IME Aachen GmbH. This company offers airborne wind measurements as a service.
This article is part of the special issue “Wind Energy Science Conference 2019”. It is a result of the Wind Energy Science Conference 2019, Cork, Ireland, 17–20 June 2019.
We thank Windtest Grevenbroich GmbH for providing validation data from a meteorological mast at their test site.
This open-access publication was funded by the RWTH Aachen University.
This paper was edited by Rebecca Barthelmie and reviewed by four anonymous referees.