Wind data collection in the atmospheric boundary layer benefits from short-term wind speed measurements using unmanned aerial vehicles. Fixed-wing and rotary-wing devices with diverse anemometer technology have been used in the past to provide such data, but the accuracy still has the potential to be increased. A lightweight drone for carrying an industry-standard precision sonic anemometer was developed. Accuracy tests have been performed with the isolated anemometer at high tilt angles in a calibration wind tunnel, with the drone flying in a large wind tunnel and with the full system flying at different heights next to a bistatic lidar reference.
The propeller-induced flow deflects the air to some extent, but this effect is compensated effectively. The data fusion shows a substantial reduction of crosstalk (factor of 13) between ground speed and wind speed. When compared with the bistatic lidar in very turbulent conditions, with a 10 s averaging interval and with the unmanned aerial vehicle (UAV) constantly circling around the measurement volume of the lidar reference, wind speed measurements have a bias between
The system was finally flown in the wake of a wind turbine, successfully measuring the spatial velocity deficit and downwash distribution during forward flight, yielding results that are in very close agreement to lidar measurements and the theoretical distribution. We believe that the results presented in this paper can provide important information for designing flying systems for precise air speed measurements either for short duration at multiple locations (battery powered) or for long duration at a single location (power supplied via cable). UAVs that are able to accurately measure three-dimensional wind might be used as a cost-effective and flexible addition to measurement masts and lidar scans.
Measurements of wind characteristics are important in the environmental science of the atmospheric boundary layer (ABL). They are crucial for predictions of meteorological processes
Despite the large variation of existing measurement techniques, there is still a gap in the wind data collection in the ABL, driving the development of small unmanned aerial vehicles (UAVs) that are equipped with sensors measuring wind velocities
There are two fundamental types that are suitable for atmospheric wind measurements: fixed-wing and rotary-wing UAVs. In fixed-wing UAVs, the wings provide the force (
Power requirement of fixed-wing vs. rotary-wing UAVs with the same weight
Rotary-wing UAVs have a lower endurance, typically by a factor of
Wind can be determined using two different approaches in UAVs: indirect methods measure the response of the UAV to the wind and can determine wind speed, azimuth and elevation directly from the sensors that are also used to control the UAV
Direct methods use dedicated wind sensors that are mounted to the UAV.
Suitable sensors should be lightweight, robust and measure a 3-D wind vector. Together with the 3-D ground speed vector of the UAV, wind speed and direction can be derived by simple vector addition. If the sensor is mounted on a gimbal, ensuring zero pitch and roll angle during flight, then a two-dimensional sensor can be sufficient to measure 2-D wind speed. In practice, the fusion of vehicle speed and wind speed can yield erroneous results due to errors in wind sensing and vehicle state estimation. These errors become visible as periodic signals in the wind data having a similar frequency as the vehicle speed, attitude or position
Several types of sensors have already been used to measure wind speeds with fixed-wing and rotary-wing UAVs. Using differential pressure sensors like pitot tubes and multi-hole pressure probes is most suitable when the wind measurement covers larger areas and the UAV is constantly moving forward at elevated speeds. They require the wind to come mainly from one direction within the cone of acceptance of the probe. Furthermore, these sensors perform best at speeds
Mechanical anemometers (e.g. cup anemometers) are rarely seen on UAVs. Most mechanical anemometers cannot be used for accurate measurements of a 3-D wind vector. Arrays of several sensors in different orientations would have to be analysed. This leads to a bulky setup and a large measurement volume. The response time is generally low
Very recently, a lidar sensor has been mounted to a rotary-wing UAV
Hot-wire anemometers can be used for high-frequency wind speed measurements and have a flat frequency response up to 7 kHz
Sonic anemometers that are attached to rotary-wing UAVs have been shown to provide accurate wind measurements
In the study of
Lately, several small-sized sonic anemometer have become commercially available (e.g. Decagon Devices DS-2, Anemoment TriSonica, FT Technologies FT205), which have subsequently been successfully applied to UAVs
In an additional experiment, the study of
The number of applications of miniature sonic sensors on UAVs is growing
Due to the simplicity of deployment, the ability to measure close to structures and the potential to uninterruptedly fly the UAV via power-tethering, a rotary-wing UAV was chosen as platform. Commercial off-the-shelf (COTS) wind-measuring drones are not yet available. Several studies, including the ones mentioned above, use COTS drones (e.g. by companies such as DJI, 3DR, Yuneec) to carry the sensor payload. However, the flight time of a drone can only be optimized for a specific payload weight. Most COTS drones with sufficient endurance (
Sonic anemometers were identified to be most suitable for the application in rotary-wing UAVs. These anemometers can sense wind from any azimuth angle from zero speed to about 50
Based on the literature review presented above, special attention must be paid for the following parameters, when designing an accurate drone-based wind-measuring system:
accuracy in 3-D flow of the sonic anemometer (mini and full size), maximization of endurance via weight minimization, accuracy of the data fusion with the UAVs attitude and speed, sensor placement: influence of propeller-induced air flow, accuracy of the full measurement system and practicability of the measurement system in the field.
The following sections describe how these parameters were analysed for the flying anemometer. The 3-D sensing performance of a miniature sonic anemometer and a precalibrated full-size sensor (with removed post to reduce weight and moment of inertia) was studied in a calibration wind tunnel. Additionally, the influence of the propeller-induced flow was analysed by flying the UAV with attached anemometer inside a large wind tunnel. Subsequently, the crosstalk between ground speed and wind speed during flight was determined. The accuracy of the drone-based measurements was analysed at several altitudes with a bistatic lidar.
Finally, the UAV was tested in a typical measurement campaign: wind turbine wakes are usually mapped using lidar
The drone was composed from COTS electronic components and a custom frame. The aim was to achieve the maximum flight time for the specified sensor payloads while keeping a total weight below 5 kg (see Fig.
Weight breakdown of the drone components.
Air speeds of up to 20
Flight properties on the OPTOkopter for flight speeds between 2 and 18
Wind speed can be derived from the sum of the relative wind vector (as measured by the sonic anemometer) and the ground speed vector (as measured by the UAV). The UAV uses ArduPilot's extended Kalman filter estimation system
Data fusion of sonic anemometry and UAV attitude, position and ground speed.
Wind speed is transformed from a body-fixed reference system (BFRS) to the terrestrial reference system (TRS) using standard rotation matrices. The airflow induced by angular velocities in roll and pitch of the UAV is also compensated for. The input and output data for this transformation are given in Fig.
A miniature sonic anemometer
Wind tunnel tests of the 3-D wind sensing capabilities. Rotation around the yaw axis (green arrow) for four different pitch angles (from left to right: 0, 10, 20 and 30
Setup of the experiment in the calibration wind tunnel of METAS. The flow is from left to right.
A suitable anemometer for application on UAVs should be able to accurately sense wind speed, azimuth and elevation. This should be possible for all pitch, roll and yaw angles that occur during a typical measurement flight of the UAV. In the proposed design, the maximum pitch angle of the UAV at the maximum air speed (20
Wind speed measurements with the TriSonica are lower than the reference (see Table
Wind tunnel test. Accuracy of a miniature sonic anemometer (TriSonica) and a full-size sonic anemometer (WindMaster). Each pitch
angle was tested with 0–360
The wind tunnel measurements of the Gill WindMaster anemometer show that bias and RMSE are small, but wind speed is overestimated by up to 3.6 % at 30
An anemometer that is mounted on a rotary-wing UAV is potentially measuring a velocity component that is induced by the propellers. It therefore potentially measures a biased wind speed and a biased elevation. The induced component most likely depends on the forward flight speed (air speed in this case). In normal free flight, every flight speed requires a certain pitch angle and propeller speed. Therefore, suitable pitch angles and throttle values (voltage sag compensated) for front and rear motors were determined by flying circles (
Motor throttle (front and rear motor pairs) and pitch angle vs. flight speed. The spread can be explained by the control loops working against external disturbances during free flight in windy conditions. Rear motors generally need to provide more thrust than the front motors to compensate for a pitch-up moment. This effect results from a combination of uneven lift distribution of the rotors in forward flight and gyroscopic precession
Setup of the wind tunnel measurement. The UAV is attached to a mount at multiple suitable pitch angles (here 10
Bias of wind speed
Only little effect of the propeller flow on the measured wind speed was found (see Fig.
After testing the performance of individual components in the measurement system, the accuracy of the full flying setup (OPTOkopter with Gill WindMaster and all compensations running) was assessed.
As mentioned in the introduction,
In a situation with zero wind, air speed and ground speed as measured by the UAV must be identical. When there is wind, these velocities will not be identical anymore. But any change in ground speed will also result in a change in air speed; hence, a spectral analysis should show peaks at the same frequencies. This is the case in the test flight (see Fig.
Amplitude spectrum of ground speed (as reported by the EKF2 in the flight controller), air speed (as reported by the sonic anemometer) and wind speed (as calculated by the data fusion) during a flight where the UAV was repeatedly oscillating in the east–west direction between two waypoints. There is a clear peak at 0.104
The FFT analysis (see Fig.
We compared the drone wind measurements with the bistatic Doppler lidar, developed at the Physikalisch-Technische Bundesanstalt (PTB) in Braunschweig, Germany
The bistatic lidar has a small, stationary measurement volume. The distance between the measurement volumes of the OPTOkopter and the lidar was difficult to assess as there was no optical reference that could help with relative positioning as the exact measurement position of the lidar is not visible. However, attempting to fly close to this volume is relatively safe if optical instruments on the UAV such as distance finders and cameras are isolated from the high laser power.
After performing several flights, we selected a measurement at 40
The OPTOkopter was always hovering at the lee side of the measurement volume. Wind speed, azimuth and elevation were sampled during multiple short flights of 10 min.
Additionally, we were measuring wind speeds while circling (4
Measurement site of the PTB lidar reference. This 3-D model of the location was calculated using photogrammetry with image data that were captured during the wind measurement flights.
Wind speed
The measurement volume of the lidar at 40
A linear Deming regression of the data in 1
Linear Deming regression of the UAV wind speed measurement at 40
Bias and RMSE of the UAV wind speed
The azimuth has a constant bias of about 2.6
Bistatic lidar and OPTOkopter hence give closely matched results, even at 10
Turbulence intensity (TI; averaging interval of 10
Despite sometimes flying very close to the lidar, we believe that the presence of the UAV did not significantly change the flow in the measurement volume of the lidar: the measurements of the OPTOkopter have been successfully compensated for propeller-induced flow (see Fig.
The wind turbine (Enercon E 70–E 4) is located in the Black Forest in southern Germany (47
Measurement site of the wind turbine study.
Wind speed and wind direction during the measurement flight. Wind speed was measured by the reference cup anemometer on top of the nacelle
Flight path of the OPTOkopter behind the wind turbine (view from the front). The drone oscillates with 5
Horizontal cross section at nacelle height through the wake of a wind turbine.
A relatively constant velocity deficit (
Data at
The environmental science of the atmospheric boundary layer benefits from wind speed measurements collected by UAVs. A suitable lightweight rotary-wing UAV was designed for carrying an anemometer. Drones can measure close to structures and they can be validated comfortably by hovering close to a reference instrument. Flight time is often an issue with UAV-based measurements. In the proposed design, the battery is responsible for 49 % of the total weight. It can be replaced by COTS power-tethering devices that allow for much longer, uninterrupted measurement flights at a single location at different altitudes up to 100 m.
The OPTOkopter uses a full-size industry-standard anemometer instead of a miniature version, as the accuracy in three-dimensional flow is better by 1 order of magnitude. Measurements at the test site of the PTB lidar have shown that three-dimensional flow is highly likely to happen in situ, even when the OPTOkopter hovers on spot at a constant altitude. Due to the high contribution of vertical flow, using a single miniature sonic anemometer does not seem to be feasible on a drone, even when the sensor is mounted on a stabilizing gimbal. The performance of an anemometer that is to be installed on a drone should be verified at suitable tilt angles in a precision wind tunnel. The maximum tilt angle needs to be determined with drone test flights at the maximum desired wind speed.
Propeller-induced flow mainly adds a vertical component to the flow without adding a horizontal component – even at large pitch angles. The vertical component can effectively be compensated by subtracting a value that is proportional to the mean motor throttle. Placing the wind sensor far away from the rotors is a key requirement for this simple correction to work. As has been shown in Fig.
Mounting an anemometer on such a long lever arm significantly increases the moment of inertia of the drone. It is therefore necessary to adjust the control loop parameters (e.g. proportional gain (
Bias and RMSE of the OPTOkopter wind measurements at 10 s averaging interval, with the PTB lidar reference. The table includes data from all flights that were done. The distance to the measurement volume of the lidar was difficult to assess, but it was smaller than 10 m in all cases. The comparison was done with the OPTOkopter hovering on spot or circling around the lidar measurement volume. Wind speed bias is generally low. RMSEs seem to increase with turbulence intensity.
The crosstalk between ground speed and wind speed is suppressed by a factor of 13, although relatively aggressive manoeuvres were flown (oscillating between two waypoints that were only 10 m apart). These results are supported by low bias and RMSE during the comparison with the bistatic lidar in hovering and circling flight mode (see Table
The analysis of the wind velocity in the wake of a wind turbine has proven the practicability of accurate UAV-based measurements. The application is not limited to point measurements. The mean wind speed on a 200
Based on the tests of the individual components and the full system, we think that mounting the anemometer on the drone does not significantly increase the measurement uncertainty of the anemometer in hovering flight. Wind speed and elevation are sensed accurately, when data fusion is performed as described, and separation between wind sensor and propellers is large enough (here 2.5 rotor diameters). Additionally, the maximum tilt of the drone must not exceed the maximum acceptance angle of the anemometer (30
There certainly is room for improvement in sensing the azimuth (see Table
We think that the devices that are designed following the propositions presented in this study are very suitable for accurate wind measurements up to 20
Data of all measurements presented in this paper (except for the PTB lidar data) and additional information on the OPTOkopter are available at
WT wrote the manuscript with input from all authors, and developed and operated the OPTOkopter together with WH. UM initiated and supported the development, and assisted with all measurements that are presented. ME constructed the PTB lidar system and its signal processing. PW and ME operated the Doppler lidar and preprocessed its 10 and 1 Hz raw data. All authors contributed to the discussion of the results.
William Thielicke, Waldemar Hübert and Ulrich Müller developed the OPTOkopter while being employed at OPTOLUTION Messtechnik GmbH, a company that is aiming to commercialize measurement services with this drone.
We thank the Technische Universität Dresden, Fakultät Maschinenwesen, Institut für Luft- und Raumfahrttechnik, Experimentelle Aerodynamik and especially Veit Hildebrand for the opportunity to fly inside the wind tunnel. We thank Klaus-Peter Neitzke (Hochschule Nordhausen) and Thomas Eipper (Technische Universität Dresden) for the assistance with measurements and photographs during the wind tunnel flights. We thank the Eidgenössische Institut für Metrologie (METAS) for the opportunity to test the sonic anemometers in their wind tunnel. We thank Erwin Schlauderer at Ökostrom Erzeugung Freiburg GmbH for allowing us to measure the wind turbine wake. We thank the ArduPilot community for developing a safe, great and open flight controller firmware.
This paper was edited by Szymon Malinowski and reviewed by two anonymous referees.