Wind Direction Measurements Research Articles

Stream Line Doppler lidar measurements of wind speed and direction with the duo-beam method in the surface air layer

The results of retrieval of wind speed and direction from measurements of radial velocity by a Stream Line pulsed coherent Doppler lidar using the duo-beam method and conical scanning are presented. These results are compared with data of a sonic anemometer (point sensor).

An Empirical Mode Decomposition Method for Sea Surface Wind Measurements From X-Band Nautical Radar Data

In this paper, sea surface wind direction and speed are obtained from X-band nautical radar images. A data control strategy is proposed to distinguish rain-free and rain-contaminated radar data. The radar data are decomposed by an ensemble empirical mode decomposition method into several intrinsic mode functions (IMFs) and a residual. A normalization scheme is applied to the first IMF to obtain the amplitude modulation (AM) component. Wind direction is determined from the residual for the rain-free and high-wind-speed rain-contaminated data, and from the AM portion of the first IMF for the low-wind-speed rain-contaminated data, based on curve fitting a harmonic function. Wind speed is determined from a combination of the residual and the AM part of the first IMF for both rain-free and rain-contaminated data using a logarithmic relationship. Results employing ship-borne radar and anemometer data collected in a sea trial off the east coast of Canada are presented. The root-mean-square differences for wind direction and speed measurements are 11.5° and 1.31 m/s, respectively, compared with reference values from anemometers.

Wind energy resource assessment for Kiribati with a comparison of different methods of determining Weibull parameters

Wind energy resource assessments at two locations in Kiribati are carried out. The wind resource on the main atoll of Tarawa is analysed along with a nearby atoll Abaiang. Measurements of wind speed, direction, ambient temperature and pressure were performed and analysed. The Tarawa site has an average wind speed of 5.355m/s at 34m above ground level (AGL) and the Abaiang site has an average wind speed of 5.4575m/s at 34m AGL. The wind direction for both the sites is predominantly East-North-East. Average diurnal wind shear coefficient correlated well with the variation in temperature. The overall average turbulence intensity was about 10% at 34m and about 13% at 20m AGL for both the sites. The Weibull parameters were obtained for both the sites using seven methods and the most accurate method, which was found to be the Moments method, was used to find the Weibull parameters and the wind power density. The Weibull parameters were also obtained for the two seasons of Kiribati – the dry and the wet seasons. A high resolution wind resource map of both the sites is obtained using Wind Atlas Analysis and Application Program (WAsP). The WAsP analysis indicates reasonably good wind power development potential for the Tarawa and Abaiang atolls. Annual energy production with five Vergnet 275kW turbines for both the locations is estimated and an economic analysis is performed, which showed a payback period of 5.42years to 8.74years.

DRIE Trenches and Full-Bridges for Improving Sensitivity of 2-D Micromachined Silicon Thermal Wind Sensor

This paper presents the design, fabrication, and performance of a micromachined silicon thermal wind sensor with improved sensitivity. Deep reactive ion etching (DRIE) trenches are fabricated between the heater and the thermistors to suppress the lateral heat conduction in the chip. In addition, eight thermistors symmetrically arranged in four directions around the heater form two Wheatstone full-bridges, resulting in about 50% increase of the sensitivity with respect to four thermistors. Based on these two methods, the sensitivity of the micromachined silicon thermal wind sensor is improved remarkably, which is verified by the experiment. The results show that the measurement wind speed range is up to 33 m/s in constant voltage (CV) mode with the initial heating power of 256 mW. The sensitivity is measured to be 29.37 mV/ms <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> at the wind speed 3.3 m/s, achieving improvement of about 226%, compared with that of the traditional wind sensor. Wind direction measurement results show that airflow direction over the full range of 360° is determined with an accuracy of ±5°. [2017-0052].

A procedure for calibrating the spinning ultrasonic wind sensors

The accuracy of wind speed and direction measurements with spinning ultrasonic wind sensors is important than ever in today’s wind industry in which, they are usually installed on the hub of the wind turbines to measure the wind speed and direction for optimized power. In this paper, extensive wind-tunnel tests have been performed to calibrate the wind speed and direction measured by a spinning single-axis ultrasonic anemometer for both static and spinning conditions. This has been carried out with static measures at various stationary angles of transducers signal path with wind direction, and with dynamic measures in which the anemometer is rotating with various rotational speeds. The velocity measured by ultrasonic anemometer in static tests is calibrated with pitot-tube data, and the interpolation of obtained calibration coefficients is used to correct the ultrasonic velocity measured in dynamic tests. It is observed that the calibrated ultrasonic wind speed measurements in dynamic tests are in a good agreement with the reference velocity. According to the results, the ultrasonic velocity measurements in both static and dynamic tests are affected by the transducers head distortions, and the shifting in acoustic pulse trajectory due to the rotational motion does not affect the anemometer measurements. The uncertainty of the calibration process for the spinning tests was found to be about 0.3%.

Observations of Lake-Breeze Events During the Toronto 2015 Pan-American Games

Enhanced meteorological observations were made during the 2015 Pan and Parapan American Games in Toronto in order to measure the vertical and horizontal structure of lake-breeze events. Two scanning Doppler lidars (one fixed and one mobile), a C-band radar, and a network including 53 surface meteorological stations (mesonet) provided pressure, temperature, humidity, and wind speed and direction measurements over Lake Ontario and urban areas. These observations captured the full evolution (prior, during, and after) of 27 lake-breeze events (73% of observation days) in order to characterize the convective and dynamic processes driving lake breezes at the local scale and mesoscale. The dominant signal of a passing lake-breeze front (LBF) was an increase in dew-point temperature of $$2.3 \pm 0.3 \,^{\circ }\hbox {C}$$ , coinciding with a $$180^{\circ }$$ shift in wind direction and a decrease in air temperature of $$2.1 \pm 0.2 \,^{\circ }\hbox {C}$$ . Doppler lidar observations over the lake detected lake breezes 1 hour (on average) before detection by radar and mesonet. On days with the synoptic flow in the offshore direction, the lidars observed wedge-shaped LBFs with shallow depths, which inhibited the radar’s ability to detect the lake breeze. The LBF’s ground speed and inland penetration distance were found to be well-correlated ( $$r = 0.78$$ ), with larger inland penetration distances occurring on days with non-opposing (non-offshore) synoptic flow. The observed enhanced vertical motion $$({>} 1\hbox { m s}^{-1})$$ at the LBF, observed by the lidar on 54% of lake-breeze days, was greater (at times $${>} 2.5\hbox { m s}^{-1}$$ ) than that observed in previous studies and longer-lasting over the lake than over land. The weaker and less pronounced lake-breeze structure over land is illustrated in two case studies highlighting the lifetime of the lake-breeze circulation and the impact of propagation distance on lake-breeze intensity.

The assessment of wind conditions by means of hot wire sensors and a modifed Wheatstone bridge architecture

In this paper we present a novel complete anemometric electronic system based on an array of hot wire sensors, provided by Telecontrolli SME, and a suitable heater feedback that achieves, thanks to a dedicated algorithm, a CMOS integrable portable solution showing a robust design, without moving parts and small dimensions. The sensor interface is implemented by means of a modified Wheatstone bridge architecture with autobalancing capability. Comparisons with a commercial cup anemometer have shown that the proposed architecture, with reduced space occupancy and low power consumption, is suitable to operate in a natural environment in the −20 to +55°C temperature range providing wind speed and direction measurement. Concerning the wind speed, a 0.6m/s sensitivity has been achieved. The integrated CMOS design of the final system is also presented and discussed in the paper.

Effect of Wind Turbine Wakes on the Performance of a Real Case WRF-LES Simulation

The main objective of this work is to estimate how much of the discrepancy between measured and modeled flow parameters can be attributed to wake effects. The real case simulations were performed for a period of 15 days with the Weather Research and Forecasting (WRF) model and nested down to a Large-Eddy Simulation (LES) scale of ∼ 100 m. Beyond the coastal escarpment, the site is flat and homogeneous and the study focuses on a meteorological mast and a northern turbine subjected to the wake of a southern turbine. The observational data set collected during the Prince Edward Island Wind Energy Experiment (PEIWEE) includes a sonic anemometer at 60 m mounted onto the mast, and measurements from the two turbines. Wake versus free stream conditions are distinguished based on measured wind direction while assuming constant expansion for the wake of the southern turbine. During the period considered the mast and northern turbine were under the southern turbine wake ∼ 16% and ∼ 11% of the time, respectively. Under these conditions, the model overestimates the wind speed and underestimates the turbulence intensity at the mast but not at the northern turbine, where the effect of wakes on the model error is unclear and other model limitations are likely more important. The wind direction difference between the southern and northern turbines is slightly underestimated by the model regardless of whether free stream or wake conditions are observed, indicating that it may be due to factors unrelated to the wake development such as surface forcings. Finally, coupling an inexpensive wake model to the high-fidelity simulation as a post-processing tool drives the simulated wind speeds at the mast significantly closer to the observed values, but the opposite is true at the coastal turbine which is in the far wake. This indicates that the application of a post-processing wake correction should be performed with caution and may increase the wind speed errors when other important sources of uncertainty in the model and data are not considered.

Validating precision estimates in horizontal wind measurements from a Doppler lidar

Abstract. Results from a recent field campaign are used to assess the accuracy of wind speed and direction precision estimates produced by a Doppler lidar wind retrieval algorithm. The algorithm, which is based on the traditional velocity-azimuth-display (VAD) technique, estimates the wind speed and direction measurement precision using standard error propagation techniques, assuming the input data (i.e., radial velocities) to be contaminated by random, zero-mean, errors. For this study, the lidar was configured to execute an 8-beam plan-position-indicator (PPI) scan once every 12 min during the 6-week deployment period. Several wind retrieval trials were conducted using different schemes for estimating the precision in the radial velocity measurements. The resulting wind speed and direction precision estimates were compared to differences in wind speed and direction between the VAD algorithm and sonic anemometer measurements taken on a nearby 300 m tower.All trials produced qualitatively similar wind fields with negligible bias but substantially different wind speed and direction precision fields. The most accurate wind speed and direction precisions were obtained when the radial velocity precision was determined by direct calculation of radial velocity standard deviation along each pointing direction and range gate of the PPI scan. By contrast, when the instrumental measurement precision is assumed to be the only contribution to the radial velocity precision, the retrievals resulted in wind speed and direction precisions that were biased far too low and were poor indicators of data quality.

An error reduction algorithm to improve lidar turbulence estimates for wind energy

Abstract. Remote-sensing devices such as lidars are currently being investigated as alternatives to cup anemometers on meteorological towers for the measurement of wind speed and direction. Although lidars can measure mean wind speeds at heights spanning an entire turbine rotor disk and can be easily moved from one location to another, they measure different values of turbulence than an instrument on a tower. Current methods for improving lidar turbulence estimates include the use of analytical turbulence models and expensive scanning lidars. While these methods provide accurate results in a research setting, they cannot be easily applied to smaller, vertically profiling lidars in locations where high-resolution sonic anemometer data are not available. Thus, there is clearly a need for a turbulence error reduction model that is simpler and more easily applicable to lidars that are used in the wind energy industry. In this work, a new turbulence error reduction algorithm for lidars is described. The Lidar Turbulence Error Reduction Algorithm, L-TERRA, can be applied using only data from a stand-alone vertically profiling lidar and requires minimal training with meteorological tower data. The basis of L-TERRA is a series of physics-based corrections that are applied to the lidar data to mitigate errors from instrument noise, volume averaging, and variance contamination. These corrections are applied in conjunction with a trained machine-learning model to improve turbulence estimates from a vertically profiling WINDCUBE v2 lidar. The lessons learned from creating the L-TERRA model for a WINDCUBE v2 lidar can also be applied to other lidar devices. L-TERRA was tested on data from two sites in the Southern Plains region of the United States. The physics-based corrections in L-TERRA brought regression line slopes much closer to 1 at both sites and significantly reduced the sensitivity of lidar turbulence errors to atmospheric stability. The accuracy of machine-learning methods in L-TERRA was highly dependent on the input variables and training dataset used, suggesting that machine learning may not be the best technique for reducing lidar turbulence intensity (TI) error. Future work will include the use of a lidar simulator to better understand how different factors affect lidar turbulence error and to determine how these errors can be reduced using information from a stand-alone lidar.

Stream Line Doppler lidar measurements of wind speed and direction with the duo-beam method in the atmospheric boundary layer

Stream Line Doppler lidar measurements of wind speed and direction with the duo-beam method in the atmospheric boundary layer

Statistical parameters of the spatiotemporal variability of the wind direction in the surface layer

Multipoint measurements of wind direction were carried out during the expedition of the Institute of Atmospheric Physics, Russian Academy of Sciences (IPA RAS), in Tsimlyansk in 2012. Spatial correlation functions for the transverse direction and temporal correlation functions for the longitudinal direction are plotted under stable and unstable stratification of the atmosphere. The longitudinal correlation radius is much higher than the transverse one, and radii in daytime realizations are larger than in nighttime. To determine the stratification conditions, an ultrasonic anemometer–thermometer was used. Autospectra of wind direction fluctuations were plotted. They include long segments of power dependence on the frequency. The spectral correlation coefficients of variations in the wind direction versus intersensor distance in the transverse direction are also calculated. A set of fast-response thermometers was used in the experiment. They allowed temperature mapping, i.e., plotting the time variations in the isothermal surface altitude. That analysis was also applied to visualization of the spatiotemporal variability of wind direction. The resulting data were used for planning the helicity measurements in the Tsimlyansk expedition in 2014.

Tracking the Indian Summer Monsoon Onset Back to the Preinstrument Period

Abstract The Indian summer monsoon onset is one of the most expected meteorological events of the world, affecting the lives of hundreds of millions of people. The India Meteorological Department has dated the monsoon onset since 1901, but its original methodology was considered subjective and it was updated in 2006. Unfortunately, the new method relies on OLR measurements, which impedes the construction of an objective onset series before the 1970s. An alternative approach is the use of the wind field, but the development of such an index is limited to the period covered by reanalysis products. In this paper historical wind records taken on board ships are used to develop a new onset series using only wind direction measurements, providing an objective record of the onset since the late nineteenth century. The new series captures the rapid precipitation increase associated with the onset, correlates well with previous approaches, and is robust against anomalous (bogus) onsets. A tendency for later-than-average onsets during the 1900–25 and 1970–90 periods and earlier-than-average onsets between 1940 and 1965 was found. A relatively stable relationship between ENSO and Indian monsoon onset dates was found; however, this link tends to be weaker during decades characterized by prevalent La Niña conditions. Furthermore, it was found that the link between the Pacific decadal oscillation (PDO) and the onset date is limited to the phases characterized by a shift from negative to positive PDO phases.

Statistical wind direction modeling for the analysis of large scale wind power generation

AbstractUnderstanding the effects of large‐scale wind power generation on the electric power system is growing in importance as the amount of installed generation increases. In addition to wind speed, the direction of the wind is important when considering wind farms, as the aggregate generation of the farm depends on the direction of the wind. This paper introduces the wrapped Gaussian vector autoregressive process for the statistical modeling of wind directions in multiple locations. The model is estimated using measured wind direction data from Finland. The presented methodology can be used to model new locations without wind direction measurements. This capability is tested with two locations that were left out of the estimation procedure. Through long‐term Monte Carlo simulations, the methodology is used to analyze two large‐scale wind power scenarios with different geographical distributions of installed generation. Wind generation data are simulated for each wind farm using wind direction and wind speed simulations and technical wind farm information. It is shown that, compared with only using wind speed data in simulations, the inclusion of simulated wind directions enables a more detailed analysis of the aggregate wind generation probability distribution. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Comparison of Different Measurement Techniques and a CFD Simulation in Complex Terrain

This paper deals with a comparison of data collected by measurements and a simulation for a complex terrain test site in southern Germany. Lidar, met mast, unmanned aerial vehicle (UAV) measurements of wind speed and direction and Computational Fluid Dynamics (CFD) data are compared to each other. The site is characterised regarding its flow features and the suitability for a wind turbine test field. A Delayed-Detached-Eddy- Simulation (DES) was employed using measurement data to generate generic turbulent inflow. A good agreement of the wind profiles between the different approaches was reached. The terrain slope leads to a speed-up, a change of turbulence intensity as well as to flow angle variations.

Orientation correction of wind direction measurements by means of staring lidar

In spite of the efforts made at the time of installation of wind vanes or ultrasonic anemometers (Sonic), there is always a remaining uncertainty of several degrees in the absolute north of such sensors. In this research a method is presented to reduce the azimuthal orientation error of wind direction sensors by means of Doppler Lidar measurements. The method is based on the comparison between the conventional sensor and a distant long range lidar pointing to it in staring mode. By comparing their line-of-sight wind speeds any misalignment between both systems can be estimated more accurately. This method was applied in an measurement campaign in the offshore wind farm alpha ventus next to the meteorological mast FINO 1. The maximum alignment error of a Sonic was reduced to below ±1°. This accurate alignment has asserted, that no bias exists between Lidar and Sonic wind speed measurements.

Wind speed and direction measurement based on arc ultrasonic sensor array signal processing algorithm

This article investigates a kind of method to measure the wind speed and the wind direction, which is based on arc ultrasonic sensor array and combined with array signal processing algorithm. In the proposed method, a new arc ultrasonic array structure is introduced and the array manifold is derived firstly. On this basis, the measurement of the wind speed and the wind direction is analyzed and discussed by means of the basic idea of the classic MUSIC (Multiple Signal Classification) algorithm, which achieves the measurements of the 360° wind direction with resolution of 1° and 0–60m/s wind speed with resolution of 0.1m/s. The implementation of the proposed method is elaborated through the theoretical derivation and corresponding discussion. Besides, the simulation experiments are presented to show the feasibility of the proposed method. The theoretical analysis and simulation results indicate that the proposed method has superiority on anti-noise performance and improves the wind measurement accuracy.

Active yaw control in a horizontal axis wind system without requiring wind direction measurement

This study presents an improved active yaw control technique for a horizontal-axis wind turbine that is driven by a full power converter system with maximum power point tracking control. The turbine is aligned to the wind direction through the error between the rotor speed determined by the optimal tip speed ratio and the real rotor speed commanded by the maximum power point tracking control. Thus, the implementation of the yaw control is not based on the measurement of the wind direction and therefore, it is not influenced by inaccuracies that may be caused by the vortex flow downstream of the blades. The proposed yaw control method is intended for large turbines, since larger increase in the generated electric energy can be attained. However, since it is cost-effective, it is affordable to be applied in low power wind systems. Selective simulation and experimental results are presented in order to demonstrate the effectiveness and the resulting improvements of the proposed control scheme. The simulation analysis is conducted in a large wind turbine with permanent magnet synchronous generator and the experimental verification in a low power real wind system with squirrel cage induction generator. However, it can also be applied to a doubly fed induction generator.

Flight paths of seabirds soaring over the ocean surface enable measurement of fine-scale wind speed and direction

Ocean surface winds are an essential factor in understanding the physical interactions between the atmosphere and the ocean. Surface winds measured by satellite scatterometers and buoys cover most of the global ocean; however, there are still spatial and temporal gaps and finer-scale variations of wind that may be overlooked, particularly in coastal areas. Here, we show that flight paths of soaring seabirds can be used to estimate fine-scale (every 5 min, ∼5 km) ocean surface winds. Fine-scale global positioning system (GPS) positional data revealed that soaring seabirds flew tortuously and ground speed fluctuated presumably due to tail winds and head winds. Taking advantage of the ground speed difference in relation to flight direction, we reliably estimated wind speed and direction experienced by the birds. These bird-based wind velocities were significantly correlated with wind velocities estimated by satellite-borne scatterometers. Furthermore, extensive travel distances and flight duration of the seabirds enabled a wide range of high-resolution wind observations, especially in coastal areas. Our study suggests that seabirds provide a platform from which to measure ocean surface winds, potentially complementing conventional wind measurements by covering spatial and temporal measurement gaps.

Sensitivity Improvement of a 2D MEMS Thermal Wind Sensor for Low-Power Applications

In this paper, we report a novel and low-cost method to improve the sensitivity of a low-power 2D microelectromechanical systems thermal wind sensor by using HF wet etching. After wet etching, the thickness of the glass substrate decreases, so that the sensor’s thermal vias become more exposed to the wind. As a result, the conductive heat transfer is weakened and the convective heat transfer is enhanced in sensor operation. Finite-element method simulations verify this analysis. Moreover, the sensor chips with different lengths of silicon vias above the substrate are successfully fabricated and tested. Measurement results show that the wet etching has no influence on the metal film sensing and heating elements of the sensor. Besides, before and after wet etching for 7 and 14 min, at the wind speed of 5 m/s, the measured sensitivities of the sensor with a total power consumption of 24.5 mW are 77.2, 98.6, and 164.1 mK/(m/s). Measurement results also show that the improved sensitivity of the sensor chip can provide a more accurate measurement in wind speed but has little effect on the wind direction measurement. Instead, the accuracy of wind direction measurement is mainly related to the structural and thermal symmetries of the wind sensor. After compensation, the proposed thermal wind sensor can detect the wind direction in a full range of 360° with an mean error of 2.3° and a maximum error of 6°.