A recursive change point estimate of the wind speed and direction

Acoustic array processing can be employed to measure the wind speed and direction based on microphone signals. Turbulent pressure fluctuations picked up by microphones are referred to as wind noise. According to Taylor's frozen turbulence hypothesis, turbulent eddies retain their shape while advecting at nearly the mean wind speed and in the wind direction. It follows that wind noise propagates accordingly across a microphone array when the inter-microphone distance is smaller than the turbulence wavelength. This property can be exploited to track the orientation of the turbulence advection, and hence to characterize the wind flow. We propose beamforming and signal subspace-based methods to estimate the wind speed and direction using a compact microphone array. In particular, the pseudospectrum of measured wind noise is computed against candidate pairs of wind speed and direction. The wind speed and direction estimates are then obtained as the maximizers of the pseudospectrum. In addition, we extend an existing time difference of arrival-based method originally derived for three microphones to an arbitrary number of microphones. We evaluate the estimation accuracy of the proposed methods separately for the wind speed and direction. Possible applications include highly integrable, portable, and inexpensive anemometers for smart sensors or action cameras.

Reliability analysis of a microwave tower for fluctuating mean wind with directional effect

This paper describes the development of a wind turbine (WT) component lifetime fatigue load variation map within an offshore wind farm. A case study on the offshore wind farm Horns Rev I is conducted with this purpose, by quantifying wake effects using the Dynamic Wake Meandering (DWM) method, which has previously been validated based on CFD, Lidar and full scale load measurements. Fully coupled aeroelastic load simulations using turbulent wind conditions are conducted for all wind directions and mean wind speeds between cut-in and cut-out using site specific turbulence level measurements. Based on the mean wind speed and direction distribution, the representative 20-year lifetime fatigue loads are calculated. It is found that the heaviest loaded WT is not the same when looking at blade root, tower top or tower base components. The blade loads are mainly dominated by the wake situations above rated wind speed and the highest loaded blades are in the easternmost row as the dominating wind direction is from West. Regarding the tower components, the highest loaded WTs are also located towards the eastern central location. The turbines with highest power production are, not surprisingly, the ones facing a free sector towards west and south. The power production results of few turbines are compared with SCADA data. The results of this paper are expected to have significance for operation and maintenance planning, where the schedules for inspection and service activities can be adjusted to the requirements arising from the varying fatigue levels. Furthermore, the results can be used in the context of remaining fatigue lifetime assessment and planning of decommissioning.

: Argus Island wind information was systematically recorded and processed over a three-year period. The recording, processing, and analysis procedures used are described. Monthly mean wind speeds with standard deviations are compared with monthly median wind speeds and quartiles. Monthly distributions of wind speed are given, as well as yearly distributions. Cumulative distribution plots compare wind-speed occurrences over the three-year period. A time plot of daily mean and maximum wind speeds is given, and diurnal variations are discussed. The distributions of wind direction for each month are also shown.

This paper aims to produce a low-complexity predictor for the hourly mean wind speed and direction from 1 to 6 h ahead at multiple sites distributed around the UK. The wind speed and direction are modelled via the magnitude and phase of a complex-valued time series. A multichannel adaptive filter is set to predict this signal on the basis of its past values and the spatio-temporal correlation between wind signals measured at numerous geographical locations. The filter coefficients are determined by minimizing the mean square prediction error. To account for the time-varying nature of the wind data and the underlying system, we propose a cyclo-stationary Wiener solution, which is shown to produce an accurate predictor. An iterative solution, which provides lower computational complexity, increased robustness towards ill-conditioning of the data covariance matrices and the ability to track time-variations in the underlying system, is also presented. The approaches are tested on wind speed and direction data measured at various sites across the UK. Results show that the proposed techniques are able to predict wind speed as accurately as state-of-the-art wind speed forecasting benchmarks while simultaneously providing valuable directional information.

This paper presents a framework for the conditional simulation of a 3D nonstationary wind field for super-long sea-crossing bridges based on wind speed time histories measured by limited anemometers on site. The variations of wind characteristics along the bridge deck with multi-elements are considered in this framework. The wind characteristics, including mean wind speeds, mean wind directions, turbulence intensities, turbulence integral scales, evolutionary wind spectra and spectral parameters, coherence functions and function parameters, at the measurement points are first obtained by analyzing the measured wind speed time histories. The wind characteristics are then extended over all the simulation points along the deck of the sea-crossing bridge using the spatial interpolation method and considering the variation of deck height above the sea level using the profiles of mean wind speeds, turbulence intensities and turbulence integral scales estimated from the measured wind speed time histories. After working out wind cross correlation functions, wind speed time histories at all simulation points over the sea-crossing bridge can be conditionally simulated. The proposed framework is finally applied to a real 22.9 km sea-crossing bridge under Typhoon Higos. The conditionally simulated wind speed time histories at all simulation points of the bridge are acceptable and consist a complete wind field which can be used for the buffeting analysis of the bridge.

The Aedes aegypti mosquito transmits three of the most serious viruses (DENV), ZIKV, and CHIKV), causing significant morbidity and mortality in humans worldwide. The objective of this research was to determine the impact of certain climatic variables on the population dynamics of the Ae. aegypti mosquito in the "Ignacio Agramonte" health area of ​​Camagüey, Cuba. A correlation was established between the total number of positive mosquito deposits and three temperature ranges; in addition, the total number of positive deposits was correlated with three humidity levels, as well as with precipitation, wind direction, wind speed and force, and atmospheric pressure. All analyses were performed using Pearson's correlation coefficient. Finally, a linear regression analysis was performed using the Durbin-Watson statistic and the ANOVA test (Fisher's F-test), which allowed for an analysis of variance. The results showed no significant correlation with any of the temperatures; however, the correlation between the total number of positive deposits and those outside the enclosures was highly significant. In the regression analysis, the model was also highly significant, with a Fisher's F-test significant at the 100% confidence level. Regarding variances, they were significant at the 95% confidence level, except for LAG12VM, where the model trend was significant and increased depending on the mean wind speed and direction frequency, both of which were regressed over 7 months. It is concluded that climate does influence the population dynamics of the Ae. aegypti mosquito.

ARMA based approaches for forecasting the tuple of wind speed and direction

Wind sensors mounted on towers and smokestacks do not always indicate the true free-air flow. To determine the probable errors in measurements of wind speed and direction around such structures, quarter-scale models have been tested in a large wind tunnel. Data on changes in wind speed and direction were obtained by using smoke, very small wind vanes, and a scale model propeller anemometer. Most emphasis has been placed on a relatively open lattice-type tower, but a solid tower and a stack were also studied. The analysis shows that in the wake of lattice-type towers disturbance is moderate to severe, and that in the wake of solid towers and stacks there is extreme turbulence, with reversal of flow. Recommendations for locating wind sensors in the wind field relative to the supporting structure are given for each of the three structures studied. Guidelines are suggested regarding probable errors in measurements of wind speed and direction around different supporting structures, as outlined below. For an open triangular tower with equal sides D, the wake is about 1-1/2D in width for a distance downwind of at least 6D. Sensors mounted 2 D out from the corner of such a tower will usually measure speeds within ± 10° of that of the undisturbed flow for an arc of about 330°. The disturbance by very dense towers and stacks is much greater. Wind sensors mounted 3 diameters out from the face of a stack will measure wind speeds within ± 10%, and directions within ± 10° of the undisturbed flow for an arc of about 180°.

This study analyzed the wind speed data of the met mast in the first commercial-scale offshore wind farm of Taiwan from May 2017 to April 2018. The mean wind speed and standard deviation, wind rose, histogram, wind speed profile, and diurnal variation of wind speed with associated changes in wind direction revealed some noteworthy findings. First, the standard deviation of the corresponding mean wind speed is somewhat high. Second, the Hellmann exponent is as low as 0.05. Third, afternoons in winter and nights and early mornings in summer have the highest and lowest wind speed in a year, respectively. Regarding the histogram, the distribution probability of wind is bimodal, which can be depicted as a mixture of two gamma distributions. In addition, the corresponding change between the hourly mean wind speed and wind direction revealed that the land–sea breeze plays a significant role in wind speed distribution, wind profile, and wind energy production. The low Hellmann exponent is discussed in detail. To further clarify the effect of the land–sea breeze for facilitating future wind energy development in Taiwan, we propose some recommendations.

In order to provide reliable parameters for wind-resistant evaluation of Xihoumen Bridge, four 3-D ultrasonic anemometers were installed in the middle of the main span of Xihoumen Bridge. A long-term field measurement was carried out. The field data such as wind speed, wind direction, attack and yaw angles, etc, were measured. Detailed analysis of the field data and further study were conducted to investigate the wind characteristics. The measured wind data are analyzed to obtain parameters such as mean wind speed and wind direction, turbulence intensities, gust factors, turbulence integral length scales and spectrum of gust components. It is found that the turbulence intensities were larger than normal, and the ratio among the three components wasn't in good agreement with the recommended quantities in Wind-resistent Design Specification for Highway Bridges of China. And there was a tendency for the longitudinal and lateral turbulence intensities and gust factors to decrease with the increase of mean wind speed. The Kaimal spectrum and Panofsky spectrum models were identified to describe the energy distribution fairly well for the wind speed component in the longitudinal and vertical direction. Key word: field measurement, turbulence intensity, gust factor

A wind power generation forecast model based on WOA-SVM is presented to exploit effective information in different data sets completely. This model addresses the difficulties associated with parameter selection, low prediction accuracy, and susceptibility to local optima in short-term wind energy prediction. The model systematically investigates the relationship between various wind parameters (mean wind speed, maximum wind speed, minimum wind speed, mean wind direction, and mean hull position) and wind power. It then evaluates the model’s performance using mean absolute error and coefficient of determination. The predictive outcomes of the WOA-SVM model are contrasted with those of the SVM model, PSO-SVM model, and extreme learning machine (ELM) model, utilising authentic data from a wind farm located in northern China in the year 2021. The overall evaluation indicates that the WOA-SVM model outperforms the other models in short-term wind energy prediction, demonstrating superior prediction accuracy.

Field Evaluation of Windbreak Protection for Orchards

The statistical prediction of local sea surface winds from large-scale, free-tropospheric fields is investigated at a number of locations over the global ocean using a statistical downscaling model based on multiple linear regression. The predictands (the mean and standard deviation of both vector wind components and wind speed) calculated from ocean buoy observations on daily, weekly, and monthly scales are regressed on upper-level predictor fields from reanalysis products. It is found that in general the mean vector wind components are more predictable than mean wind speed in the North Pacific and Atlantic, while in the tropical Pacific and Atlantic the difference in predictive skill between mean vector wind components and wind speed is not substantial. The predictability of wind speed relative to vector wind components is interpreted by an idealized model of the wind speed probability density function, which indicates that in the midlatitudes the mean wind speed is more sensitive to the vector wind standard deviations (which generally are not well predicted) than to the mean vector winds. In the tropics, the mean wind speed is found to be more sensitive to the mean vector winds. While the idealized probability model does a good job of characterizing month-to-month variations in the mean wind speed in terms of the vector wind statistics, month-to-month variations in the standard deviation of speed are not well modeled. A series of Monte Carlo experiments demonstrates that the inconsistency in the characterization of wind speed standard deviation is the result of differences of sampling variability between the vector wind and wind speed statistics.

Wind speed measurements within a maize row canopy were carried out to investigate the horizontal and vertical variability of the mean wind speed and its standard deviation. Attention was given to finding adequate scaling parameters of the within-canopy wind speed profiles under various atmospheric stratification states. A validation of existing model simulations was also carried out. The horizontal mean wind speed and its standard deviation can vary about 20% from its spatial mean value. During day time and night time the friction velocity appears to be a good scaling parameter. Clear nights, however, are exceptions, when the wind speed above the crop drops to a very low value. Then the free convection velocity appears to be an appropriate scaling parameter for the within-canopy processes. The canopy models of Wilson & Shaw [Journal of Applied Meteorology (1977) 16, 1197-1205] and Li et al. [Boundary-Layer Meteorology (1985) 33, 77-84] were found to simulate the spatially averaged mean wind profile within the range of the horizontal variability. (Abstract retrieved from CAB Abstracts by CABI’s permission)