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

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 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.

On differences and similarities of applied wind comfort criteria

This article suggests the application of multiresolution analysis by Wavelet Transform—WT and Echo State Networks—ESN for the development of tools capable of providing wind speed and power generation forecasting. The models were developed to forecast the hourly mean wind speeds, which are applied to the wind turbine’s power curve to obtain wind power forecasts with horizons ranging from 1 to 24 h ahead, for three different locations of the Brazilian Northeast. The average improvement of Normalized Mean Absolute Error—NMAE for the first six, twelve, eighteen and twenty-four hourly power generation forecasts obtained by using the models proposed in this article were 70.87%, 71.99%, 67.77% and 58.52%, respectively. These results of improvements in relation to the Persistence Model—PM are among the best published results to date for wind power forecasting. The adopted methodology was adequate, assuring statistically reliable forecasts. When comparing the performance of fully-connected feedforward Artificial Neural Networks—ANN and ESN, it was observed that both are powerful time series forecasting tools, but the ESN proved to be more suited for wind power forecasting.

Neste trabalho o ciclo diário do vento à superfície é investigado com ênfase na variação sazonal e nas circulações locais. Os dados da estação automática instalada a 25-30 km de Petrolina, e da estação convencional localizada na cidade são utilizados. Os resultados mostram que as velocidades médias horárias aumentam (diminuem) no decorrer do trimestre abril-maio-junho (outubro-novembro-dezembro), devido à intensificação (enfraquecimento) da alta subtropical do Atlântico Sul. No trimestre julho-agosto-setembro os valores são máximos e muito próximos, com direção no intervalo 120-130 graus. O ciclo diário tem um máximo de intensidade às 8-9 HL (hora local) e dois mínimos, um às 5-6 HL e outro às 17-18 HL, em todos os meses. Os hodógrafos do vento local nas duas estações mostram circulações fracas de janeiro a maio, uma característica que pode estar relacionada com o maior teor de umidade do solo e, consequentemente, com valores mais baixos do gradiente do fluxo de calor sensível. No restante do ano, período de estiagem, as circulações locais são comparativamente fortes. Os hodógrafos apresentam forma irregular que indica um regime preferencial na direção sudeste-noroeste. As circulações locais são mais intensas às 9 e 18 HL, horários de máxima e mínima velocidade no ciclo diário, respectivamente.

A substantial part of South Africa is subject to more than one strong wind source. The effect of that on extreme winds is that higher quantiles are usually estimated with a mixed strong wind climate estimation method, compared to the traditional Gumbel approach based on a single population. The differences in the estimated quantiles between the two methods depend on the values of the Gumbel distribution parameters for the different strong wind mechanisms involved. Cluster analysis of the distribution parameters provides a characterization of the effect of the relative differences in their values, and therefore the dominance of the different strong wind mechanisms. For gusts, cold fronts tend to dominate over the coastal and high-lying areas, while other mechanisms, especially thunderstorms, are dominant over the lower-lying areas in the interior. For the hourly mean wind speeds cold fronts are dominant in the south-west, south and east of the country. On the West Coast the ridging of the Atlantic Ocean high-pressure system dominate in the south, while the presence of a deep trough or coastal low pressure system is the main strong wind mechanism in the north. In the central interior cold fronts tend to share their influence almost equally with other synoptic-scale mechanisms.

Wind speed is the most critical factor that determines wind power potential and generation. In this paper, the wind speed data of multiple years from various observation sites in North Dakota, U.S. was analyzed to assess the wind power potential. The study first applied probability density functions (PDFs) to characterize the wind speed data and fit the distributions at various heights for each observation site. The fitted distributions were then used to estimate the wind power potential based on the theoretical cubic power relationship between energy potential and wind speed. Due to the complexity of functions, the numerical integration approach was employed. The following major findings were obtained from this empirical study: (1) Weibull distribution is not always the best function to fit wind speed data, while gamma and lognormal distributions produce better fitting in many occasions; (2) For different height levels at one observation site, the best performing distributions may be different; (3) The estimation accuracies of wind energy potential based on the fitted wind speed distributions range from -4% to 3.8%; (4) The rank of energy potential estimation accuracies is not always consistent with that of goodness-of-fit for wind speed distributions. In addition, a simplified approach that only relies on the hourly mean wind speed to estimate wind power potential is evaluated. Based on the theoretical cubic relationship for wind power estimation, it was found that the simplified approach may provide significantly lower estimates of wind power potential by 42-54%. As such, this approach will become more practical if this amount of difference is to be compensated. Key words: Wind speed; Distribution; Goodness-of-fit; Wind power potential; North Dakota

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.

Mean wind speeds and mean wind directions are important input parameters for environmental models and applications. In many practical cases, anemometer sensor provides measurements of direction and wind speed, supposed to be Gaussian and piecewise stationary. In this work we use a von Mises distribution and a Log-normal distribution in order to model sensor data with distributions having a physical reality. We derive from these distributions an ¿on-line¿ recursive estimate of the change point in the wind speed and direction processes. We present numerical results obtained using synthetic and real data to demonstrate the ability of these estimates.

Theoretical investigations of the statistical properties of the wave front perturbed by atmospheric turbulence are presented. They are deduced from the calculation of the two-dimensional spatial covariance and the temporal cross spectrum of the angle-of-arrival fluctuations with a finite outer scale over a pair of circular pupils as in the case of the grating scale monitor or any other Shack–Hartmann-type sensor. Both calculations lead to integral expressions that are numerically evaluated and hold for any baseline vector in the mean wave-front plane. It is proposed to retrieve the wave-front outer scale L0 from estimations of this two-dimensional spatial covariance, normalized by the angle-of-arrival structure function. To eliminate instrument vibration errors, the covariance and the structure function are estimated from measurements obtained by mechanically independent and mechanically coupled devices, respectively. The angle-of-arrival temporal cross spectrum is calculated for any mean wind velocity vector. It is shown that the baseline component in the mean wind direction affects the phase of the angle-of-arrival temporal cross spectrum, whereas the component in the perpendicular direction affects the modulus. From simultaneous measurements of the phase of the angle-of-arrival temporal cross spectrum obtained with two nonparallel baselines, one can calculate the mean wind speed and direction, which allows estimation of the coherence time for techniques of optical observation at high angular resolution through the atmosphere.

Assessment of inhomogeneity in environmental conditions in a Norwegian fjord for design of floating bridges

The deformation of tamarack, including crown deformation and tree ring asymmetry, was calibrated against the mean wind speed and direction at seven meteorological stations in Newfoundland. The indices, based on regression equations, showed that tree deformation is caused primarily by summer southwesterlies. A crown deformation ratio, D, gave a good estimate of mean windflow in complex terrain. A compression index, C, describing tree-ring asymmetry, gave a reasonably good estimate of mean wind flow over flat terrain but was unreliable for estimating wind flow over hilly terrain.

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.

Since 1990s, the Ground-Based Velocity Track Display (GBVTD) – family of techniques, including the Generalized Velocity Track Display (GVTD), have been used to retrieve the kinematic structure of Tropical cyclones (TCs) using data from a single Doppler radar. This study builds on the GVTD formulation and proposes a single Doppler TC inner core mean wind retrieval technique, called GVTD-Mean Wind (GVTD-MW), that can improve the retrieved TC inner core structures and the understanding of TC intensity change. Results from analytical wind field experiments demonstrate that GVTD-MW can estimate mean wind speed and direction, with errors below 10% and 6°, respectively. GVTD-MW estimated mean winds are not sensitive to magnitude and direction of the along-beam mean wind component, vortex size, and uncertainty of the TC center. GVTD-MW was applied to Typhoon Nock-Ten (2004) and Typhoon Koinu (2023). For Nock-Ten, the significant cross-beam mean wind component enabled GVTD-MW to reduce the retrieval errors compared to GVTD. Consequently, the root mean square error (RMSE) for the entire volume decreased from 8.8 m s−1 (GVTD) to 7.6 m s−1 (GVTD-MW) when compared with radar observations. For Koinu, where the cross-beam mean winds were much smaller than those in Nock-Ten, GVTD-MW also improved the accuracy of storm structure retrieval. These findings demonstrate the characteristics and impacts of GVTD-MW in improving the TC structures from single-Doppler radar observations.

Variations in wind velocity and direction due to the mounting structure are studied using Gill anemometers mounted on 1.8 m long arms of a 232 m high tower. The tower «shadowing» effect for the observed range, from 0 to 20 m/s, is in linear correlation with the velocity. The polar diagrams of the variations in wind speed and direction are presented as a function of the wind's direction. The horizontal component decreases by 15% when the anemometer is on the windward side of the tower, and by a maximum of 80% on the lee side. In the latter case the decrease is highly sensitive to wind direction. Variations in direction do not exceed 15 degrees.