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Probabilistic modeling of the directional wind speed has been an important topic in the fields of wind energy assessment as well as structural wind-resistant design. The mixture model is required for the probabilistic modeling on the directional wind speed as the wind climates often show the mixture nature. For accuracy and robustness concerns in engineering applications, a generalized bivariate mixture (GBM) model of the directional wind speed is developed by using the copula-based bivariate distribution function. In this model, the marginal distributions and connection function for each sub-model can be flexibly defined. In parametrizing the model, the data-driven parametric estimation including a data preprocess and an automatic unsupervised fitting process is presented. The performance of GBM model is verified using two data sets and compared with the offset elliptical normal mixture (OENM) model. The robustness of GBM model is found for its good performance in fitting to the targets with or without smoothing process, and in tackling directional wind speed with the extremely complex probability characteristic. [ABSTRACT FROM AUTHOR]

In order to reduce the estimation uncertainty for the wind energy output, the accurate assessment of the wind speed distribution plays an important role. Due to the influence of the complex terrain, the wind speed data in mountainous area may show the heavy tail. The tail has an important effect on the annual energy production of the wind turbine because of the power output being the cubic of the wind speed. Hence the heavy tail should be highlighted before choosing the proper distributions. To characterize the heavy tail, the box plot, QQ plot and conditional mean exceedance are used. In addition, the wind climate in the mountainous area is very complicated and typically mixed by local storms and large-scale wind flows. The distribution of the wind speed is prone to exhibit bimodal or even multi-modal nature. The conventional unimodal distributions such as Weibull may not well portray these distributions. The mixture distribution has more flexibilities to handle this challenge. Both generalized extreme value distribution and generalized Pareto distribution are considered to formulate the mixture distribution in modeling the tail. The maximum likelihood method and the constrained maximum likelihood method are used to estimate distribution parameters. The comprehensive evaluation criteria are adopted to determine the most appropriate distribution of four sets of wind speed in the mountainous area of Yunnan, China. Graphical tools, such as histogram density curve and QQ plot, are also used for evaluation. Results show that heavy tail distribution should be considered for the wind data in mountainous areas and the proposed mixture model with the constrained maximum likelihood method for the parameter estimation will be superior for the wind energy potential estimation. [ABSTRACT FROM AUTHOR]

The coastal region of China suffers from severe typhoons. Hazard mitigation and risk management require a good estimation of the typhoon wind speed. In this study, an enhanced circular subregion method (CSM) is proposed. The efforts are mainly made in three aspects. First, a new treatment is developed to model the observed central pressure difference better. Second, the appropriate size for the enhanced CSM is discussed. Studies indicate that 500 km is appropriate. Third, Nataf transformation is introduced to incorporate the correlations among typhoon key parameters in wind hazard estimation. The enhanced CSM combined with a typhoon wind field model is used to assess the typhoon hazard for nine cities located in the coastal region of mainland China. Results show the design wind speeds predicted by traditional CSM are lower than those by the enhanced CSM for nine cities with an averaged difference of 4.3% for 50-year and 4.9% for 100-year return periods. The maximum difference was 6.1% in Fuzhou for 100-year return periods. Results also show the consideration of correlations among typhoon key parameters results in a change of the design wind speed at less than 2% for nine cities. Note that the consideration of correlations may not be neglected in other countries. [ABSTRACT FROM AUTHOR]

With the development of mountain areas, more wind-sensitive infrastructures are constructed. In the design of these infrastructures, the wind loading cannot be accurately obtained from the code based on the flat area. Hence, it is of great importance to study the mountain wind characteristics. In this study, the wind field measurement was initiated in a mountain area of western China. After the examination of the measured data, two typical wind events including the thunderstorm wind and thermally developed wind are highlighted. To extract and separate these wind events, an automatic classification method is proposed. The thunderstorm wind is analyzed in order to capture the rapid variation of its maximum wind speed, mean temperature and mean humidity through the boxplot method while the analysis of thermally developed winds relies on the correlation between the mean wind speed and mean temperature. Since the thunderstorm wind is relatively more important for wind engineering, its wind characteristic is focused hereafter and analyzed in detail based on the ultrasonic anemometer data. The characteristics of the thermally developed wind and other wind will be the matter of further studies and investigations. Results show that the characteristics of the thunderstorm wind measured in the mountainous area have no significant difference in comparison with those in the flat area. Due to the limited data, the above results deserve further investigations when more measurements become available. • Field measurement was conducted to study intense wind characteristics in mountain. • Thunderstorm and thermally developed winds (TW, TDW) are spotted in mountain. • TW and TDW are separated out by the proposed automatic classification method. • Transient characteristics of TW in mountain area are well discussed. • Characteristics of TW in mountain area are found to be similar to those in flat area. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Refined variational mode decomposition with adaptive level number is developed. • Conditional kernel density estimation with optimized bandwidth is proposed. • Based on above techniques, real time decomposition-based hybrid method is offered. • Deterministic and probabilistic predictions are provided by the hybrid method. • Case studies show hybrid method performs well in reliability and stability. Abstract Short-term wind speed prediction is an important task for the wind energy development. However, due to intermittency and uncertainty of wind resources, it is difficult to be achieved only by the deterministic prediction. Hence, there is a motivation to develop a novel method to further consider the uncertainty. The originality is to develop an innovative hybrid wind speed forecasting model based on empirical mode decomposition, variational mode decomposition, sample entropy and conditional kernel density estimation. More specifically, the original data are decomposed in real time by the refined variational mode decomposition, where the number of decomposition levels is adaptively determined by empirical mode decomposition and sample entropy. Then, conditional kernel density estimation with the bandwidth optimized by normal reference criterion is used to obtain the predictive probability density function for each subseries, by which the expectations and variances are derived. Finally, the deterministic prediction is produced by summarizing these expectations and the probabilistic prediction interval is obtained by the covariance and the Gaussian distribution assumption on target wind speed. It should be emphasized that some hypotheses are adopted in the proposed method: the correlations among different subseries can be characterized by the covariance and the target wind speed follows Gaussian distribution. Two case studies based on the measured data are used to evaluate the performance of the proposed method. The results show the improvement by the proposed method is up to 20% compared with empirical mode decomposition-based method. The main conclusion is that the proposed method may provide the better prediction over other models for the wind speed data with nonstationarity. [ABSTRACT FROM AUTHOR]

The short-term wind speed prediction is important for the connection of wind power to the grid. However, it is challenging due to the stochastic and intermittent characteristics of wind speed. In this paper, a novel wind speed prediction method named as EWT-ARIMA-LSSVM-GPR-DE-GWO method is proposed, where the empirical wavelet transform (EWT) is applied to decompose the original wind speed signal into multiple intrinsic mode functions (IMF), and various prediction models including the statistical model of autoregressive integrated moving average (ARIMA), artificial intelligence models of least squares support vector machine (LSSVM) and Gaussian process regression (GPR) are used for predicting the IMF in sequence from low frequency to high frequency based on the superiority of each model. Differential evolution-grey wolf optimizer (DE-GWO) algorithm is used to optimize the hyperparameters of artificial intelligence prediction models of LSSVM and GPR. The proposed short-term wind speed prediction method shows superior performance in both accuracy and stability with validation by the measurement data. Both the EWT signal decomposition and DE-GWO hyperparameter optimization are effective to promote the prediction accuracy for wind speeds with large non-stationary. • A novel short-term wind speed prediction method is proposed. • The effect of signal decompositon on the wind speed prediction is analyzed. • The hyperparameter optimization is used to improve the prediction performance. [ABSTRACT FROM AUTHOR]

Accurately forecasting wind speed is a critical mission for the exploitation and utilization of wind power. To improve the prediction accuracy, the nonlinearity and nonstationarity embedded in wind speed time series should be reduced. Because the subseries has less nonlinearity and nonstationarity after decomposition, the decomposition-based forecasting methods are widely adopted to provide the higher predictive accuracy. However, latest studies showed the real time decomposition-based forecasting methods could be worse than the single forecasting models. The aim of this study is to improve the performance of the real time decomposition-based forecasting method after the factors attributed to its unsatisfactory performance are uncovered. In this paper, the feature selection and error correction are adopted in the real time decomposition-based forecasting method to enhance the prediction accuracy. In the proposed method, the raw wind speed time series is decomposed into a number of different subseries by ensemble empirical mode decomposition; then two feature selection methods including kernel density estimation-based Kullback-Leibler divergence and energy measure are used to reduce the disturbance of illusive components; further the least squares support vector machine is adopted to establish the one-step ahead forecasting models for the remaining subseries; finally, the hybrid of least squares support vector machine and generalized auto-regressive conditionally heteroscedastic model is introduced to correct resulting error component if its inherent correlation and heteroscedasticity cannot be neglected. Based on two sets of measured data, the results of this study show that: (1) the real time decomposition-based method may be ineffective in practice; (2) both the feature selection and error correction can improve forecasting performance in comparison with the real time decomposition-based method; (3) compared with other involved methods, the proposed hybrid method has the satisfactory performance in both accuracy and stability. [ABSTRACT FROM AUTHOR]

The classical spectral representation method (SRM) has been extensively used in the simulation of multivariate stationary Gaussian random processes. Due to the application of fast Fourier transform (FFT), the simulation is usually efficient. However, for processes with a large number of simulation points, it becomes necessary to enhance the simulation efficiency. One example is the wind velocity field along a large-span bridge, where hundreds of wind velocity fluctuations are required. In the case of bridges built over a homogeneous terrain such as coastal area or flat plain, the wind velocity field can be modeled as a multivariate homogeneous random process i.e., the auto power spectral densities (PSDs) at evenly-spaced simulation points are same and the cross PSD is a function of separation distance between two simulation points. Furthermore, in some applications, additional simulation points need to be included to a set of uniformly distributed points in order to make the wind velocity field consistent to the structural dynamic analysis requirement. In this paper, a hybrid approach of space–time random-field based SRM and proper orthogonal decomposition (POD)-based interpolation is developed for simulating the above wind velocity process. In this approach, the random-field based SRM is used to simulate the multivariate homogeneous random process composed of a set of uniformly distributed simulation locations while POD-based interpolation is used to conditionally generate the wind velocities at a few unevenly distributed points using the previously simulated wind velocities. The idea of the former is based on transforming the simulation of the homogeneous random process into that of the corresponding space–time random field where the phase angle is assumed to be zero and the coherence function must be an even function in terms of separation distance. Through this procedure, customary requirement for spectral matrix decomposition is eliminated and application of two dimensional FFT can improve the simulation efficiency dramatically. The shortcomings of this method include a slight approximation regarding the simulated sample and the non-ergodicity for the correlation function. The numerical example of a homogeneous wind velocity field along a bridge deck shows that the proposed random field-based method is very efficient in terms of accuracy and efficiency when the number of simulation locations is large and the POD-based interpolation also has good performance. [ABSTRACT FROM AUTHOR]

Asphalt shingle roofing that is widely used in US has been observed to be prone to wind damage. Also roofing damage often creates the opening that leads rain water into the building and may cause significant further loss to interior contents. Correctly estimating wind damage for asphalt shingle roofing has its significance from the perspective of wind loss mitigation and wind risk management. This paper focuses on the application and validation of the asphalt shingle roof damage estimation method presented in the previous paper (Huang et. al, 2015, “Data-based probabilistic damage estimation for asphalt shingle roofing.” J. Struct. Eng., http://dx.doi.org/10.1061/(ASCE)ST.1943-541X.0001300, thereafter denoted as DBPDE method). Firstly, the DBPDE method is employed to estimate the asphalt shingle roofing damage for a single residence. Secondly, the estimation procedure is extended to a group of residences in an area. Finally, the shingle resistance estimation and the validation of the damage estimation method are conducted using field survey data. [ABSTRACT FROM AUTHOR]

A refined full-order method is presented for estimating the extreme wind load effects of rigid structures with given mean recurrence intervals (MRIs) by combining the distributions of annual maximum wind speed and extreme load coefficients. This refined method is capable of dealing with any type of asymptotic extreme value distribution. With this full-order method, the predictions of wind load effects by using distributions of annual maximum wind velocity pressure and wind speed are compared that provide information on the sensitivity of predictions to the upper tail of wind speed distribution. The efficacy of the first-order method is examined. The influences of the type of distributions and the variations of annual maximum wind speed and extreme load coefficient on the predictions are quantified. Finally, the first- and full-order methods are extended to wind load effects of dynamically sensitive structures which facilitate a comprehensive probabilistic analysis as compared to the Monte Carlo simulation schemes used in literature. It is pointed out that 78% fractile extreme load coefficient can be used for defining the characteristic load effects of both rigid and dynamically sensitive structures. The wind load factor is insensitive to the variation of extreme load coefficient. It can be approximately estimated through the wind speed factor and the growth rate of extreme wind load effect with increasing wind speed. The result concerning the wind load factor justifies the advantage of specifying design wind speeds with various MRIs in reducing the uncertainties of design wind loading. [ABSTRACT FROM AUTHOR]

• An enhanced method to simulate ergodic multivariate stochastic processes is developed. • The novel simulation formulation possesses a faster convergence rate. • The two-dimensional fast Fourier transform algorithm can expedite the simulation efficiency. The spectral representation method (SRM) has been extensively used in the simulation of multivariate stationary Gaussian stochastic processes. In this study, an enhanced SRM for the simulation of ergodic multivariate stochastic processes is developed. Through shifting a frequency increment, the enhanced SRM offers a faster convergence rate of probabilistic characteristics in comparison with conventional SRM. The computational efficiency is enhanced as the Cholesky decomposition is only required at single-indexed frequencies. Additionally, a two-dimensional fast Fourier transform (FFT) algorithm is proposed to expedite simultaneously estimation of the double summation of cosine functions in both frequency and space dimensions. Accordingly, compared to the traditional FFT-based SRM, in which FFT is only used for the summation in the frequency dimension, the simulation efficiency is further enhanced significantly. The numerical example concerning the simulation of wind velocity field demonstrates that the proposed method offers a faster convergence rate and a higher level of efficiency. [ABSTRACT FROM AUTHOR]

This study proposes a novel damping separation method based on operating state-measured data collected from a 6.7 MW offshore wind turbine tower to provide various damping ratios caused by different loads. First, the operating frequencies and total damping ratios for various operating states are identified using the dynamic responses under normal operating conditions. Subsequently, the natural and harmonic frequencies caused by the rotation of the blades are distinguished using rotor speed data and the Campbell diagram. Finally, to determine the various damping ratios of different modal orders of offshore wind turbines under multiple load conditions using only actual response data, a new damping equation based on different damping sources is proposed. Filtering strategies for total damping ratios corresponding to different damping sources with varying vibration amplitudes and wind speed thresholds were proposed. The outlier elimination method was introduced to remove error-scattered points, and the average value calculation was used to identify various damping ratios. The actual measured data of a 6.7 MW offshore wind turbine tower was studied, and the results show that the proposed method can effectively separate various damping ratios. • A new damping separation method is proposed, providing the various damping ratios caused by different loads. • The total damping ratios are divided into three parts, i.e. structural, aero-induced , and hydro-induced damping ratios. • A new damping equation is proposed based on the different damping sources to determine the various damping ratios. • The filtering strategies for total damping ratios corresponding to different damping sources are proposed. [ABSTRACT FROM AUTHOR]

Extreme winds such as thunderstorm winds typically exhibit nonstationary characteristics. When these winds act on the structure, the time domain analysis method is used considering the structural and/or aerodynamic nonlinearity in the wind-induced structural response analysis. Because the time domain analysis method needs a sufficient number of wind speed samples, the efficient simulation of nonstationary winds becomes a crucial task. The time series method is more suitable in generating the nonstationary process due to the high efficiency. As a typical time series model, the autoregressive-generalized autoregressive conditional heteroskedasticity (AR-GARCH) model could be a good candidate to characterize these samples. In this paper, the method based on the multivariate AR-GARCH model is proposed to model and simulate the multivariate nonstationary wind speed process. Specifically, Baba-Engle-Kraft-Kroner (BEKK) model is adopted since it can maintain the positive definiteness of conditional covariance matrix. The numerical simulations based on samples with different time-varying variances obtained by spectral representation method (SRM) are given to demonstrate the applicability of the proposed method. The measured thunderstorm wind speed is adopted to illustrate and validate the accuracy of the proposed method. The results show that the time-varying variance and cross correlation can be satisfactorily maintained. • The AR-GARCH model is proposed to model and simulate the multivariate nonstationary wind speed process. • The Baba-Engle-Kraft-Kroner model is adopted to maintain the positive definiteness of conditional covariance matrix. • Numerical simulations based on processes with different time-varying variances are used for validation. • Measured thunderstorm wind speeds from Texas Tech University are adopted to illustrate the accuracy. [ABSTRACT FROM AUTHOR]

Flutter analysis that governs the bridge aerodynamic instability is crucial during the wind-resistance design period of long-span bridges. However, randomness existing in the wind load and structure can bring about obstacles in the determination of the flutter onset and thus can lead the bridge to precarious situations. Probabilistic analysis considering the uncertainties from different sources is necessary for the risk assessment of bridge flutter. In this paper, a framework for probabilistic analysis of bridge flutter is developed. Bimodal coupled flutter serving as the classical flutter problem is considered. In the probabilistic analysis, modal damping ratio and critical wind speed are reformulated with generalized flutter analysis, based on which their moments are estimated by adopting point estimate method. The proposed framework is validated with experimental data. Results show that both the accuracy and efficiency can be retained. • A framework for the probabilistic flutter analysis is proposed by introducing the point estimate method. • The framework is validated with experimental data, and possesses higher efficiency compared to the sampling method. • The influences of uncertainties from different sources has been investigated using this framework. [ABSTRACT FROM AUTHOR]

Strong wind is one kind of the most destructive natural disasters devastating transmission towers. The damage and even collapse of transmission towers caused by strong winds occur frequently. Wind-induced fragility assessment of the transmission tower is of great significance for the safety and economy of transmission tower. It provides not only a basis for the disaster mitigation and risk assessment of the government, but the reference for the design, maintenance and retrofit of the transmission tower as well. In this paper, wind-induced fragility analysis and failure probability evaluation of the transmission tower with considering wind directionality are addressed. Firstly, the wind-induced fragility analysis method including two variants are presented. Then an engineering case is used to illustrate the fragility analysis of the transmission tower: the random finite element model of the transmission tower and the fluctuating wind speed and related wind load are simulated; the static non-linear buckling analysis under different wind attack angles is conducted to determine the threshold displacements in damage and collapse state of the tower; the incremental dynamic analysis is carried out to obtain the maximum displacement at the tower top and the fragility curve of the transmission tower is determined accordingly. Furthermore, the failure probability of the tower is determined considering wind directionality. Finally, the procedure to determine the optimal layout of the transmission tower is proposed, which is based on the overall damage and collapse probability of transmission tower under any layout in one year. The results show that the most unfavorable wind attack angle is 45°, then 0°, 60° and 90°, and the most favorable layout is 90° in damage and collapse state for the illustrated tower. • The wind-induced fragility analysis method including two variants are addressed and compared. • An approach to calculate the damage and collapse probability of the tower considering the wind directionality is proposed. • Given the wind speed statistical information, the layout optimization of the tower is analyzed. [ABSTRACT FROM AUTHOR]

RANS (Reynolds-averaged Navier-Stokes) has become one of the mainstream methods to determine the wind field in the wind resource assessment of the complex terrain. However, before the simulation of wind field by RANS, defining the inlet boundary condition often depends on user's experience, and lacks of theoretical guidance, which brings great uncertainties to the accuracy of simulation results. In this paper, based on the vertical multi-point wind speed data (including wind direction) obtained from the on-site measurement and the wind speed data simulated by RANS, a data fusion method called the RANS-VMM (Vertical Multi-point on-site Measurement) method is proposed. In this method, through the polar coordinate diagram (PCD) that can express the wind direction and wind profile index at the same time, the vertical multi-point measured wind speed of wind measurement tower and the RANS simulation results are fused via multiple iterations, which can reconstruct the wind field distribution with spatial variability in complex mountainous areas, thus improving the accuracy of wind resource assessment in the complex terrain. The evaluation and verification of the proposed method are conducted through an real wind farm with complex terrain, and the results indicate that the RANS-VMM method can improves the accuracy of wind resource assessment in the complex terrain. • A wind resource assessment method considering vertical multi-point measured wind field data for mountainous wind farm. • The polar coordinate diagram (PCD) is proposed, which can express the wind direction and wind profile index. • Vertical multi-point wind speed of wind measurement tower and the RANS simulation results are fused via multiple iterations. • Wind field distribution with spatial variability in complex mountainous areas can be reconstruct. • The evaluation and verification of the proposed method are conducted through an real wind farm with complex terrain. [ABSTRACT FROM AUTHOR]

The wind pressure at the flow separation region of building surfaces generally exhibits non-Gaussian characteristics and is modeled as a non-Gaussian random process. To evaluate the structural response under this non-Gaussian excitation, the time-domain response analysis is commonly used, especially in order to take into account the structural and aerodynamics nonlinearity. This makes the simulation of stationary non-Gaussian multivariate random processes becomes an essential task. The traditional correlation distortion simulation method based on the Hermite polynomial model (HPM) is widely used because it has relatively satisfactory simulation efficiency and is easy for the use. Nonetheless, its application range and simulation accuracy are restricted by the effective zone and modeling accuracy of the moment-based HPM. In this study, a novel correlation distortion simulation method using moment-based piecewise HPM (PHPM) is proposed, where the formulas of the PHPM corresponding to four cases, correlation distortion relationship and simulation scheme are developed. In comparison with the method using HPM, the proposed method has not only wider application range, but also higher simulation accuracy. To demonstrate the effectiveness of the proposed method, the simulations of measured univariate and multivariate non-Gaussian wind pressure data are conducted. Results show that for the example of wind pressures out of the monotonic region, which cannot be simulated by the method using HPM, the PDF and PSD of the simulated samples by the proposed method agree with the targets well. For the other examples, the simulation accuracy of the proposed method is overall higher than that of the simulation method using the moment-based HPM. These verify that the proposed method has larger applicability and higher accuracy than the simulation method using the HPM. Note that the proposed method can also be applied to simulate other non-Gaussian excitations such as the wind speed in the complex area. • The simulation of stationary non-Gaussian multivariate wind pressures is addressed. • The Hermite polynomial model (HPM) and piecewise HPM (PHPM) are involved. • A correlation distortion simulation method using moment-based PHPM is proposed. • The proposed method can extend the application range of the method using HPM. • Results show that the proposed method is of higher accuracy than that using HPM. [ABSTRACT FROM AUTHOR]

The study of thunderstorm outflows and their actions on structures is a dominant topic of wind engineering. This paper contributes to knowledge in this field through a systematic analysis of the thunderstorm outflows recorded by 9 anemometers installed at different heights along the 325 m high Beijing Meteorological Tower. Analyses are carried out through a directional decomposition strategy. The characteristics of a wide dataset of thunderstorm signals are analysed in a statistical environment coherent with the traditional one for synoptic winds; in this stage of the research, estimations of the mean wind speed and direction, the turbulence parameters and gust factor are carried out considering the records as disjoint from each other. Based on this choice, the results are compared with those obtained in the northern Mediterranean, providing a preliminary answer to the crucial question whether thunderstorms have similar properties in different areas, or, better, what of their properties are similar and what depend on the area itself. Besides to previous researches, the time evolution of the wind speed and direction profile is investigated with reference to a thunderstorm test case event. The systematic analysis of the full dataset of thunderstorm outflow fields is postponed to future researches. • A series of thunderstorm outflow records are detected in Beijing urban area. • A novel directional decomposition strategy recently formulated is implemented. • The characteristics of a wide dataset of thunderstorm signals are analysed in a statistical environment. • The results are compared with those obtained in the northern Mediterranean preliminarily. • The time evolution of the wind speed and direction profile is investigated condidering a test case. [ABSTRACT FROM AUTHOR]