Non-stationary Wind Research Articles

Fast simulation of fully non-stationary wind fields using a new matrix factorization assisted interpolation method

The classical spectral representation method and its improved versions have been widely used to generate non-stationary wind fields. However, they are not efficient for the simulation of non-stationary wind fields with time-varying coherence (i.e., fully non-stationary wind fields) due to the extensive computational demand regarding spectral matrix decomposition or harmonic superposition. To this end, this study develops a new matrix factorization assisted interpolation method, which is composed of an improved interpolation scheme and the monotone projected Barzilai-Borwein based non-negative matrix factorization (MPBB-NMF). Specifically, an improved interpolation scheme is firstly developed to quantify the desired distribution of time-frequency interpolation nodes, and only the decomposed spectra at these nodes are calculated by Cholesky decomposition. Then, MPBB-NMF with a fast global convergence is used to decouple these spectra into products of non-negative time- and frequency-dependent functions. Finally, one-dimensional interpolation is performed for these decoupled functions, which makes it possible to accelerate the harmonic superposition using a small amount of fast Fourier transforms (FFTs). Obviously, the proposed method realizes the decomposition of spectral matrix in the decoupling form. Besides reducing of the number of Cholesky decomposition and invoking FFT, its decoupling only aims at the low dimensional matrix and the interpolation merely involves a few decoupled items. Numerical simulation on the fully non-stationary wind field of a long-span bridge demonstrates the effectiveness and superiority of the proposed method.

Modeling Nonstationary Non-Gaussian Hurricane Wind Velocity and Gust Factor

This study estimated the wind gust factor and turbulence intensity for hurricane winds that are modeled as a nonstationary non-Gaussian process. The estimation considered the time-varying mean wind velocity, and the time–frequency decomposition using S-transform characterized the time-varying amplitude and frequency content of the fluctuating wind. Winds simulated by applying a newly developed algorithm were used to augment the sample size. Results indicate that the standardized power spectral density function of hurricane winds can be represented in terms of the reduced frequency but with time-varying mean wind velocity. The standardized fluctuating wind is only weakly non-Gaussian; the average skewness and kurtosis coefficients are not very sensitive to whether the 10-or 60-min time-varying mean wind speed is considered. The assessed relation between the gust factor and turbulence intensity indicates that such a relationship is influenced by whether the instantaneous aspect of the nonstationary process is considered. There is large uncertainty in the developed relationship. By removing samples associated with a low mean wind velocity, the largest value of the estimated gust factor decreases. The consideration of the non-Gaussian aspect of the fluctuating wind to estimate the gust factor could be important as turbulence intensity increases.

Frequency Domain Analysis of Alongwind Response and Study of Wind Loads for Transmission Tower Subjected to Downbursts

Downburst is one of the high-intensity winds that cause transmission tower failures. The regulations of transmission tower-line systems under downburst wind loads cannot meet the design requirements at present. In this paper, the calculation formulas of the background and resonant components of transmission tower under downburst wind loads are obtained, based on the modal analysis theory of non-stationary wind for the single-degree-of-freedom system in the frequency domain. The effects of structural dynamic characteristics, damping ratio, and mean wind speed vertical profile on dynamic effect on structural response are discussed. Then the equivalent static wind load (ESWL) is obtained according to the maximum response and compared with the finite element method (FEM) in the time domain. Applications of these formulas are addressed to the cases from the empirical model of Holmes and field record of a rear flank downdraft (RFD). The results show that the maximum responses obtained by the current formulas match well with those from the modal decomposition method and dynamic analysis with FEM. The internal forces of tower members calculated by ESWL based on maximum response are closer to the results from FEM than those calculated by downburst loads recommended in ASCE guidelines. The presented framework can be used to assist the wind-resistant design of transmission towers considering downburst wind load.

Change point detection-based simulation of nonstationary sub-hourly wind time series

In this paper, we present a wind speed simulation method by detecting change points in multivariate nonstationary wind speed time series data. The change point detection method identifies changes in the covariance structure and decomposes the nonstationary multivariate time series into stationary segments. Parametric and nonparametric techniques are also provided to model and simulate new time series within each stationary segment. The proposed simulation approach retains the statistical properties of the original time series (as obtained from a Numerical Weather Prediction system) and therefore, can be employed for simulation-based analysis of power systems planning and operations problems. We demonstrate the capabilities of the change point detection method through computational experiments conducted on wind speed time series at five-minute resolution. We also conduct experiments on the economic dispatch problem to illustrate the impact of nonstationarity in wind generation on conventional generation and location marginal prices.

Short-time wind power prediction using hybrid kernel extreme learning machine

A hybrid short-time wind power forecasting technique based on variational mode decomposition (VMD) and kernel extreme learning machine (KELM) is proposed in this paper. The non-stationary historical wind data is initially decomposed into various modes using VMD technique, which is subsequently passed through the proposed KELM (Gaussian and wavelet-based) and conventional ELM [without weight optimisation and with optimisation – chaotic firefly optimisation algorithm (CFA)], respectively, in order to predict the 30 minutes and 1 hour ahead wind power, respectively. It is observed that, rather than optimising the arbitrary input layer weights of VMD-ELM technique, the proposed Gaussian-based EMDKELM technique illustrates the most effective and accurate short-time wind power predictions for some diverse seasons. The overall results presented in the simulation (through MATLAB simulation platform) and result section are satisfactory and indicates the proposed Gaussian-based EMD-KELM technique as a highly potential prediction technique for real-time applications in power systems. The proposed model can be tested in the industry having wind generation or wind power plant where this can be applied in order to predict the future wind power to schedule the load profile in efficient manner. This can be also validated through wind test bench system.

Practical Approach to Digitally Simulate Nonsynoptic Wind Velocity Profiles and Its Implications on the Response of Monopole Towers

AbstractIn the numerical simulation of stochastic nonstationary wind loads, rapidly varying wind speed fluctuations about a deterministic mean value typically represent the sole source of random va...

Database of extreme waves generated during the passage of a cold front in Rio Grande do Sul coast, southern Brazil

This datapaper supports the use of a database generated from wavefield simulations with the WAVEWATCH III model in waters off the coast of Rio Grande do Sul in the South Atlantic Ocean. In the WAVEWATCH III simulations, three domains are generated as a part of a numerical experiment to set up the best configuration. This database includes all input and output files for the two best-fit simulations. Bathymetry and wind files at 10 m above the surface are available as input files. The period of simulation and non-stationary wind data input corresponds to March 22-28, 2016. The date was chosen because it is related to the passage of a cold front through the area of interest. The different parameterizations used and with which good results were obtained in the simulations with the model are also described. The WAVEWATCH III output files contain the spatial and temporal distribution of the wavefield in the area of interest, as well as the outputs for point locations consistent with the location of on-site records. For the two best-fit domains, the following variables were obtained: mean wind speed (m s-1), sea-air temperature difference (°C), wave height (m), mean wavelength (m), mean wave period (s), mean wave direction (degrees), mean directional propagation (degrees) and friction velocity (m s-1). All these variables are provided in NetCDF format and will serve as a reference for future wave modeling work in the region, and the results will be able to be compared with those obtained in the database.

Data-driven simulation of multivariate nonstationary wind velocity with explicit introduction of the time-varying coherence functions

An S-transform (ST) based data-driven simulation methodology is proposed of multivariate nonstationary wind velocity with explicit introduction of the time-varying coherence functions. This framework differs from that based on the evolutionary spectral theory that uniform or non-uniform modulation functions needs to be assigned. In defining the time-frequency power spectral density (TFPSD) in accordance with the ST coefficients, the Parseval's theorem is considered to ensure energy preservation. The proposed method is firstly applied to simulated nonstationary wind velocity. For comparisons, the results of the discrete orthonormal S transform (DOST) are also included. The research demonstrates that the ST based results agree well with the original, but a bias exists between the results simulated by DOST and the original, indicating that the feasibility of the proposed method. Employing four measured downburst records to simulate multivariate nonstationary wind velocity by the proposed method further validates its feasibility. In order to enhance the simulation fidelity, the inverse S-transform (IST) based regulation and control method (IST-RCM) of high precision is developed, where the balancing parameter is defined and formulated to express quantitatively the relationship between accuracy and efficiency. It is found that the results with IST-RCM get more close to the actual wind velocity records.

A novel hybrid approach based on variational heteroscedastic Gaussian process regression for multi-step ahead wind speed forecasting

Accurate wind speed forecasting is the key to safe and economic operation of electric power and energy systems. As a Bayesian nonparametric method, Gaussian process regression (GPR) has provided competitive forecasting results in recent years. However, conventional GPR model assumes that the noise obeys Gaussian distribution and the variance of the noise in the whole data set is a constant, which is not appropriate for some problems. Motivated by this, this study makes the first attempt to study the ability of the variational heteroscedastic GPR (VHGPR) model in wind speed forecasting. The Marginalized Variational (MV) approximation is employed to approximate the heteroscedastic Gaussian process in the VHGPR model. What’s more, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is employed to transfer the nonstationary wind speed series into a certain number of subseries with more regularity such that the forecasting performance of VHGPR can be enhanced. The proposed method is compared with the other twelve benchmark models for 1- to 3- step ahead wind speed forecasting. Experiments results on four real-world datasets demonstrate that VHGPR with the CEEMDAN decomposition strategy is able to obtain better forecasting results for wind speed time series.

Fast simulation of nonstationary wind velocity fields by proper orthogonal decomposition interpolation

The classic spectral representation method (SRM) has been extensively utilized in simulating nonstationary wind velocity fields because of its great precision and simplicity. However, this method is computationally expensive when it is applied to the situation with a great number of simulation positions. Although several attempts have been made to alleviate the computational burden, the efficiency improvement may be limited. To this end, this study develops a proper orthogonal decomposition (POD) interpolation-enhanced approach to expedite the simulation of nonstationary wind fields by SRM. Firstly, the Cholesky decomposition is only executed for the spectral matrices at time-frequency interpolation points and the corresponding computational cost is therefore reduced greatly. The POD interpolation is then used to approximately decouple each decomposed spectrum into a small amount of time and frequency functions, thereby invoking the fast Fourier transform to speed up the summing of cosine functions. The POD operation is merely executed for the low-dimensional matrix and the interpolation operation solely aims at the few time and frequency functions. As a result, the proposed approach has the potential to considerably improve the simulation efficiency. Finally, numerical examples with the simulation of two different nonstationary wind fields validate its precision and efficiency.

A Combined Forecasting System Based on Modified Multi-Objective Optimization for Short-Term Wind Speed and Wind Power Forecasting

Wind speed and wind power are two important indexes for wind farms. Accurate wind speed and power forecasting can help to improve wind farm management and increase the contribution of wind power to the grid. However, nonlinear and non-stationary wind speed and wind power can influence the forecasting performance of different models. To improve forecasting accuracy and overcome the influence of the original time series on the model, a forecasting system that can effectively forecast wind speed and wind power based on a data pre-processing strategy, a modified multi-objective optimization algorithm, a multiple single forecasting model, and a combined model is developed in this study. A data pre-processing strategy was implemented to determine the wind speed and wind power time series trends and to reduce interference from noise. Multiple artificial neural network forecasting models were used to forecast wind speed and wind power and construct a combined model. To obtain accurate and stable forecasting results, the multi-objective optimization algorithm was employed to optimize the weight of the combined model. As a case study, the developed forecasting system was used to forecast the wind speed and wind power over 10 min from four different sites. The point forecasting and interval forecasting results revealed that the developed forecasting system exceeds all other models with respect to forecasting precision and stability. Thus, the developed system is extremely useful for enhancing forecasting precision and is a reasonable and valid tool for use in intelligent grid programming.

A Nonstationary Wind Speed Frequency Model over South Korea: In the Context of Bayesian Mixture Distribution Model

Kim, H.-J.; Kim, Y.-T.; Kim, T.-W., and Kwon, H.-H., 2021. A nonstationary wind speed frequency model over South Korea: In the context of Bayesian mixture distribution model. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 196–200. Coconut Creek (Florida), ISSN 0749-0208. It has been well recognized that extreme wind speed processes often feature nonstationary behavior, which may not be effectively modeled within a stationary frequency modeling framework. Furthermore, univariate Gumbel distribution has been commonly used for wind speed frequency analysis in Korea. However, the distributional changes in extreme wind speed have been globally observed, including Korea. More specifically, the univariate Gumbel distribution based wind speed frequency analysis often failed to describe multimodal behaviors which are mainly influenced by distinct climate conditions. In this perspective, this study explores a mixture distribution based nonstationary frequency (MDNF) model in a changing climate within a Bayesian framework. It was found that the MDNF model can effectively account for the time-varying moments (i.e., mean and variance) in a two-component mixture distribution. The MDNF model showed more robust results for describing the upper tail of the distribution, which plays a crucial role in estimating the design wind speed.

Inelastic Response of Base-Isolated Tall Buildings under Nonstationary Winds: Response History Analysis and Statistical Linearization Approach

AbstractThis study investigated uncoupled crosswind and alongwind responses of base-isolated tall buildings under nonstationary wind excitations. The base isolation system has hysteretic restoring ...

A data-driven deep sequence-to-sequence long-short memory method along with a gated recurrent neural network for wind power forecasting

Large amounts of wind power generation have an impact not only on energy markets but also on wholesale and retail market designs. Simultaneously, technological issues arise as a result of the need to ensure the smooth operation of the power grid. In the long run, high-quality wind data series are required to generate model results that lead to robust policy advice. Several techniques are commonly used to forecast short-term, nonstationary, and nonlinear wind speeds. These techniques are lacking in model optimization and data processing abilities. This lack of expertise is posing a significant challenge for reliable and stable wind power forecasting. Furthermore, the medium and long-term forecasting criteria for models (e.g., robustness, accuracy, speed) are higher, making it difficult to obtain reliable results. This study used the extended deep sequence-to-sequence long short-term memory regression (STSR-LSTM) model for time-series wind power forecasting to overcome these challenges for accurate forecasting decisions. The statistical-learning technique is used to improve the dependability of the derived features as well as the expected performance. Three different locations/sites were used and analyzed (e.g., Belgian, DSO-Connected, Elia), three different forecasting classes (e.g., week-ahead forecast, day-ahead forecast, and most recent forecast), three different seasons (e.g., monthly, seasonal, and annual), and three experimental setups. The performance of Deep STSR-LSTM is validated using two existing models and four performance evaluation indexes. Despite the fact that the input wind power load curve had a lot of variation, we were able to achieve higher forecast accuracy. Using different sites and classes, similar forecast accuracy was achieved for each season (e.g., monthly, seasonal, and annual).

Wind effects on large cooling tower in velocity fields of different non-stationary levels

The non-stationarity of the realistic wind velocity field strongly influences wind effects on structures. Related research studies are limited, as it is difficult to simulate non-stationary wind velocity fields using wind tunnel model tests and numerical simulations. In this paper, wind effects on a 167 m high cooling tower in velocity fields of different non-stationary levels are investigated based on a full-scale measurement campaign. First, numerical analyses are employed to identify the quasi-steady region on circular cylindrical structures. Second, wind velocity samples are extracted from the full-scale wind pressure samples measured in the quasi-steady region on the actual large cooling tower, and quantitatively evaluated for their non-stationary levels using a wavelet packet transform-based indicator. Third, effects of the non-stationarity of the velocity field on wind loads on the large cooling tower are revealed based on different non-stationarity cases. Through these works, it is found that the non-stationary level of the wind velocity field largely influences wind effects on the cooling tower in most cases, and the wind effects obtained in a velocity field of high non-stationarity are generally more unfavourable than those obtained in a velocity field of low non-stationarity.

A Short-Term Wind Powerprediction Model Based on CEEMD and WOA-KELM

Effective short-term wind power prediction is crucial to the optimal dispatching, stability, and operation cost control of a power system. In order to deal with the intermittent and fluctuating characteristics of wind power timing series signals, a hybrid forecasting model is proposed, based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Whale Optimization Algorithm (WOA)- Kernel Extreme Learning Machine (KELM), to predict short-term wind power. Firstly, the non-stationary wind power time series is decomposed into a series of relatively stationary components by CEEMD. Then, the components are used as the training set for the KELM prediction model, in which the initial values and thresholds are optimized by WOA. Finally, the predicted output values of each component are superimposed, to obtain the final prediction of the wind power values. The experimental results show that the proposed prediction method can reduce the complexity of the prediction with a small reconstruction error. Furthermore, performance is greater, in terms of prediction accuracy and stability, with lower computational cost than other benchmark models.

Simulating nonstationary non-Gaussian vector process based on continuous wavelet transform

In the present study, we propose a new iterative algorithm based on discretized continuous wavelet transform (CWT) to simulate nonstationary non-Gaussian vector process for prescribed marginal probability distribution functions and the time-scale power spectral density function matrix. The algorithm is designed for a CWT pair within the wavelet analysis framework. It can be used with analytical wavelets satisfying the admissibility condition and can cope with time-dependent and time-independent coherence. The proposed algorithm is applied to generate nonstationary downburst winds and seismic ground motions at multiple sites within the wavelet analysis framework. The numerical validation analysis confirms that the proposed algorithm can lead to the simulated records matching the prescribed marginal probability distribution, marginal time-scale power spectral density function and coherence function.

Extracting Time-Varying Mean Component of Non-Stationary Winds Utilizing Vondrak Filter and Genetic Algorithm: A Wind Engineering Perspective

The time-varying mean (TVM) component plays a vital role in the characterization of non-stationary winds, whereas it is difficult to extract the TVM accurately or to validate it quantitively. To deal with this problem, this paper first develops two additional conditions for the TVM extraction from the perspective of structural wind-induced vibration response, then presents an approach, based on the combination of Vondrak filter and genetic algorithm (Vondrak-G), to derive the optimal TVM from non-stationary wind speed records as well as its turbulence characteristics (i.e. gust factor, turbulence intensity, and turbulence integral length scale). Furthermore, the wind characteristics obtained by the Vondrak-G approach are compared with those by a conventional approach derived for stationary winds, demonstrating that the results by the Vondrak-G approach are evidently more accurate. This paper aims to provide an effective method for accurately extracting the TVM and then evaluating wind characteristics of the non-stationary wind.

Field measurement study of wind characteristics at different measuring positions along a bridge in a mountain valley

Wind field characteristics were investigated with a non-stationary wind speed model at a bridge in a mountain valley, where five anemometers were set along the bridge span for long-term field measurements. The mean wind characteristics, such as mean wind speed and direction, were studied with a traditional steady wind-speed model, which showed the significant influence of the terrain on the mean wind characteristics. In addition, data for certain strong-wind weather conditions were measured, and the non-stationary analysis of strong wind samples were conducted using the run test and wavelet analysis. Subsequently, the characteristics of the fluctuating wind—time-varying mean wind speed, turbulence intensity, turbulence integral scale, and power spectral density (PSD)—were compared based on the two wind speed models. The comparative results indicated that the mountain valley wind field was substantially affected by the terrain and exhibited a strong non-stationarity. However, there were certain differences between the wind field characteristics of the two wind speed models, and the recommended value was not completely consistent for the field measurements in the current specification.

Fast simulation of large-scale non-stationary wind velocities based on adaptive interpolation reconstruction scheme

Fast simulation of large-scale non-stationary wind velocities based on adaptive interpolation reconstruction scheme