Wind Speed Time Series Research Articles

Experimental study on the effect of drop size in rain erosion test and on lifetime prediction of wind turbine blades

Rain erosion of turbine blades causes loss of production and expensive repairs in the wind energy sector. There is a common consensus, that the size of impacting rain drops has a governing effect on the added damage. However, the literature lacks systematic experimental studies on the topic. In the present paper the effects of drop sizes in Rain Erosion Tests (RET) are studied for a commercial polyurethane based top coat applied to glass fiber-epoxy specimens. The tests are conducted applying a whirling arm RET at impact velocities ranging from 90 to 150 m/s and with four different rain field setups generating mean droplet diameters of 0.76 mm, 1.90 mm, 2.38 mm and 3.50 mm respectively. The time to damage at the end of incubation is determined by inspection of photographs captured inline at regular intervals through the test.Data sets of time to damage as function of local rotor velocity are extracted for each of the four different rain fields. From this data VQ curves (V for Velocity, Q for Quantity) are presented for different representations of quantity, namely specific impacts (DNV GL), specific impacts (ASTM) and accumulated impingement. The slope of the velocity-impingement (VH) curves vary with drop size.We propose a drop size dependent empirical model for impingement (H) to damage as function of impact velocity (v), H (v) = cv−m, where the scale parameter m and the shift parameter c are functions of the drop size.The drop size-dependent impingement model is then applied for computing the expected leading edge lifetime of a virtual 15 MW IEA reference turbine at 18 different meteorological stations in Northern Europe based on 10-min time series of rain intensity and wind speeds observed during several years. The drop size dependent model predicted on average 2.35 times longer lifetime compared to models based on the standard 2.38 mm drop size. Comparing the sites, the model shows a factor 3 variation of added damage per meter of accumulated rain between sites because of differences in concurrent wind during the rain events.

Matérn process-based simulation of wind speed time series

This work proposes a practical methodological framework for simulating long-term discrete wind speed time series, for applications to renewable energy systems. The framework is based on the Matérn stochastic process, that is used to identify and extract the short and long-term periodic components present in the mean and variance of a wind speed time series, and to characterize the residual random component. The procedure of construction of synthetic wind speed time series is developed, rooted in a fast numerical simulation algorithm for the Matérn process. We empirically validate the usefulness of the proposed framework by numerical experiments based on three real historical wind speed datasets. The results show consistent statistical similarities between the historical and simulated wind speed time series of data.

Hourly day ahead wind speed forecasting based on a hybrid model of EEMD, CNN-Bi-LSTM embedded with GA optimization

In this paper, a novel hybrid model of decomposition and deep learning embedded with GA optimization was proposed to forecast 24-hour ahead wind speed. The historical wind speed time series was pre-processed and then decomposed into intrinsic mode functions (IMFs) using Ensemble Empirical Mode Decomposition. Each IMFs then was trained and tested through a models of CNN-Bidirectional LSTM model. The hyperparameters of the hybrid CNN-Bi-LSTM model was optimized using GA. CNN can extract the internal characteristics of the time series directly meanwhile Bi-LSTM network can utilize the information in both forward and backward directions completely. The forecasting results of each IMFs were reconstructed to obtain the final forecast. The proposed method was applied to real WS dataset in Hanoi compared with 6 other methods. The result shows that the proposed method has demonstrated much better performance than the other methods.

Interval deep learning architecture with rough pattern recognition and fuzzy inference for short-term wind speed forecasting

In recent decades, wind power is rapidly becoming a significant energy resource due to environmental considerations. The accuracy of wind energy forecasts is closely dependent on the prediction of wind speed time series. In this paper, a novel solution for ultra-short-term and short-term wind speed forecasting is introduced. The proposed method consists of a novel real-valued Deep Belief Network (DBN) with a new Rough feature extraction layer (RFEL) and a Fuzzy Type II Inference System (FT2IS) for robust supervised regression. To learn meaningful unsupervised features from the underlying wind speed data, real-valued input units are computed to better approximate the wind data distribution compared to the existing deep learning models. The proposed differentiable RFEL can be applied to any neural network to efficiently extract noise invariant features. A Takagi-Sugeno-Kang (TSK) system with interval Gaussian membership functions is employed for the supervised forecasting task. The high generalization capacity of the proposed unsupervised feature learning model incorporated into the robust RFEL and FT2IS leads to accurate predictions for highly varying wind speed time series. Numerical results on the Western Wind Dataset reveal significant performance improvements compared to recently proposed Deep Learning Architectures (DLAs), including the DBN, Stacked Autoencoder (SAE), and hybrid methodologies that leverage backtracking and metaheuristic optimization.

Extreme wind speed ramp events: A measurement-based approach for improving the modelling of ultimate loads for wind turbine design

Ramp events, i.e., significant changes in wind speed in a short time period, have become critically important to end-users. However, only a few studies address their impact on wind turbine loads. To the best of the authors’ knowledge, these results have not yet been validated with measurements. Therefore, this paper aims to investigate the impact of extreme wind speed ramps on ultimate wind turbine loads using eight months of offshore measurements. We also compare the measured loads with simulations following the International Electrotechnical Commission (IEC) extreme turbulence model, in order to improve the modelling of ultimate loads. This is because events with a 10-min horizontal wind speed standard deviation higher than the prescribed IEC turbulence class, in line with other research, are primarily associated with ramp events. They are found to be design driving for the blade root flap-wise moments below and beyond rated wind speed, but not in the transition region. The high-frequency analysis of these moments showed a sudden pitch transition from the inactive to the active region. In general, the loads associated with ramp events did not exceed the simulations. In addition, non-ramp related extreme loads around rated wind speed, which exceeded the simulations, were associated with standard deviations slightly above the normal turbulence model (NTM) of IEC for a waked turbine, indicating the impact of wake added turbulence. In conclusion, for the ultimate load analysis, the wind speed time series should include a sudden pitch transition from the inactive to the active region in addition to wake added turbulence.

A one-year long turbulence simulation using a WRF-LES based modeling system at Østerild

Here we show an intercomparison between turbulence simulations and measurements performed at a 250-m tall meteorological mast located at the wind turbine test center of Østerild in northern Denmark. The Weather Research and Forecasting model is used to perform the simulations. We use the model’s nesting capability to downscale the forcing reanalysis down to sub-kilometer domains, which are run in a large-eddy simulation fashion, thus being able to partly resolve turbulence. We find that the simulations are able to reproduce accurately the wind climatology of the site when compared to the observations at the mast. The wind power densities derived from the time series of 10-min mean wind speeds from simulations and observations differ by less than 2%. The observed turbulence conditions, in the form of histograms of the wind speed standard deviation, seem fairly well reproduced by the simulations, although there seems to be a tendency of lower simulated turbulence levels. This is also partly reflected when studying the behavior of the turbulence intensity with increasing mean wind speeds. Spectral analysis on the mean wind speed time series reveals that the simulations are able to reproduce the energy content of the observations rather well, but we find a slight larger variability of simulated winds at frequencies larger than 1 h.

Wind Speed Forecasting Using Attention-Based Causal Convolutional Network and Wind Energy Conversion

As one of the effective renewable energy sources, wind energy has received attention because it is sustainable energy. Accurate wind speed forecasting can pave the way to the goal of sustainable development. However, current methods ignore the temporal characteristics of wind speed, which leads to inaccurate forecasting results. In this paper, we propose a novel SSA-CCN-ATT model to forecast the wind speed. Specifically, singular spectrum analysis (SSA) is first applied to decompose the original wind speed into several sub-signals. Secondly, we build a new deep learning CNN-ATT model that combines causal convolutional network (CNN) and attention mechanism (ATT). The causal convolutional network is used to extract the information in the wind speed time series. After that, the attention mechanism is employed to focus on the important information. Finally, a fully connected neural network layer is employed to get wind speed forecasting results. Three experiments on four datasets show that the proposed model performs better than other comparative models. Compared with different comparative models, the maximum improvement percentages of MAPE reaches up to 26.279%, and the minimum is 5.7210%. Moreover, a wind energy conversion curve was established by simulating historical wind speed data.

A short-term wind speed prediction method based on interval type 2 fuzzy model considering the selection of important input variables

Wind speed prediction is a challenging task in wind energy resource prediction. Wind speed time series, which are a highly variable data source, require highly nonlinear time characteristics for prediction. Most prediction models use hybrid models to improve prediction accuracy, which increases the complexity of the model. This paper proposes an interval type-2 (IT2) wind speed prediction model based on the selection of important input variables. First, the two-stage fuzzy curve are used to determine the model input so as to reduce redundant input and further reduce the complexity of the model. Second, a Gaussian membership function with a variable centre is used to further improve the prediction accuracy of the model. Finally, the recursive least squares method is used to complete the identification of the conclusion parameters. The proposed model has high generalisability and IT2 robustness, and can thus realise deterministic wind speed prediction. The wind speed prediction results for a wind farm in Colorado, USA, in two time periods show that compared with the type-1 fuzzy model and other type-2 fuzzy models, the proposed model provides more accurate short-term wind speed forecasting.

The annual rate of independent events – A key interpretation for traditional extreme value distributions of wind velocity

AbstractThe extreme value theory has been object of engineering studies for more than a century. The analysis of extreme winds plays a key role for complex civil structures and a driving role in different stages of wind turbines lifetime. Most of extremes probability models depend on the annual rate of independent events (ARIE) which has been traditionally considered a constant value. The authors have embraced a recent belief considering the ARIE as a function of the wind velocity. Even though a certain agreement has been achieved across the researches, some issues are still pending. In this regard, the paper shows that the annual, seasonal and daily fluctuations embedded in time series of the mean wind speeds, constrain its probability distribution and time correlation to be physically consistent. Besides, a new physical interpretation of the ARIE is presented, expressing how the independence across wind observations increases with the wind speed, up to the point that all yearly observations are independent if larger than a suitable speed value. Such a tendency is not revealed if the annual, seasonal and daily fluctuations are excluded by the analysis, leading to a deceitful shape of the ARIE. Finally, the paper shows how the velocity‐dependent ARIE model is consistent with the conventional asymptotic extreme value theory, if a sufficiently large left‐censorship applies to the dataset. The study of the ARIE presented in this paper is based on long‐term Monte Carlo simulation of the mean wind speed.

Wind and Sea Breeze Characteristics for the Offshore Wind Farms in the Central Coastal Area of Taiwan

Renewable energy is crucial for achieving net zero emissions. Taiwan has abundant wind resources and most major wind farms are offshore over the Taiwan Strait due to a lack of space on land. A thorough study that includes time series modeling of wind speed and sea breeze identification and evaluation for Taiwan’s offshore wind farms was conducted. The time series modeling identified two periodic (annual and diurnal) components and an autoregressive model for multiple-year wind speed time series. A new method for sea breeze type identification and magnitude evaluation is proposed. The method (named as EACH) utilizes a vector and an ellipse to represent the wind condition of a day. Verification of the type identification determined by the new method in two cases of different seasons has been conducted by using surface weather charts and wind data measured by lidar. It is a concise, effective, and programmable way to filter a number of dates for type identification and speed change precursor of sea breeze. We found that the typical daily wind power production of corkscrew sea breeze in Central Taiwan is more than 33 times that of pure sea breeze and more than 9 times that of backdoor sea breeze, which highlights the impact of sea breeze types on wind power.

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.

Data‐driven generation of synthetic wind speeds: A comparative study

The increasing sophistication of wind turbine design and control generates a need for high-quality wind data. The relatively limited set of available measured wind data may be extended with computer generated data, for example, to make reliable statistical studies of energy production and mechanical loads. Here, a data-driven model for the generation of surrogate wind speeds is compared with two state-of-the-art time series models that can capture the probability distribution and the autocorrelation of the target wind data. The proposed model, based on the phase-randomised Fourier transform, can generate wind speed time series that possess the power spectral density of the target data and converge to their generally non-Gaussian probability distribution with an arbitrary, user-defined precision. The model performance is benchmarked in terms of probability distribution, power spectral density, autocorrelation, and nonstationarities such as the diurnal and seasonal variations of the target data. Comparisons show that the proposed model can outperform the selected models in reproducing the statistical descriptors of the input datasets and is able to capture the nonstationary diurnal and seasonal variations of the wind speed.

Wind Speed Prediction for Offshore Sites Using a Clockwork Recurrent Network

Offshore sites show greater potential for wind energy utilization than most onshore sites. When planning an offshore wind power farm, the speed of offshore wind is used to estimate various operation parameters, such as the power output, extreme wind load, and fatigue load. Accurate speed prediction is crucial to the running of wind power farms and the security of smart grids. Unlike onshore wind, offshore wind has the characteristics of random, intermittent, and chaotic, which will cause the time series of wind speeds to have strong nonlinearity. It will bring greater difficulties to offshore wind speed predictions, which traditional recurrent neural networks cannot deal with for lacking in long-term dependency. An offshore wind speed prediction method is proposed by using a clockwork recurrent network (CWRNN). In a CWRNN model, the hidden layer is subdivided into several parts and each part is allocated a different clock speed. Under the mechanism, the long-term dependency of the recurrent neural network can be easily addressed, which can furthermore effectively solve the problem of strong nonlinearity in offshore speed winds. The experiments are performed by using the actual data of two different offshore sites located in the Caribbean Sea and one onshore site located in the interior of the United States, to verify the performance of the model. The results show that the prediction model achieves significant accuracy improvement.

Wind speed forecasting based on model selection, fuzzy cluster, and multi-objective algorithm and wind energy simulation by Betz's theory

Wind energy is of increasing interest to wind farm administrators as a clean and renewable energy source. Accurate wind speed forecasting and effective wind energy simulation can increase the capability of wind power combined with a grid and decrease the operating cost of wind farms. However, many previous studies have been restricted to wind speed forecasting, ignoring wind energy simulations. Thus, grid management cannot effectively estimate the power production of wind farms and leads to an increase in the abandonment wind rate in wind farms. In this study, a wind farm auxiliary management system is developed, which includes two modules: wind speed forecasting and wind energy simulation. In the wind speed forecasting module, first, a data mining algorithm is used to analyze different features of wind speed time series data in a wind farm. Subsequently, a feature selection algorithm is used to determine the representative wind speed time series of the wind farm, and it is combined with a data preprocessing method to effectively eliminate the noise of the original wind speed time series. Second, six hybrid neural network forecasting models based on a modified multi-objective algorithm are established to forecast wind speed. Finally, they are combined with a model selection strategy to yield the best forecasting value for each time point. In the wind energy simulation module, using Betz's theory, the physical transformation process of a wind turbine is estimated to determine the range of wind power generation.

Generating long-term sub-hourly wind speed time series by coupling mesoscale models with full-scale spectra

Time series-based wind studies are gaining more and more interest and importance within the wind resource community. Benefits of such time series-based analysis being, among others, more accurate production and loss estimations, better representation of ramp-up and extreme events, and improvements in the dimensioning of energy storage systems. While mesoscale Numerical Weather Prediction (NWP) models can simulate long-term winds that capture non-stationary weather patterns, it is known that they are not able to properly resolve sub-scale processes leading to a smoothing effect. Recent studies presented spectral models that describe the full atmospheric spectrum of boundary layer winds which connect the microscale turbulent movements to the large, mesoscale fluctuations. In this work, mesoscale simulations from the Weather Research and Forecasting (WRF) model are coupled with stochastic turbulence simulations using state-of-the-art full-scale boundary-layer spectra to efficiently bridge the spectral gap between the mesoscale and the turbulence fluctuations, without requiring any local measurements nor expensive CFD simulations. The study provides a practical step-by-step approach to generate long wind speed time series at high sampling rate at any desired location and height.

Wind speed prediction based on CEEMD-SE and multiple echo state network with Gauss-Markov fusion.

The purpose of this study is to improve the prediction accuracy of wind speed. The wind speed has the characteristics of unstable, non-stationary, and non-linear, so it is difficult to predict the wind speed. This study proposes a prediction model based on the complementary ensemble empirical mode decomposition-sample entropy and multiple echo state network (ESN) with Gauss-Markov fusion for wind speed. The proposed prediction model consists of the following steps: (a) using the complementary ensemble empirical mode decomposition algorithm, it decomposes the initial wind speed time series and obtains some components with different scales, and (b) using the sample entropy algorithm, it determines the complexity of each component. The components whose entropy is larger than the original wind speed remain unchanged, while the components whose entropy is smaller than the original wind speed are merged into one. The reconstructed component greatly reduces the number of prediction models. (c) After reconstruction, the ESN has good regression prediction ability, so it is chosen as the prediction model of each component. The gray wolf optimization algorithm is introduced to optimize the parameters of the ESN. (d) The Gauss-Markov algorithm is adopted to fuse the predicted values of multiple ESN models. The variance of the predicted value obtained using the Gauss-Markov fusion is less than that of the single ESN model, which significantly increases the prediction accuracy. In order to verify the prediction performance of the proposed model, the actual ultra-short-term and short-term wind speed sample data are compared. At the same time, seven prediction models are chosen as the comparison model. Finally, through the comparison of the prediction error and its histogram distribution, eight performance indicators, Pearson's correlation coefficient, and Diebold-Mariano test, all the results show that the proposed prediction model has high prediction accuracy.

Santa Ana Winds: Fractal-Based Analysis in a Semi-Arid Zone of Northern Mexico

A fractal analysis based on the time series of precipitation, temperature, pressure, relative humidity, and wind speed was performed for 16 weather stations located in the hydrographic basin of the Guadalupe River in Baja California, Mexico. Days on which the phenomenon known as Santa Ana winds occurs were identified based on the corresponding criteria of wind speed (≥4.5 m/s) and wind direction (between 0° and 90°). Subsequently, the time series was formed with data representing the days on which this phenomenon occurs in each of the analyzed weather stations. A time series was additionally formed from the days in which the Santa Ana winds condition does not occur. Hurst exponents and fractal dimension were estimated applying the rescaled range method to characterize the established time series in terms of characteristics of persistence, anti-persistence, or randomness along with the calculation of the climate predictability Index. This enabled the behavior and correlation analysis of the meteorological variables associated with Santa Ana winds occurrence. Finally, this type of research study is instrumental in understanding the regional dynamics of the climate in the basin, and allows us to establish a basis for developing models that can forecast the days of occurrence of the Santa Ana winds, in such a way that actions or measures can be taken to mitigate the negative consequences generated when said phenomenon occurs, such as fires and droughts.

On the influence of wind speed model resolution on the global technical wind energy potential

Previous estimations of the global technical onshore wind energy potential (WEP) range between 64 and 690 PWh/yr. The diversity of WEP estimates is caused by various applied wind resource data sets, geographical restrictions, and wind turbine features. So far, little attention was paid to the wind speed model resolution and hub height influence on national and global WEP. Thus, this study's goals were to compare WEP estimates from highly and coarsely resolved wind speed data in 120 and 140 m hub height. The wind resource was assessed based on ERA5 wind speed time series at a horizontal resolution of 0.25° × 0.25° and L-moments from the highly resolved Global Wind Speed Model (GloWiSMo) available at 250 m × 250 m horizontal resolution. Theoretical distributions and a generic 3.3 MW wind turbine power curve were used to estimate the average annual energy yield at a 1000 m × 1000 m global grid. A total of twelve geographical restrictions were specified to exclude inaccessible areas from WEP estimations. Using highly resolved wind speed data at 120 m hub height, global WEP was estimated to be 404 PWh/yr. It decreases by 16.1% (339 PWh/yr) using ERA-5 wind speed data, whereas, at 140 m hub height, WEP increases by 11.8% (452 PWh/yr). The results presented highlight the relevance of spatial resolution of wind speed data for wind resource assessment from local to global scales.

A New Neural Networks Approach Used to Improve Wind Speed Time Series Forecasting

Generally, wind turbines convert the energy of wind into electricity. In this order, it is essential to predict accurately this source’s availability and intensity at the same location and height where wind electric generators will be installed, and therefore obtain reliable time-series data. The problem of meteorological time series prediction can be formulated as a system identification problem. To improve the prediction of these meteorological time series, we describe then use an application of a new neural networks approach in this paper. This novel, robust, and reliable forecasting method is based on the application of a new learning algorithm that allows a renewal of learning data, with time. For our algorithm a neural network is developed to estimate just one value y (t+1), then it is taken up with a new learning set enriched by data freshly measured. The obtained results showed a good agreement between measured and predicted series, and the mean relative error over the whole data set, which are not exceeding 5 %.

Inter-event Times Statistic in Stationary Processes: Nonlinear ARMA Modeling of Wind Speed Time Series

The random sequence of inter-event times of a level-crossing is a statistical tool that can be used to investigate time series from complex phenomena. Typical features of observed series as the skewed distribution and long range correlations are modeled using non linear transformations applied to Gaussian ARMA processes. We investigate the distribution of the inter-event times of the level-crossing events in ARMA processes in function of the probability corresponding to the level. For Gaussian ARMA processes we establish a representation of this indicator, prove its symmetry and that it is invariant with respect to the application of a non linear monotonic transformation. Using simulated series we provide evidence that the symmetry disappears if a non monotonic transformation is applied to an ARMA process. We estimate this indicator in wind speed time series obtained from three different databases. Data analysis provides evidence that the indicator is non symmetric, suggesting that only highly non linear transformations of ARMA processes can be used in modeling. We discuss the possible use of the inter-event times in the prediction task.