scholarly journals The Compound Inverse Rayleigh as an Extreme Wind Speed Distribution and its Bayes Estimation

2021 ◽  
Author(s):  
Elio Chiodo ◽  
Maurizio Fantauzzi ◽  
Giovanni Mazzanti
Keyword(s):  
Wind Speed ◽  
Prior Information ◽  
Extreme Values ◽  
Rayleigh Distribution ◽  
Bayes Estimation ◽  
Large Set ◽  
Wind Speeds ◽  
Extreme Wind ◽  
Extreme Wind Speed ◽  
Extreme Wind Speeds

The paper deals with the Compound Inverse Rayleigh distribution, shown to constitute a proper model for the characterization of the probability distribution of extreme values of wind-speed, a topic which is gaining growing interest in the field of renewable generation assessment, both in view of wind power production evaluation and the wind-tower mechanical reliability and safety. The first part of the paper illustrates such model starting from its origin as a generalization of the Inverse Rayleigh model - already proven to be a valid model for extreme wind-speeds - by means of a continuous mixture generated by a Gamma distribution on the scale parameter, which gives rise to its name. Moreover, its validity to interpret different field data is illustrated, also by means of numerous numerical examples based upon real wind speed measurements. Then, a novel Bayes approach for the estimation of such extreme wind-speed model is proposed. The method relies upon the assessment of prior information in a practical way, that should be easily available to system engineers. In practice, the method allows to express one’s prior beliefs both in terms of parameters, as customary, and/or in terms of probabilities. The results of a large set of numerical simulations – using typical values of wind-speed parameters - are reported to illustrate the efficiency and the accuracy of the proposed method. The validity of the approach is also verified in terms of its robustness with respect to significant differences compared to the assumed prior information.

scholarly journals Stochastic Extreme Wind Speed Modeling and Bayes Estimation under the Inverse Rayleigh Distribution

2020 ◽  
Vol 10 (16) ◽  
pp. 5643
Author(s):  
Elio Chiodo ◽  
Luigi Pio Di Noia
Keyword(s):  
Wind Speed ◽  
Prior Information ◽  
Extreme Values ◽  
Risk Index ◽  
Rayleigh Distribution ◽  
Bayes Estimation ◽  
Large Set ◽  
Extreme Wind ◽  
System Engineer ◽  
Extreme Wind Speed

Inverse Rayleigh probability distribution is shown in this paper to constitute a valid model for characterization and estimation of extreme values of wind speed, thus constituting a useful tool of wind power production evaluation and mechanical safety of installations. The first part of this paper illustrates such a model and its validity to interpret real wind speed field data. The inverse Rayleigh model is then adopted as the parent distribution for assessment of a dynamical “risk index” defined in terms of a stochastic Poisson process, based upon crossing a given value with part of the maximum value of wind speed on a certain time horizon. Then, a novel Bayes approach for the estimation of such an index under the above model is proposed. The method is based upon assessment of prior information in a novel way which should be easily feasible for a system engineer, being based upon a model quantile (e.g., the median value) or, alternatively, on the probability that the wind speed is greater than a given value. The results of a large set of numerical simulation—based upon typical values of wind-speed parameters—are reported to illustrate the efficiency and the precision of the proposed method, also with hints to its robustness. The validity of the approach is also verified with respect to the two different methods of assessing the prior information.

scholarly journals Extreme wind speed prediction in mountainous area with mixed wind climates

2022 ◽  
Author(s):  
Teng Ma ◽  
Wei Cui ◽  
Lin Zhao ◽  
Yejun Ding ◽  
Genshen Fang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Mountainous Area ◽  
Construction Site ◽  
Wind System ◽  
Mountainous Areas ◽  
Wind Speeds ◽  
Extreme Wind ◽  
Synoptic Wind ◽  
Extreme Wind Speed ◽  
Extreme Wind Speeds

Abstract In addition to common synoptic wind system, the mountainous terrain forms a local thermally driven wind system, which makes the mountain wind system have strong terrain dependence. Therefore, in order to estimate the reliable design wind speeds for structural safety, the samples for extreme wind speeds for certain return periods at mountainous areas can only come from field measurements at construction site. However, wind speeds measuring duration is usually short in real practice. This work proposes a novel method for calculating extreme wind speeds in mountainous areas by using short-term field measurement data and long-term nearby meteorological observatory data. Extreme wind speeds in mountainous area are affected by mixed climates composed by local-scale wind and large scale synoptic wind. The local winds can be recorded at construction site with short observatory time, while the extreme wind speeds samples from synoptic wind climate from nearby meteorological station with long observatory time is extracted for data augmentation. The bridge construction site at Hengduan Mountains in southwestern China is taken as an example in this study. A 10-month dataset of field measurement wind speeds is recorded at this location. This study firstly provides a new method to extract wind speed time series of windstorms. Based on the different windstorm features, the local and synoptic winds are separated. Next, the synoptic wind speeds from nearby meteorological stations are converted and combined with local winds to derive the extreme wind speeds probability distribution function. The calculation results shows that the extreme wind speed in the short return period is controlled by the local wind system, and the long-period extreme wind speed is determined by the synoptic wind system in the mountain area.

Extreme wind speed climatology over Greece

2021 ◽  
Author(s):  
Georgios Blougouras ◽  
Chris G. Tzanis ◽  
Kostas Philippopoulos
Keyword(s):  
Wind Speed ◽  
Confidence Intervals ◽  
Wind Turbines ◽  
Economic Losses ◽  
Return Periods ◽  
Wind Speeds ◽  
Return Levels ◽  
Extreme Wind ◽  
Extreme Wind Speed ◽  
Extreme Wind Speeds

<p>Extreme wind speeds are a multifaceted environmental risk. They may cause considerable damage to infrastructure (e.g., bridges, private property), they can jeopardize maritime and aviation activities, and sometimes even human safety. Furthermore, the design of wind turbines for on and off-shore wind farms requires a study of the return periods of extreme wind speeds in combination with the lifespan of the wind turbines. Windstorms also result in major economic losses and cause up to 80 % of the natural hazards' long term insurance loss in Europe. The scope of this work is to identify location-specific extreme wind speed thresholds and obtain accurate estimates of exceedances for multiple future horizons. In this context, the Extreme Value Analysis framework is used for providing the return periods and the respective return levels of extreme wind speeds. The Peaks Over Threshold method is utilized for the 10 m wind speed for a domain centered over Greece, in Southeastern Mediterranean. Wind speed data at 10 m are extracted from the ERA5 reanalysis dataset that provides hourly estimates of surface wind speed with a horizontal resolution of 0.25°x0.25°, from 1979/01/01 up to 2019/12/31 (i.e., 41 years). The thresholds are selected using the Mean Residual Life plots, which is the most reliable method for identifying accurate threshold values. The seasonal analysis of the exceedances is discussed in terms of the physical mechanisms in the region. The exceedances are modelled using the Generalized Pareto Distribution, whose shape and scale parameters (<em>ξ</em> and <em>σ</em>, respectively) are estimated using the Maximum Likelihood Estimation method. The return levels and their confidence intervals are estimated for return periods up to 100 years. Geographic Information Systems are used for mapping future projections of extreme wind speeds and the corresponding confidence intervals. The results are discussed in terms of identifying high-risk areas and the findings could assist in informed decision-making in the wind energy industry. The proposed methodological framework could be extended to other areas characterized by particularly high wind speeds and the results can contribute towards sustainable investments and support adaptation mechanisms.</p>

scholarly journals Non-stationary statistical estimation of wind speeds of rare recurrence in the Azov and Black Sea region

2021 ◽  
pp. 15-22
Author(s):  
V.P. Evstigneev ◽  
◽  
V.A. Naumova ◽  
N.A. Lemeshko ◽  
◽  
...  
Keyword(s):  
Wind Speed ◽  
Black Sea ◽  
Statistical Estimation ◽  
Extreme Values ◽  
Distribution Functions ◽  
Statistical Distribution ◽  
Black Sea Region ◽  
Wind Speeds ◽  
Extreme Wind ◽  
Extreme Wind Speeds

In the paper statistical distribution of the highest wind speed per year in the Azov and Black Sea region was analyzed using the data of 33 meteorological stations for 1958-2013. A statistical estimation of the wind speed extremes was carried out by approximation of the empirical sample with a function of Generalized distribution of Extreme Values (GEV) and by extrapolating it to the low probabilities region. We used two methodologies and applied statistical distribution functions corresponding to them. The first method is based on the assumption of stationarity of parameters of the GEV function. The second one is based on the non-stationary assumption of time dependence of extremum localization parameter μ. It was found, that for 13 out of 33 stations of the region, non-stationary GEV-function turned out to be adequate to describe extreme wind speeds.

scholarly journals At What Time of Day Do Daily Extreme Near-Surface Wind Speeds Occur?

2014 ◽  
Vol 27 (11) ◽  
pp. 4226-4244 ◽  
Author(s):  
Robert Fajber ◽  
Adam H. Monahan ◽  
William J. Merryfield
Keyword(s):  
Extreme Values ◽  
Mechanistic Model ◽  
Surface Wind ◽  
Time Of Day ◽  
Near Surface ◽  
Wind Speeds ◽  
Boundary Layer Processes ◽  
Early Afternoon ◽  
Extreme Wind ◽  
Extreme Wind Speeds

Abstract The timing of daily extreme wind speeds from 10 to 200 m is considered using 11 yr of 10-min averaged data from the 213-m tower at Cabauw, the Netherlands. This analysis is complicated by the tendency of autocorrelated time series to take their extreme values near the beginning or end of a fixed window in time, even when the series is stationary. It is demonstrated that a simple averaging procedure using different base times to define the day effectively suppresses this “edge effect” and enhances the intrinsic nonstationarity associated with diurnal variations in boundary layer processes. It is found that daily extreme wind speeds at 10 m are most likely in the early afternoon, whereas those at 200 m are most likely in between midnight and sunrise. An analysis of the joint distribution of the timing of extremes at these two altitudes indicates the presence of two regimes: one in which the timing is synchronized between these two layers, and the other in which the occurrence of extremes is asynchronous. These results are interpreted physically using an idealized mechanistic model of the surface layer momentum budget.

scholarly journals An Improved Typhoon Simulation Method Based On Latin Hypercube Sampling Method

2021 ◽  
Author(s):  
Jianpeng Sun ◽  
Guanjun Lv ◽  
Wenfeng Huang ◽  
Rong Wang ◽  
Xiaogang Ma
Keyword(s):  
Sampling Method ◽  
Correlation Coefficients ◽  
Latin Hypercube Sampling ◽  
Simulation Method ◽  
Return Periods ◽  
Latin Hypercube ◽  
Wind Speeds ◽  
Extreme Wind ◽  
Extreme Wind Speed ◽  
Extreme Wind Speeds

Abstract In order to further improve the prediction accuracy of typhoon simulation method for extreme wind speed in typhoon prone areas, an improved typhoon simulation method is proposed by introducing the Latin hypercube sampling method into the traditional typhoon simulation method. In this paper, the improved typhoon simulation method is first given a detailed introduction. Then, this method is applied to the prediction of extreme wind speeds under various return periods in Hong Kong. To validate this method, two aspects of analysis is carried out, including correlation analysis among typhoon key parameters and prediction of extreme wind speeds under various return periods. The results show that the correlation coefficients among typhoon key parameters can be maintained satisfactorily with this improved typhoon simulation method. Compared with the traditional typhoon simulation method, extreme wind speeds under various return periods obtained with this improved typhoon simulation method are much closer to the results obtained with historical typhoon wind data.

scholarly journals Global Estimates of Extreme Wind Speed and Wave Height

2011 ◽  
Vol 24 (6) ◽  
pp. 1647-1665 ◽  
Author(s):  
J. Vinoth ◽  
I. R. Young
Keyword(s):  
Wind Speed ◽  
Wave Height ◽  
Return Period ◽  
Significant Wave Height ◽  
Extreme Values ◽  
Significant Wave ◽  
Extreme Wind ◽  
Extreme Wind Speed ◽  
Global Estimates ◽  
Buoy Measurements

Abstract A long-term dataset of satellite altimeter measurements of significant wave height and wind speed, spanning 23 years, is analyzed to determine extreme values corresponding to a 100-yr return period. The analysis considers the suitability of both the initial distribution method (IDM) and peaks-over-threshold (POT) approaches and concludes that for wave height both IDM and POT methods can yield reliable results. For the first time, the global POT results for wave height show spatial consistency, a feature afforded by the larger dataset. The analyses also show that the POT approach is sensitive to spatial resolution. Since wind speed has greater spatial and temporal variability than wave height, the POT approach yields unreliable results for wind speed as a result of undersampling of peak events. The IDM approach does, however, generate extreme wind speed values in reasonable agreement with buoy estimates. The results show that the altimeter database can estimate 100-yr return period significant wave height to within 5% of buoy measurements and the 100-yr wind speed to within 10% of buoy measurements when using the IDM approach. Owing to the long dataset and global coverage, global estimates of extreme values can be developed on a 1° × 1° grid when using the IDM and a coarser 2° × 2° for the POT approach. The high-resolution 1° × 1° grid together with the long duration of the dataset means that finescale features not previously identified using altimeter data are clearly apparent in the IDM results. Goodness-of-fit tests show that the observed data conform to a Fisher–Tippett Type 1 (FT-1) distribution. Even in regions such as the Gulf of Mexico where extreme forcing is produced by small-scale hurricanes, the altimeter results are consistent with buoy data.

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