Triggering Mechanism of Extreme Wind over the Complex Mountain Area in Dali Region on the Yunnan-Guizhou Plateau, China

Wind disasters are responsible for significant physical destruction, injury, loss of life, and economic damage. This study examined the extreme wind triggering mechanism over a typical mountain area with complex terrain, i.e., Dali city in Yunnan Province on the Yunnan-Guizhou Plateau in China. Using the observation data, we first optimized the Weather Research and Forecasting (WRF) model configuration and parametrization schemes for better simulating the wind in this area using a 1-month simulation. Then, the triggering mechanism of extreme wind was investigated by performing a series of sensitive experiments based on a typical extreme wind case. The results indicate that terrain uplift is critical for triggering the local 8–9-scale (the wind velocity between 17.2 and 24.4 m/s) extreme winds over high topography regions. When a large-scale atmospheric circulation is passing, accompanied with regional terrain lifting, the instantaneous wind velocity can reach 9- to 10-scale (the mean wind velocity between 20.8 and 28.4 m/s), causing broken power lines. These results suggest that it is essential to avoid sites where these factors can affect the operation of power transmission lines, or to establish warning systems in the existing systems.

Estimation of the Tower Shape Effect on the Stress–Strain Behavior of Wind Turbines Operating under Offshore Boundary Conditions

The metal tower, or the pylon, is one of the most important elements in the construction of a wind turbine. It has the role of supporting the entire wind turbine, and it also allows access for inspection and conducting planned maintenance and repairs. Moreover, the tower ensures support for the structure and strengthens the whole assembly. It has a particularly important role, as it has to face very severe weather conditions. The present study aims to analyze the forces and moments resulting from the action of the wind on the tower of a wind turbine. Two important load cases are considered, namely, the load under operating conditions and the ultimate load under 50 year wind conditions. For this purpose, cylindrical and conical geometric shapes of the tower were chosen. These were analyzed under the action of both normal and extreme wind speeds. Then, the behavior of the two towers under the action of the wind speed for a location in the Black Sea was analyzed. Finally, in an attempt to make the structure more economical, the thickness of the shell was reduced.

Extreme wind speed prediction in mountainous area with mixed wind climates

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.

Gaussian mixture model for extreme wind turbulence estimation

Uncertainty quantification is a necessary step in wind turbine design due to the random nature of the environmental loads, through which the uncertainty of structural loads and responses under specific situations can be quantified. Specifically, wind turbulence has a significant impact on the extreme and fatigue design envelope of the wind turbine. The wind parameters (mean and standard deviation of 10-minute wind speed) are usually not independent, and it will lead to biased results for structural reliability or uncertainty quantification assuming the wind parameters are independent. A proper probabilistic model should be established to model the correlation among wind parameters. Compared to univariate distributions, theoretical multivariate distributions are limited and not flexible enough to model the wind parameters from different sites or direction sectors. Copula-based models are used often for correlation description, but existing parametric copulas may not model the correlation among wind parameters well due to limitations of the copula structures. The Gaussian mixture model is widely applied for density estimation and clustering in many domains, but limited studies were conducted in wind energy and few used it for density estimation of wind parameters. In this paper, the Gaussian mixture model is used to model the joint distribution of mean and standard deviation of 10-minute wind speed, which is calculated from 15 years of wind measurement time series data. As a comparison, the Nataf transformation (Gaussian copula) and Gumbel copula are compared with the Gaussian mixture model in terms of the estimated marginal distributions and conditional distributions. The Gaussian mixture model is then adopted to estimate the extreme wind turbulence, which could be taken as an input to design loads used in the ultimate design limit state of turbine structures. The wind turbulence associated with a 50-year return period computed from the Gaussian mixture model is compared with what is utilized in the design of wind turbines as given in the IEC 61400-1.

An Improved Typhoon Simulation Method Based On Latin Hypercube Sampling Method

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.

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

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.

The CSTools (v4.0) Toolbox: from Climate Forecasts to Climate Forecast Information

Despite the wealth of existing climate forecast data, only a small part is effectively exploited for sectoral applications. A major cause of this is the lack of integrated tools that allow the translation of data into useful and skilful climate information. This barrier is addressed through the development of an R package. CSTools is an easy-to-use toolbox designed and built to assess and improve the quality of climate forecasts for seasonal to multi–annual scales. The package contains process-based state-of-the-art methods for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination and multivariate verification, as well as basic and advanced tools to obtain tailored products. Due to the design of the toolbox in individual functions, the users can develop their own post-processing chain of functions as shown in the use cases presented in this manuscript: the analysis of an extreme wind speed event, the generation of seasonal forecasts of snow depth based on the SNOWPACK model and the post-processing of data to be used as input for the SCHEME hydrological model.

A probabilistic model for estimating ocean-spray distribution in extreme tropical cyclone winds

ABSTRACT. Serious gaps in knowledge about ocean spray at wind speeds over 28 m/s remain difficult to fill by observation or experiment; yet refined study of the thermodynamics of Tropical Cyclones (including typhoons and hurricanes) requires assessment of the hypothesis that ‘spray cooling’ at extreme wind speeds may act to reduce (i) the initial temperature of saturated air rising in the eyewall and so also (ii) the input of mechanical energy into the airflow as a whole. Such progressive reductions at higher speeds could, for example, make any possible influence, of future global warming on Tropical Cyclone intensification largely se1f-limiting. In order to help in extrapolation of knowledge on ocean spray to extreme wind speeds, a probabilistic analysis is introduced which allows for the effects of gusts, gravity and evaporation on droplet distributions yet all other respect is as simple as possible. Preliminary indications from this simplified analysis appear to confirm the potential importance of spray cooling.