Applied statistics for extreme wind estimate

Abstract

The assessment of extreme wind speeds is a crucial issue for securing structural safety of wind turbines and inquiring largest loads to which turbines must be prepared to undergo.International standards suggest applying the Gumbel method of fitting the annual maxima to their theoretic probability distribution. Yet, often, wind databases are too short to apply such methods with statistical significance, and other procedures are commonly adopted [such as peaks over threshold (POT) and independent storms], which involve dependency on arbitrary thresholds for filtering data and issues of sub-asymptocity, i.e. how well the selected dataset fits to density functions describing the distribution of peaks or extreme values.The present paper aims at contributing to such currently ongoing debate, providing a statistical analysis of the application of POT and independent storms methods on wind time series of various lengths from different geographical areas.The CERN data analysis framework ROOT has been employed for guaranteeing excellent standards of computational precision and wealth of statistical information. Analysis of uncertainties in the wind speeds estimates and tests of the goodness of fit of the datasets to the proper distributions have been carried on. An algorithm for choosing the optimum thresholds was developed, which encapsules and compromises the statistical complexity of the methods. A declustering procedure has been carried on for discriminating proper peaks in the POT method: it has been tested that such declustering provides a dramatic improvement of the statistical quality of the method.

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

“…The analysis of extreme winds is not only the foundation of the ultimate limit states design of any new civil structure, infrastructure or industrial machinery sensitive to the wind action, but also the base of the reappraisal of the original design anytime the question of a lifetime extension comes. In the wind energy field, accurate predictions of extreme winds and resulting loads are fundamental for turbine design (e.g 1–4 …”

“…In the wind energy field, accurate predictions of extreme winds and resulting loads are fundamental for turbine design (e.g. [1][2][3][4] ), operation under extreme events, and in turn for the resilience of the entire power infrastructure. Furthermore, the wind energy industry is called to address the lifetime extension of an extensive number of wind turbines.…”

“…Asymptotic EV distributions are recommended by international standards for the design of wind turbines along with the POT or MIS approaches. 1 For instance, the International Electrotechnical Commission recommends the use of the Gumbel analysis for the prediction of extreme wind speeds. 9 In a previous study, the authors observed a large dispersion in the estimate of design wind speeds when the abovementioned EV distributions are applied to the same dataset.…”

A method to estimate the probability of strong winds occurrence using weather radar data

“…The assessment of the extreme wind speed is fundamental in the structural design of wind turbines. Regarding this requirement, Larsen et al 4 coupled the atmospheric Weather Research and Forecasting (WRF) model and Spectral Wave model for Near shore (SWAN) to account for the impact of surface waves in the storm wind calculations, while some others use statistical models for this perpose 5,6 . These researches have tried to estimate the magnitude of the extreme winds in a region in order to help design of a wind farm and is not applicable for forecasting the storm in future.…”

“…perpose. 5,6 These researches have tried to estimate the magnitude of the extreme winds in a region in order to help design of a wind farm and is not applicable for forecasting the storm in future. In distinct in this article, we proposed a method for assessing the probability of storm occurrence at each moment, which is useful for the daily operation of a wind farm.…”