Inverse Burr distribution for extreme wind speed prediction: Genesis, identification and estimation

Chiodo, E.; Falco, Pasquale De

doi:10.1016/j.epsr.2016.08.028

Cited by 39 publications

(28 citation statements)

References 30 publications

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“…[34] The Gamma and Burr distributions are convenient in economics [35] or engineering for time-variant phenomena. [31,38] In Table 12, the Beta distribution is presented in its standardized form. It can be easily adapted to any range x x ] , [ 1 .…”

Section: Resultsmentioning

confidence: 99%

Quantification of Tafel coefficients according to passive/active state of steel carbonation‐induced corrosion in concrete

Duprat

Larrard

2019

Materials & Corrosion

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Statistical quantification of Tafel coefficients is investigated in this study for isolated steel rebar embedded in concrete. The survey is supported by a wide experimental campaign carried out earlier to characterize the passive and active states of carbonation‐induced corrosion of steel. Electrochemical measurements (polarization resistance, corrosion potential, Tafel coefficients) and gravimetric estimations of iron loss were regularly conducted over 417 days on 108 concrete specimens. The statistical analysis reveals that the mean value of Tafel coefficients, both cathodic and anodic, is higher under active corrosion, which seems to contradict the general tendency found for chloride‐induced corrosion, while their coefficient of variation is smaller. The statistical inference was based on the first step of distributions fitting the experimental data and then on the second step of goodness‐of‐fit tests. The most suitable of the distributions proposed were the Burr, Rayleigh, and Gamma distributions. A similar analysis was made for the corrosion potential and polarization resistance. The findings of the study will be valuable for probabilistic approaches to corrosion where probabilistic distributions are required.

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Section: Resultsmentioning

confidence: 99%

Quantification of Tafel coefficients according to passive/active state of steel carbonation‐induced corrosion in concrete

Duprat

Larrard

2019

Materials & Corrosion

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“…33 When the wind speed is higher than the cutoff value of the turbine, the electrical generator has to be stopped to preserve the correct operation of the turbine, and the electrical energy produced is zero. 33 When the wind speed is higher than the cutoff value of the turbine, the electrical generator has to be stopped to preserve the correct operation of the turbine, and the electrical energy produced is zero.…”

Section: Calm and Extreme Wind Speedsmentioning

confidence: 99%

“…34 The inverse Weibull distribution is similar to the Weibull distribution, very flexible for those distributions characterized by a long asymmetric tail on the right, ie, by high frequencies at extreme wind speeds (EWS). 33 Morgan et al, 43 using the wind speed time series collected at 178 offshore stations, showed that the three-parameter Weibull, Kappa, and Wakeby distributions are the most accurate among the 11 distributions considered. In some works, [36][37][38][39] the inverse Weibull distribution was considered the best for modelling EWS when compared with the Gumbel distribution.…”

Section: Calm and Extreme Wind Speedsmentioning

confidence: 99%

“…Even more important than the correct fitting of the wind speed distributions characterized by long CWS is the correct representation of the EWS, owing to problems related to the structural safety of turbines. 33 When the wind speed is higher than the cutoff value of the turbine, the electrical generator has to be stopped to preserve the correct operation of the turbine, and the electrical energy produced is zero. Usually, this cutoff value of the turbine is close to the EWS range.…”

Section: Calm and Extreme Wind Speedsmentioning

confidence: 99%

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A correction to the unimodal and bimodal truncated normal distributions for a more accurate representation of extreme and calm wind speeds

2019

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Summary The use of wind speed probability density functions is a standard practice to represent different wind regimes. Generally, these regimes are distinguished by the following three characteristics: the shape of the distribution in the central wind speeds, amount of the calm wind speeds (CWS), and extreme wind speeds (EWS). An in‐depth review has highlighted that none of the parametric distributions available is suitable to represent the three main characteristics at the same time. To overcome this gap, the use of the corrected mixture of two truncated normal distributions (CMTTND) and corrected single truncated normal distribution (CTND) are proposed to represent, respectively, bimodal and unimodal wind speed distribution shapes. The CMTTND and CTND are obtained by introducing a correction, respectively, to the mixture of two truncated normal distributions (MTTND) and to the single truncated normal distribution (TND). The MTTND and TND permit an accurate representation of distributions with high levels of CWS. The CMTTND and CTND employ a new parameter, to accurately quantifying also the relative frequencies associated with EWS. The performance of the CMTTND and CTND was assessed using a goodness‐of‐fit (GOF) test and statistical measures of error in the evaluation of the characteristic mean wind speeds. The analytical expressions of these mean wind speeds are obtained and validated by a numerical integration method for the first time in this work. The accuracy of these distributions is compared with that of other conventional probability distribution models, of which three are unimodal and six bimodal, in four Italian locations and three American locations. The analysis of the results showed that the CTND and CMTTND allow obtaining high GOF of the experimental distributions with R2 and RMSE higher and lower than, respectively, 0.977 and 0.054. Moreover, the CTND results in the most accurate distribution in the estimation of the characteristic mean wind speeds in the case of localities with unimodal experimental distributions and the CMTTND in the case of localities with bimodal experimental distributions. Contrary to other distribution, CTND and CMTTND accuracies grow by increasing the grade of the characteristic mean wind speed by reaching also estimation values lower than 2% of the real ones. This is a great advantage in the wind energy source determination in a location since the available energy depends on the mean cubic wind speed.

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“…Morgan et al Reference [10] evaluated the capability of numerous theoretical distributions to capture the extreme tails of x values in North American offshore areas and found that the two-parameter Lognormal distribution (L) is suitable. The two-parameter Weibull distribution (Wei) was applied by [19] and the three-parameter inverse Burr distribution by [20] to evaluate extreme x values.…”

Section: Introductionmentioning

confidence: 99%

Global Gust Climate Evaluation and Its Influence on Wind Turbines

et al. 2017

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Strong gusts negatively affect wind turbines in many ways. They (1) harm their structural safety; (2) reduce their wind energy output; and (3) lead to a shorter wind turbine rotor blade fatigue life. Therefore, the goal of this study was to provide a global assessment of the gust climate, considering its influence on wind turbines. The gust characteristics analyzed were: (1) the gust speed return values for 30, 50 and 100 years; (2) the share of gust speed exceedances of cut-out speed; and (3) the gust factor. In order to consider the seasonal variation of gust speed, gust characteristics were evaluated on a monthly basis. The global monthly wind power density was simulated and geographical restrictions were applied to highlight gust characteristics in areas that are generally suitable for wind turbine installation. Gust characteristics were computed based on ERA-interim data on a 1 • × 1 • spatial resolution grid. After comprehensive goodness-of-fit evaluation of 12 theoretical distributions, Wakeby distribution was used to compute gust speed return values. Finally, the gust characteristics were integrated into the newly developed wind turbine gust index. It was found that the Northeastern United States and Southeast Canada, Newfoundland, the southern tip of South America, and Northwestern Europe are most negatively affected by the impacts of gusts. In regions where trade winds dominate, such as eastern Brazil, the Sahara, southern parts of Somalia, and southeastern parts of the Arabian Peninsula, the gust climate is well suitable for wind turbine installation.

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Inverse Burr distribution for extreme wind speed prediction: Genesis, identification and estimation

Cited by 39 publications

References 30 publications

Quantification of Tafel coefficients according to passive/active state of steel carbonation‐induced corrosion in concrete

Quantification of Tafel coefficients according to passive/active state of steel carbonation‐induced corrosion in concrete

A correction to the unimodal and bimodal truncated normal distributions for a more accurate representation of extreme and calm wind speeds

Global Gust Climate Evaluation and Its Influence on Wind Turbines

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