A refined analysis and simulation of the wind speed macro-meteorological components

Torrielli, Alessio; Repetto, Maria Pia; Solari, Giovanni

doi:10.1016/j.jweia.2014.05.006

Cited by 17 publications

(13 citation statements)

References 22 publications

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“…This very long duration does not imply that the climate is assumed to remain constant over such a long period, but it is needed to control the statistical errors in the distribution tails: for example, a 500,000 year record is required to define the once in 50-year response to an accuracy of 1%. This is an active field of research and development in wind engineering and recent examples of time-series wind models are the Fourier series approach of Torrielli et al (2013Torrielli et al ( , 2014 and the autoregressive filter approach of Harris (2014).…”

Section: Introductionmentioning

confidence: 99%

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A statistical model of the seasonal-diurnal wind climate at Adelaide

Cook¹

2015

AMOJ

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The seasonal-diurnal variation of surface winds observed at Adelaide Airport (South Australia) over 40 years is modelled in terms of the zonal-meridional wind vectors. Calms are assessed separately from the wind vectors. The joint probability density function of the wind vectors is modelled as the sum of a number of disjoint bivariate normal distributions in the zonal-meridional plane. An overview of the data suggests three diurnal mechanisms: sea-to-land breezes, land-to-sea breezes and downslope drainage flows off the Adelaide Hills, together with two non-diurnal large-scale weather mechanisms that are modulated by the annual migration of the sub-tropical ridge. It is shown that, typically, two of the three diurnal components contribute to the joint probability density function for any given hour of day and month, together with the two large-scale weather components. Optimisation is used to quantify the model parameters directly from the joint probability density function. The robustness of the methodology is verified using standard "Bootstrapping" methods. The model elucidates the principal climate mechanisms at Adelaide in terms of the seasonaldiurnal variation of the joint statistics of wind speed and direction using just the mean, annual, semi-annual, diurnal and semi-diurnal harmonics.

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Section: Introductionmentioning

confidence: 99%

“…Calms form a special case, where P = 1 and p is represented by a Dirac delta function at the origin. Temperate wind climates are often modelled by two components, "calms" (V = 0) and "winds" (V > 0) using the "three-parameter Weibull" distribution (Torrielli et al 2014) which follows directly from Eqn. 1 and 2:…”

Section: Introductionmentioning

confidence: 99%

A statistical model of the seasonal-diurnal wind climate at Adelaide

Cook¹

2015

AMOJ

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“…The macro-meteorological spectrum of the wind velocity in Figure 2 is derived by a suitable combination of power spectrum density functions (PSDF) of multiple registrations as described in Refs. 25,28 The PSDF in Figure 2A shows a broadband component with a series of major narrowband spikes relative to the annual cycle, the daily cycle and the relative subharmonics. Annual cycle refers to a set of harmonics defined over the frequency band centered around 1/year frequency (Figure 2B), while the daily cycle refers to a set of harmonics defined over the frequency band centered around 1/day (Figure 2D), the same for the other main cycles.…”

Section: Wind Climatementioning

confidence: 99%

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

Torrielli¹,

Repetto

Solari

2022

Wind Energy

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The 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.

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“…The increased randomness of wind in finer temporal windows makes forecasting unfeasible, and the study of wind speed in the frequency domain can fill this gap. Moreover, the wind power spectrum can be used to plan reliable schedules through fluctuation forecasts [6] and the identification of periodic deterministic components of a time series [7].…”

Section: Introductionmentioning

confidence: 99%

“…Power spectrum analysis for wind data has been recognized as a very useful tool to aim a more detailed description of wind speed variability [1,6,7]. It enhances both the study of identification of variation patterns and the distribution of turbulent energy over the frequency domain [8].…”

Section: Introductionmentioning

confidence: 99%

Regarding the influence of the Van der Hoven spectrum on wind energy applications in the meteorological mesoscale and microscale

Soberanis

Mérida

2015

Renewable Energy

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a b s t r a c tWe demonstrate the use of high frequency data (HFD) to reproduce the power spectrum shown by Van der Hoven in 1957. His work represents the basis of wind energy standards such as averaging and variability in the frequency domain. Our results unveil discrepancies with Van der Hoven's approach, which can be related to constraints in the computing capabilities in the 1950's. We show a major eddyenergy peak at a period of 2 days and a smaller eddy-energy peak contribution at frequencies higher than the region known as the spectrum gap. The variance calculated by the area under the curve indicated that the spectral energy is mainly due to the Power Spectral Density (PSD) values located in the microscale region. We calculated the economic value of this energy based on the turbulence kinetic energy of the wind data set. We also conclude that, given the results of the present study, HFD analysis in the frequency domain uncover eddy energy peaks that determine energy fluctuations in the short and long terms. This information is lost every time data are erased from current monitoring systems.

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A refined analysis and simulation of the wind speed macro-meteorological components

Cited by 17 publications

References 22 publications

A statistical model of the seasonal-diurnal wind climate at Adelaide

A statistical model of the seasonal-diurnal wind climate at Adelaide

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

Regarding the influence of the Van der Hoven spectrum on wind energy applications in the meteorological mesoscale and microscale

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