A new combination of conditional environmental distributions

Horn, Jan-Tore H.; Bitner‐Gregersen, Elzbieta M.; Krokstad, Jørgen R; Leira, Bernt J.; Amdahl, Jørgen

doi:10.1016/j.apor.2018.01.010

Cited by 41 publications

(20 citation statements)

References 25 publications

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“…This value is close to the reference value of mean wind power density of 88.14 W/m 2 which is obtained using Eq. (31). It indicates that the region under study stands in class 1 10 , which belongs to the low-wind speed wind power development area, and is generally not suitable for wide wind turbine establishment and wind farm investment.…”

Section: Figure 4 Histogram Of Circular Variablementioning

confidence: 99%

Wind energy potential assessment based on wind speed, its direction and power data

Wang

Liu

2021

Preprint

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Based on wind speed, direction and power data, an assessment method of wind energy potential using finite mixture statistical distributions is proposed. Considering the correlation existing and the effect between wind speed and direction, the angular-linear modeling approach is adopted to construct the joint probability density function of wind speed and direction. For modeling the distribution of wind power density and estimating model parameters, based on expectation-maximization algorithm, a two-component three-parameter Weibull mixture distribution is chosen as wind speed model, and a von Mises mixture distribution with nine components and six components are selected as wind direction and relation circular variable models, respectively. A comprehensive technique of model selection, which includes Akaike information criterion, Bayesian information criterion, the coefficient of determination R2 and root mean squared error, is used to select the optimal model in all candidate models. The proposed method is applied to averaged 10-minute field monitoring wind data and compared with the other estimation methods and judged by the values of R2 and root mean squared error, histogram plot and wind rose diagram. The results show that the proposed method is effective and the area under study is not suitable for wide wind turbine applications, and the estimated wind energy potential would be inaccuracy without considering the influence of wind direction.

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Section: Figure 4 Histogram Of Circular Variablementioning

confidence: 99%

Wind energy potential assessment based on wind speed, its direction and power data

Wang

Liu

2021

Preprint

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“…The maximum fatigue utilization occurs in the cross‐section located 4 m below the seabed. In this cross‐section, the maximum fatigue utilization occurs in the southwest/northeast direction due to a high incidence of wind and wind‐generated sea from southwest …”

Section: Two‐stage Adaptation Of a Control Strategy For Fatigue Load mentioning

confidence: 99%

“…In this cross-section, the maximum fatigue utilization occurs in the southwest/northeast direction due to a high incidence of wind and wind-generated sea from southwest. 60 (A) (B) FIGURE 5 Results for the first stage adaptation of tower feedback control (TFC). Normal operation without tower feedback control is referred to as baseline control (baseline).…”

Section: First-stage Controls Adaptationmentioning

confidence: 99%

Post‐installation adaptation of offshore wind turbine controls

et al. 2020

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The cost of offshore wind energy can be reduced by incorporating control strategies to reduce the support structures' load effects into the structural design process. While effective in reducing the cost of support structures, load‐reducing controls produce potentially costly side effects in other wind turbine components and subsystems. This paper proposes a methodology to mitigate these side effects at the wind farm level. The interaction between the foundation and the surrounding soil is a major source of uncertainty in estimating the safety margins of support structures. The safety margins are generally closely correlated with the modal properties (natural frequencies, damping ratios). This admits the possibility of using modal identification techniques to reassess the structural safety after installing and commissioning the wind farm. Since design standards require conservative design margins, the post‐installation safety assessment is likely to reveal better than expected structural safety performance. Thus, if load‐reducing controls have been adopted in the structural design process, it is likely permissible to reduce the use of these during actual operation. Here, the probabilistic outcome of such a two‐stage controls adaptation is analyzed. The analysis considers the structural design of a 10 MW monopile offshore wind turbine under uncertainty in the site‐specific soil conditions. Two control strategies are considered in separate analyses: (a) tower feedback control to increase the support structure's fatigue life and (b) peak shaving to increase the support structure's serviceability capacity. The results show that a post‐installation adaptation can reduce the farm‐level side‐effects of load‐reducing controls by up to an order of magnitude.

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“…Obviously, for the construction of environmental contours, there will be a need for a joint model for circular‐linear data, and different proposals have recently been made for such data, eg, based on a conditional modelling approach, or based on copula‐like constructions involving a circular joining density; see also previous studies . Model selection for several copula‐based joint circular‐linear models for wind data is discussed in Soukissian and Karathanasi, and it is reported that models using the circular joining density proposed by Johnson and Wehrly perform well.…”

Section: Joint Distribution Of Wind Speed and Directionmentioning

confidence: 99%

Environmental contours for circular‐linear variables based on the direct sampling method

2019

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Environmental contours are often used in the design of engineering structures to identify extreme environmental conditions that may give rise to extreme loads and responses. The perhaps most common application of environmental contours is for wave climate variables such as significant wave height and wave period. However, for the design of wind energy installations, the joint distribution of wind speed and wind direction may be equally important. In this case, joint modelling of linear (wind speed) and circular (wind direction) variables are needed, and methods for establishing environmental contours for circular-linear variables will be required.In this paper, different ways of establishing environmental contours for circular-linear variables will be presented and applied to a joint distribution model for wind speed and wind direction.In particular, the direct sampling approach to environmental contours will be modified to the case where one of the variables is cyclic. In addition, contours based on exceedance planes in polar coordinates will be established, and circular-linear contours will also be calculated based on the inverse FORM (I-FORM) approach.

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A new combination of conditional environmental distributions

Cited by 41 publications

References 25 publications

Wind energy potential assessment based on wind speed, its direction and power data

Wind energy potential assessment based on wind speed, its direction and power data

Post‐installation adaptation of offshore wind turbine controls

Environmental contours for circular‐linear variables based on the direct sampling method

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