On the choice of data transformation for modelling time series of significant wave height

Cunha, Cleyzer Adrian da; Soares, C. Guedes

doi:10.1016/s0029-8018(98)00014-6

Cited by 52 publications

(26 citation statements)

References 6 publications

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“…Such a procedure consists of determining the transformation function f, generation of realizations of the process {X t } and then transforming the generated samples of {X t } into samples of {Y t } using f. A number of such models for the significant wave height have been proposed in the literature (e.g. Cunha and Guedes Soares (1999), Walton and Borgman (1990) for the univariate time series for significant wave height, H s , Guedes Soares and Cunha (2000), Monbet and Prevosto (2001) for the bivariate time series for significant wave height and mean wave period, (H s , T) and DelBalzo et al (2003) for the multivariate time series for significant wave height, mean wave period and mean wave direction, (H s , T, H m )). However, it is noted that the duration statistics of transformed Gaussian processes has been demonstrated not to fit too well with data, even though the occurrence probability is correctly modelled (Jenkins 2002).…”

Section: Stationary Modelsmentioning

confidence: 99%

Long-term time-dependent stochastic modelling of extreme waves

Vanem

2010

Stoch Environ Res Risk Assess

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This paper presents a literature survey on timedependent statistical modelling of extreme waves and sea states. The focus is twofold: on statistical modelling of extreme waves and space-and time-dependent statistical modelling. The first part will consist of a literature review of statistical modelling of extreme waves and wave parameters, most notably on the modelling of extreme significant wave height. The second part will focus on statistical modelling of time-and space-dependent variables in a more general sense, and will focus on the methodology and models used also in other relevant application areas. It was found that limited effort has been put on developing statistical models for waves incorporating spatial and long-term temporal variability and it is suggested that model improvements could be achieved by adopting approaches from other application areas. In particular, Bayesian hierarchical space-time models were identified as promising tools for spatio-temporal modelling of extreme waves. Finally, a review of projections of future extreme wave climate is presented.

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Section: Stationary Modelsmentioning

confidence: 99%

Long-term time-dependent stochastic modelling of extreme waves

Vanem

2010

Stoch Environ Res Risk Assess

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“…(3) (Cunha and Guedes Soares, 1999). For wave period, it is only necessary to remove seasonal trends.…”

Section: Synthetic Climate Dataset Generationmentioning

confidence: 99%

Advanced logistics planning for offshore wind farm operation and maintenance activities

et al. 2015

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Offshore wind turbine technology is moving forward as a cleaner alternative to the fossil fuelled power production. However, there are a number of challenges in offshore; wind turbines are subject to different loads that are not often experienced onshore and more importantly challenging wind and wave conditions limit the operability of the vessels needed to access offshore wind farms. As the power generation capacity improves constantly, advanced planning of Operation and Maintenance (O&M)activities, which supports the developers in achieving reduced downtime, optimised availability and maximised revenue, has gained vital importance. In this context, the focus of this research is the investigation of the most cost-effective approach to allocate O&M resources which may include helicopter, crew transfer vessels, offshore access vessels, and jack-up vessels. This target is achieved through the implementation of a time domain Monte-Carlo simulation approach which includes analysis of environmental conditions (wind speed, wave height, and wave period), operational analysis of transportation systems, investigation of failures (type and frequency), and simulation of repairs. The developed methodology highlights how the O&M fleets can be operated in a cost-effective manner in order to support associated day-to-day O&M activities and sustain continuous power production

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“…For wave data, the Box-Cox shape parameter, λ = 0 and the transformation takes the form of Eq. 2 where Y t is the transformed time series and µ ln(Hst) is a Fourier Series estimate of the time series of the logarithm means [8].…”

Section: ) Ar Modelmentioning

confidence: 99%

Wave height forecasting to improve off-shore access and maintenance scheduling

Dinwoodie

Catterson

McMillan

2013

2013 IEEE Power &Amp; Energy Society General Meeting

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Abstract-This paper presents research into modelling and predicting wave heights based on historical data. Wave height is one of the key criteria for allowing access to off-shore wind turbines for maintenance. Better tools for predicting wave height will allow more accurate identification of suitable "weather windows" in which access vessels can be dispatched to site. This in turn improves the ability to schedule maintenance, reducing costs related to vessel dispatch and recall due to unexpected wave patterns.The paper outlines the data available for wave height modelling. Through data mining, different modelling approaches are identified and compared. The advantages and disadvantages of each approach, and their accuracies for a given site implementation, are discussed.

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On the choice of data transformation for modelling time series of significant wave height

Cited by 52 publications

References 6 publications

Long-term time-dependent stochastic modelling of extreme waves

Long-term time-dependent stochastic modelling of extreme waves

Advanced logistics planning for offshore wind farm operation and maintenance activities

Wave height forecasting to improve off-shore access and maintenance scheduling

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