The estimation of wave height and wind speed persistence statistics from cumulative probability distributions

Kuwashima, Susumu; Hogben, N.

doi:10.1016/0378-3839(86)90004-9

Cited by 25 publications

(9 citation statements)

References 1 publication

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“…According to their investigations, six to eight different states in the Markov model are sufficient to adequately model the marine environmental parameters. Finally, they also show that the Markov model has a better performance than the Kuwashima-Hogben model [58] in terms of persistence distribution. Monbet and Marteau [59] present a continuous-space discrete time Markov model of higher order to model significant wave height peak period and wind speed.…”

Section: Weather and External Factorsmentioning

confidence: 87%

Decision Support Models for Operations and Maintenance for Offshore Wind Farms: A Review

Seyr

Muskulus

2019

Applied Sciences

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This paper reviews the state of the art in offshore wind farm operations and maintenance with a focus on decision support models for the scheduling of maintenance. Factors influential to maintenance planning are collected from the literature and their inclusion in state-of-the-art models is discussed. Methods for modeling and optimization are presented. The methods currently used and possible alternatives are discussed. The existing models are already able to aid the decision-making process. They can be improved by applying more advanced mathematical methods, including uncertainties in the input, regarding more of the influential factors, and by collecting, analyzing, and subsequently using more accurate data.

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Section: Weather and External Factorsmentioning

confidence: 87%

Decision Support Models for Operations and Maintenance for Offshore Wind Farms: A Review

Seyr

Muskulus

2019

Applied Sciences

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“…Kuwashima and Hogben carried out regression analysis on calm and storm duration distribution parameters for sites predominantly in the North Sea. If the three Weibull parameters required for the wave height exceedence distribution are known, then according to their schema, it is possible, for any threshold wave height, to estimate the corresponding mean storm and calm durations τ x ( H th ) and τ n ( H th ) and respective shape factors α x ( H th ) and α n ( H th ) that define the distribution.…”

Section: Methodology For Estimating Access Delaysmentioning

confidence: 99%

Offshore wind turbine maintenance access: a closed‐form probabilistic method for calculating delays caused by sea‐state

Feuchtwang¹,

Infield²

2012

Wind Energy

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Offshore wind energy is fast developing and with it a growing understanding of the challenge to maintain high levels of turbine availability and to keep down maintenance costs. Loss of turbine availability is, of course, related to component failure rate but is also highly dependent on access to the turbine, and this in turn reflects the wind and sea conditions occurring at the site as well as the operational limits of the vessels and plant being used.A computational approach has been developed on the basis of probability calculations, enabling very fast estimates to be made of offshore access probabilities and expected delays. These can be used directly to explore the impact of different parameters such as key component reliability, time to repair and access constraints at specific offshore sites. The methodology used is derived and explained in detail. Different numerical techniques are available to calculate the probability distributions and their parameters as required by the methodology. These are presented and contrasted. Example applications of the methodology are provided for two specific sites that provide a degree of validation and also allow comparison of the different numerical approaches to probability distribution identification. It is shown that the accessibility calculated using the developed method is believable in the context of operational access data for the sites in question. Copyright © 2012 John Wiley & Sons, Ltd.

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“…By defining a H s limit for the task, the maximum H s which the task can be performed in, the waiting time is calculated at each point in the domain using t window along with the statistical methodology presented by Walker et al [27]. This is based on the National Maritime Institute (NMI) method, a modified version of Graham's method that was originally proposed by Kuwashima and Hogben [28]. It is a recommended protocol from the EquiMar Project [29].…”

Section: Parameter (Class)mentioning

confidence: 99%

A model to map levelised cost of energy for wave energy projects

et al. 2018

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An economic model has been developed which allows the spatial dependence of wave energy levelised cost of energy (LCOE) to be calculated and mapped in graphical information system (GIS) software. Calculation is performed across a domain of points which define hindcast wave data; these data are obtained from wave propagation models like Simulating WAves Nearshore (SWAN). Time series of metocean data are interpolated across a device power matrix, obtaining energy production at every location. Spatial costs are calculated using Dijkstra's algorithm, to find distances between points from which costs are inferred. These include the export cable and operations, the latter also calculated by statistically estimating weather window waiting time. A case study is presented, considering the Scottish Western Isles and using real data from a device developer. Results indicate that, for the small scale device examined, the lowest LCOE hotspots occur in the Minches. This area is relatively sheltered, showing that performance is device specific and does not always correspond to the areas of highest energy resource. Sensitivity studies are performed, examining the effects of cut-in and cutout significant wave height on LCOE, and month on installation cost. The results show that the impact of these parameters is highly location-specific.

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The estimation of wave height and wind speed persistence statistics from cumulative probability distributions

Cited by 25 publications

References 1 publication

Decision Support Models for Operations and Maintenance for Offshore Wind Farms: A Review

Decision Support Models for Operations and Maintenance for Offshore Wind Farms: A Review

Offshore wind turbine maintenance access: a closed‐form probabilistic method for calculating delays caused by sea‐state

A model to map levelised cost of energy for wave energy projects

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