A model of the trajectories of ice fragments thrown from a rotating wind turbine blade is used to estimate the ground impact locations that could occur under different scenarios. Wind speed, ejection position on the blade and turbine rotation rate all play a role in determining the impact point, as well as mass, density and drag coefficient of the ice fragment. For ‘compact’ ice fragments, the trajectory depends on the combination CDA∕M where CD is the drag coefficient, A is the frontal area and M the mass of the ice fragment. Sensitivity tests show that ice fragments can travel further laterally for low CD and further downwind for high CD. For plate‐like fragments, aerodynamic lift can increase the distance travelled if the plate maintains an orientation to maximize lift. Although this may be a relatively rare event, we provide an example where a 1 kg plate‐like fragment could travel up to 350 m from the base of the turbine. Copyright © 2011 John Wiley & Sons, Ltd.
A near-complete 4 year data set of 10 min average 80 m wind speeds is used to examine the impact of missing data on monthly and yearly estimates of mean wind speed and energy production from a generic wind turbine. Missing data is a source of uncertainty in wind energy resource assessment studies. Quantifying that uncertainty can improve the reliability of P90 and related wind farm energy production estimates. An empirical relationship between missing data percentage and relative uncertainty in monthly mean wind speed is derived. Relationships between uncertainties in monthly average wind speed and uncertainties in monthly energy production are also explored. In many cases with monthly data losses of 10% or less the contribution to the overall uncertainty in annual energy production will be small (<1%), but with substantial losses in cold winters, typically caused by icing; the uncertainties can become more significant. The data set is also used to indicate uncertainties associated with short data periods. Annual average wind speed estimates based on less than a complete year's data also add significant uncertainty to wind resource assessments.
Competition within the energy generation industry provides an incentive for developers to build offshore wind farms with a low levelised cost of energy. Therefore, there is a need for design optimisation to reduce costs and increase energy capture. A sequential approach to optimise turbine placement and cable layout is presented, using a heuristic k-opt algorithm and mixed-integer linear programming respectively. Energy storage is considered as a means to further improve the cable selection process. A case study is carried out on the Lillgrund offshore wind farm and the resulting layout improves energy capture by 6%. Cable costs are increased but the electrical losses are reduced such that there is an overall saving over the project lifetime of 20%. Energy storage as a means to peak shave the power seen by a cable in order to reduce electrical losses or de-rate a cable section was found to be impractically large and not profitable. Future work will consider secondary revenue streams to remedy this.
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