SILSOE RESEARCH INSTITUTE Spatial variation of wind speeds between sites Andrew Quinn

SILSOE RESEARCH INSTITUTE 2 Why consider wind? Many problems require a knowledge of the natural environment Design and construction, Transport risk analysis Pollution control, Pollen and aphid movement Wind Power, Forestry management, etc etc Some require single design values Others require distributions

SILSOE RESEARCH INSTITUTE 3 Wind speed distribution How does this change with site? Data from Shap Meteorological site, hourly maximum gust 4/1994 – 3/2003

SILSOE RESEARCH INSTITUTE 4 General form of the CDF Weibull distribution Where c and k are site specific constants Fits both mean hourly data and gust data

SILSOE RESEARCH INSTITUTE 5 Extreme value analysis Majority of previous studies Typical approach is to take linear approximation to the tail of the distribution Where LHS is known as reduced variate

SILSOE RESEARCH INSTITUTE 6 Gumbel plot Data from Shap meteorological site - hourly mean and gust up to 1 year return period

SILSOE RESEARCH INSTITUTE 7 Extreme value analysis Shap data hourly mean and gust up to 1 year return period Gumbel plot of Shap gust extrapolated to 50-year return period Peak values Such approaches require long data sets Therefore not local to sites of interest

SILSOE RESEARCH INSTITUTE 8 Wind Speed map methods BS6399:Part2, Eurocode 1, ESDU Wind Atlas method Miller et al (1998) Fig. 5. Estimated 50-year return hourly-mean wind speed for the United Kingdom. Values given in m/s. After Abild et al (1992) Design wind speed methods for dealing with spatial effects

SILSOE RESEARCH INSTITUTE 9 Objective Obtain a wind speed distribution for a site: Objective (no subjective estimates of parameters) Realistic (rather than a conservative design value) Based on short-term data records Consistent with other methods (EVA)

SILSOE RESEARCH INSTITUTE 10 Approach Consider the wind distribution at two sites Where c A and k A known (from long records) Define U B + such that

SILSOE RESEARCH INSTITUTE 11 Approach For consistency with standard EVA where assume k A = k B (i.e. distributions same shape) General form E(U B + ) E(γU A )

SILSOE RESEARCH INSTITUTE 12 Evaluating γ from short term records using ranked simultaneous data 9 years data 3 months (winter) data

SILSOE RESEARCH INSTITUTE 13 Reliability of short term data

SILSOE RESEARCH INSTITUTE 14 Solution Thus we know Long term wind speed probability distribution from a reference site Can calculate expected wind speed & return period Relationship between two sites Objective Realistic Small data set

SILSOE RESEARCH INSTITUTE 15 Example anomalous results

SILSOE RESEARCH INSTITUTE 16 including wind direction

SILSOE RESEARCH INSTITUTE 17 Conclusions Method for objective, realistic estimates based on short term site data Data from 8 sites used and estimates for hourly mean and gust wind speeds Accuracy level similar to direct MO records Wind direction can be a significant factor

SILSOE RESEARCH INSTITUTE Spatial variation of wind speeds between sites Acknowledgements Roger Hoxey, Chris Hampson, Nick Teer and the other members of the project team at SRI Russell Pottrill, William Bradbury and David Deaves (Atkins)