The use of satellite altimeter data for spatial mapping of the wave resource is examined. A new algorithm for estimating wave period from altimeter data is developed and validated, which enables estimates of wave energy converter (WEC) power to be derived. Maps of the long-term mean WEC power from altimeter data are of a higher spatial resolution than is available from global wave model data. They can be used for identifying promising wave energy locations along particular stretches of coastline, before a detailed study using nearshore models is undertaken. The accuracy of estimates of WEC power from wave model data is considered. Without calibration estimates of the mean WEC power from model data can be biased of the order of 10-20%. The calibration of wave model data is complicated by non-linear dependence of model parameters on multiple factors, and seasonal and interannual changes in biases. After calibration the accuracy in the estimate of the historic power production at a site is of the order of 5%, but the changing biases make it difficult to specify the accuracy more precisely. The accuracy of predictions of the future energy yield from a WEC is limited by the accuracy of the historic data and the variability in the resource. The variability in 5, 10 and 20 year mean power levels is studied for an area in the north of Scotland, and shown to be greater than if annual power anomalies were uncorrelated noise. The sensitivity of WEC power production to climate change is also examined, and it is shown that the change in wave climate over the life time of a wave farm is likely to be small in comparison to the natural level of variability. It is shown that despite the uncertainty related to variability in the wave climate, improvements in the accuracy of historic data will improve the accuracy of predictions of future WEC yield. The topic of extreme wave analysis is also considered. A comparison of estimators for the generalised Pareto distribution (GPD) is presented. It is recommended that the Likelihood-Moment estimator should be used in preference to other estimators for the GPD. The use of seasonal models for extremes is also considered. In contrast to assertions made in previous studies, it is demonstrated that non-seasonal models have a lower bias and variance than models which analyse the data in separate seasons.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:516194 |
| Date | January 2009 |
| Creators | Mackay, Edward B. L. |
| Contributors | Bahaj, Abubakr |
| Publisher | University of Southampton |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://eprints.soton.ac.uk/79448/ |
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