Statistical profiling of site wind resource speed and directional characteristics. Issue 6 (1st November 2013)

• Record Type: Journal Article
• Authors: Stephen, Bruce; Galloway, Stuart; McMillan, David; Anderson, Lucy; Ault, Graham
• Is Part Of: IET renewable power generation. Volume 7:Issue 6(2013)
• Page Start: 583
• Page End: 592
• Publication Date: 2013-11-01
• Subjects: power generation economics -- power system simulation -- probability -- statistical analysis -- statistical distributions -- wind power plants -- wind turbines; Statistical profiling -- site wind resource speed representation -- directional characteristics -- wind farm construction -- reliable plant margin forecasting -- meteorological factor -- topological factor -- dynamic loading -- turbine structure -- probability density estimation -- mixture modelling approach -- statistical distribution -- Scotland -- power system modeling -- visualisation -- resource assessment illustration; Renewable energy sources -- Periodicals; 333.79405
• DOI: 10.1049/iet-rpg.2012.0202
• Languages: English
• ISSNs: 1752-1416

Abstract

Abstract : Construction of a wind farm without a reliable plant margin forecast can jeopardise potential returns on investment from the outset. Meteorological and topological factors influence the wind characteristics across any site which in turn affects wind farm output, critical for localised generation, and also the dynamic loading of the turbine structure. The models developed in this study follow the generally advocated use of probability density estimation as a means of representing wind resource characteristics but, owing to differences, in characterisation that may be encountered, do not assume a single distribution form across all sites. A mixture modelling approach is adopted that removes the need for choosing distribution forms on a site by site basis. Advancing previous work constructing statistical distributions over congruent wind speed and direction observations of the wind resource characteristics at a given site, the proposed model, as a consequence of using a mixture distribution, captures both recurring regimes in the site behaviour along with their frequency of occurrence. Preliminary results using data sets from a diverse range of locations in Scotland demonstrate the variation in the forms of model learned; comparisons of the model with current and alternate practices are given through visualisation and resource assessment illustrations.