<p>The Faroe Islands is a small mountainous island group in the north east Atlantic Ocean, located far from any other mainland. The closes adjacent land being Shetland ~300 km away. One electrical power company exists on the islands, distributing power to the ~50.000 citizens. Approximately half of the electrical power comes from renewable energy sources (wind and hydro) and the other half from oil [1]. The political goal is to have the electrical system running 100% on renewable energy sources by 2030. This will presumable be achieved by implementing a significant amount of wind power [2]. The climate in the Faroe Islands is very windy, making it a good area for harvesting wind energy.</p><p>As wind is a fluctuating power source, analyzing the wind field and its characteristics is of great importance, when planning implementation of a significant amount of wind power into the power grid. Smoothening of the wind power can be achieved different ways, one being with spatial dispersion of wind farms seen in other studies [3,4]. The spectral characteristics and the smoothening effect of spatial dispersed sites based on wind farm data and meteorological wind speed measurements in the Faroe Islands was shown in a poster presentation at EMS2019 [5]. However, implementing more wind farms requires knowledge of new sites. There have been made NWP calculations of the wind in the Faroe Islands for the period July 2016 to June 2017. NWP are beneficial in the way that they give valuable information at unknown sites, which may be used for wind farm planning. However, NWP calculations are based on a given setup of a simplified reality. Hence, validating any NWP model is needed.</p><p>There exists wind measurements at various heights from two meteorological masts at the time period of the mentioned NWP model calculations in the Faroe Islands. The aim of this study is to compare auto- and cross-spectral characteristics of the sets of modelled and measured data. The results will give an insight on the value of NWP derived data for grid integration studies in a region with complex topography.</p><div><br><div>
<p>[1] Framlei&#240;sluroknskapur 2018, SEV, (see http://www.sev.fo/Default.aspx?ID=67)</p>
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<p>[2] Hansen, H., Nielsen, T., Thomsen, B., and Andersen, K., 2018, Energilagring p&#229; F&#230;r&#248;erne, Teknisk opsamlingsrapport. Dansk Energi. (see http://www.os.fo/media/1187/1-teknisk-opsamlingsrapport-energilagring-paa-faer-erne.pdf)</p>
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<p>[3] Beyer, H. G., Luther, J., and Steinberger-Willms, R., 1993, Power fluctuations in spatially dispersed wind turbine systems, Solar Energy, Vol. 50, No. 4, pp. 297-305.</p>
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<p>[4] Pearre, N. S. and Swan, L. G., 2018, Spatial and geographic heterogeneity of wind turbine farms for temporally decoupled power output, Energy, Vol. 145, pp. 417-429.</p>
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<p>[5] Poster presentation at the European Meteorology Society annual meeting 2019, 9-13 September, Copenhagen, Denmark.</p>
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Small signal fault analysis for renewable energy (Wind) power system distributed generation by using MATLAB software (Simulink)
