Abstract. This paper, based on the pursuit of scientific articles published and recorded in the last five years (2010-2014) patents on VAWT technology, gives an image of the current situation of the treated technology.From data extracted we know: The different models that are working with different geometries, distinguishing between Savonius, Darrieus, hybrid of both (D+S), models dedicated to Offshore technology and what can be applied generally (D&S) on both types of VAWT (controllers, electric generators, materials ...).The main countries that research and develop VAWT technology, globally and at European level and the number of dedicated studies and patents each.Multiple applications that can be given in fields such as building, industrial environment, social areas, civil engineering and other more.Future trends for VAWT, which can be seen in our environment, both rural and urban, as has already happened with other renewable technologies for electricity production, as HAWT and photovoltaic (PV), becoming part of the mix of renewable energy technology and business network of the future, thereby contributing to the reduction of CO 2 production and economic growth.

Wind energy is gaining special interest worldwide due to the necessity of reducing pollutant emissions and employ renewable resources. Traditionally, horizontal axis wind turbines have been employed but certain situations require vertical axis wind turbines. With a view to improve the efficiency of a vertical axis wind turbine Savonius type, the present work proposes a bioinspired design blade profile relying on the Fibonacci spiral. This shape is repeatedly presented in nature and thus it leads to a bio-inspired blade profile. A numerical model was carried out and it was found that the Fibonacci shape improves the performance of the original Savonius shape, based on semicircular blade profiles. Particularly, the Fibonacci blade profile increases around 14% the power in comparison with the Savonius blade profile. Besides this comparison between Savonius and Fibonacci, a research study was carried out to improve the efficiency of the Fibonacci turbine. To this end, the effect of several parameters was analyzed: number of blades, aspect ratio, overlap, separation gap, and twist angle. Improvements on the average power greater than 30% were obtained.

The present work proposes an artificial neural network (ANN) to analyze vertical axis wind turbines of the Savonius type. These turbines are appropriate for low wind velocities due to their low starting torque. Nevertheless, their efficiency is too low. In order to improve the efficiency, several modifications are analyzed. First of all, an innovative blade profile biologically inspired is proposed. After that, the influence of several parameters such as the aspect ratio, overlap, and twist angle was analyzed through a CFD (computational fluid dynamics) model. In order to characterize the most appropriate combination of aspect ratio, overlap, and twist angle, an artificial neural network is proposed. A data set containing 125 data points was obtained through CFD. This data set was used to develop the artificial neural network. Once established, the artificial neural network was employed to analyze 793,881 combinations of different aspect ratios, overlaps, and twist angles. It was found that the maximum power coefficient, 0.3263, corresponds to aspect ratio 7.5, overlap/chord length ratio 0.1125, and twist angle 112°. This corresponds to a 32.4% increment in comparison to the original case analyzed with aspect ratio 1, overlap 0, and twist angle 0.