International Journal of Energy and Environmental Research (IJEER)

EA Journals

Wind Turbine

Construction and Comparative Study of a Standalone Savonius and Darrieus Vertical Axis Wind Turbine (Published)

In this work, a Savonius Vertical Axis Wind Turbine (VAWT) and its Darrieus counterpart was designed and constructed based on a preliminary study involving wind pattern analysis of an identified study area, the Usmanu Danfodio University, Sokoto, Nigeria. The turbines were constructed using wood and metals and the resulting blades were field tested under various wind conditions available in the study area. The results of the test were compared in terms of start-up wind speed and their rotor’s Revolution Per Minute (RPM) values. The result of this test showed that the Savonius rotor proves to self-start at a wind speed of 2.46 ms-1 with a minimum Revolution Per Minute (RPM) of 46 and maximum RPM of 89 at a wind speed of 9.28 ms-1. The Darrieus blade proves to self-start at a higher wind speed of 3.8 ms-1 with a minimum RPM of 54 and a maximum RPM of 93 and at a wind speed of 8.9 ms-1 under same prevailing atmospheric conditions of the study area.  When both standalone results were integrated into a single system, an equivalent of a combined Savonius-Darrieus type of VAWT resulted and for which a reinforcement in RPM was observed.  It was recommended that a combined Darrieus and Savonius VAWT when constructed will optimize the high rotational efficiency of Darrieus and the high self-starting capabilities of Savonous VAWT.

Keywords: RPM, Renewable Energy, Wind Turbine, Wind speed, darrieus, savonius

Prediction of Energy Gains From Jordanian Wind Stations Using Artificial Neural Network (Published)

System and environmental parameters affecting the output of the wind farm system at different stations in Jordan have been computationally investigated, using artificial neural network (ANN). For the several variables identified, statistical analysis was employed to indicate their relative significance to the targeted output, with the aid of the Pearson’s correlation coefficients. ANN shows proficiency in the prediction of the original experimental data for all the stations and turbines. In the simulation, the energy gain increases with the increase in the system and environmental parameters. However, there appears to be a phenomenon of threshold value in the output parameter, which limits the impacts of change in the input parameters on the eventual response of the output. It can be deduced that there is a minimum energy gain value below which increase in any of the system/environmental parameters will not have positive impact on the energy output. Findings show that the turbine characteristics, like rotor diameter and hub height, have more significant impact on the energy gain than the environmental factor like wind speed. The uniqueness of this work is that it predicts the important output of the wind farm system based on the logical arrangement of detailed parameters that are found in all operational units of the system in order to elicit desired effects.

Keywords: Artificial Neural Network, Energy Gains, Rotor Diameter., Wind Farm, Wind Turbine

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