Tag Archives: 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

Comparative Analysis of Floating Offshore Wind Turbine Foundation For Renewable Energy Generation (Published)

The offshore energy industry concerning wind energy is moving its focus towards deeper water locations, which are more fitting to floating rather than bottom fixed support structures. The selection of the appropriate support structure type is an important factor in making the offshore energy industry reliable and efficient for product delivery. The study aims to develop a methodology for the evaluation and selection of an optimum structure to support offshore wind power with emphasis on three different selected support structures for wind turbines offshore based on the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method with modification using the pairwise comparison for obtaining the weighted vector and also using the Analytic Hierarchy Process (AHP) method for verification of approach. These methods were able to check for consistency of the weights employed in the analysis and provide a means for validating the weight of attributes. The scores obtained from the three methods used to carry out the multi-criteria decision-making analysis (TOPSIS, modified TOPSIS and AHP) and this shows how all the alternatives rank concerning each other. The Spar buoy option scores 41.00% (TOPSIS), 42.45% (Modified TOPSIS) and 29.15% (AHP) while the Semi-sub option scores 32.75% (TOPSIS), 20.90% (Modified TOPSIS) and 37.32% (AHP) and the TLP options scores 26.25% (TOPSIS), 36.64% (Modified TOPSIS) and 33.53% (AHP). The option with the best performance across all decision-making approaches is the Spar buoy platform with the TLP being the next preferred and the Semi-sub being the least preferred.

Keywords: Floating platform, Wind Turbine, offshore renewable energy, offshore structures, weight analysis

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