Alex Maurício Araújo

A Preliminary Approach of the Technical Feasibility of Offshore Wind Projects along the Brazilian Coast

This work seeks to demonstrate the technical feasibility of offshore wind projects by calculating the preliminary estimate of wind energy production in the Brazilian marine environment, through a simplified methodology based on currently available data in a case study performed with the application of wind turbines. The Exclusive Economic Zone of Brazil is about 3.6 million km2, an area that can be harnessed for offshore wind energy production. The case study was conducted in Itamaracá Island, Pernambuco state with the aim of supplying the local energy demands. An analysis was carried out on the local wind conditions through an ocean wind map measured at a height of 10 m, and therefore, the wind speed was extrapolated to 90 m using the logarithmic law. The Weibull frequency distribution and the annual energy production were calculated. The results presented showed that three wind turbines at a rated power of 3 MW, including a calculated annual average wind speed of 7.15 m/s would generate around 30,000 MWh/year, which would be sufficient to ensure energy throughout the year in Itamaracá Island.

AN APPROACH TO SIMULATE WIND FIELDS AROUND AN URBAN ENVIRONMENT FOR WIND ENERGY APPLICATION

This work proposes an approach to simulate wind flow fields around an urban environment with the aim of evaluating the potential impact of buildings on the general wind patterns and power production using the current generation of commercial wind turbines. The simulation process was performed with the aid of accessible computational tools that can potentially render the proposed procedure applicable in other cases of interest. The roughness of the urban environment was defined as the association of roughness map, topography, and an alternative process for obtaining the volumetry of buildings. A case study was conducted in a region located at the district of Boa Viagem (Recife-PE) for assessing the applicability of the approach. Scenarios were designed in order to simulate wind flow patterns and pre-identify sites that have suitable wind energy potential for electric power production by investigating the combination of wind speed magnitude and turbulence intensity. From the results obtained, it was possible to identify zones of potential wind sources that are not detected in classical wind atlas probably due to the influence of the built environment on local wind flow patterns.

Simulación de la Producción de Energía Eléctrica con Aerogeneradores de Pequeño Tamaño

This paper presents a simulation of electricity production from several small wind turbines in the wind regime of Olinda-PE-Brazil, taking into account a number of records wind speeds and temperature in 1998, with the aim of developing a performance analysis tool that allows its selection. The hourly wind speed data were discretized at appropriate intervals to allow better handling of the information contained in the curves of electricity, supplied by manufacturers. The tool allows a comparative analysis of the performance of wind turbines, both in the aspect of the electrical energy production as its efficiency in function of wind speed. The results show that of the local wind regime influenced the production of electricity and that the data provided by manufacturers must be certified.

Extreme Wind Speeds and Their Prediction for Wind Turbines

The precise knowledge of occurrence of extreme wind speed for wind turbine design is of utmost importance. This paper describes the extreme value theory, especially the Fisher-Tippett generalized extreme value distributions. On this basis, Gumbels hypothesis that merely recording of annual extreme events for a duration of ten years or more can provide the probability of occurrence of an extreme event (maximum or minimum). Gumbels cumulative distribution function (two parameter function) provides the exceedances of wind speed from a given value. The fitting of a regression to the data provides the unknown parameters. However, at the extreme end of the plot, the function is biased. This has been corrected by Gringorten method. The work also calculates the mean recurrence interval of, say, occurrence of a 50-year wind. The proposed methodology to estimate extreme wind speed is simple and straightforward. It is hoped that its use would be a valuable tool in designing wind turbines.

Meeting Peak Load Demand by an Offshore Wind Farm in Brazil

The Itamaracá island close to the state of Pernambuco in Brazil receives thousands of visitors during the period of long summer vacations (3 months), increasing the peak demand by a factor of almost 200%. The purpose of this work is to demonstrate that the offshore wind turbines can meet peak energy demand and at the same time reduce impact on conventional energy generation during other months as well. The method involves a nautical wind map at a height of 10 m, and extrapolating it for the desired height by logarithmic law. The annual wind energy output employs Weibull probability density function. The wind turbine siting is chosen with the aid of the nautical chart. The results obtained demonstrate that three wind turbines would generate about 30,000 MWh per year, sufficient for meeting electricity demand in the island.

Defining Capacity Factor by Weibull Shape Parameter and Mean Wind Speed

This workmodels capacity factor in terms of wind turbine operating characteristics, mean wind speed, and shape parameter of Weibull probability density function. The methodology used involves fewer parameters than have been used by other workers. We have used a nondimensional speed parameter (x) as the ratio of the wind speed (V) to the mean wind speed (Vm). The operating characteritics of a megawatt wind turbine cut-in (Vin), rated (Vr) and cut-out (Vout) speeds are divided by Vm, resulting into nondimensional speeds: xin, xr, and xout, respectively. Similarly, the Weibull scale parameter is also transformed by the Vm. The final model of the capacity factor constitutes four variables: xin, xr, xout and k. The analysis of model shows that, in general, low values of nondimensional speed xr results into higher values of capacity factor. The shape parameter also influences the value of capacity factor. High values of shape parameter provides, in general, low value of capacity factor. For example, a turbine designed for shape parameter of about 2 if installed at a site where the shape parameter is about 4 or more than the value of capacity factor may reduce by almost 10%. The analysis also shows that if one wishes to achieve higher values of capacity factor for a turbine desined for k = 2 to k = 4 would necessitate increase in the mean wind speed of the new site (k = 4).