Janardan Rohatgi

Wind turbulence and atmospheric stability — Their effect on wind turbine output

This paper analyses the effect wind turbulence on the output of large-sized wind turbines. A brief account of atmospheric stability and vertical temperature gradient is explained. The principal objective of this paper is to give an account of underlying principles and concepts without going into the depth of this topic for the wind energy engineering community.

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.

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).

The influence of atmospheric stability on wind turbine output: Technical note

SYNOPSIS This technical note aims to provide a brief outline of atmospheric turbulence in the lower boundary layer so as to be able to understand the vertical wind profiles above the earths surface. In addition to the cited references, a bibliography has been added for those wishing to gain a deeper understanding of the underlying principles and concepts.