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Among types of renewable energy resources, wind energy is considered the fastest developing and most competitive. Counter-rotating turbines (CD-RWT) were adopted in order to enhance the efficiency of the system and overcome the difficulty of large-scale wind turbine installation and maintenance. This paper is devoted for design and predicting the actual performance of a counter-rotating multi-rotor horizontal axis wind turbine. The wind turbine rotor is initially designed by Q blade software based on the modified blade element momentum theory and is used to assess and analyze the design and performance as well. The wind turbine blades have been manufactured for both rotors with a radius of front rotor and rear rotor of 0.61 m and 1.17 m respectively and fixed to a single 600W PMSG generator. Results showed that the power coefficient at low wind speed is very satisfactory and put within the acceptable range. The most important improvement was fixing the aerodynamic deficit at the root of back-rotor blades and therefore improving the self-starting characteristics. The constructed wind turbine start-up has been rated from 2 m/s to 4.5 m/s as the maximum speed, which is consistent with the wind characteristics of Iraq. The coefficient of performance (cp) was found 0.46,0.41 for single wind rotors and 0.50 for twin-rotors experimentally. While cp was found-for single rotors wind turbine 0.49,0.43 and for twin rotor 0.54 theoretically. A comparison between theoretical and experimental results was conducted and its revealed good closeness to the overall outcome. [ABSTRACT FROM AUTHOR]

Wind energy is one of the most important kinds of renewable energy resources in the world. The most effective, friendly to the environment and inexpensive power that can produce by wind. In this paper introduces a new software was designed and implemented “Wind Energy Analysis Tool” (WEATb) based on MATLAB Graphical User Interface (GUI) to analyze deeply wind energy in a selected location in south of Iraq. This software has included two main tools. The first tool (wind data analysis tool), processing the missing data, presented wind speed characteristics, estimate (mean wind speed Um, standard deviation S.D, the shape parameter k, scale parameter c, wind power density WPD, wind energy intensity WEI) using Weibull distribution based on standard deviation method, and plotting many graphs such as wind rose. The second tool (wind turbine analysis tool), the annual energy production kWh, capacity factor CF, and cost of energy $c/kWh can be estimated. This tool used to select the optimal wind turbine in the specific site depending on the minimum cost per kWh or maximum capacity factor generated from wind energy system. [ABSTRACT FROM AUTHOR]