Building a Classification Map of Wind Turbine Characteristics Compatible with the Winds of Middle and Southern Regions in Iraq

The research creates classification maps of wind turbine operational speeds based on the wind regimes of four governorates in central and southern Iraq: Wasit, Diwaniyah, Maysan, and Dhiqar. High-resolution wind data from GEOSUN resource maps, together with statistical analysis of the Weibull distribution, are used to derive site-specific shape and scale parameters, which are then utilized to calculate the ideal cut-in, rated, and cut-out wind speeds for each location. A turbine performance index integrates capacity factor and normalized power output to determine the turbine speed combination that optimizes energy production for the local wind distribution. The resultant maps exhibit distinct geographical gradients: in all four governorates, cut-in, rated, and cut-out speeds consistently escalate towards the eastern regions of the research area, therefore broadening the range of technologically suitable turbines. Quantitatively, Wasit demonstrates the highest rated wind speeds, ranging from approximately 11.1 to 14.9 m per second, and cut-out speeds from about 20.5 to 27.6 m per second, indicating superior wind resource quality relative to other governorates. In contrast, Diwaniyah is suitable for lower-speed turbines, with minimum rated speeds between 8.9 and 9.5 m per second and minimum cut-out speeds around 16.6 to 17.6 m per second. Analysis of wind direction indicates that around fifty percent of the wind power potential originates from the northwest sector, suggesting that turbines should be aligned toward the northwest to optimize yearly energy acquisition. The maps serve as an effective decision support instrument that connects quantitative wind resource assessment to turbine operational specifications, facilitating expedited preliminary turbine selection, enhanced energy efficiency, and diminished dependence on traditional fossil fuel power plants in areas experiencing persistent electricity deficits.