To enhance wind farm efficiency and economic benefits

this study developed a wind turbine selection optimization method based on multi-source data fusion.By integrating data from meteorological stations

mesoscale databases

and wind measurement towers

a wind resource assessment model was established.A technical-economic comparison of five mainstream 3.0 MW turbine models was conducted

and a multi-objective optimization model was constructed.The results were validated using CFD simulation and lifecycle economic evaluation.The study shows that this approach reduces wind speed prediction error to within 3%.E turbine demonstrated superior performance under low wind speed and high turbulence conditions

with a power coefficient of 0.487.The project’s internal rate of return ranged from 10.2% to 14.8%

providing scientific support for wind farm planning.