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Wind resource assessment, which is an important component of the wind power project, provides the basis for the wind farm micro-site and economic evaluation. Fluid dynamics and computational fluid dynamics have provided theoretical calculation methods for wind resource assessment, but the application methods need to be optimized continuously. WAsP (Wind Atlas Analysis and Application Program), which is applied by many wind power projects, would give great errors in complex terrains. Much research has been taken on solving the problem.Firstly, this paper summarized the wind resource assessment studies, including theoretical research, such as wind speed change, turbulence, wind speed prediction and forecast, turbulence in wake reign, and the application models adopted by WAsP , such as the roughness model, the orographic model and the shelter model. After the research on wind speed prediction analyzed, some improvements of the predicting methods need to be done.Then, two issues of predicting wind speed have been studied: one is the estimate of wind speed statistics parameters. The distribution of m order moment of wind speed stays the same with the wind speed-Weibull distribution, and the empirical formula of the shape parameter k with origin moment methods is given. Then, the adaptability and accuracy of the empirical formula is inspected. After comparison of new method and traditional methods with the data of the 70m wind tower in Zhangbei Danjinghe wind farm, it is proved that new method is simple to compute and of greater accuracy and adaptation to a wider scope. The other one is the greater error of predicting wind speed in complex terrains with WAsP. RIX (ruggedness index), which classifies terrains with steepness based on separation of airflow, is a better index of terrain complexity. By introducing RIX and comparing the mutual predictions with measured results of the 3 wind towers in Zhangbei Danjinghe wind farm, the impact of the RIX difference between the terrains of observation points and predicting points and the empirical formulas are given. At last, the methods are introduced to Zhangbei Danjinghe wind farm project, and the results computed with traditional methods are amended. The map of wind power distribution is given.
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