Practical power curve estimation is necessary for evaluating the actual power output of a wind farm; since a power curve provided by the wind turbine manufacture will be different with the actual power curve following several years of operation. It can be estimated using the collected power output data including wind power generation and wind speed. This data is commonly ill-distributed due to a noticeable number of outliers

which impose a serious bias to the estimation models obtained from this data. It introduces an interesting challenge in estimation of a power curve. In this paper

an intelligent algorithm is proposed for estimating a power curve using the measured data while modeling and bias errors

imposed to the estimation model by the outliners

are minimized. More specifically

this algorithm is designed based on the Statistical Analysis Software (SAS) programming software package in order to facilitate analyzing and managing big datasets of wind speed and wind power generation. The effectiveness and practical application of the proposed algorithm is demonstrated using a real-world dataset.