Electric power big data is an important resource for electric power development and comes from the procedures of electricity production and energy utilization. Wind power operating data is the major part of electric power big data. With the dramatic increase of wind power penetration, it is of great significance for wind farm operation, control and integration research by collection, processing and analysis of real historical operating data from wind farms. However, amounts of data collected from wind farms usually contain abnormal data, which have adverse impact on the study of fluctuation characteristics of wind power, wind power prediction, etc. The main source of abnormal data existed in wind farm historical operation data is analyzed and a model for eliminating abnormal data based on quartile method is presented. In the cases of missing data, methods based on patterns of similarity between neighboring wind farms outputs and multi-point cubic spline are used on the basis of historical data to reconstruct the discontinuous time series respectively. The case study indicates that the presented models are efficient for eliminating abnormal data and reconstructing missing data, which can be applied in practical engineering.