| Summary: | With the increasing integration of large-scale wind farms, the stochastic characteristics of wind speed and the coupling relationship of geographically distributed wind farms become ineligible. Correlation investigation of wind farms based on copula theory is able to lay good foundation for further optimization and schedule in power systems. In this paper, three data types, including wind speed, calculated wind power, and actual wind power, are applied to explore the correlation of two geographically close wind farms. After graphical analysis and comparison of the time series, the structural and compositional characteristics of correlation by different data types are investigated based on mixed copula. Moreover, a two-stage filtration method is proposed to evaluate different types of copulas. Finally, taking into account the practical conditions after careful examination of actual wind power dataset, the practical conditions are applied to the calculated wind power dataset. Correlation research based on the adjusted calculated wind power dataset is further explored and revealed that it is more practical and prospective to provide reference for further power system operation with high penetration of wind farms.
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