Wind turbine group active power collaborative optimization method based on wake flow time-space characteristics

Abstract

The invention relates to a wind turbine group active power collaborative optimization method based on wake flow time-space characteristics, and the method comprises the steps: obtaining real-time operation data of a target wind turbine group, and carrying out the preprocessing of the real-time operation data, so as to obtain standard operation data; based on a wake effect model corresponding to a pre-constructed target wind turbine group, performing space-time characteristic analysis on the standard operation data to obtain a space-time characteristic analysis result; and based on the wake effect model and the time-space characteristic analysis result, establishing a collaborative optimization strategy corresponding to the target wind turbine group to optimize the active power of the target wind turbine group according to the real-time operation data to obtain the target active power corresponding to the target wind turbine group, and using the target active power to control the target wind turbine group to execute the corresponding operation. Therefore, the problems that according to an existing wind turbine group optimization method, the wake effect cannot be considered dynamically, the operation states of the multiple draught fans are difficult to coordinate, and optimal distribution of active power of the wind turbine group is difficult to achieve are solved.