Wind power plant fan site selection method and device

By using whale school algorithm in wind farms to solve the minimization objective function in the grid, the problem of insufficient optimization of location location of wind farms in the prior art was solved, and more efficient wind farm power generation was achieved.

CN119720760APending Publication Date: 2025-03-28XEMC WINDPOWER CO LTD
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Patent Information

Application Number
CN202411781119.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-05
Publication Date
2025-03-28

AI Technical Summary

Technical Problem

The solution that cannot be guaranteed to be solved in the location of the wind farm in the prior art is the global optimal solution or near-optimal solution, resulting in the insufficient power generation of the wind farm.

Method used

The whale school algorithm is used to solve the minimization objective function in the divided grid to determine the optimal position of the fan. This method finds the global optimal solution or better solution by determining the minimization objective function and dividing the planning area into multiple grids, and using the whale school algorithm for optimization.

Benefits of technology

Through this method, the power generation of the wind farm can be effectively improved, and the global optimal solution or better solution can be found as much as possible, solving the problem of insufficient optimization in the existing technology.

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Abstract

The embodiment of the invention provides a wind power plant fan site selection method and device, and relates to the technical field of wind power generation. The method comprises the following steps: determining a minimization objective function, wherein the value of the minimization objective function is reduced along with the increase of the annual energy of the wind power plant; dividing the planning area into a plurality of grids; a minimization objective function is solved based on the divided grids through a whale swarm algorithm to obtain a target site selection result, elements in each whale individual represent the grid where the fans are located, the number of the elements in each whale individual is the same as the number of the fans of the wind power plant, and the number of the elements in each whale individual is the same as the number of the fans of the wind power plant; the fitness value of each whale individual is a value obtained through calculation based on the whale individual and the minimum target function, and the target site selection result is used for indicating the position of the fan. Therefore, a globally optimal solution or a better solution is obtained as far as possible, and the generating capacity of the wind power plant is effectively improved.
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