Fan arrangement method and device based on multi-population genetic algorithm

Abstract

The invention provides a fan arrangement method and device based on a multi-population genetic algorithm. The method comprises the following steps: randomly generating a plurality of initial populations, and enabling chromosomes in each population to take a machine position as a chromosome and take power generation capacity as an appropriate value function; optimizing each population by adopting agenetic algorithm; completing one-time optimization on each population, and transferring chromosomes among the populations; selecting an optimal chromosome from each population, and adding the optimal chromosome into an essence population; and taking the position represented by the chromosome in the essence population as the optimal machine position. According to the fan arrangement method and device based on the multi-population genetic algorithm, the problem of local convergence of an original fan arrangement mode can be solved, and global optimization is achieved.

Description

technical field [0001] The invention relates to the technical field of wind power generation, in particular to a wind turbine arrangement method and device based on a multi-population genetic algorithm. Background technique [0002] With the promotion of online bidding policies in the wind power industry, the need to increase revenue and reduce costs is becoming more and more urgent. In the past, most wind turbine layouts were manually arranged, and subjective factors were greatly affected, making it difficult to achieve true optimization of machine position layout. The intelligent algorithm is introduced into the arrangement of wind turbines to automatically search for the optimal layout of wind turbines, which can make full use of wind farm resources and improve economic benefits. [0003] Intelligent optimization algorithms, especially genetic algorithms, have been widely used in various industries. There have been some articles on fan layout based on genetic algorithm. ...

AI Technical Summary

Problems solved by technology

However, the simple application of genetic algorithm tends to fall into local convergence. For example, in a certain high-density area in a wind farm, the algorithm automatically searches for several locations with high power generation, and after comparing with the surrounding locations, it is found that this arrangement scheme is the best. Excellent stop searching

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