Wind farm multi-model draught fan optimized arrangement method based on genetic algorithm

Abstract

The invention relates to a wind farm multi-model draught fan optimized arrangement method based on a genetic algorithm. The method includes the following steps that (1) a wind farm region is divided into square meshes which are the same in size according to the diameter of a draught fan, and an integer matrix which is the same in line and row is generated randomly to be used as the initial solution of the algorithm; (2) the individual fitness value of a current generation is calculated; (3) parent individuals participating in crossover are selected through even random selection operators, and then filial generation individuals are generated by the adoption of improved crossover and mutation operators; (4) repairing operators are introduced to the individuals in a population; (5) a Tabu operator is introduced to an optimal solution of the current generation of the population, the optimal solution is used as the initial solution of a Tabu algorithm, and the neighborhood solution of the optimal solution is searched for; (6) whether the biggest number of iterations is reached or not is judged, if yes, the multi-model draught fan optimized arrangement is completed, and if not, the step (2) is executed again. Compared with the prior art, the wind farm multi-model draught fan optimized arrangement method based on the genetic algorithm has the advantages of being visual in coding mode, good in performance index, high in local search capacity, high in expansibility, high in practicability and the like.

Description

technical field [0001] The invention relates to a method for optimally arranging multi-type wind turbines in a wind farm, in particular to a method for optimally arranging multi-type wind turbines in a wind farm based on a genetic algorithm. Background technique [0002] The micro-site selection technology of wind farms is a key scientific issue for the rational planning of the wind power industry. It can fundamentally eliminate mistakes in wind turbine selection and site selection. Contribute to the realization of reasonable decision-making and scientific development of wind farm construction. However, how to make full use of wind energy resources at different heights of wind farms and further improve the economic benefits of wind farms is still one of the difficult problems in the field of micro-site selection. [0003] The actual wind farm site selection mainly includes two parts: macro site selection and micro site selection. The macro-site selection of wind farms is t...

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Problems solved by technology

Although choosing such a research object reduces the difficulty of algorithm design and actual wind farm construction, however, in the near-surface layer, due to the wind shear effect, the wind speed changes significantly with height, and the specific performance is that the wind speed increases with height, so only the A wind farm with a single type of wind turbine will affect the power output of the wind farm and the utilization rate of wind energy resources to a certain extent, thereby reducing the production efficiency and economic indicators of the entire wind farm

[0005] Among the documents and patents related to the present invention, documents J.Castro Mora, J.M.Calero Barón, J.M.Riquelme Santos, and M.Burgos Payán, "An evolutive algorithm for wind farm optimal design," Neurocomputing, pp.2651-2658, 2007 A solution to the problem of optimal arrangement of multi-type wind turbines is proposed, but the number of wind turbines in the optimization results in the literature is small and the influence of wake flow is not considered, which does not meet the requirements of actual wind farm construction; the patent "A Real-coded Genetic Algorithm Based Wind farm micro-site selection optimization method" (application publication number is CN102142103A) proposed to use the genetic algorithm of real number coding method to solve the micro-site selection optimization of wind farms, but the research object is a wind farm composed of a single model, and does not consider multi-model wind turbines The optimal arrangement of wind energy resources at different heights cannot be fully utilized

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