A method for optimizing the arrangement of multi-model wind turbines in a wind farm

Abstract

The invention discloses a wind farm multi-type wind driven generator arrangement optimization method based on a genetic algorithm nested in a particle swarm optimization. The genetic algorithm is used for choosing wind driven generator position. The particle swarm optimization is used for obtaining the optimal solution of the wind driven generator position type selection and the optimal solution is adopted as fitness of the wind driven generator position. The use of the generic algorithm guarantees that a feasible solution can be obtained for solving the nonlinear strong coupling optimization problem. The use of the particle swarm optimization not only guarantees that when various types of the wind driven generators have lots of parameters, and the type selection solution is obtained rapidly but also guarantees that the two algorithm are used in a nest mode, and when iteration times are too many, the calculation time is not likely to be too long. The wind driven generator position coordinate is directly encoded rather than choosing the checkerboards after the wind farm area is divided into checkerboards, and continuous search is capable of being conducted in the wind farm scope. The wind farm multi-type wind driven generator arrangement optimization method based on the genetic algorithm nested in the particle swarm optimization doesn't need to divide the wind farm into square grids, and compared with the prior art, the performance index is better, the position scheme is more accurate and the practicability is stronger.

Description

technical field [0001] The invention relates to a method for optimizing the arrangement of multi-type wind generators in a wind farm, in particular to a method for optimizing the arrangement of multi-type wind generators in a wind farm based on a genetic algorithm nested particle swarm algorithm. Background technique [0002] Wind energy is a non-polluting, renewable new energy. In a modern society where energy is scarce and traditional energy pollutes the environment seriously, the wind power industry has become one of the vigorously developed new energy industries. The micro-site selection of wind farms is a necessary step for the rational planning of the wind power industry. The micro-site selection of wind farms before the construction of wind farms can effectively improve the utilization efficiency of wind energy, increase the service life of wind turbines, reduce the operation and maintenance costs of wind farms and wind power generation costs, so as to achieve reasona...

AI Technical Summary

Problems solved by technology

However, the research objects are all simplified conceptual wind farms, most of which only choose a single type of wind turbine, divide the wind farm into grids similar to a checkerboard, and code "0" and "1" for each grid to represent Whether the wind turbine is installed or not, it is impossible to conduct a continuous search in the two-dimensional space of the wind farm area. At the same time, the height of a single type of wind turbine is the same, and the power curve of the wind turbine is consistent, so it is impossible to make full use of the wind energy resources distributed in the three-dimensional space.

Therefore, the traditional wind turbine arrangement optimization algorithm cannot effectively improve the wind farm production efficiency and reduce the wind farm production cost, and needs further improvement and performance improvement

[0004] Among the documents and patents related to this patent, the document Castro Mora, J et al. published the paper "An evolutive algorithm for wind farm optimal design" in Neurocomputing in 2007, and proposed the problem of multi-model fan arrangement optimization and gave A solution, but the optimization does not take into account the wake effect between fans

The patent "A Genetic Algorithm-based Optimal Arrangement Scheme for Multi-model Wind Turbine in Wind Farm" (Application Publication No.: CN103793566A) proposes to use genetic algorithm to solve the problem of multi-type wind turbine arrangement, but the search method in the wind farm area It is artificially divided into a grid that is a multiple of the diameter of the fan, and the location of the fan arrangement is relatively rough and not accurate enough

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