Method for optimizing site selection and model selection of wind driven generators suitable for complex constraint condition

Abstract

The invention discloses a method for optimizing the site selection and the model selection of wind driven generators suitable for a complex constraint condition. For an optimization problem of the site selection and the model selection of the wind driven generators, when nonlinear constraint conditions exist such as minimum annual power generation and shortest distance between wind driven generators, a genetic algorithm is used for site selection of the wind driven generators and a particle swarm optimization (PSO) algorithm is used for acquiring the optimal scheme of the model selection of the wind driven generators at such site. The method sets the objective function of the PSO algorithm, adds a penalty function in order to add a minimum power generation limiting condition thereto, and is in nested use with a genetic algorithm as a wind driven generator model selection optimization algorithm. In the genetic algorithm, the objective function of the genetic algorithm is set, and the penalty function is added to the shortest wind driven generator distance, that is, a safe distance limiting condition is added in order to achieve position model optimization of mixed installation of multiple models of wind driven generators in a wind power plant. The method can solve the optimization problem with the complex nonlinear constraint condition, achieves a better performance index, a more accurate model selection scheme, and better practicality.

Description

technical field [0001] The invention relates to a method for optimizing the arrangement of multi-type wind power generators in a wind farm, in particular to an optimization method for site selection and type selection of wind power generators applicable to complex constraint conditions. 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 reasonable decisi...

AI Technical Summary

Problems solved by technology

At the same time, in the process of micro-site selection of wind farms, there are also various restrictions, such as the need to meet the safety distance and the minimum power generation of wind farms in normal years, etc.

[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 Turbines in Wind Farms" (Application Publication No.: CN103793566A) proposes the use of genetic algorithms to solve the problem of multi-model wind turbine arrangement, but the optimal algorithm for fan model selection adopted It does not consider the globality of the optimization algorithm, which is relatively rough and not accurate enough

These studies do not consider complex constraints or assume very simple assumptions, which are not suitable for use in micro-site selection practice

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