Improved method for solving fan power curve parameter model based on genetic algorithm

A technology of power curve and genetic algorithm, which is applied in the field of solving fan power curve parameter models based on genetic algorithm, can solve problems such as huge time-consuming

Inactive Publication Date: 2015-11-18
HEBEI UNIV OF TECH
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Problems solved by technology

Repeatedly performing function value calculations for hundreds or even...

Method used

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  • Improved method for solving fan power curve parameter model based on genetic algorithm
  • Improved method for solving fan power curve parameter model based on genetic algorithm
  • Improved method for solving fan power curve parameter model based on genetic algorithm

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Experimental program
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Effect test

Embodiment

[0044] The power curve parameter model of a fan is:

[0045] where u is the wind speed, P is the power, is an undetermined parameter in the parametric model.

[0046] The operating data of the fan is shown in Table 1, drawn to two-dimensional coordinates as figure 1

[0047] Table 1 fan operation data

[0048]

[0049] step 1):

[0050] The range of wind speed is the range of the data in row 2 in the above table, namely (0, 25)

[0051] The range of power is the range of data in row 3 in the above table, namely (0, 2500)

[0052] The total amount of fan operation data is 10000

[0053] Step (2):

[0054] Set the grid size as (1, 100), that is, draw a straight line parallel to the power axis for each 1 unit, and draw a straight line parallel to the wind speed axis for each 100 units. The grid is as follows figure 1 As shown, a total of 625 grids are divided, and the grids are numbered from 1 to 625 in order from left to right and top to bottom.

[0055] Step (3):...

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Abstract

The invention discloses an improved method for solving a fan power curve parameter model based on a genetic algorithm. According to the method, each parameter in the fan power curve parameter model is determined through fan operation data in an operational process of wind power units. But in a process for solving the parameters of the model by using the genetic algorithm, the fitness of all individuals in a population is calculated in each iterative process, and when the data volume of the fan operation data is increased, the calculation amount of a fitness function and an evaluation function of the population is increased accordingly and the space complexity and time complexity of a program are also increased. Therefore, the fan operation data is subjected to grid-based clustering and weight assignment first and then each parameter in the parameter model is determined by applying the genetic algorithm, in the method. By clustering the fan operation data, the increment of the calculation amount of the fitness function and the evaluation function caused by the increment of the data volume of the fan operation data can be avoided; and by assigning weights to clustering points, the model can adapt to most original data points and the credibility of the model is enhanced.

Description

technical field [0001] The invention belongs to the technical field of wind power generation, and in particular relates to an improved method for solving a fan power curve parameter model based on a genetic algorithm. Background technique [0002] At present, with the proposal and implementation of the national new energy development strategy, my country's wind power industry has entered a stage of leapfrog development. With the rapid development of wind power technology in my country, the proportion of wind power in my country's total power generation has increased year by year, the proportion of wind power in power supply has increased rapidly, and the installed capacity of wind power has also increased year by year. [0003] Among them, the power characteristic curve of the wind turbine is an important index for evaluating the performance of the unit, evaluating the generating capacity of the unit and predicting the annual power generation. The power characteristic curve...

Claims

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Application Information

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IPC IPC(8): G06F17/50
Inventor 刘宁张家安李志刚王华君杨彦杰孟心怡高艳红李轩赵凡
Owner HEBEI UNIV OF TECH
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