Oscillating float wave power generation method based on multi-population genetic algorithm

A technology of wave power generation and genetic algorithm, applied in the direction of calculation, calculation model, prediction, etc., can solve the problems of single individual, the final result of wave energy capture rate is not the optimal solution, etc., to achieve the effect of optimal capture rate

Inactive Publication Date: 2019-01-01
GUANGDONG UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The present invention provides an oscillating float based on a multi-population genetic algorithm to overcome the technical defect that the existing traditional genetic algorithm tends to make the individuals in the population tend to be single quickly, resulting in the final result of the wave energy capture rate not being the optimal solution. type wave power generation method

Method used

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  • Oscillating float wave power generation method based on multi-population genetic algorithm
  • Oscillating float wave power generation method based on multi-population genetic algorithm
  • Oscillating float wave power generation method based on multi-population genetic algorithm

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0064] Such as figure 1 As shown, an oscillating buoy type wave power generation method based on multi-population genetic algorithm includes the following steps:

[0065] S1: According to the oscillating float type wave power generation system, build a mathematical model of the power generation system;

[0066] S2: Analyze the output power of the system through the mathematical model of the power generation system to obtain the wave energy capture rate;

[0067] S3: Calculate the multi-population genetic algorithm according to the wave energy capture rate;

[0068] S4: Simulate the multi-population genetic algorithm, and complete the capture of the optimal wave energy solution of the multi-population genetic algorithm.

[0069] More specifically, the step S1 includes the following steps:

[0070] S11: The oscillating float type wave power generation system includes floats, mass blocks, springs and linear generators, using cylindrical floats, including:

[0071] f b (t)=-K...

Embodiment 2

[0117] When the wave period T is 2s, the evolution process of the wave energy capture rate by running the genetic algorithm and the multi-population genetic algorithm five times is as follows: Figure 4 , Figure 5 shown.

[0118] In the specific implementation process, as shown in Table 1, the optimization results obtained by the genetic algorithm for five times are all different, indicating that the optimal solution is still likely to rise, and the stability of the algorithm is not good; and the algorithm has repeatedly fallen into local optimum, and there is premature The case of convergence;

[0119] Table 3 The 5th optimal solution of the genetic algorithm with a period of 2s and the corresponding R g 、K g

[0120] i time

R g

K g

Optimal solution

1

373.9551

-512.9914

0.98755

2

301.1134

-588.2173

0.9997

3

272.6653

-633.5932

0.99586

4

344.1337

-454.0162

0.99279

5

342.1119

-521....

Embodiment 3

[0125] When the wave period T is 3s, the evolution process of the wave energy capture rate by running the genetic algorithm and the multi-population genetic algorithm five times is as follows: Figure 6 , Figure 7 shown.

[0126] In the specific implementation process, as shown in Table 5, the optimization results obtained by the genetic algorithm for five times are not the same, indicating that the optimal solution may still rise, and the stability of the algorithm is not good; The case of convergence;

[0127] Table 5 The 5th optimal solution of the genetic algorithm with a period of 3s and the corresponding R g 、K g

[0128] i time

R g

K g

Optimal solution

1

282.74563

-1018.7063

0.96875

2

332.72775

-1003.9816

0.97481

3

383.4442

-739.5465

0.98318

4

360.1364

-772.3339

0.99123

5

355.2249

-892.5923

0.98865

[0129] In the specific implementation process, as shown in Table 6,...

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Abstract

The invention provides an oscillating float wave power generation method based on a multi-population genetic algorithm, which comprises the following steps: according to an oscillating float wave power generation system, a mathematical model of the power generation system is built; The output power of the system is analyzed by the mathematical model of the power generation system, and the wave energy capture rate is obtained. According to the wave energy capture rate, a multi-population genetic algorithm is obtained. The multi-population genetic algorithm is simulated to capture the optimal solution of wave energy of multi-population genetic algorithm. The invention provides an oscillating float wave power generation method based on a multi-population genetic algorithm, which combines a multi-population genetic optimization algorithm with an MPPT of wave power generation to capture wave energy, thereby completing an optimal wave energy capture rate.

Description

technical field [0001] The present invention relates to the technical field of wave power generation, and more specifically, relates to an oscillating float type wave power generation method based on a multi-population genetic algorithm. Background technique [0002] Wave power generation is a form of new energy generation. Wave energy has high power density, good predictability, and good development potential. How to realize the maximum power point tracking of wave energy is a key technology in wave power generation research. Genetic algorithm is an effective target optimization tool, which has been widely used in photovoltaic cell arrays and wind power generation systems. However, the traditional genetic algorithm only has the genetic operation of a single population. Under the influence of individuals with a large fitness function value, it is easy to make the individuals in the population tend to be single quickly, and the population update stops quickly and is prone to ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/00
CPCG06N3/006G06Q10/04G06Q50/06Y02E40/70Y04S10/50
Inventor 吴丹琦杨俊华邹子君
Owner GUANGDONG UNIV OF TECH
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