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Non-linear fitting optimal arraying method based on shadow magnification

A nonlinear fitting and optimal technology, applied in the field of nonlinear fitting optimal array based on shadow magnification, can solve problems such as the inability to meet the needs of refined design, and achieve the coordination and unity of land use control indicators and capital investment methods. The effect of the optimal cost and optimal layout spacing synergistic unity

Pending Publication Date: 2022-01-28
CHINA SINOGY ELECTRIC ENG
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Inspired by the idea of ​​zero-sum game, after satisfying the conditions of "optimum inclination" and "optimum array spacing", the optimal LCOE cost is also limited by land use control indicators, capital investment methods and other factors
Obviously, how to maximize the optimal LCOE cost, the traditional linear interpolation scheme cannot meet the needs of fine design

Method used

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  • Non-linear fitting optimal arraying method based on shadow magnification
  • Non-linear fitting optimal arraying method based on shadow magnification
  • Non-linear fitting optimal arraying method based on shadow magnification

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0030] Embodiment one: if figure 1 , figure 2 with image 3 As shown, a nonlinear fitting optimal array method based on shadow magnification includes the following steps:

[0031] S1: Obtain array data samples and form a data matrix according to the data samples;

[0032] In practical applications, the acquisition of array data samples and formation of a data matrix according to the data samples include: acquisition of winter solstice and vernal equinox maximum non-blocking day data, string slope length data, string length data, inclination data, latitude data, brackets Spacing data, north-south, east-west slope data and power generation data, build a data matrix characterized by inclination, array spacing and power generation.

[0033] In practical applications, the data samples in this embodiment can be exemplified in the following table:

[0034]

[0035] In practical applications, by adjusting the inclination of the array, the array spacing D on the ground, the dat...

Embodiment 2

[0053] Embodiment two: if figure 1 , figure 2 , image 3 with Figure 4 As shown, an optimal array system based on shadow magnification, the optimal array system includes:

[0054] An acquisition module, configured to acquire array data samples;

[0055] A matrix module, for forming a data matrix according to the data samples;

[0056] In practical applications, the acquisition module is connected to a matrix module, the matrix module is connected to a preprocessing module, the preprocessing module is connected to an extraction extension module, and the extraction extension module is connected to a neural network.

[0057] In practical applications, obtaining array data samples and forming a data matrix based on the data samples includes: obtaining data on the winter solstice and vernal equinox maximum non-blocking day data, string slope length data, string length data, inclination data, latitude data, and bracket spacing data , north-south, east-west slope data and powe...

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Abstract

The invention discloses a non-linear fitting optimal arraying method based on shadow magnification. The method comprises the following steps of: acquiring an arraying data sample and forming a data matrix according to the data sample; performing data preprocessing on the data matrix; performing characteristic quantity extraction and implicit rule extension on the preprocessed data matrix; inputting the expanded data matrix into a pre-constructed neural network for training; and enabling the trained neural network to output an optimal arrangement characteristic parameter. Based on environment data, a certain number of event analog simulation is carried out, data characteristic quantity mining and expansion are carried out for discretized distribution event data, and nonlinear fitting is carried out through a trained and tested Kohonen neural network clustering model to output optimal characteristic parameters. The photovoltaic design cost per kilowatt-hour is optimal, so that the optimal inclination angle, the optimal array spacing, the land control index and the capital investment mode are coordinated and unified.

Description

technical field [0001] The invention relates to array design, in particular to a nonlinear fitting optimal array method based on shadow magnification. Background technique [0002] In the traditional array design, the maximum non-blocking design is based on the time period of 9:00 to 15:00 on the winter solstice, so as to determine the "best inclination" and "best array spacing" to achieve the maximum power generation design. Inspired by the idea of ​​zero-sum game, after satisfying the conditions of "best inclination" and "best array spacing", the optimal LCOE cost is also limited by land use control indicators, capital investment methods and other factors. Obviously, how to maximize the optimal LCOE cost, the traditional linear interpolation scheme cannot meet the needs of refined design. Contents of the invention [0003] In view of the above-mentioned shortcomings currently existing, the present invention provides a non-linear fitting optimal array method based on sha...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06N3/04G06N3/08
CPCG06F30/27G06N3/04G06N3/08Y02E40/70Y04S10/50
Inventor 万露苏欣周洲张敬昂王元龙陶岳来臧藏施蒋娟
Owner CHINA SINOGY ELECTRIC ENG
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