CN216053046U - Optimal array distribution system based on shadow multiplying power - Google Patents
Optimal array distribution system based on shadow multiplying power Download PDFInfo
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Abstract
The utility model discloses an optimal arraying system based on shadow multiplying power, which comprises: the acquisition module is used for acquiring the array data samples; the matrix module is used for forming a data matrix according to the data samples; the preprocessing module is used for preprocessing data of the data matrix; the extraction extension module is used for extracting characteristic quantity and extending implicit rules of the preprocessed data matrix; and the neural network is trained by using the expanded data matrix and is used for outputting the optimal array characteristic parameters. Through the connection work of a plurality of modules, a certain amount of event simulation is carried out depending on environmental data, data characteristic quantity mining and expansion are carried out aiming at discretely distributed event data, and nonlinear fitting is carried out through a trained and tested Kohonen neural network clustering model to output optimal characteristic parameters. The optimal photovoltaic design electricity cost is realized, and the optimal inclination angle, the optimal array spacing, the land control index and the capital investment mode are coordinated and unified.
Description
Technical Field
The utility model relates to an arraying system, in particular to an optimal arraying system based on shadow multiplying power.
Background
In the traditional arrangement design, the maximum non-shielding design is carried out on the basis of a 9: 00-15: 00 time period of winter solstice days, and the optimal inclination angle and the optimal arrangement distance are determined to realize the maximum power generation design. Inspired by the theory of zero sum game, after the conditions of the optimal inclination angle and the optimal arrangement spacing are met, the optimal LCOE cost is also limited by factors such as land control indexes, capital investment modes and the like. It is clear how to maximize the best LCOE cost, the traditional linear interpolation scheme cannot meet the requirement of fine design. A new system is required to implement this function.
SUMMERY OF THE UTILITY MODEL
In view of the existing defects, the utility model provides an optimal arrangement system based on shadow multiplying power, which can realize optimal photovoltaic design electricity cost through the connection work of a plurality of modules, so that the optimal inclination angle, the optimal arrangement distance, the land control index and the capital investment mode are coordinated and unified.
In order to achieve the purpose, the utility model adopts the following technical scheme:
an optimal arraying system based on shadow multiplying power, the optimal arraying system comprising:
the acquisition module is used for acquiring the array data samples;
the matrix module is used for forming a data matrix according to the data samples;
the preprocessing module is used for preprocessing data of the data matrix;
the extraction extension module is used for extracting characteristic quantity and extending implicit rules of the preprocessed data matrix;
and the neural network is trained by using the expanded data matrix and is used for outputting the optimal array characteristic parameters.
According to one aspect of the utility model, the acquisition module is coupled to a matrix module, the matrix module is coupled to a preprocessing module, the preprocessing module is coupled to an extraction extension module, and the extraction extension module is coupled to a neural network.
In accordance with one aspect of the utility model, the extraction extension module includes a GAN network learning unit that includes a generator and a discriminator.
According to one aspect of the utility model, the generator receives the noise samples and generates data to be sent to a discriminator, which outputs a discrimination result, which is returned to the generator by a loss function LG and to the discriminator by a loss function LD.
According to one aspect of the utility model, the optimal arraying characteristic parameters comprise optimal inclination angles, optimal arraying intervals and maximum power generation quantity characteristic parameters.
The implementation of the utility model has the advantages that: the utility model discloses an optimal arraying system based on shadow multiplying power, which comprises: the acquisition module is used for acquiring the array data samples; the matrix module is used for forming a data matrix according to the data samples; the preprocessing module is used for preprocessing data of the data matrix; the extraction extension module is used for extracting characteristic quantity and extending implicit rules of the preprocessed data matrix; and the neural network is trained by using the expanded data matrix and is used for outputting the optimal array characteristic parameters. Through the connection work of a plurality of modules, a certain amount of event simulation is carried out depending on environmental data, data characteristic quantity mining and expansion are carried out aiming at discretely distributed event data, and nonlinear fitting is carried out through a trained and tested Kohonen neural network clustering model to output optimal characteristic parameters. The optimal photovoltaic design electricity cost is realized, and the optimal inclination angle, the optimal array spacing, the land control index and the capital investment mode are coordinated and unified.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic structural diagram of an optimal arraying system based on shadow multiplying power according to the present invention;
FIG. 2 is a schematic diagram of a structure of a GAN network learning unit of an optimal arraying system based on shadow multiplying power according to the present invention;
FIG. 3 is a schematic diagram of a work flow of an optimal arraying system based on shadow multiplying power according to the present invention;
fig. 4 is a Kohonen neural network topology structure diagram of an optimal arraying system based on shadow multiplying power according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, fig. 2, fig. 3 and fig. 4, an optimal arraying system based on shadow magnification includes:
the acquisition module is used for acquiring the array data samples;
the matrix module is used for forming a data matrix according to the data samples;
in practical application, the acquisition module is connected with the matrix module, the matrix module is connected with the preprocessing module, the preprocessing module is connected with the extraction extension module, and the extraction extension module is connected with the neural network.
In practical application, obtaining the arrayed data samples and forming the data matrix according to the data samples includes: acquiring maximum unconcealed day data of winter solstice and spring equinox, group string inclined plane length data, group string length data, inclination angle data, latitude data, bracket interval data, south-north and east-west gradient data and generating capacity data, and establishing a data matrix taking the inclination angle, the arrangement interval and the generating capacity as characteristics.
In practical applications, the data sample of this embodiment can be listed as follows:
in practical application, winter solstice day and spring minutes day data output of a flat ground array interval D, a mountain land declination angle delta, a sun hour angle omega, a sun altitude angle alpha and a sun azimuth angle beta is realized by adjusting an array inclination angle, flat ground shadows are realized, the distance between the front row and the rear row of a square array is realized, only the south and north direction has a slope, and the distance between the front row and the rear row and the left row and the right row of the east and the north row and the south row and the north row and the east and the west row and the south and the north row have the slope are output, wherein multiple groups of data acquisition is realized by adjusting the array inclination angle, the south and north, the east and west direction slopes, and the generated energy data acquisition is completed by PVsyst software.
The preprocessing module is used for preprocessing data of the data matrix;
in practical application, the data preprocessing of the data matrix comprises: and carrying out Weber-Fisher law normalization processing, wavelet decomposition and filter setting and reconstruction on the data matrix.
In practical application, Weber-Fisher's law
Indicating that the generating capacity is in direct proportion to the logarithm of the inclination angle and the distance of a certain interval; wherein
For evaluating the parameter coefficient, the reference quantity is the theoretically calculated inclination angle, distance and generated energy.
In practical applications, wavelet decomposition and filter setting and reconstruction thereof perform function reduction and denoising of noisy data on a data matrix and form noise samples.
The extraction extension module is used for extracting characteristic quantity and extending implicit rules of the preprocessed data matrix;
in practical application, the extraction extension module comprises a GAN network learning unit, and the GAN network learning unit comprises a generator and an arbiter.
In practical application, the generator receives the noise sample and generates data to be sent to the discriminator, the discriminator outputs a discrimination result, and the discrimination result is returned to the generator through the loss function LG and returned to the discriminator through the loss function LD.
In practical application, the characteristic quantity extraction and implicit rule extension of the preprocessed data matrix comprise: and performing GAN network learning training on the noise sample and the preprocessed data matrix aiming at the characteristic quantity and implicit rule extension.
In practical application, the GAN network learning training comprises: the noise sample is input into the generator to generate generated data, the generated data and the preprocessed data matrix are input into the discriminator to be discriminated, and the discrimination result is returned to the generator through the loss function LG and returned to the discriminator through the loss function LD, so that the data matrix is expanded.
And the neural network is trained by using the expanded data matrix and is used for outputting the optimal array characteristic parameters.
In practical application, inputting the expanded data matrix into a pre-constructed neural network for training comprises the following steps: and inputting the expanded data matrix into a Kohonen neural network clustering model for training and testing.
In practical application, the training and testing of the expanded data matrix input into the Kohonen neural network clustering model comprises the following steps: by connecting weights omegaijImplementing input layer feature vectors alphai(i ═ 1, 2, … m) and competition layer matrix βj(j is 1, 2, …, n) and the weight ω of input layer matrix neuron i and output neuron jijCalculating Euclidean values
Minimum Euclidean value djThe corresponding input layer matrix neuron i is a winning neuron, the best matching of the competition layer matrix neuron j is realized by adjusting the winning neuron and the adjacent weight, the similar aggregation is realized by gradual iteration, and a winning connotation matrix gamma is established by the best matching neuron and the adjacent neurons thereofk(k 1, 2, …, n), iterative optimization of the neuron weight coefficients τ is achieved within the context of an inclusion matrix, i.e.
ωij＝ωij+ε(αi-ωij)
Wherein posτ，posjTo win over the neuron tau, j positions of the connotation matrix,
ε is the limit threshold and learning rate, respectively.
In practical application, the trained neural network outputs the optimal array characteristic parameters, including: and outputting the optimal inclination angle, the optimal array spacing and the maximum generating capacity characteristic parameter by the trained neural network through nonlinear fitting.
In practical application, the method realizes the establishment of a non-linear fitting clustering model of shadow multiplying power by inputting inclination angle, spacing and power generation data samples, using a Weber-Fisher law, an association clustering theory and a Kohonen neural network, and outputting an optimal inclination angle, an optimal array spacing and a maximum power generation characteristic parameter.
In practical application, a certain amount of small sample database is formed by a large amount of simulation experiment data acquisition, the Weber-Fisher law, wavelet decomposition and filter denoising and reconstruction are combined, the Kohonen neural network model of a large data sample set is trained and tested by resisting the internal recessive rule expansion of a generation network, and finally the optimal inclination angle, the optimal array spacing and the maximum generating capacity characteristic parameters are output. The scheme of the utility model realizes the nonlinear optimization of shadow multiplying power optimization.
The implementation of the utility model has the advantages that: the utility model discloses an optimal arraying system based on shadow multiplying power, which comprises: the acquisition module is used for acquiring the array data samples; the matrix module is used for forming a data matrix according to the data samples; the preprocessing module is used for preprocessing data of the data matrix; the extraction extension module is used for extracting characteristic quantity and extending implicit rules of the preprocessed data matrix; and the neural network is trained by using the expanded data matrix and is used for outputting the optimal array characteristic parameters. Through the connection work of a plurality of modules, a certain amount of event simulation is carried out depending on environmental data, data characteristic quantity mining and expansion are carried out aiming at discretely distributed event data, and nonlinear fitting is carried out through a trained and tested Kohonen neural network clustering model to output optimal characteristic parameters. The optimal photovoltaic design electricity cost is realized, and the optimal inclination angle, the optimal array spacing, the land control index and the capital investment mode are coordinated and unified.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention disclosed herein are intended to be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (5)
1. An optimal arraying system based on shadow multiplying power, characterized in that the optimal arraying system comprises:
the acquisition module is used for acquiring the array data samples;
the matrix module is used for forming a data matrix according to the data samples;
the preprocessing module is used for preprocessing data of the data matrix;
the extraction extension module is used for extracting characteristic quantity and extending implicit rules of the preprocessed data matrix;
and the neural network is trained by using the expanded data matrix and is used for outputting the optimal array characteristic parameters.
2. The optimal arrangement system based on shadow multiplying power as claimed in claim 1, wherein the acquisition module is connected with a matrix module, the matrix module is connected with a preprocessing module, the preprocessing module is connected with an extraction expansion module, and the extraction expansion module is connected with a neural network.
3. The shadow magnification-based optimal arraying system according to claim 1, wherein the extraction extension module includes a GAN network learning unit, and the GAN network learning unit includes a generator and a discriminator.
4. The shadow magnification-based optimal arraying system according to claim 3, wherein the generator receives noise samples and generates data to be sent to a discriminator, the discriminator outputs a discrimination result, and the discrimination result is returned to the generator through a loss function LG and returned to the discriminator through a loss function LD.
5. The shadow magnification-based optimal arraying system according to claim 1, wherein the optimal arraying characteristic parameters include an optimal inclination angle, an optimal arraying distance and a maximum power generation amount characteristic parameter.
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