Fishing-light complementary photovoltaic power station optimization method based on power generation efficiency influence factor analysis

Abstract

The invention provides a fishing-light complementary photovoltaic power station optimization method based on power generation efficiency influence factor analysis, and the method comprises the steps: collecting influence factor data, inputting the influence factor data into a trained neural network model, and predicting and outputting the power generation efficiency of a fishing-light complementary photovoltaic power station; s3, judging whether the power generation efficiency meets a preset threshold value or not, if not, carrying out optimization adjustment on the influence factors, after the optimization operation is completed, returning to the step S1 to carry out a new round of data acquisition, sequentially executing the steps S2 and S3, and continuously monitoring the power generation efficiency until the preset threshold value is met, so as to realize the optimization of the fishing-light complementary photovoltaic power station.

Description

Technical Field

[0001] The present invention relates to the technical field of fishery-photovoltaic complementary photovoltaic power stations, and in particular to an optimization method for fishery-photovoltaic complementary photovoltaic power stations based on analysis of factors affecting power generation efficiency. Background Art

[0002] As a new energy utilization model, the photovoltaic power station for fishery and photovoltaic complementation combines photovoltaic power generation with fishery farming, realizing the comprehensive utilization of land and water. However, the power generation efficiency of the photovoltaic power station for fishery and photovoltaic complementation is affected by many factors, such as light intensity, photovoltaic module angle, water surface reflectivity, fishery farming activities, weather conditions, etc. At present, the optimization methods for the photovoltaic power station for fishery and photovoltaic complementation mainly focus on traditional empirical analysis and simple mathematical models, which makes it difficult to accurately predict and optimize the power generation efficiency and further optimize the photovoltaic power station for fishery and photovoltaic complementation. Summary of the invention

[0003] The purpose of the present invention is to solve the above-mentioned difficulties in the prior art and provide an optimization method for a fishery-photovoltaic complementary power station based on the analysis of factors affecting power generation efficiency. By comprehensively analyzing the factors affecting power generation efficiency and using a neural network model for accurate prediction and optimization, the power generation efficiency of the fishery-photovoltaic complementary power station can be improved.

[0004] The present invention is achieved through the following technical solutions:

[0005] A method for optimizing a fishery-solar complementary photovoltaic power station based on analysis of factors affecting power generation efficiency, the method comprising the following steps:

[0006] Step S1: collecting influencing factor data, the data including meteorological data of the fishery-solar complementary photovoltaic power station, the latitude of the project site, the photovoltaic array tracking mode, the photovoltaic module angle, and the orientation of the photovoltaic system array;

[0007] Step S2: inputting the meteorological data, the latitude of the project site, the photovoltaic array tracking mode, the photovoltaic module angle, and the orientation of the photovoltaic system array into the trained neural network model to predict and output the power generation efficiency of the fishery-solar complementary photovoltaic power station;

[0008] Step S3: Determine whether the power generation efficiency meets the preset threshold. If not, optimize and adjust the influencing factors. After completing the above optimization operation, return to step S1 for a new round of data collection, and execute steps S2 and S3 in sequence to continuously monitor the power generation efficiency until the preset threshold is met.

[0009] Furthermore, the meteorological data includes light intensity, temperature, humidity, and wind speed.

[0010] Furthermore, the photovoltaic array tracking modes include fixed, single-axis tracking, and dual-axis tracking.

[0011] Furthermore, before step S2, the step of constructing the neural network model includes training and optimizing the neural network model using sample data to obtain a trained neural network model.

[0012] Compared with the prior art, the present invention has the following advantages: the present invention proposes an optimization method for a fishery-photovoltaic complementary photovoltaic power station based on the analysis of factors affecting power generation efficiency, collects data on influencing factors, inputs the data on influencing factors into a trained neural network model, and predicts and outputs the power generation efficiency of the fishery-photovoltaic complementary photovoltaic power station; determines whether the power generation efficiency meets a preset threshold, and when it does not meet the threshold, optimizes and adjusts the influencing factors. After completing the above optimization operation, returns to step S1 for a new round of data collection, and executes steps S2 and S3 in sequence, continuously monitors the power generation efficiency, and achieves the optimization of the fishery-photovoltaic complementary photovoltaic power station. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the specific implementation of the present invention or the technical solution in the prior art, the drawings required for describing the specific implementation or the prior art will be briefly introduced below. Obviously, the drawings described below are only one implementation of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0014] Figure 1 The figure is a flow chart of an optimization method for a fishery-solar complementary photovoltaic power station based on the analysis of factors affecting power generation efficiency.

[0015] Figure 2 It is the structure of a neural network model. DETAILED DESCRIPTION

[0016] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the technical scheme in the specific implementation mode of the present invention is clearly and completely described below to further illustrate the present invention. Obviously, the specific implementation mode described is only a part of the implementation mode of the present invention, rather than all styles.

[0017] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0018] As attached Figure 1 As shown, the present invention discloses a method for optimizing a fishery-solar complementary photovoltaic power station based on analysis of factors affecting power generation efficiency, the method comprising the following steps:

[0019] Step S1: collecting influencing factor data, the data including meteorological data of the fishery-solar complementary photovoltaic power station, the latitude of the project site, the photovoltaic array tracking mode, the photovoltaic module angle, and the orientation of the photovoltaic system array;

[0020] Step S2: inputting the meteorological data, the latitude of the project site, the photovoltaic array tracking mode, the photovoltaic module angle, and the orientation of the photovoltaic system array into the trained neural network model to predict and output the power generation efficiency of the fishery-solar complementary photovoltaic power station;

[0021] Step S3: Determine whether the power generation efficiency meets the preset threshold. If not, optimize and adjust the influencing factors. After completing the above optimization operation, return to step S1 for a new round of data collection, and execute steps S2 and S3 in sequence to continuously monitor the power generation efficiency until the preset threshold is met.

[0022] Furthermore, the meteorological data includes light intensity, temperature, humidity, and wind speed.

[0023] Furthermore, the photovoltaic array tracking modes include fixed, single-axis tracking, and dual-axis tracking.

[0024] Furthermore, before step S2, the step of constructing the neural network model includes training and optimizing the neural network model using sample data, using a back propagation algorithm and a stochastic gradient descent algorithm, and continuously adjusting the weights and biases of the neural network model to obtain a trained neural network model.

[0025] The optimization of fish-solar hybrid photovoltaic power plants is highly dependent on the prediction of power generation efficiency, however, accurate prediction of power generation efficiency is extremely challenging. Therefore, in this work, we introduce a two-branch neural network model for power generation efficiency prediction. The first branch of the proposed network uses a convolutional network for feature learning, and the second branch learns features through sequentially connected BiGRU modules, which are then integrated together to form a single but representative feature vector, which is used as the input of the attention mechanism to further select for power generation efficiency prediction.

[0026] Furthermore, the neural network model adopted in the present invention is a dual-branch neural network model. After preprocessing the meteorological data, the latitude of the project site, the photovoltaic array tracking mode, the photovoltaic component angle, and the orientation of the photovoltaic system array, a feature vector F0 is constructed as the input of the dual-branch neural network model, and the feature vector F0 is input into the first feature extraction branch and the second feature extraction branch respectively to obtain features F1 and F2. The features F1 and F2 are feature fused, and the fused features are input into the self-attention module, and the predicted output is obtained through Dense layer prediction.

[0027] Furthermore, the first feature extraction branch includes a convolution layer, a maximum pooling layer, a convolution layer, a maximum pooling layer, and a flattening layer connected sequentially.

[0028] Furthermore, the second feature extraction branch includes a first BiGRU, a second BiGRU, and a flattening layer connected in sequence to obtain a feature Fb, and the Fb is input into a weight processing module to obtain a feature F2, and the F2 is obtained by the following formula:

[0029]

[0030] Among them, w=sigmoid(Fb), w represents weight, Relu() represents Relu operation, and linear() represents linear transformation.

[0031] By inputting Fb into the weight processing module, the extracted features are further optimized and the gradient disappearance can be prevented.

[0032] Furthermore, the feature fusion of the features F1 and F2 is specifically performed based on the Concat layer.

[0033] The above describes the main technical features and basic principles of the present invention and the related advantages. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the concept or basic features of the present invention. Therefore, no matter from which point of view, the above specific embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes that fall within the meaning and scope of the equivalent elements of the claims are included in the present invention.

[0034] In addition, it should be understood that although the present specification is described according to various implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each implementation method may also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A method for optimizing a fishery-solar complementary photovoltaic power station based on analysis of factors affecting power generation efficiency, characterized in that: The method comprises the following steps: Step S1: collecting influencing factor data, the data including meteorological data of the fishery-solar complementary photovoltaic power station, the latitude of the project site, the photovoltaic array tracking mode, the photovoltaic module angle, and the orientation of the photovoltaic system array; Step S2: inputting the meteorological data, the latitude of the project site, the photovoltaic array tracking mode, the photovoltaic module angle, and the orientation of the photovoltaic system array into the trained neural network model to predict and output the power generation efficiency of the fishery-solar complementary photovoltaic power station; Step S3: Determine whether the power generation efficiency meets the preset threshold. If not, optimize and adjust the influencing factors. After completing the above optimization operation, return to step S1 for a new round of data collection, and execute steps S2 and S3 in sequence to continuously monitor the power generation efficiency until the preset threshold is met.

2. The method for optimizing a fishery-solar complementary photovoltaic power station based on analysis of factors affecting power generation efficiency according to claim 1, characterized in that: The meteorological data include light intensity, temperature, humidity, and wind speed.

3. The method for optimizing a fishery-solar complementary photovoltaic power station based on analysis of factors affecting power generation efficiency according to claim 1, characterized in that: The photovoltaic array tracking modes include fixed, single-axis tracking, and dual-axis tracking.

4. The method for optimizing a fishery-solar complementary photovoltaic power station based on analysis of factors affecting power generation efficiency according to claim 1, characterized in that: Before step S2, the construction of the neural network model includes using sample data to train and optimize the neural network model, using a back propagation algorithm and a stochastic gradient descent algorithm, and continuously adjusting the weights and biases of the neural network model to obtain a trained neural network model.

5. The method for optimizing a fishery-solar complementary photovoltaic power station based on analysis of factors affecting power generation efficiency according to claim 1, characterized in that: The neural network model is a dual-branch neural network model. After preprocessing the meteorological data, the latitude of the project site, the photovoltaic array tracking mode, the photovoltaic component angle, and the orientation of the photovoltaic system array, a feature vector F0 is constructed as the input of the dual-branch neural network model. The feature vector F0 is input into the first feature extraction branch and the second feature extraction branch respectively to obtain features F1 and F2. The features F1 and F2 are fused, and the fused features are input into the self-attention module, and the predicted output is obtained through Dense layer prediction.

6. The method for optimizing a fishery-solar complementary photovoltaic power station based on analysis of factors affecting power generation efficiency according to claim 5, characterized in that: The first feature extraction branch includes a convolution layer, a maximum pooling layer, a convolution layer, a maximum pooling layer, and a flattening layer connected in sequence.

7. The method for optimizing a fishery-solar complementary photovoltaic power station based on analysis of factors affecting power generation efficiency according to claim 5, characterized in that: The second feature extraction branch includes a first BiGRU, a second BiGRU, and a flattening layer connected in sequence to obtain a feature Fb, and the Fb is input into a weight processing module to obtain a feature F2. The weight processing module is: Among them, w=sigmoid(Fb), w represents weight, Relu() represents Relu operation, and linear() represents linear transformation.

8. The method for optimizing a fishery-solar complementary photovoltaic power station based on analysis of factors affecting power generation efficiency according to claim 5, characterized in that: The feature fusion of the features F1 and F2 is specifically performed based on the Concat layer.

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