A system and method for predicting the amount of power generated by a photovoltaic installation at sea

Abstract

The application discloses a kind of applied to offshore photovoltaic power generation quantity prediction system and method, it is related to photovoltaic power generation quantity prediction technical field, it is solved that the prior art in offshore photovoltaic power generation quantity is predicted, without considering that salt film can exist on photovoltaic panel before prediction, directly through the initial power generation efficiency of photovoltaic is predicted, leading to the technical problem that prediction accuracy is not high;The application obtains the image of offshore photovoltaic platform photovoltaic panel and salt fog concentration;By analyzing the image of photovoltaic panel, salt film data is obtained;Salt film power generation efficiency loss factor is calculated based on salt film data;The initial power generation efficiency of current photovoltaic panel is calculated based on salt film power generation efficiency loss factor;Photovoltaic power generation efficiency is calculated based on salt fog concentration and initial power generation efficiency;The power generation of offshore photovoltaic is predicted based on photovoltaic power generation efficiency, solve the above technical problem.

Description

Technical Field

[0001] This invention belongs to the field of photovoltaic power generation prediction, specifically a prediction system and method for offshore photovoltaic power generation. Background Technology

[0002] With the increasing global demand for clean energy, offshore photovoltaic power generation, as a renewable energy utilization method with great potential, has received widespread attention.

[0003] The prior art (invention patent application number 2022106044366) discloses a method and system for predicting the power generation of photovoltaic panels on offshore platforms. The method includes: acquiring relevant data for predicting the power generation of photovoltaic panels; the relevant data includes: solar radiation of the prediction date and prediction sea area, photovoltaic panel area, photovoltaic panel efficiency, power efficiency, temperature influence coefficient of seawater temperature on photovoltaic panel power generation efficiency, wave influence coefficient of sea waves on photovoltaic panel power generation, cloud cover coefficient of cloud cover on photovoltaic panel power generation, and solar radiation attenuation coefficient of seawater covering photovoltaic panels on their power generation. Based on the relevant data, a custom photovoltaic panel power generation prediction model is used to predict the power generation of photovoltaic panels. This solves the problem that the power generation prediction of existing offshore platforms is inaccurate due to factors such as the angle of the photovoltaic panels being affected by sea waves and the surface being covered by seawater, and effectively improves the accuracy of photovoltaic panel power generation prediction on offshore platforms. However, existing technologies do not consider the dynamic impact of salt film on power generation efficiency. Salt film is a coating layer formed by the crystallization of salt on the surface of photovoltaic panels after seawater evaporates. It is mainly caused by the evaporation of water after sea spray is deposited. When predicting the power generation of offshore photovoltaics, existing technologies do not take into account that there may already be a salt film on the photovoltaic panels before the prediction. Therefore, predicting directly based on the initial power generation efficiency of photovoltaics is obviously not very accurate.

[0004] Therefore, this invention proposes a prediction system and method for offshore photovoltaic power generation, which solves the above-mentioned problems. Summary of the Invention

[0005] The present invention aims to at least solve one of the technical problems existing in the prior art; to this end, the present invention proposes a prediction system and method for offshore photovoltaic power generation, which solves the technical problem that the prior art does not take into account the possibility that there is already a salt film on the photovoltaic panel before the prediction, and directly predicts based on the initial power generation efficiency of the photovoltaic, resulting in low prediction accuracy.

[0006] To achieve the above objectives, a first aspect of the present invention provides a prediction system for offshore photovoltaic power generation, comprising: a data acquisition module, a data analysis module, and a power generation prediction module;

[0007] Data acquisition module: used to acquire images of photovoltaic panels on offshore photovoltaic platforms and salt spray concentration;

[0008] Data Analysis Module: This module analyzes images of photovoltaic panels to obtain salt film data. This data includes the salt film coverage area and thickness.

[0009] The salt film power generation efficiency loss factor is calculated based on the salt film data; the initial power generation efficiency of the current photovoltaic panel is calculated based on the salt film power generation efficiency loss factor; and the photovoltaic power generation efficiency is calculated based on the salt spray concentration and the initial power generation efficiency.

[0010] Power generation prediction module: Predicts the power generation of offshore photovoltaic systems based on photovoltaic power generation efficiency.

[0011] Preferably, acquiring images of the photovoltaic panels on the offshore photovoltaic platform and the salt spray concentration includes:

[0012] The image of the current photovoltaic panel is acquired using an image acquisition device;

[0013] Salt spray concentration is collected in real time using data sensors.

[0014] Preferably, the analysis of the photovoltaic panel image includes:

[0015] The image of the photovoltaic panel is converted into a black and white binary image using a binarization method, and the salt film coverage area in the black and white binary image is obtained; wherein, the binarization method includes: global thresholding or adaptive thresholding;

[0016] The salt film coverage ratio is obtained by dividing the number of pixels in the salt film-covered area of ​​the black-and-white binary image by the total number of pixels in the black-and-white binary image.

[0017] The coverage area of ​​the salt film is obtained by multiplying the coverage ratio of the salt film by the total area of ​​the photovoltaic panels.

[0018] The image of the photovoltaic panel is converted into a grayscale image; the coverage area of ​​the salt film in the black and white binary image is mapped to the grayscale image, and the average grayscale value of the salt film coverage area in the grayscale image is obtained.

[0019] The average gray value of the salt film coverage area is identified by a pre-trained salt film thickness recognition model to obtain the corresponding salt film thickness.

[0020] It should be noted that the salt film is a coating layer formed on the surface of photovoltaic panels by the crystallization of salt (such as sodium chloride and calcium sulfate) after seawater evaporates. It is mainly caused by the evaporation of water after sea spray is deposited. It reduces the light transmittance (blocks incident light), increases surface reflection and scattering, and causes electrochemical corrosion (such as oxidation of metal frames and peeling of glass coatings), resulting in a significant decrease in the power generation efficiency of photovoltaic panels.

[0021] Preferably, the training method for the salt film thickness recognition model includes:

[0022] Images of several salt film thicknesses and the average gray value of the salt film-covered areas in the images were obtained from a historical database.

[0023] The average gray values ​​of the salt film-covered area in the image are integrated into standard input data, and the salt film thickness corresponding to the average gray values ​​of the salt film-covered area in the image is integrated into standard output data.

[0024] An artificial intelligence model is trained based on standard input data and standard output data to obtain a salt film thickness recognition model; the artificial intelligence model includes: convolutional neural network or deep belief network.

[0025] It should be noted that the historical database stores images of different salt film thicknesses obtained through manual measurement.

[0026] Preferably, the step of calculating the salt film power generation efficiency loss factor based on salt film data includes:

[0027] The salt film coverage area is denoted as YS, and the salt film thickness is denoted as YH;

[0028] The transmittance T corresponding to the current salt film thickness is calculated using the formula T=e^(-α×YH); where α is the light absorption coefficient of the salt film.

[0029] The overall average transmittance of the photovoltaic panels on the current photovoltaic platform is calculated using the formula Tavg=(YS / S)×T+(1-(YS / S))×T0; where Tavg is the overall average transmittance, S is the total area of ​​the photovoltaic panels on the photovoltaic platform, and T0 is the initial transmittance.

[0030] The salt film power generation efficiency loss factor is calculated using the formula Δη=η0×(1-(Tavg / T0)); where Δη is the salt film power generation efficiency loss factor and η0 is the initial power generation efficiency.

[0031] It should be noted that the light absorption coefficient α of the salt film was determined experimentally; for example, the transmittance (T1, T2) at different salt film thicknesses (H1, H2) was measured by a spectrometer, i.e., T1=e^(-α×H1) and T2=e^(-α×H2), from which we can derive: α=ln(T1 / T2) / (H2-H1);

[0032] Initial transmittance refers to the proportion of incident light that passes through the surface glass or protective layer of a photovoltaic panel to reach the solar cell when the panel is completely clean and free of pollution; initial power generation efficiency refers to the power generation efficiency of the photovoltaic panel on the photovoltaic platform without a salt film.

[0033] Preferably, the calculation of the initial power generation efficiency of the photovoltaic panel based on the salt film power generation efficiency loss factor includes:

[0034] The initial power generation efficiency of the photovoltaic system is obtained by calculating the difference between the initial power generation efficiency and the loss factor of the salt film power generation efficiency.

[0035] Preferably, the calculation of photovoltaic power generation efficiency based on salt spray concentration and initial power generation efficiency includes:

[0036] The current salt spray concentration is identified by a pre-trained salt spray deposition rate recognition model, and the corresponding salt spray deposition rate is obtained.

[0037] Obtain the preset time period for the required predicted power generation; divide the preset time period into several time periods i according to the preset time interval; where i = {1,2,3,…,N}, and N is the total number of time periods after division;

[0038] Through the formula ηi=η a The photovoltaic power generation efficiency for each time period is calculated using the formula ×e^(-β×R×ti); where ηi refers to the photovoltaic power generation efficiency in the i-th time period, β is the proportionality coefficient, and ηi is the photovoltaic power generation efficiency in the ith time period. a R is the initial power generation efficiency of photovoltaics, R is the salt spray deposition rate, and ti refers to the duration of the i-th time period.

[0039] It should be noted that the preset time period refers to the overall time range for which the photovoltaic power generation needs to be predicted, such as the next 24 hours; if the preset time interval is 1 hour, that is, it is divided once every hour, then the next 24 hours will be divided into 24 1-hour time periods, N=24.

[0040] The preset time interval is set according to the actual required accuracy. The smaller the preset time interval, the higher the accuracy of the predicted power generation.

[0041] The proportionality coefficient β is set by those skilled in the art based on experience or experimental calibration, and reflects the degree to which photovoltaic power generation efficiency is affected by salt spray deposition rate.

[0042] Preferably, the training method for the salt spray deposition rate identification model includes:

[0043] Based on the historical salt spray database, several salt spray concentrations and corresponding salt spray deposition rates were obtained;

[0044] An artificial intelligence model is trained based on training data to obtain a salt spray deposition rate recognition model. The training data includes training input data and training output data. The training input data is the salt spray concentration, and the training output data is the salt spray deposition rate corresponding to the salt spray concentration.

[0045] It should be noted that the salt spray deposition rate corresponding to the salt spray concentration in the historical salt spray database was obtained manually.

[0046] Preferably, the prediction of offshore photovoltaic power generation based on photovoltaic power generation efficiency includes:

[0047] The power generation of offshore photovoltaic power is predicted by a pre-set prediction model.

[0048] The preset prediction model is: E=∑(ηi×Gi×S×ti); where ∑ is the summation symbol, the summation range is (1,N), and Gi is the light intensity in the i-th time period.

[0049] It should be noted that the light intensity at different times is obtained through a weather forecasting platform.

[0050] A second aspect of the present invention provides a method for predicting offshore photovoltaic power generation, comprising:

[0051] Acquire images of photovoltaic panels on offshore photovoltaic platforms and salt spray concentrations;

[0052] Salt film data was obtained by analyzing images of photovoltaic panels;

[0053] The salt film power generation efficiency loss factor was calculated based on the salt film data.

[0054] The initial power generation efficiency of the photovoltaic panel is calculated based on the salt film power generation efficiency loss factor.

[0055] The photovoltaic power generation efficiency is calculated based on the salt spray concentration and the initial power generation efficiency.

[0056] Predicting the power generation of offshore photovoltaic systems based on photovoltaic power generation efficiency.

[0057] Compared with the prior art, the beneficial effects of the present invention are:

[0058] 1. Existing technologies for predicting offshore photovoltaic (PV) power generation do not consider the possibility of a salt film already present on the PV panel before prediction, directly relying on the initial power generation efficiency of the PV system, resulting in low prediction accuracy. This invention acquires PV panel images in real time using image acquisition equipment, accurately extracts the salt film coverage area and average grayscale value of the covered area using binarization and grayscale analysis techniques, and dynamically calculates the current salt film thickness using a pre-trained salt film thickness recognition model. Then, it quantifies the attenuation of light transmittance by the salt film using a formula, ultimately calculating the overall average light transmittance. Based on the overall average light transmittance, it calculates the salt film power generation efficiency loss factor, thereby correcting the initial power generation efficiency and obtaining the initial power generation efficiency. The power generation prediction module dynamically predicts PV power generation efficiency over different time periods based on factors such as real-time salt spray deposition rate, the corrected initial power generation efficiency, and light intensity, and accumulates the total power generation. This effectively solves the problem of existing technologies neglecting the dynamic impact of the salt film on power generation efficiency, not only improving prediction accuracy but also better reflecting the performance of the PV system under actual operating conditions, providing solid data support for optimizing the operation and maintenance strategies of offshore PV power plants.

[0059] 2. Existing technologies neglect the impact of salt spray environments on photovoltaic power generation efficiency. In salt spray environments, salt deposits on the surface of photovoltaic panels to form a salt film. As the salt spray concentration increases and time progresses, the thickness of the salt film gradually increases, leading to a decrease in light transmittance and an increase in surface reflection and scattering, thereby significantly reducing the actual power generation efficiency of the photovoltaic panel. This invention utilizes a pre-trained artificial intelligence model to accurately identify the corresponding salt spray deposition rate based on real-time salt spray concentration, thus quantifying the salt film growth rate. The overall prediction period is subdivided into multiple smaller time periods (e.g., one hour per period), and the photovoltaic power generation efficiency for each time period is dynamically calculated using a formula based on the salt spray deposition rate, the corrected initial power generation efficiency, and the duration of each smaller time period. This method can more accurately reflect the impact of salt film growth on power generation efficiency over time. Combining light intensity and photovoltaic panel area, an additive model is used to comprehensively predict the power generation throughout the entire prediction period, ensuring that the prediction results not only consider the power generation efficiency loss in the initial state but also fully incorporate the continuous impact of future salt spray deposition rates on power generation efficiency. This effectively solves the problem of existing technologies neglecting the dynamic changes of the salt film and the impact of salt spray environments on photovoltaic power generation efficiency during actual operation, leading to inaccurate predictions. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 This is a schematic diagram of the system modules according to an embodiment of the present invention;

[0062] Figure 2 This is a schematic diagram of the salt film data acquisition method according to an embodiment of the present invention;

[0063] Figure 3 This is a schematic diagram of the method steps in an embodiment of the present invention. Detailed Implementation

[0064] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] Please see Figure 1 The first aspect of the present invention provides a prediction system for offshore photovoltaic power generation, comprising: a data acquisition module, a data analysis module, and a power generation prediction module;

[0066] Data acquisition module: used to acquire images of photovoltaic panels on offshore photovoltaic platforms and salt spray concentration;

[0067] Data Analysis Module: This module analyzes images of photovoltaic panels to obtain salt film data. This data includes the salt film coverage area and thickness.

[0068] The salt film power generation efficiency loss factor is calculated based on the salt film data; the initial power generation efficiency of the current photovoltaic panel is calculated based on the salt film power generation efficiency loss factor; and the photovoltaic power generation efficiency is calculated based on the salt spray concentration and the initial power generation efficiency.

[0069] Power generation prediction module: Predicts the power generation of offshore photovoltaic systems based on photovoltaic power generation efficiency.

[0070] Acquire images of photovoltaic panels on offshore solar platforms and salt spray concentrations, including:

[0071] The image of the current photovoltaic panel is acquired using an image acquisition device;

[0072] Salt spray concentration is collected in real time using data sensors.

[0073] See Figure 2 By analyzing images of photovoltaic panels, including:

[0074] The image of the photovoltaic panel is converted into a black and white binary image using a binarization method, and the salt film coverage area in the black and white binary image is obtained; wherein, the binarization method includes: global thresholding or adaptive thresholding;

[0075] The salt film coverage ratio is obtained by dividing the number of pixels in the salt film-covered area of ​​the black-and-white binary image by the total number of pixels in the black-and-white binary image.

[0076] The coverage area of ​​the salt film is obtained by multiplying the coverage ratio of the salt film by the total area of ​​the photovoltaic panels.

[0077] The image of the photovoltaic panel is converted into a grayscale image; the coverage area of ​​the salt film in the black and white binary image is mapped to the grayscale image, and the average grayscale value of the salt film coverage area in the grayscale image is obtained.

[0078] The average gray value of the salt film coverage area is identified by a pre-trained salt film thickness recognition model to obtain the corresponding salt film thickness.

[0079] Training methods for salt film thickness recognition models include:

[0080] Images of several salt film thicknesses and the average gray value of the salt film-covered areas in the images were obtained from a historical database.

[0081] The average gray values ​​of the salt film-covered area in the image are integrated into standard input data, and the salt film thickness corresponding to the average gray values ​​of the salt film-covered area in the image is integrated into standard output data.

[0082] An artificial intelligence model is trained based on standard input data and standard output data to obtain a salt film thickness recognition model; the artificial intelligence model includes: convolutional neural network or deep belief network.

[0083] The salt film power generation efficiency loss factor was calculated based on salt film data, including:

[0084] The salt film coverage area is denoted as YS, and the salt film thickness is denoted as YH;

[0085] The transmittance T corresponding to the current salt film thickness is calculated using the formula T=e^(-α×YH); where α is the light absorption coefficient of the salt film.

[0086] The overall average transmittance of the photovoltaic panels on the current photovoltaic platform is calculated using the formula Tavg=(YS / S)×T+(1-(YS / S))×T0; where Tavg is the overall average transmittance, S is the total area of ​​the photovoltaic panels on the photovoltaic platform, and T0 is the initial transmittance.

[0087] The salt film power generation efficiency loss factor is calculated using the formula Δη=η0×(1-(Tavg / T0)); where Δη is the salt film power generation efficiency loss factor and η0 is the initial power generation efficiency.

[0088] The initial power generation efficiency of the photovoltaic panel is calculated based on the salt film power generation efficiency loss factor, including:

[0089] The initial power generation efficiency of the photovoltaic system is obtained by calculating the difference between the initial power generation efficiency and the loss factor of the salt film power generation efficiency.

[0090] The photovoltaic power generation efficiency is calculated based on salt spray concentration and initial power generation efficiency, including:

[0091] The current salt spray concentration is identified by a pre-trained salt spray deposition rate recognition model, and the corresponding salt spray deposition rate is obtained.

[0092] Obtain the preset time period for the required predicted power generation; divide the preset time period into several time periods i according to the preset time interval; where i = {1,2,3,…,N}, and N is the total number of time periods after division;

[0093] Through the formula ηi=η a The photovoltaic power generation efficiency for each time period is calculated using the formula ×e^(-β×R×ti); where ηi refers to the photovoltaic power generation efficiency in the i-th time period, β is the proportionality coefficient, and ηi is the photovoltaic power generation efficiency in the ith time period. a R is the initial power generation efficiency of photovoltaics, R is the salt spray deposition rate, and ti refers to the duration of the i-th time period.

[0094] Training methods for salt spray deposition rate identification models include:

[0095] Based on the historical salt spray database, several salt spray concentrations and corresponding salt spray deposition rates were obtained;

[0096] An artificial intelligence model is trained based on training data to obtain a salt spray deposition rate recognition model. The training data includes training input data and training output data. The training input data is the salt spray concentration, and the training output data is the salt spray deposition rate corresponding to the salt spray concentration.

[0097] Predicting the power generation of offshore photovoltaic systems based on photovoltaic power generation efficiency includes:

[0098] The power generation of offshore photovoltaic power is predicted by a pre-set prediction model.

[0099] The preset prediction model is: E=∑(ηi×Gi×S×ti); where ∑ is the summation symbol, the summation range is (1,N), and Gi is the light intensity in the i-th time period.

[0100] For example: a near-shore floating photovoltaic power station, where each photovoltaic panel has an area of ​​10m². 2 The total number of photovoltaic panels is 50, and the total area of ​​the photovoltaic panels on the entire platform is S = 500m². 2 The 24-hour power generation of offshore photovoltaic panels is predicted as follows:

[0101] 1. System parameter settings;

[0102] Photovoltaic panel parameters:

[0103] Initial power generation efficiency η0 = 20% (without salt film).

[0104] Initial light transmittance T0 = 90% (clean condition).

[0105] The light absorption coefficient of the salt film is α = 0.05 μm. -1 (Experimental measurement value).

[0106] Environmental parameters:

[0107] Forecast period: the next 24 hours, divided into hourly intervals (N=24).

[0108] Time-of-day light intensity (Gi): Obtained from a weather forecasting platform, assuming typical values ​​for a sunny day:

[0109] 0-6 AM (Nighttime): Gi = 0 W / m 2 (No light);

[0110] 7-10 AM (morning): Gi = 300 W / m 2 ;

[0111] 11:00 AM to 1:00 PM (noon): Gi = 1000 W / m 2 ;

[0112] 2 PM to 5 PM: Gi = 800 W / m 2 ;

[0113] 18:00–24:00 (Evening to Night): Gi = 0 W / m 2 ;

[0114] Salt spray related parameters:

[0115] Salt spray concentration: Real-time monitoring value is 10 mg / m³ 3 ;

[0116] Salt spray deposition rate identification model: Input salt spray concentration 10 mg / m³ 3 The output salt spray deposition rate R = 0.5 mg / (m²) 2 ·h);

[0117] Proportionality coefficient β: experimentally calibrated to 0.002h -1 ·mg -1 ·m 2 .

[0118] 2. Data acquisition module;

[0119] Image acquisition and salt film analysis:

[0120] Images of the photovoltaic panel surface were captured using a drone;

[0121] The salt film coverage area, YS = 95.25 m², was obtained using the binarization method. 2 ;

[0122] The image is converted to a grayscale image. Assuming the average grayscale value of the salt film region in the grayscale image is 120 (200 for the clean region), the output salt film thickness YH = 20μm is obtained by mapping through a pre-trained CNN model.

[0123] 3. Data Analysis Module;

[0124] Calculation of salt film power generation efficiency loss factor:

[0125] The transmittance T corresponding to the current salt film thickness is calculated using the formula T=e^(-α×YH);

[0126] Substituting the data, we get T≈36.79%;

[0127] The overall average light transmittance of the photovoltaic panels on the current photovoltaic platform is calculated using the formula Tavg=(YS / S)×T+(1-(YS / S))×T0.

[0128] Substituting the data, we get Tavg≈79.86%;

[0129] The salt film power generation efficiency loss factor is calculated using the formula Δη=η0×(1-(Tavg / T0)).

[0130] Substituting the data, we get Δη≈1.99%;

[0131] Initial power generation efficiency η a =η0-Δη=18.01%;

[0132] Dynamic photovoltaic power generation efficiency calculation (time-of-use calculation):

[0133] Hourly efficiency ηi:

[0134] Using the formula ηi=η a ×e^(-β×R×ti).

[0135] Calculation example:

[0136] Hour 1 (7:00): η1≈17.98%.

[0137] 12th hour (12 noon): After 5 hours of salt spray deposition, η12≈17.68%.

[0138] 24 hours (24:00 at night): η24≈17.24%.

[0139] 4. Power generation prediction module;

[0140] Time-of-use power generation calculation: E=∑(ηi×Gi×S×ti);

[0141] The final calculation yielded E = 651.8 kWh.

[0142] Compared with existing methods:

[0143] If the effects of salt film and salt spray are ignored, and the initial efficiency η0 = 20% is used directly, then: E≈740kWh;

[0144] Error analysis: The predicted value of 651.8 kWh in this invention is about 12% lower than that of existing methods, and the prediction accuracy is higher (efficiency loss is caused by salt film and salt spray).

[0145] See Figure 3 A second aspect of the present invention provides a method for predicting offshore photovoltaic power generation, comprising:

[0146] Acquire images of photovoltaic panels on offshore photovoltaic platforms and salt spray concentrations;

[0147] Salt film data was obtained by analyzing images of photovoltaic panels;

[0148] The salt film power generation efficiency loss factor was calculated based on the salt film data.

[0149] The initial power generation efficiency of the photovoltaic panel is calculated based on the salt film power generation efficiency loss factor.

[0150] The photovoltaic power generation efficiency is calculated based on the salt spray concentration and the initial power generation efficiency.

[0151] Predicting the power generation of offshore photovoltaic systems based on photovoltaic power generation efficiency.

[0152] Some of the data in the above formula are calculated by removing dimensions and taking their numerical values. The formula is the closest to the real situation obtained by software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0153] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A prediction system for offshore photovoltaic power generation, characterized in that, include: Data acquisition module, data analysis module, and power generation prediction module; Data acquisition module: used to acquire images of photovoltaic panels on offshore photovoltaic platforms and salt spray concentration; Data Analysis Module: This module analyzes images of photovoltaic panels to obtain salt film data. This data includes the salt film coverage area and thickness. The salt film power generation efficiency loss factor is calculated based on the salt film data; the initial power generation efficiency of the current photovoltaic panel is calculated based on the salt film power generation efficiency loss factor; and the photovoltaic power generation efficiency is calculated based on the salt spray concentration and the initial power generation efficiency. Power generation prediction module: Predicts the power generation of offshore photovoltaic platforms based on photovoltaic power generation efficiency.

2. The prediction system for offshore photovoltaic power generation according to claim 1, characterized in that, The acquisition of images of photovoltaic panels on offshore photovoltaic platforms and salt spray concentrations includes: The image of the current photovoltaic panel is acquired using an image acquisition device; Salt spray concentration is collected in real time using data sensors.

3. The prediction system for offshore photovoltaic power generation according to claim 1, characterized in that, The analysis of images of photovoltaic panels includes: The image of the photovoltaic panel is converted into a black and white binary image using a binarization method, and the salt film coverage area in the black and white binary image is obtained; wherein, the binarization method includes: global thresholding or adaptive thresholding; The salt film coverage ratio is obtained by dividing the number of pixels in the salt film-covered area of ​​the black-and-white binary image by the total number of pixels in the black-and-white binary image. The coverage area of ​​the salt film is obtained by multiplying the coverage ratio of the salt film by the total area of ​​the photovoltaic panels. The image of the photovoltaic panel is converted into a grayscale image; the coverage area of ​​the salt film in the black and white binary image is mapped to the grayscale image, and the average grayscale value of the salt film coverage area in the grayscale image is obtained. The average gray value of the salt film coverage area is identified by a pre-trained salt film thickness recognition model to obtain the corresponding salt film thickness.

4. The prediction system for offshore photovoltaic power generation according to claim 3, characterized in that, The training method for the salt film thickness recognition model includes: Images of several salt film thicknesses and the average gray value of the salt film-covered areas in the images were obtained from a historical database. The average gray values ​​of the salt film-covered area in the image are integrated into standard input data, and the salt film thickness corresponding to the average gray values ​​of the salt film-covered area in the image is integrated into standard output data. An artificial intelligence model is trained based on standard input data and standard output data to obtain a salt film thickness recognition model; the artificial intelligence model includes: convolutional neural network or deep belief network.

5. A prediction system for offshore photovoltaic power generation according to claim 1, characterized in that, The salt film power generation efficiency loss factor calculated based on salt film data includes: The salt film coverage area is denoted as YS, and the salt film thickness is denoted as YH; The transmittance T corresponding to the current salt film thickness is calculated using the formula T=e^(-α×YH); where α is the light absorption coefficient of the salt film. The overall average transmittance of the photovoltaic panels on the current photovoltaic platform is calculated using the formula Tavg=(YS / S)×T+(1-(YS / S))×T0; where Tavg is the overall average transmittance, S is the total area of ​​the photovoltaic panels on the photovoltaic platform, and T0 is the initial transmittance. Through formula The salt film power generation efficiency loss factor is calculated by η=η0×(1-(Tavg / T0)); where, η is the salt film power generation efficiency loss factor, and η0 is the initial power generation efficiency.

6. The prediction system for offshore photovoltaic power generation according to claim 1, characterized in that, The calculation of the initial power generation efficiency of the photovoltaic panel based on the salt film power generation efficiency loss factor includes: The initial power generation efficiency of the photovoltaic system is obtained by calculating the difference between the initial power generation efficiency and the loss factor of the salt film power generation efficiency.

7. A prediction system for offshore photovoltaic power generation according to claim 1, characterized in that, The photovoltaic power generation efficiency calculated based on salt spray concentration and initial power generation efficiency includes: The current salt spray concentration is identified by a pre-trained salt spray deposition rate recognition model, and the corresponding salt spray deposition rate is obtained. Obtain the preset time period for the required predicted power generation; divide the preset time period into several time periods i according to the preset time interval; where i={1,2,3,…,N}, and N is the total number of time periods after division; Through the formula ηi=η a The photovoltaic power generation efficiency for each time period is calculated using the formula ×e^(-β×R×ti); where ηi refers to the photovoltaic power generation efficiency in the i-th time period, β is the proportionality coefficient, and ηi is the photovoltaic power generation efficiency in the ith time period. a R is the initial power generation efficiency of photovoltaics, R is the salt spray deposition rate, and ti refers to the duration of the i-th time period.

8. A prediction system for offshore photovoltaic power generation according to claim 7, characterized in that, The training method for the salt spray deposition rate identification model includes: Based on the historical salt spray database, several salt spray concentrations and corresponding salt spray deposition rates were obtained; An artificial intelligence model is trained based on training data to obtain a salt spray deposition rate recognition model. The training data includes training input data and training output data. The training input data is the salt spray concentration, and the training output data is the salt spray deposition rate corresponding to the salt spray concentration.

9. A prediction system for offshore photovoltaic power generation according to claim 7, characterized in that, The prediction of offshore photovoltaic power generation based on photovoltaic power generation efficiency includes: The power generation of offshore photovoltaic power is predicted by a pre-set prediction model. The preset prediction model is: E=∑(ηi×Gi×S×ti); where ∑ is the summation symbol, the summation range is (1,N), and Gi is the light intensity in the i-th time period.

10. A prediction method using the prediction system for offshore photovoltaic power generation as described in any one of claims 1-9, characterized in that, include: Acquire images of photovoltaic panels on offshore photovoltaic platforms and salt spray concentrations; Salt film data was obtained by analyzing images of photovoltaic panels; The salt film power generation efficiency loss factor was calculated based on the salt film data. The initial power generation efficiency of the photovoltaic panel is calculated based on the salt film power generation efficiency loss factor. The photovoltaic power generation efficiency is calculated based on the salt spray concentration and the initial power generation efficiency. Predicting the power generation of offshore photovoltaic systems based on photovoltaic power generation efficiency.

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