A method, device, terminal and medium for predicting the temperature distribution of a photovoltaic module

Abstract

The present invention discloses a method, device, terminal and medium for predicting the temperature distribution of a photovoltaic module. The method includes: performing grid processing on the photovoltaic module to be predicted and assigning an initial preset temperature to each grid; obtaining the surface wind speed distribution of the photovoltaic module through wind speed field simulation based on the ambient wind speed; calculating the heat transfer coefficients of each grid respectively in combination with air humidity, temperature, surface wind speed and preset temperature to construct the heat dissipation equations for the front and back surfaces of each grid; respectively constructing the heat absorption equations for each grid; respectively constructing the heat transfer equations for the sides of each grid; establishing a heat balance equation to solve the temperature of each grid, and using the temperature solution results of each grid in the current iteration as their corresponding preset temperatures for iterative calculation until a preset termination condition is met, and outputting the finally solved temperatures of each grid. The present invention improves the spatial resolution of temperature field prediction and realizes higher-precision prediction of the temperature distribution of photovoltaic modules.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation, and particularly to a method, device, terminal and medium for predicting the temperature distribution of a photovoltaic module. Background Art

[0002] The power generation power of a solar cell is affected by various environmental factors, with strong volatility and difficulty in prediction. Temperature is an important factor affecting the output power of a photovoltaic module and is also an important indicator reflecting the working conditions of the photovoltaic module. In order to achieve high-precision photovoltaic power prediction, accurate prediction of the temperature of the photovoltaic module is essential.

[0003] Currently, the mainstream temperature prediction methods mostly adopt the correlation analysis of environmental parameters and historical temperature rise data. For example, "Photovoltaic power generation power prediction based on the XGBoost-LSTM combined model" disclosed in the prior art. However, such prediction methods have the following significant defects: First, the temperature prediction method based on regression modeling does not fully consider environmental factors and performs poorly under abnormal meteorological conditions. Since the training data set rarely covers extreme weather samples (such as extremely high or low temperature weather that is rare in history), the temperature prediction of the model under special working conditions has a large deviation. Second, the existing models generally adopt a homogenization hypothesis, regarding the photovoltaic panel as a whole with uniform temperature for prediction, ignoring the temperature spatial distribution characteristics caused by material property differences, shadow occlusion or local aging. In fact, the temperature differences between different parts of the photovoltaic module are obvious and cannot be ignored, especially under strong environmental wind speeds. Third, there are obvious limitations in the selection of environmental variables in the existing methods: neither the micro-environment wind speed gradient formed by the structural layout inside the photovoltaic array is considered, nor the potential impact of air relative humidity on the heat dissipation efficiency is taken into account, which makes it difficult for the model to accurately reflect the true heat exchange process under complex meteorological conditions.

[0004] Therefore, there is an urgent need to develop a highly accurate and reliable method for predicting the temperature distribution of a photovoltaic module to improve the accuracy of photovoltaic power generation prediction, so as to better cope with the severe challenges posed by the current large number of photovoltaic power generation systems being connected to the power grid to the dispatching, operation stability and power quality of the power system. Summary of the Invention

[0005] To solve the deficiencies in the prior art, the present invention provides a method, device, terminal and medium for predicting the temperature distribution of a photovoltaic module. The present invention fully considers the influence of air humidity and wind speed distribution on the temperature of the photovoltaic module, and through grid processing, improves the spatial resolution of the temperature field prediction, realizes a higher-precision prediction of the temperature distribution of the photovoltaic module, and further improves the accuracy of photovoltaic power generation prediction, and can also provide data support for the maintenance and repair of each part of the photovoltaic module.

[0006] The present invention adopts the following technical solutions.

[0007] In a first aspect, the present invention provides a method for predicting the temperature distribution of a photovoltaic module, the method comprising:

[0008] Step 1: Perform grid division on the photovoltaic module to be predicted. Each grid is regarded as having uniform material and isothermal, and an initial preset temperature is assigned to each grid.

[0009] Step 2: Obtain the surface wind speed distribution of the photovoltaic module through wind speed field simulation based on the ambient wind speed.

[0010] Step 3: Calculate the heat transfer coefficients of each grid respectively by combining air humidity, temperature, surface wind speed and preset temperature, so as to construct the heat loss equations for the front and back surfaces of each grid. ;

[0011] Step 4: Construct the heat absorption equations for each grid respectively based on irradiance and photovoltaic conversion efficiency. ;

[0012] Step 5: Construct the heat transfer equations for the side surfaces of each grid respectively based on Fourier's law. ;

[0013] Step 6: Establish a heat balance equation based on , and to solve the temperature of each grid, and use the temperature solution results of each grid in the current iteration as their corresponding preset temperatures for iterative calculation until the preset termination condition is met, and output the finally solved temperatures of each grid, that is, obtain the temperature distribution of the photovoltaic module.

[0014] Optionally, in step 3, the steps of calculating the heat transfer coefficients of each grid by combining air humidity, temperature, surface wind speed and preset temperature include:

[0015] Step 3.1: Calculate air density, viscosity and thermal conductivity according to air humidity and temperature.

[0016] Step 3.2: Calculate the natural convection heat transfer coefficients of the front / back surfaces of each grid respectively according to the preset temperature, air temperature, viscosity and thermal conductivity of each grid.

[0017] Step 3.3: Calculate the forced convection heat transfer coefficients of the front / back surfaces of each grid respectively according to the surface wind speed, air density and viscosity of each grid.

[0018] Step 3.4: Calculate the radiation heat transfer coefficients of the front / back surfaces of each grid respectively according to air temperature and preset temperature.

[0019] Optionally, the calculation formulas for air density, viscosity and thermal conductivity are as follows respectively:

[0020]

[0021] In the formula, , and respectively represent the density, viscosity and thermal conductivity of air; is the temperature of air; is the relative humidity of air; and respectively represent the molar masses of dry air and water vapor; and respectively represent the atmospheric pressure and the vapor pressure of saturated water vapor; and respectively represent the viscosity and thermal conductivity of dry air; and respectively represent the viscosity and thermal conductivity of water vapor; and are respectively the correction factor from ideal gas to real gas and the compressibility factor of air; represents the correction parameter from dry air to water vapor; represents the correction parameter from water vapor to dry air.

[0022] Optionally, in step 3.3, the steps of calculating the positive / negative surface forced convection heat transfer coefficient of each grid include:

[0023] Calculating the critical length and the characteristic length of the positive / negative surface of each grid respectively based on the density and viscosity of air, and determining the air flow types of the positive / negative surface of each grid, including: turbulent flow, mixed flow and laminar flow, so as to calculate the positive / negative surface forced convection heat transfer coefficients of each grid respectively through the following formula:

[0024]

[0025] In the formula, is the forced convection heat transfer coefficient, is the surface wind speed of the positive / negative surface of the grid with coordinates .

[0026] Optionally, the calculation formula for the ratio of the critical length and the characteristic length of the positive / negative surface of each grid is as follows:

[0027]

[0028] In the formula, refers to the critical Reynolds number; and are the viscosity and density of air, respectively.

[0029] Optionally, the step of determining the air flow type on the front / back surface of each grid includes:

[0030] When the ratio is less than 0.05, the air flow type is turbulent flow;

[0031] When the ratio is not less than 0.05 and not greater than 0.95, the air flow type is mixed flow;

[0032] When the ratio is greater than 0.95, the air flow type is laminar flow.

[0033] Optionally, in step 3.2, the expressions for calculating the natural convection heat transfer coefficients on the front / back surfaces of each grid are as follows:

[0034]

[0035] In the formula, and are the natural convection heat transfer coefficients on the front and back surfaces of the grid, respectively; is the Prandtl number; is the characteristic length of the grid; is the Grashof number; is the acceleration of gravity; is the volume change coefficient; represents the coordinate the preset temperature of the grid, is the temperature of the air; and are the viscosity and thermal conductivity of the air, respectively, is the angle between the grid and the horizontal plane.

[0036] Optionally, in step 3, the heat dissipation equation for the front and back surfaces of each grid is constructed as follows:

[0037]

[0038] In the formula, represents the heat dissipation power of the front and back surfaces of the grid with the coordinate ; and are the natural convection heat transfer coefficients on the front and back surfaces of the grid, respectively; and are the forced convection heat transfer coefficients on the front and back surfaces of the grid, respectively; and are the radiation heat transfer coefficients on the front and back surfaces of the grid, respectively; is the power sum coefficient; represents the temperature to be solved for the grid with coordinates of the grid; is the temperature of the air; is the area of a single grid.

[0039] Optionally, in step 4, the heat absorption equation for each grid is constructed as follows:

[0040]

[0041] In the formula, represents the heat absorption power of the radiation converted into heat energy in the grid with coordinates of the grid; is the light transmissivity of the light-transmitting glass; is the absorption ratio of the light coefficient; is the irradiance; is the photoelectric conversion efficiency of the photovoltaic module under standard test conditions; and are the air temperature and irradiance under standard test conditions respectively; is the power temperature coefficient of the photovoltaic module; represents the temperature to be solved for the grid with coordinates of the grid; is the light radiation coefficient; is the area of a single grid.

[0042] Optionally, in step 5, the heat transfer equation for the side surfaces of each grid is constructed as follows:

[0043]

[0044] In the formula, represents the heat transfer power of the heat transfer on the side surface of the grid with coordinates of the grid; , and represent the preset temperatures of the grids with coordinates , and respectively, = -1, 0, 1; and are the distances of the adjacent grids in the axis direction and in the plane respectively; is the power of the heat dissipated from the side edge grid of the photovoltaic module; is the thickness of the photovoltaic module; is the thermal conductivity of the photovoltaic panel material.

[0045] Optionally, the heat balance equation is as follows:

[0046]

[0047] In the formula, , and respectively represent the heat dissipation power of the front and back surfaces of the grid with coordinates the heat absorption power of the radiation converted into heat energy, and the heat transfer power of the heat transferred through its side surfaces.

[0048] In a second aspect, the present invention provides a photovoltaic module temperature distribution prediction device that operates according to the steps of any one of the methods in the first aspect of the present invention. The device includes:

[0049] A gridification module for gridifying the photovoltaic module to be predicted. Each grid is regarded as having a uniform material and isothermal, and an initial preset temperature is assigned to each grid;

[0050] A simulation module for obtaining the surface wind speed distribution of the photovoltaic module through wind speed field simulation based on the environmental wind speed;

[0051] A first construction module for calculating the heat transfer coefficients of each grid respectively by combining air humidity, temperature, surface wind speed and preset temperature to construct the heat dissipation equations of the front and back surfaces of each grid ;

[0052] A second construction module for constructing the heat absorption equations of each grid respectively based on irradiance and photovoltaic conversion efficiency ;

[0053] A third construction module for constructing the side heat transfer equations of each grid respectively based on Fourier's law ;

[0054] An iterative solution module for establishing a heat balance equation based on , and to solve the temperature of each grid, and using the temperature solution results of each grid in the current iteration as their corresponding preset temperatures for iterative calculation until a preset termination condition is met, and outputting the finally solved temperatures of each grid, that is, obtaining the temperature distribution of the photovoltaic module.

[0055] In a third aspect, the present invention provides a terminal, including a processor and a storage medium;

[0056] The storage medium is used to store instructions;

[0057] The processor is used to operate according to the instructions to execute the steps of any one of the methods in the first aspect of the present invention.

[0058] Fourthly, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the method described in any one of the first aspects of the present invention are implemented.

[0059] The beneficial effects of the present invention are as follows. Compared with the prior art,

[0060] 1. The present invention adopts a grid-based discrete processing technology to decompose the continuous photovoltaic panel surface into refined grid units with independent thermal boundary conditions. Each micro-grid unit maintains a homogeneous characteristic at the thermodynamic level. By solving the heat balance equations of each grid unit, the temperature gradient distribution characteristics on the surface of the component are accurately reconstructed, breaking through the inherent limitation of the homogenization treatment of the traditional model, significantly improving the spatial resolution of the temperature field prediction, not only providing refined data support for the evaluation of the power generation efficiency of the photovoltaic system, but also enabling the early identification and positioning of abnormal states of the component.

[0061] 2. The present invention integrates multi-scale meteorological parameters. Based on the conventional irradiation parameters, different from the limitation of the traditional method of simplifying the wind speed into a uniform field and ignoring the influence of humidity, the present invention fully considers the influence of air humidity and wind speed distribution on the temperature of the photovoltaic module, analyzes the influence mechanism of micro-meteorological environment parameters on the thermodynamic characteristics of the photovoltaic array, quantitatively analyzes the perturbation effect of the photovoltaic array on the local flow field, and combines the correction mechanism of humidity on the convective heat transfer coefficient to construct a heat transfer equation with the synergistic effect of multi-dimensional environmental parameters, significantly improving the reliability of temperature prediction under complex meteorological conditions, not only providing key technical support for the efficiency optimization of the photovoltaic power station, but also showing important application value in the field of health management of photovoltaic equipment, providing an important basis for equipment status monitoring.

[0062] 3. Different from the traditional data-driven method, the present invention adopts a mechanism modeling path based on the heat conduction equation and the principle of fluid dynamics. By constructing a three-dimensional steady-state heat transfer differential equation , and , a theoretical calculation framework independent of historical data is realized, which can be based on multi-source input data such as ambient temperature, light intensity, wind speed and air humidity without relying on historical data, fundamentally solving the problem of prediction inaccuracy under extreme meteorological conditions and realizing a higher-precision prediction of the temperature distribution of photovoltaic modules.

[0063] 4. For the non - linear coupling problem existing in the heat balance equation, an adaptive iterative algorithm is used. By constructing an initial hypothesis matrix of the temperature field, an initial preset temperature is assigned to each grid, and an error feedback adjustment mechanism is established. When the continuous iteration results meet the preset termination conditions (when the temperature difference between two iterations is lower than the set value or the maximum iteration step length is reached), the calculation process is terminated. This algorithm achieves an optimized balance between calculation efficiency and accuracy; the present invention realizes the temperature distribution prediction that cannot be achieved by traditional models, lays a foundation for further high - precision power prediction, and can also play an important role in the maintenance and overhaul of photovoltaic modules. Brief Description of the Drawings

[0064] Figure 1 is the algorithm flow chart of the method for predicting the temperature distribution of a photovoltaic module in an embodiment of the present invention;

[0065] Figure 2 is the structural schematic diagram of the grid division of a photovoltaic module in an embodiment of the present invention;

[0066] Figure 3 is the relationship curve diagram of (a) density, (b) viscosity, and (c) thermal conductivity of air in an embodiment of the present invention with temperature and relative humidity RH ;

[0067] Figure 4 is the wind speed distribution of the front (a) and back (b) of an inclined photovoltaic flat plate under different environmental wind speeds in an embodiment of the present invention (unit: m / s);

[0068] Figure 5 is the temperature distribution of an inclined photovoltaic flat plate under different wind speeds when the relative humidity in an embodiment of the present invention is (a) RH = 0%, (b) RH = 50%, (c) RH = 100% (unit: K);

[0069] Figure 6 is the relationship curve diagram of the highest / lowest temperature of a photovoltaic panel with (a) air relative humidity and (b) environmental wind speed in an embodiment of the present invention. Among them, the environmental wind speed in (a) is 0.1 m / s, and the relative humidity in (b) RH is 0%;

[0070] Figure 7 is the temperature distribution result predicted by the present invention according to the measured environmental data in an embodiment of the present invention (unit: K);

[0071] Figure 8 is the structural principle block diagram of the device for predicting the temperature distribution of a photovoltaic module in an embodiment of the present invention. Detailed Embodiment

[0072] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in the present invention are only a part of the embodiments of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the protection scope of the present invention.

[0073] Embodiment 1:

[0074] Referring to Figure 1 , the embodiment of the present invention provides a method for predicting the temperature distribution of a photovoltaic module, specifically including the following steps:

[0075] Step 1: Perform grid processing on the photovoltaic module to be predicted. Each grid is regarded as having uniform material and isothermal, and an initial preset temperature is assigned to each grid.

[0076] To reduce the computational amount, accelerate the prediction speed, and facilitate the calculation of the temperature distribution, in this embodiment, the structure of the photovoltaic module is first simplified and gridded; referring to Figure 2 , the photovoltaic module is regarded as an inclined flat cuboid, ignoring the structures such as the brackets and columns of the photovoltaic module and only retaining the panel, and is divided into M×N grids; generally, each grid corresponds to a device in the photovoltaic module, and the grid division of photovoltaic modules with different specifications will also be different; the photovoltaic modules in practical applications are generally composed of several small photovoltaic devices spliced in series and parallel; each grid corresponds to a photovoltaic device and is regarded as having uniform material and isothermal. This step is a pre-step of the algorithm, so it is not shown in the Figure 1 algorithm block diagram.

[0077] Furthermore, the embodiment of the present invention adopts a grid-based discrete processing technology to deconstruct the continuous photovoltaic panel into a micro-element system with independent thermodynamic characteristics. Each micro-element grid maintains a homogeneous characteristic at the thermodynamic level, and the temperature of each grid is calculated through a distributed temperature field to achieve accurate thermal state analysis. This architecture design significantly improves the spatial resolution of the temperature field prediction, not only providing refined data support for the evaluation of the power generation efficiency of the photovoltaic system, but also enabling the early identification and positioning of component abnormal states.

[0078] Step 2: Obtain the surface wind speed distribution of the photovoltaic module through wind speed field simulation based on the ambient wind speed.

[0079] According to the simplification in Step 1, a digital model of the photovoltaic module is established, and then the geometric model is imported into the simulation software COMSOL. The ambient wind speed is input, and the k~ε turbulence module built into the software is used to perform wind speed field simulation on it to obtain the surface wind speed distribution on the front and back of the photovoltaic module, that is, the surface wind speed on the front and back of each grid. Referring to Figure 4. It should be noted that the method of simulating the wind speed distribution using existing simulation software is not the research content of the present invention, and the specific simulation process will not be elaborated.

[0080] Step 3: Calculate the heat transfer coefficients of each grid respectively by combining air humidity, temperature, surface wind speed and preset temperature, so as to construct the heat dissipation equations for the front and back surfaces of each grid ;

[0081] Through research, it is found that air humidity will affect the density, viscosity and thermal conductivity of air. Figure 3 Figures (a), (b) and (c) in RH respectively plot the relationship curves of the density, viscosity and thermal conductivity of air with temperature and relative humidity RH . Among them, the relative humidity Figure 3 plots curves for a total of 11 values from 0 to 100% with a step of 10%. It can be observed RH that at a temperature of 0°C, the change in humidity has little effect on the density, viscosity and thermal conductivity of air, because at low temperatures, water is more likely to exist in a liquid or even solid state, and even if the water vapor in the air is in a saturated state ( RH = 100%), the proportion in the air is still negligible. However, as the temperature increases, the influence of humidity on the density, viscosity and thermal conductivity of air becomes significant, and all three decrease significantly with the increase in humidity. At 100°C, the density, viscosity and thermal conductivity of saturated humid air are about 37.5%, 45% and 21.5% lower than those of dry air respectively. Since the thermophysical parameters of air (including density, viscosity and thermal conductivity) will greatly affect the heat dissipation of photovoltaic modules, in the embodiments of the present invention, by introducing the influence of air humidity, each physical parameter in the heat transfer coefficient is corrected; thus, the specific process of Step 3 includes:

[0082] Step 3.1: Calculate the density, viscosity and thermal conductivity of air according to air humidity and temperature, and their calculation formulas are as follows:

[0083]

[0084] In the formula, , and respectively represent the density, viscosity and thermal conductivity of air; and respectively refer to the temperature and relative humidity of air, which are input data; and respectively represent the molar masses of dry air and water vapor, which are fixed values; and represent the atmospheric pressure and the vapor pressure of saturated water vapor respectively. The former can generally be regarded as a constant value at a fixed location, while the latter is calculated from an empirical formula fitted with historical data; is the ideal gas constant; and represent the viscosity and thermal conductivity of dry air respectively; and represent the viscosity and thermal conductivity of water vapor respectively; and are the correction factor from ideal gas to real gas and the compressibility factor of air respectively; represents the correction parameter from dry air to water vapor; represents the correction parameter from water vapor to dry air.

[0085] As an embodiment of the present invention, each parameter , , , , , , , and can be calculated by the following formulas respectively:

[0086]

[0087] In the formula, , , , , , , , and are all empirical parameters of mature research and are all in the International System of Units. Among them, takes 3.53624×10 -4 , takes 2.93228×10 -5 , takes 2.61474×10 -7 , takes 8.57538×10 -9 ; takes -10.7588, takes 6.32529×10 -2 , takes -2.53591×10 -4 ; Take 6.33784×10 7 ; Take 7×10 -9 , Take -1.47184×10 -9 , Take 1734.29; Take 1.04×10 -15 , Take -3.35297×10 -18 , Take 3645.09; Take -0.98601, Take 0.09080125, Take -1.17635575×10 -4 , Take 1.2349703×10 -7 , Take -5.7971299×10 -11 ; Take -2.276501×10 -3 , Take 1.2598485×10 -4 , Take -1.4815235×10 -7 , Take 1.73550646×10 -10 , Take -1.066657×10 -13 , Take 2.47663035×10 -17 ; Take 80.58131868, Take 0.4000549451; Take 17.61758242, Take 0.05558941059, Take 1.663336663×10 -4 ; Take 0.7073034146, Take -2.703615165×10 -2 , Take 4.36088211×10 -3 , Take -4.662575642×10 -5 , Take 1.034693708×10 -6; According to the above formula, the air density, viscosity, and thermal conductivity can be calculated based on the predicted meteorological data (temperature and relative humidity), referring to Figure 3 .

[0088] Step 3.2: Calculate the natural convection heat transfer coefficients on the front / back sides of each grid according to the preset temperature, air temperature, viscosity, and thermal conductivity of each grid.

[0089] To calculate the heat dissipation of air on the front and back sides of each grid, it is necessary to calculate the natural convection heat transfer coefficient; the calculation formulas for the natural convection heat transfer coefficients on the front and back sides provided in the embodiments of the present invention are as follows:

[0090]

[0091] In the formula, and are the natural convection heat transfer coefficients on the front and back sides of the grid, respectively; is the Prandtl number, which takes a fixed value here; is the characteristic length of the grid (i.e., the length of the grid projected onto the horizontal plane, ), which is a fixed value for a determined grid under a determined wind direction; is the acceleration due to gravity; is the volume change coefficient; represents the preset temperature of the grid with coordinates , is the air temperature; and are the viscosity and thermal conductivity of the air, respectively; is the angle between the grid and the horizontal plane (such as Figure 2 ). Among them, the grid with coordinates is the grid ranked in the m th column and the n th row in the entire photovoltaic panel; m = 1, 2... M; n = 1, 2... N.

[0092] Step 3.3: Calculate the forced convection heat transfer coefficients on the front / back sides of each grid according to the surface wind speed, air density, and viscosity of each grid;

[0093] To calculate the heat dissipation of air on the front and back sides of each grid, it is also necessary to calculate the forced convection heat transfer coefficient of the air; before calculating the forced convection heat transfer coefficient, it is necessary to first divide the flow type of the air, and the division basis is the ratio of the critical length to the characteristic length. Calculate the critical length and the characteristic length of the front and back sides of each grid based on the air density and viscosity, and their ratio is as follows:

[0094]

[0095] In the formula, refers to the critical Reynolds number, which takes a fixed value; and are the viscosity and density of air respectively, obtained from Step 3.1; is the surface wind speed of the grid with coordinates , obtained from Step 2. Among them, the wind speeds on the front and back of the same grid may be different, and their ratio and the corresponding forced convective heat transfer coefficient need to be calculated separately. When the ratio is less than 0.05, the fluid is considered to be in a fully turbulent state; when the ratio is greater than 0.95, the fluid is considered to flow in a laminar manner at this time; when the ratio is not less than 0.05 and not greater than 0.95, the fluid is in a transitional state from laminar flow to turbulent flow, i.e., mixed flow. The calculation formulas for the forced convective heat transfer coefficient used in laminar flow, turbulent flow, and mixed flow in the present invention are as follows:

[0096]

[0097] In the formula: is the forced convective heat transfer coefficient, is the surface wind speed on the front / back of the grid with coordinates . In this step, based on the above formulas, the forced convective heat transfer coefficient is calculated using the wind speeds on the front and back of the grid obtained in Step 2 and the air viscosity and density obtained in Step 3.

[0098] Step 3.4: Calculate the radiative heat transfer coefficients on the front / back of each grid according to the air temperature and the preset temperature respectively.

[0099] In this embodiment, the radiative heat transfer coefficients for the front / back of each grid to radiate heat to the environment can be calculated respectively by the following formulas:

[0100]

[0101] In the formula, and are the radiative heat transfer coefficients on the front / back of the grid respectively; and are the front and back emissivity coefficients of the photovoltaic module respectively; is the Stefan-Boltzmann constant; is the angle between the photovoltaic panel and the ground, referring to Figure 2 ; and are the preset temperature and the air temperature of the grid with coordinates respectively. Based on the above formulas, the radiative heat transfer coefficients on the front / back of each grid can be calculated.

[0102] Step 3.5: Construct the heat dissipation equations for the front and back surfaces of each grid according to the calculation results of Steps 3.1 - 3.4 .

[0103] In this embodiment, the natural convection heat transfer coefficients, forced convection heat transfer coefficients, and radiation heat transfer coefficients of the front / back surfaces of each grid are calculated respectively according to Steps 3.1 - 3.4, and thus the power sum of the heat dissipated from the front / back surfaces of each grid, that is, the heat dissipation equations for the front and back surfaces can be expressed :

[0104]

[0105] In the formula: represents the heat dissipation power of the front and back surfaces of the grid with coordinates ; and are the natural convection heat transfer coefficients of the front and back surfaces of the grid respectively; and are the forced convection heat transfer coefficients of the front and back surfaces of the grid respectively; and are the radiation heat transfer coefficients of the front and back surfaces of the grid respectively; is the power summation coefficient, generally taking a value of 2 - 4. Preferably, in this embodiment takes a value of 3; represents the temperature to be solved of the grid with coordinates ; is the temperature of the air; is the area of a single grid.

[0106] Step 4: Construct the heat absorption equations for each grid based on the irradiance and photovoltaic conversion efficiency ;

[0107] The main heat source of the photovoltaic module is the radiant energy that fails to be converted into electrical energy; for each grid, this part of the energy can be expressed by the following formula:

[0108]

[0109] In the formula: represents the heat absorption power of the radiation converted into heat energy in the grid with coordinates ; is the light transmission rate of the lighting glass; is the absorption ratio of the light coefficient; is the irradiance; is the photoelectric conversion efficiency of the photovoltaic module under the standard test environment; and are the air temperature and irradiance under the standard test environment respectively. (In this embodiment, the air temperature = 25 °C, irradiance = 1000 W / m 2 ); is the power temperature coefficient of the photovoltaic module; represents the coordinates of the temperature to be solved for the grid; is the light radiation coefficient; is the area of a single grid.

[0110] Step 5: Based on Fourier's law, establish the heat transfer equations for the sides of each grid ;

[0111] Based on Fourier's law of heat conduction, the present invention can deduce the power of heat flow on the sides of each grid, that is, the heat transfer on the sides of each grid The equation is as follows:

[0112]

[0113] In the formula: represents the coordinates of the heat transfer power of the heat transfer on the side of the grid; = -1, 0, 1; , and respectively represent the coordinates , and of the preset temperature of the grid; and are respectively the adjacent grids in the axial direction and the distance in the plane; is the power of heat dissipation of the grid at the edge of the side of the photovoltaic module (only exists on the edge grid); is the thickness of the photovoltaic module; is the thermal conductivity of the photovoltaic panel material.

[0114] Step 6: Establish a heat balance equation based on , and to solve the temperature of each grid, and use the temperature solution results of each grid in the current iteration as their corresponding preset temperatures for iterative calculation until the preset termination condition is met, and output the finally solved temperatures of each grid, that is, obtain the temperature distribution of the photovoltaic module.

[0115] Specifically, in Steps 3 to 5, the heat dissipation power of the upper and lower surfaces, the power of obtaining heat, and the power of heat flow on the sides of each grid are obtained respectively. From this, a heat balance equation can be constructed for each grid:

[0116]

[0117] In the formula, , and respectively represent the heat dissipation power of the front and back surfaces of the grid with coordinates , the heat absorption power of the radiation converted into heat energy, and the heat transfer power of the heat transferred through its side surfaces. By solving this equation for each grid, the temperature of the grid can be obtained, and then the temperature distribution of the entire photovoltaic module can be obtained.

[0118] Furthermore, due to the existence of a large number of complex formula nestings and a large number of non-linear relationships, it is extremely difficult to directly solve the heat balance equation. In order to reduce the difficulty of solving and accelerate the calculation speed, in the embodiment of the present invention, for the non-linear coupling problem existing in the equation, an iterative solution algorithm is adopted; first, a preset temperature matrix is given to the photovoltaic module, and each temperature value in the matrix corresponds to the initial preset temperature of each grid, and is substituted into some formulas (i.e., in step 3.2, step 3.4, and step 5), while the temperature to be solved in step 3.5 and step 4 is solved as an unknown (the relationship between is linear and easy to solve), and then the obtained by solving is compared with the preset : If the result does not meet the preset convergence threshold (the difference between the two is higher than the set value) and the upper limit of the iteration step number is not reached, is substituted into (let ), and the calculation is restarted for iteration; until the result meets the preset convergence threshold (the difference between the two is lower than the set value) or reaches the maximum iteration step length, the calculation process is terminated, as shown in Figure 1 ; the iterative algorithm of the present invention realizes an optimal balance between calculation efficiency and accuracy, and finally the temperature distribution of the photovoltaic module can be obtained by solving, and it is drawn as a heat map as shown in Figure 5 .

[0119] The advantages of the photovoltaic module temperature distribution prediction method provided by the present invention are described below through specific experimental data analysis.

[0120] Extract Figure 5 part of the data in Figure 6 to draw

[0121] Figure 7 , which more intuitively reflects the influence of air humidity and wind speed on the temperature of the photovoltaic module, indicating that air humidity and wind speed should not be ignored in the temperature prediction of the photovoltaic module, and verifying the necessity of the present invention to take air humidity and wind speed distribution into consideration.The prediction results of the temperature distribution of the photovoltaic module according to the present invention are shown. Among them, the predicted maximum and minimum temperatures are 28.3 °C (301.8 K) and 12.9 °C (286.4 K), respectively; while the true values of the maximum and minimum temperatures in the experiment are 28.8 °C and 13.0 °C, respectively, and the errors are less than 1 °C. The prediction results are consistent with the experiment, which proves the reliability of the present invention.

[0122] In addition, the most direct application of the temperature prediction model of the present invention is for power generation prediction. Table 1 below compares the effects of the algorithm of the present invention and the traditional temperature prediction method for power generation prediction with the measured results. Among them, the traditional prediction algorithm refers to the algorithm that does not consider the air humidity, the surface wind speed distribution and the temperature difference at different positions within the module, and regards the photovoltaic module as a homogeneous whole. It can be clearly found from the comparison of the experimental result data in Table 1 that, compared with the traditional method, the error of the present invention for power generation prediction is smaller. The experimental results prove the superiority of the photovoltaic module temperature distribution prediction method proposed by the present invention.

[0123] Table 1: Comparison of the effects of different temperature prediction methods for power generation prediction and measured data (retained to two decimal places)

[0124]

[0125] The beneficial effects of the present invention are as follows. Compared with the prior art,

[0126] 1. The present invention adopts the grid-based discrete processing technology to decompose the continuous photovoltaic panel surface into refined grid units with independent thermal boundary conditions. Each micro-grid unit maintains a homogeneous characteristic at the thermodynamics level. By solving the heat balance equations of each grid unit, the surface temperature gradient distribution characteristics of the module are accurately reconstructed, breaking through the inherent limitation of the homogeneous processing of the traditional model, and significantly improving the spatial resolution of the temperature field prediction. It not only provides refined data support for the evaluation of the power generation efficiency of the photovoltaic system, but also enables the early identification and positioning of abnormal states of the module.

[0127] 2. The present invention integrates multi-scale meteorological parameters. On the basis of conventional irradiation parameters, different from the limitation of the traditional method of simplifying the wind speed into a uniform field and ignoring the influence of humidity, the present invention fully considers the influence of air humidity and wind speed distribution on the temperature of the photovoltaic module, analyzes the influence mechanism of micro-meteorological environment parameters on the thermodynamic characteristics of the photovoltaic array, quantitatively analyzes the perturbation effect of the photovoltaic array on the local flow field, and combines the correction mechanism of humidity on the convective heat transfer coefficient to construct a heat transfer equation with the synergistic effect of multi-dimensional environmental parameters, significantly improving the reliability of temperature prediction under complex meteorological conditions. It not only provides key technical support for the efficiency optimization of photovoltaic power stations, but also shows important application value in the field of photovoltaic equipment health management, providing an important basis for equipment status monitoring.

[0128] 3. Different from traditional data-driven methods, the present invention adopts a mechanism modeling approach based on the heat conduction equation and the principles of fluid dynamics. By constructing a three-dimensional steady-state heat transfer differential equation 、 and , a theoretical calculation framework independent of historical data is realized. It can be based on multi-source input data such as ambient temperature, light intensity, wind speed, and air humidity without relying on historical data, fundamentally solving the problem of inaccurate prediction under extreme meteorological conditions and achieving a higher-precision prediction of the temperature distribution of photovoltaic modules.

[0129] 4. Aiming at the non-linear coupling problem in the heat balance equation, an adaptive iterative algorithm is used. By constructing an initial hypothesis matrix of the temperature field, an initial preset temperature is assigned to each grid, and an error feedback adjustment mechanism is established. When the continuous iteration results meet the preset termination conditions (when the temperature difference between two iterations is lower than the set value or the maximum iteration step length is reached), the calculation process is terminated. This algorithm realizes an optimal balance between calculation efficiency and accuracy; the present invention realizes the temperature distribution prediction that cannot be achieved by traditional models, lays a foundation for further high-precision power prediction, and can also play an important role in the maintenance and repair of photovoltaic modules.

[0130] Embodiment 2:

[0131] As Figure 8 shown, the present invention provides a device for predicting the temperature distribution of photovoltaic modules. The device is used to implement the steps of the method in Embodiment 1 above. Specifically, the device includes:

[0132] A gridification module, which is used to gridify the photovoltaic module to be predicted. Each grid is regarded as having uniform material and isothermal, and an initial preset temperature is assigned to each grid;

[0133] A simulation module, which is used to obtain the surface wind speed distribution of the photovoltaic module through wind speed field simulation based on the ambient wind speed;

[0134] A first construction module, which is used to calculate the heat transfer coefficients of each grid respectively by combining air humidity, temperature, surface wind speed, and preset temperature, so as to construct the heat loss equations of the front and back surfaces of each grid ;

[0135] A second construction module, which is used to construct the heat absorption equations of each grid respectively based on irradiance and photovoltaic conversion efficiency ;

[0136] A third construction module, which is used to construct the heat transfer equations of the sides of each grid respectively based on Fourier's law ;

[0137] An iterative solution module, which is used to establish based on 、 and The heat balance equation is used to solve the temperature of each grid, and the temperature solution results of each grid in the current iteration are used as their corresponding preset temperatures for iterative calculation until the preset termination condition is met, and the finally solved temperatures of each grid are output, that is, the temperature distribution of the photovoltaic module is obtained.

[0138] The photovoltaic module temperature distribution prediction device provided by the embodiments of the present invention and the photovoltaic module temperature distribution prediction method provided by Embodiment 1 are based on the same technical concept and can produce the beneficial effects described in Embodiment 1. The content not described in detail in this embodiment can be referred to in Embodiment 1.

[0139] Embodiment 3:

[0140] A terminal provided by an embodiment of the present invention includes a processor and a storage medium;

[0141] The storage medium is used to store instructions;

[0142] The processor is used to operate according to the instructions to execute the steps of the method described in any one of Embodiment 1.

[0143] Embodiment 4:

[0144] A computer-readable storage medium provided by an embodiment of the present invention stores a computer program, and when the program is executed by a processor, the steps of the method described in any one of Embodiment 1 are implemented.

[0145] The present disclosure may be a system, a method, and / or a computer program product. The computer program product may include a computer-readable storage medium having thereon computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0146] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device such as a punch card or raised structures in grooves having instructions stored thereon, and any suitable combination of the foregoing. The computer-readable storage medium as used herein is not construed as being a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., an optical pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0147] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to respective computing / processing devices, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include a copper transmission cable, an optical fiber transmission, a wireless transmission, a router, a firewall, a switch, a gateway computer, and / or an edge server. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0148] Computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider). In some embodiments, by using the state information of the computer-readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer-readable program instructions to implement various aspects of the present disclosure.

[0149] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the specific implementation manners of the present invention can still be modified or equivalently replaced, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A method for predicting the temperature distribution of a photovoltaic module, characterized in that, The method includes the following steps: Step 1: Perform grid processing on the photovoltaic module to be predicted. Each grid is regarded as having uniform material and isothermal, and an initial preset temperature is assigned to each grid; Step 2: Obtain the surface wind speed distribution of the photovoltaic module through wind speed field simulation based on the ambient wind speed; Step 3: Calculate the heat transfer coefficients of each grid respectively by combining air humidity, temperature, surface wind speed and the preset temperature, so as to construct the heat dissipation equations for the front and back surfaces of each grid ; Step 4: Construct the heat absorption equation for each grid based on irradiance and photovoltaic conversion efficiency ; Step 5: Based on Fourier's law, establish the heat transfer equations for the sides of each grid respectively ; Step 6: Establish a heat balance equation based on , and to solve the temperature of each grid, and use the temperature solution results of each grid in the current iteration as their corresponding preset temperatures for further iterative calculation until the preset termination conditions are met, and output the finally solved temperatures of each grid, that is, obtain the temperature distribution of the photovoltaic module; In Step 3, the steps of calculating the heat transfer coefficients of each grid by combining air humidity, temperature, surface wind speed and preset temperature include: Step 3.1: Calculate air density, viscosity and thermal conductivity according to air humidity and temperature; Step 3.2: Calculate the natural convection heat transfer coefficients of the front / back sides of each grid respectively according to the preset temperature, air temperature, viscosity and thermal conductivity of each grid; Step 3.3: Calculate the forced convection heat transfer coefficients of the front / back sides of each grid respectively according to the surface wind speed, air density and viscosity of each grid; Step 3.4: Calculate the radiation heat transfer coefficients of the front / back sides of each grid respectively according to air temperature and preset temperature; The calculation formulas for the air density, viscosity and thermal conductivity are as follows respectively: In the formula, , and respectively represent the density, viscosity and thermal conductivity of air; is the temperature of air; is the relative humidity of air; and respectively represent the molar masses of dry air and water vapor; and respectively represent the atmospheric pressure and the vapor pressure of saturated water vapor; and respectively represent the viscosity and thermal conductivity of dry air; and respectively represent the viscosity and thermal conductivity of water vapor; and are respectively the correction factor from ideal gas to real gas and the compressibility factor of air; represents the correction parameter from dry air to water vapor; represents the correction parameter from water vapor to dry air; In Step 3.3, the steps of calculating the forced convection heat transfer coefficients of the front / back sides of each grid include: Calculate the critical length of the front / back of each grid based on the density and viscosity of air respectively and the characteristic length ratio to determine the air flow types of the front / back of each grid, including: turbulent flow, mixed flow and laminar flow, so as to calculate the forced convective heat transfer coefficients of the front / back of each grid respectively through the following formula In the formula, is the forced convection heat transfer coefficient, is the surface wind speed on the front / back of the grid with coordinates ; The critical length of the front / back of each grid and the characteristic length The calculation formula for the ratio is as follows: In the formula, refers to the critical Reynolds number; and are the viscosity and density of air, respectively; The steps of determining the air flow types on the front / back sides of each grid include: When the ratio is less than 0.05, the air flow type is turbulent flow; When the ratio is not less than 0.05 and not greater than 0.95, the air flow type is mixed flow; When the ratio is greater than 0.95, the air flow type is laminar flow; In Step 3.2, the expressions for calculating the natural convection heat transfer coefficients of the front / back sides of each grid are as follows respectively: In the formula, and are the natural convection heat transfer coefficients on the front and back of the grid, respectively; is the Prandtl number; is the characteristic length of the grid; is the Grashof number; is the acceleration due to gravity; is the coefficient of volume expansion; represents the preset temperature of the grid with coordinates , is the temperature of the air; and are the viscosity and thermal conductivity of the air, respectively, is the angle between the grid and the horizontal plane; In step 3, establish the heat dissipation equations for the front and back surfaces of each grid as follows: In the formula, represents the heat dissipation power of the front and back surfaces of the grid with coordinates ; and are the natural convection heat transfer coefficients of the front and back surfaces of the grid respectively; and are the forced convection heat transfer coefficients of the front and back surfaces of the grid respectively; and are the radiation heat transfer coefficients of the front and back surfaces of the grid respectively; is the power summation coefficient; represents the temperature to be solved of the grid with coordinates ; is the temperature of the air; is the area of a single grid. In step 4, establish the heat absorption equation for each grid as follows: In the formula, represents the endothermic power of the radiation conversion into heat energy at the coordinate in the grid; is the transmittance of the lighting glass; is the absorption ratio of the lighting coefficient; is the irradiance; is the photoelectric conversion efficiency of the photovoltaic module under the standard test environment; and are the air temperature and irradiance under the standard test environment respectively; is the power temperature coefficient of the photovoltaic module; represents the temperature to be solved of the grid at the coordinate ; is the lighting radiation coefficient; is the area of a single grid. In step 5, establish the heat transfer equation for the sides of each grid as follows: In the formula, represents the heat transfer power of the side of the grid with coordinates transferring heat; , and respectively represent the preset temperatures of the grids with coordinates , and , where = -1, 0, 1; and are the distances between adjacent grids in the axis direction and in the plane respectively; is the power of heat dissipation of the grid at the side edge of the photovoltaic module; is the thickness of the photovoltaic module; is the thermal conductivity of the photovoltaic panel material; In Step 6, the heat balance equation is as follows: In the formula, , and respectively represent the heat dissipation power of the front and back surfaces of the grid with coordinates , the heat absorption power of the radiation converted into heat energy, and the heat transfer power of the heat transferred through its side surface.

2. A photovoltaic module temperature distribution prediction device, which operates the photovoltaic module temperature distribution prediction method as described in claim 1, characterized in that the device including: A grid module for performing grid processing on the photovoltaic module to be predicted. Each grid is regarded as having uniform material and isothermal, and an initial preset temperature is assigned to each grid; A simulation module for obtaining the surface wind speed distribution of the photovoltaic module through wind speed field simulation based on the ambient wind speed; The first building block is used to calculate the heat transfer coefficients of each grid by combining air humidity, temperature, surface wind speed, and a preset temperature respectively, so as to construct the heat loss equations for the front and back surfaces of each grid ; A second construction module, configured to construct an absorbed heat equation for each grid based on irradiance and photovoltaic conversion efficiency respectively ; The third construction module is used to construct the heat transfer equation of the side surface of each grid based on Fourier's law ; An iterative solution module for establishing a heat balance equation based on , and to solve the temperature of each grid, and using the temperature solution results of each grid in the current iteration as their corresponding preset temperatures for further iterative calculation until the preset termination condition is met, and outputting the finally solved temperatures of each grid, that is, obtaining the temperature distribution of the photovoltaic module.

3. A terminal, including a processor and a storage medium; characterized in that: The storage medium is used for storing instructions; The processor is used to operate according to the instructions to execute the steps of the method according to Claim 1.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, the steps of the method according to Claim 1 are implemented.

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