Photovoltaic module temperature distribution prediction method and device, terminal and medium

Abstract

The invention discloses a photovoltaic module temperature distribution prediction method and device, a terminal and a medium, and the method comprises the steps: carrying out the grid processing of a to-be-predicted photovoltaic module, and endowing each grid with an initial preset temperature; acquiring surface wind speed distribution of the photovoltaic module through wind speed field simulation based on the environment wind speed; respectively calculating each heat transfer coefficient of each grid by combining the air humidity, the temperature, the surface wind speed and the preset temperature so as to construct a front and back surface heat loss equation # imgabs0 # of each grid; respectively constructing an absorption heat equation # imgabs1 # of each grid; respectively constructing a side heat transfer equation # imgabs2 # of each grid; and establishing a heat balance equation to solve the temperature of each grid, taking the current temperature solving result of each grid as the corresponding preset temperature, carrying out iterative calculation until a preset termination condition is met, and outputting the finally solved temperature of each grid. According to the invention, the spatial resolution of temperature field prediction is improved, and higher-precision photovoltaic module temperature distribution prediction is realized.

Description

Technical Field

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

[0002] The power generation of solar cells is affected by a variety of environmental factors, with strong fluctuations and difficult to predict. Temperature is an important factor affecting the output power of photovoltaic modules, and it is also an important indicator reflecting the working conditions of photovoltaic modules. In order to achieve high-precision photovoltaic power prediction, accurate photovoltaic module temperature prediction is essential.

[0003] The current mainstream temperature prediction methods mostly use the correlation analysis between environmental parameters and historical temperature rise data, such as the "Photovoltaic power prediction based on XGBoost-LSTM combined model" disclosed in the prior art. However, this type of prediction method has 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; because the training data set rarely covers extreme weather samples (such as historically rare ultra-high or low temperature weather), the temperature prediction of the model under special working conditions has a large deviation. Second, the existing models generally adopt the homogenization assumption, treating the photovoltaic panel as a whole with uniform temperature for prediction, ignoring the temperature spatial distribution characteristics caused by differences in material properties, shadows or local aging; in fact, the temperature difference in different parts of the photovoltaic module is quite obvious and cannot be ignored, especially under strong ambient wind speed. Third, the existing methods have obvious limitations in the selection of environmental variables: they neither consider the micro-environmental wind speed gradient formed by the structural layout inside the photovoltaic array, nor ignore the potential impact of relative humidity on heat dissipation efficiency, which makes it difficult for the model to accurately reflect the real 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 photovoltaic modules to improve the accuracy of photovoltaic power generation prediction, so as to better cope with the severe challenges posed by the current large-scale integration of photovoltaic power generation systems into the power grid to the scheduling, operation stability and power quality of the power system. Summary of the invention

[0005] In order to solve the deficiencies in the prior art, the present invention provides a method, device, terminal and medium for predicting the temperature distribution of photovoltaic modules. The present invention fully considers the influence of air humidity and wind speed distribution on the temperature of photovoltaic modules, and improves the spatial resolution of temperature field prediction through grid processing, thereby achieving higher-precision prediction of photovoltaic module temperature distribution, thereby improving the accuracy of photovoltaic power generation prediction, and can also provide data support for the maintenance and inspection of various parts of photovoltaic modules.

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

[0007] In a first aspect, the present invention provides a method for predicting temperature distribution of a photovoltaic module, the method comprising: Step 1: Grid the PV panels to be predicted. Each grid is considered to have 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 by combining air humidity, temperature, surface wind speed and preset temperature to construct the heat loss equation 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: Construct the side heat transfer equation for each grid based on Fourier's law ; Step 6: Build based , and The heat balance equation is used to solve the temperature of each grid, and the temperature solution of each grid is used as its corresponding preset temperature for iterative calculation until the preset termination condition is met. The final solved temperature of each grid is output, that is, the temperature distribution of the photovoltaic module is obtained.

[0008] Optionally, in step 3, the step of calculating each heat transfer coefficient of each grid in combination with air humidity, temperature, surface wind speed and preset temperature includes: Step 3.1: Calculate air density, viscosity and thermal conductivity based on air humidity and temperature; Step 3.2: Calculate the front / back natural convection heat transfer coefficient of each grid according to the preset temperature, air temperature, viscosity and thermal conductivity of each grid; Step 3.3: Calculate the positive / back forced convection heat transfer coefficient of each grid according to the surface wind speed, air density and viscosity of each grid; Step 3.4: Calculate the front / back radiation heat transfer coefficient of each grid based on the air temperature and the preset temperature.

[0009] Optionally, the calculation formulas for the air density, viscosity and thermal conductivity are as follows:

[0010] In the formula, , and They represent the density, viscosity and thermal conductivity of air respectively; is the temperature of the air; is the relative humidity of the air; and denote the molar masses of dry air and water vapor, respectively; and They represent the atmospheric pressure and the pressure of saturated water vapor respectively; and represent the viscosity and thermal conductivity of dry air respectively; and represent the viscosity and thermal conductivity of water vapor respectively; and They are the correction factor from ideal gas to real gas and the compressibility factor of air respectively; Indicates the correction parameter from dry air to water vapor; Represents and correction parameter for water vapor to dry air.

[0011] Optionally, in step 3.3, the step of calculating the front / back forced convection heat transfer coefficient of each grid includes: Calculate the critical length of the front / back sides of each mesh based on the density and viscosity of the air and characteristic length The ratio of is used to determine the air flow types on the front / back sides of each grid, including turbulent flow, mixed flow, and laminar flow. The forced convection heat transfer coefficients on the front / back sides of each grid are calculated by the following formula:

[0012] In the formula, is the forced convection heat transfer coefficient, The coordinates are Surface wind speed on the front / back side of the grid.

[0013] Optionally, the critical length of the front / back sides of each grid is and characteristic length The calculation formula of the ratio is as follows:

[0014] In the formula, Refers to the critical Reynolds number; and are the viscosity and density of air respectively.

[0015] Optionally, the step of determining the air flow type on the front / back side of each grid includes: When the ratio is less than 0.05, the air flow type is turbulent; 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.

[0016] Optionally, in step 3.2, the expressions for calculating the natural convection heat transfer coefficient of the front / back sides of each grid are as follows:

[0017] 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; is the characteristic length of the grid; is the Grashof number; is the acceleration due to gravity; is the volume variation coefficient; The coordinates are The preset temperature of the grid, is the temperature of the air; and are the viscosity and thermal conductivity of air, is the angle between the grid and the horizontal plane.

[0018] Optionally, in step 3, construct the heat loss equation for the front and back surfaces of each grid as follows:

[0019] In the formula, The coordinates are The heat dissipation power of the front and back surfaces of the grid; and are the natural convection heat transfer coefficients on the front and back sides of the grid, respectively; and are the forced convection heat transfer coefficients on the front and back sides of the grid, respectively; and are the radiation heat transfer coefficients on the front and back sides of the grid, respectively; is the power sum coefficient; The coordinates are The temperature of the grid to be solved; is the temperature of the air; is the area of ​​a single grid.

[0020] Optionally, in step 4, construct the heat absorption equation for each grid as follows:

[0021] In the formula, The coordinates are The heat absorption power of the grid that converts radiation into heat energy; is the transmittance of the illuminated glass; is the light coefficient absorption ratio; is the irradiance; The photoelectric conversion efficiency of photovoltaic modules under standard test conditions; and are the air temperature and irradiance under the standard test environment; is the power temperature coefficient of the photovoltaic module; The coordinates are The temperature of the grid to be solved; is the light radiation coefficient; is the area of ​​a single grid.

[0022] Optionally, in step 5, construct the lateral heat transfer equation for each grid as follows:

[0023] In the formula, The coordinates are The heat transfer power of the sides of the grid to transfer heat; , and Respectively represent the coordinates , and The preset temperature of the grid, =-1, 0, 1; and The adjacent grids are Axis direction and Distance in a plane; The power of heat dissipated by 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.

[0024] Optionally, the heat balance equation is as follows:

[0025] In the formula, , and Respectively represent the coordinates The heat dissipation power of the front and back surfaces of the grid, the heat absorption power that converts radiation into thermal energy, and the heat transfer power that transfers heat from its sides.

[0026] In a second aspect, the present invention provides a photovoltaic module temperature distribution prediction device, which runs the steps of any method described in the first aspect of the present invention, and the device comprises: The gridding module is used to grid the photovoltaic modules 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 is used to obtain the surface wind speed distribution of the photovoltaic module through wind speed field simulation based on the ambient wind speed; The first construction module is used to calculate the heat transfer coefficients of each grid by combining air humidity, temperature, surface wind speed and preset temperature to construct the heat loss equation of the front and back surfaces of each grid. ; The second building block is used to construct the absorbed heat equation of each grid based on irradiance and photovoltaic conversion efficiency. ; The third building block is used to construct the side heat transfer equation of each grid based on Fourier's law. ; Iterative solution module, used to establish , and The heat balance equation is used to solve the temperature of each grid, and the temperature solution of each grid is used as its corresponding preset temperature for iterative calculation until the preset termination condition is met. The final solved temperature of each grid is output, that is, the temperature distribution of the photovoltaic module is obtained.

[0027] In a third aspect, the present invention provides a terminal, including a processor and a storage medium; The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps of any one of the methods described in the first aspect of the present invention.

[0028] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods described in the first aspect of the present invention.

[0029] The beneficial effect of the present invention is that, compared with the prior art, 1. The present invention adopts grid discrete processing technology to deconstruct the continuous photovoltaic panel surface into refined grid units with independent thermal boundary conditions. Each micro-element grid maintains homogeneous characteristics at the thermodynamic level. By solving the heat balance equation of each grid unit, the temperature gradient distribution characteristics of the component surface are accurately reconstructed, breaking through the inherent limitations of the traditional model homogenization processing and significantly improving the spatial resolution of temperature field prediction. It not only provides refined data support for the evaluation of photovoltaic system power generation efficiency, but also enables early identification and positioning of abnormal status of components.

[0030] 2. The present invention integrates multi-scale meteorological parameters. On the basis of conventional irradiation parameters, different from the traditional method that simplifies wind speed into a uniform field and ignores the limitation of humidity, the present invention fully considers the influence of air humidity and wind speed distribution on the temperature of photovoltaic modules, analyzes the influence mechanism of micro-meteorological environmental parameters on the thermodynamic characteristics of photovoltaic arrays, quantitatively analyzes the disturbance effect of photovoltaic arrays on local flow fields, and combines the correction mechanism of humidity on convective heat transfer coefficient to construct a heat transfer equation with the synergistic effect of multi-dimensional environmental parameters, which significantly improves 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.

[0031] 3. Different from the traditional data-driven method, the present invention adopts a mechanism modeling path based on the heat conduction equation and fluid dynamics principles, by constructing a three-dimensional steady-state heat transfer differential equation , and , realizing a theoretical calculation framework that is independent of historical data. 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. It fundamentally solves the problem of inaccurate prediction under extreme meteorological conditions and realizes higher-precision prediction of photovoltaic module temperature distribution.

[0032] 4. Aiming at the nonlinear coupling problem existing in the heat balance equation, an adaptive iterative algorithm is used to construct an initial assumption matrix of the temperature field, assign an initial preset temperature to each grid, and establish an error feedback adjustment mechanism. When the results of continuous iterations meet the preset termination conditions (the temperature difference between two iterations is lower than the set value or reaches the maximum iteration step), the calculation process is terminated. The algorithm achieves an optimal balance between calculation efficiency and accuracy. The present invention realizes temperature distribution prediction that traditional models cannot do, lays the foundation for further high-precision power prediction, and can also play an important role in the maintenance and repair of photovoltaic modules. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is an algorithm flow chart of a photovoltaic module temperature distribution prediction method in an embodiment of the present invention; Figure 2 is a schematic diagram of a structure in which a photovoltaic module is divided into grids in an embodiment of the present invention; Figure 3 is the (a) density, (b) viscosity, and (c) thermal conductivity of air in the embodiment of the present invention, temperature, and relative humidity RH The relationship curve diagram of Figure 4 is the wind speed distribution of (a) the front side and (b) the back side of the inclined photovoltaic panel under different environmental wind speeds in the embodiment of the present invention (unit: m / s); Figure 5 is the relative humidity (a) in the embodiment of the present invention RH = 0%, (b) RH = 50%, (c) RH = 100% Temperature distribution of the tilted photovoltaic panel under different wind speeds (unit: K); Figure 6 1 is a graph showing the relationship between the maximum / minimum temperature of the photovoltaic panel and (a) relative air humidity and (b) ambient wind speed in the embodiment of the present invention, wherein the ambient wind speed in (a) is 0.1 m / s and the relative humidity in (b) is RH 0%; Figure 7 is the temperature distribution result predicted by the present invention according to the experimentally measured environmental data in the embodiment of the present invention (unit: K); Figure 8 A block diagram of the structural principles of a photovoltaic module temperature distribution prediction device in an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in the present invention are only part of the embodiments of the present invention, not all of the embodiments. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0035] Embodiment 1: Reference Figure 1 The embodiment of the present invention provides a method for predicting the temperature distribution of a photovoltaic module, which specifically includes the following steps: Step 1: Grid the PV panels to be predicted. Each grid is considered to have uniform material and isothermal, and an initial preset temperature is assigned to each grid. In order to reduce the amount of calculation, speed up the prediction speed and facilitate the calculation of temperature distribution, this embodiment first simplifies the structure and grids the photovoltaic components; Figure 2 , the photovoltaic module is regarded as an inclined flat rectangular block, ignoring the support, column and other structures of the photovoltaic module, only the panel is retained, and it is divided into M×N grids; in general, the grids correspond to the devices in the photovoltaic module one by one, and the grid division of photovoltaic modules of different specifications will also be different; photovoltaic modules in actual applications are generally composed of several small photovoltaic devices connected in series and parallel; each grid corresponds to a photovoltaic device, which is regarded as uniform and isothermal. This step is the pre-step of the algorithm, so it is not reflected in Figure 1 The algorithm block diagram is shown in the figure.

[0036] It is further explained that the embodiment of the present invention adopts grid discrete processing technology to deconstruct the continuous photovoltaic panel surface into a micro-element system with independent thermodynamic properties. Each micro-element grid maintains homogeneous characteristics at the thermodynamic level, and accurate thermal state analysis is achieved by calculating the temperature of each grid through a distributed temperature field. This architectural design significantly improves the spatial resolution of temperature field prediction, which not only provides refined data support for the evaluation of photovoltaic system power generation efficiency, but also enables early identification and positioning of abnormal component conditions.

[0037] Step 2: Obtain the surface wind speed distribution of the photovoltaic module through wind speed field simulation based on the ambient wind speed; According to the simplification of step 1, the photovoltaic module is digitally modeled, and then the geometric model is imported into the simulation software COMSOL. The ambient wind speed is input, and the wind speed field is simulated using the built-in k~ε turbulence module of the software 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. Figure 4 It should be noted that the method of simulating wind speed distribution using existing simulation software can be used, which is not the research content of the present invention, and the specific simulation process will not be described in detail.

[0038] Step 3: Calculate the heat transfer coefficients of each grid by combining air humidity, temperature, surface wind speed and preset temperature to construct the heat loss equation for the front and back surfaces of each grid. ; Studies have shown that air humidity affects air density, viscosity and thermal conductivity. Figure 3 Figures (a), (b), and (c) plot the density, viscosity, and thermal conductivity of air versus temperature and relative humidity, respectively. RH The relationship curve of relative humidity. RH The curves with 11 values ​​from 0 to 100% are drawn with a step length of 10%. Figure 3 It can be found that at a temperature of 0°C, humidity changes have almost no effect on air density, viscosity, and thermal conductivity. This is because water tends to exist in liquid or even solid form at low temperatures, even if the water vapor in the air is saturated ( RH = 100%), the proportion in the air is still negligible. However, as the temperature rises, the influence of humidity on air density, viscosity and thermal conductivity becomes non-negligible, and all three decrease significantly with the increase of humidity. At 100°C, the density, viscosity and thermal conductivity of saturated wet air are approximately 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) can greatly affect the heat dissipation of photovoltaic modules, the embodiment of the present invention introduces the influence of air humidity to correct the physical parameters in the heat transfer coefficient; therefore, the specific process of step 3 includes: Step 3.1: Calculate air density, viscosity and thermal conductivity based on air humidity and temperature. The calculation formulas are as follows:

[0039] In the formula, , and They represent the density, viscosity and thermal conductivity of air respectively; and They refer to the temperature and relative humidity of the air, respectively, which are input data; and They represent the molar masses of dry air and water vapor, respectively, and are constant values; and They represent atmospheric pressure and saturated water vapor pressure respectively. The former can generally be regarded as a constant 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 They are the correction factor from ideal gas to real gas and the compressibility factor of air respectively; Indicates the correction parameter from dry air to water vapor; Represents and correction parameter for water vapor to dry air.

[0040] As an embodiment of the present invention, each parameter , , , , , , , and Can be calculated by the following formulas:

[0041] In the formula, , , , , , , , and All of them are empirical parameters from mature research and are in the International System of Units. Take 3.53624×10 -4 , Take 2.93228×10 -5 , Take 2.61474×10 -7 , Take 8.57538×10 -9 ; Take -10.7588, Take 6.32529×10 -2 , Take -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 density, viscosity and thermal conductivity of air can be calculated based on the predicted meteorological data (temperature and relative humidity). Figure 3 .

[0042] Step 3.2: Calculate the front / back natural convection heat transfer coefficient of each grid according to the preset temperature, air temperature, viscosity and thermal conductivity of each grid.

[0043] In order to calculate the heat dissipation of the front and back sides of each grid to the air, it is necessary to calculate the natural convection heat transfer coefficient; the calculation formula of the natural convection heat transfer coefficient of the front and back sides provided by the embodiment of the present invention is as follows:

[0044] 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 is a fixed value here; is the characteristic length of the grid (i.e. the length of the grid projected onto the horizontal plane, ), is a constant value for a certain grid under a certain wind direction; is the acceleration due to gravity; is the volume variation coefficient; The coordinates are The preset temperature of the grid, is the temperature of the air; and are the viscosity and thermal conductivity of air respectively; is the angle between the grid and the horizontal plane (e.g. Figure 2 ). The coordinates are That is, the first m Ledi n A grid of rows; m =1,2…M; n =1,2…N.

[0045] Step 3.3: Calculate the positive / back forced convection heat transfer coefficient of each grid according to the surface wind speed, air density and viscosity of each grid; In order to calculate the heat dissipation of the front and back of each grid to the air, it is also necessary to calculate the forced convection heat transfer coefficient of the air. Before calculating the forced convection heat transfer coefficient, the flow type of the air must be divided according to the ratio of the critical length to the characteristic length. The critical length of the front / back of each grid is calculated based on the density and viscosity of the air. and characteristic length The ratios are as follows:

[0046] In the formula, Refers to the critical Reynolds number, which takes a fixed value; and are respectively the viscosity and density of air, taken from step 3.1; The coordinates are The grid surface wind speed is taken from step 2. Among them, the front and back wind speeds of the same grid may be different, and their ratios and the corresponding forced convection heat transfer coefficients need to be calculated separately. When the ratio is less than 0.05, the fluid is considered to be completely turbulent; when the ratio is greater than 0.95, it is considered that the fluid is flowing in a laminar manner; and when the ratio is not less than 0.05 and not greater than 0.95, the fluid is in a transition state from laminar flow to turbulent flow, that is, mixed flow. The forced convection heat transfer coefficient calculation formulas used in the present invention for laminar flow, turbulent flow and mixed flow are as follows:

[0047] Where: is the forced convection heat transfer coefficient, The coordinates are The surface wind speed on the front / back side of the grid. This step combines the above formulas to calculate the forced convection heat transfer coefficient using the wind speed on the front / back side of the grid obtained in step 2 and the air viscosity and density obtained in step 3.

[0048] Step 3.4: Calculate the front / back radiation heat transfer coefficient of each grid based on the air temperature and the preset temperature.

[0049] In this embodiment, the radiation heat transfer coefficient of each grid's front / back surface radiating heat to the environment can be calculated using the following formulas:

[0050] In the formula, and are the radiation heat transfer coefficients of the front / back sides of the grid, respectively; and They 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, refer to Figure 2 ; and The coordinates are The preset temperature and air temperature of the grid. Based on the above formula, the radiation heat transfer coefficient of the front / back side of each grid can be calculated.

[0051] Step 3.5: Based on the calculation results of steps 3.1-3.4, construct the heat loss equation for the front and back surfaces of each grid .

[0052] In this embodiment, the natural convection heat transfer coefficient, forced convection heat transfer coefficient and radiation heat transfer coefficient of the front and back sides of each grid are calculated according to steps 3.1-3.4, thereby expressing the power and heat loss equation of the front and back surfaces of each grid. :

[0053] Where: The coordinates are The heat dissipation power of the front and back surfaces of the grid; and are the natural convection heat transfer coefficients on the front and back sides of the grid, respectively; and are the forced convection heat transfer coefficients on the front and back sides of the grid, respectively; and are the radiation heat transfer coefficients on the front and back sides of the grid, respectively; is the exponentiation coefficient, generally 2 to 4, preferably, in this implementation The value is 3; The coordinates are The temperature of the grid to be solved; is the temperature of the air; is the area of ​​a single grid.

[0054] Step 4: Construct the heat absorption equation for each grid based on irradiance and photovoltaic conversion efficiency ; The main source of heat in photovoltaic panels is the radiation energy that cannot be converted into electrical energy; for each grid, this part of energy can be expressed by the following formula:

[0055] Where: The coordinates are The heat absorption power of the grid that converts radiation into heat energy; is the transmittance of the illuminated 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, and the irradiance = 1000 W / m 2 ); is the power temperature coefficient of the photovoltaic module; represents the temperature to be solved for the grid with coordinates ; is the light radiation coefficient; is the area of a single grid.

[0056] Step 5: Based on Fourier's law, establish the heat transfer equation for the side surfaces of each grid ; Based on Fourier's heat transfer law, the present invention can deduce the power of the heat flow on the side surfaces of each grid, that is, the heat transfer on the side surfaces of each grid The equation is as follows:

[0057] In the formula: represents the heat transfer power of the heat transfer on the side surface of the grid with coordinates ; = -1, 0, 1; , and respectively represent the preset temperatures of the grids with coordinates , and ; and are the distances in the axis direction and in the plane between adjacent grids respectively; is the power of the heat dissipated by the grid at the edge of the side surface 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.

[0058] 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.

[0059] Specifically, steps 3 to 5 respectively obtain the upper and lower surface heat dissipation power, heat acquisition power, and side heat flow power of each grid, thereby constructing a heat balance equation for each grid:

[0060] In the formula, , and Respectively represent the coordinates The heat dissipation power of the front and back surfaces of the grid, the heat absorption power of converting radiation into heat energy, and the heat transfer power of transferring heat from the side. 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.

[0061] It is further explained that it is extremely difficult to directly solve the heat balance equation due to the presence of a large number of complex nested formulas and a large number of nonlinear relationships. In order to reduce the difficulty of solving and speed up the calculation, the embodiment of the present invention adopts an iterative solution algorithm for the nonlinear coupling problem existing in the equation; 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., the temperature values ​​in steps 3.2, 3.4 and 5). ), and the temperature to be solved in steps 3.5 and 4 is Solve for the unknown number ( The relationship between is linear and easy to solve), and then the obtained With preset Comparison: If the result does not meet the preset convergence threshold (the difference between the two is higher than the set value) and does not reach the upper limit of the number of iterations, Substitution (make ), restart the calculation and iterate; the calculation process is terminated when the result meets the preset convergence threshold (the difference between the two is lower than the set value) or the maximum iteration step is reached, such as Figure 1 As shown; the iterative algorithm of the present invention achieves an optimal balance between computational efficiency and accuracy, and the temperature distribution of the photovoltaic module can be finally obtained by solving the problem, which can be plotted as a thermal diagram as shown in Figure 5 shown.

[0062] The advantages of the photovoltaic module temperature distribution prediction method provided by the present invention are explained below through specific experimental data analysis.

[0063] extract Figure 5 Plotting some of the data in Figure 6 , which more intuitively reflects the impact of air humidity and wind speed on the temperature of photovoltaic modules, indicating that air humidity and wind speed should not be ignored in the temperature prediction of photovoltaic modules, and verifies the necessity of taking air humidity and wind speed distribution into consideration in the present invention.

[0064] Figure 7 The prediction results of the temperature distribution of photovoltaic modules by the present invention are demonstrated, wherein the predicted maximum and minimum temperatures are 28.3 ℃ (301.8 K) and 12.9 ℃ (286.4 K), respectively; while the actual values ​​of the maximum and minimum temperatures in the experiment are 28.8 ℃ and 13.0 ℃, respectively, with errors of less than 1 ℃. The prediction results are consistent with the experiment, proving the reliability of the present invention.

[0065] In addition, the most direct application of the temperature prediction model of the present invention is for power generation prediction. The following Table 1 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 an algorithm that does not consider air humidity, surface wind speed distribution and temperature differences at different positions in the component, and it regards the photovoltaic component as a uniform whole. It can be clearly found from the comparison of the experimental results data in Table 1 that compared with the traditional method, the error of the present invention when used for power generation prediction is smaller. The experimental results prove the superiority of the photovoltaic component temperature distribution prediction method proposed by the present invention.

[0066] Table 1: Comparison of the effects of different temperature prediction methods on power generation prediction and measured data (retain two decimal places)

[0067] The beneficial effect of the present invention is that, compared with the prior art, 1. The present invention adopts grid discrete processing technology to deconstruct the continuous photovoltaic panel surface into refined grid units with independent thermal boundary conditions. Each micro-element grid maintains homogeneous characteristics at the thermodynamic level. By solving the heat balance equation of each grid unit, the temperature gradient distribution characteristics of the component surface are accurately reconstructed, breaking through the inherent limitations of the traditional model homogenization processing and significantly improving the spatial resolution of temperature field prediction. It not only provides refined data support for the evaluation of photovoltaic system power generation efficiency, but also enables early identification and positioning of abnormal status of components.

[0068] 2. The present invention integrates multi-scale meteorological parameters. On the basis of conventional irradiation parameters, different from the traditional method that simplifies wind speed into a uniform field and ignores the limitation of humidity, the present invention fully considers the influence of air humidity and wind speed distribution on the temperature of photovoltaic modules, analyzes the influence mechanism of micro-meteorological environmental parameters on the thermodynamic characteristics of photovoltaic arrays, quantitatively analyzes the disturbance effect of photovoltaic arrays on local flow fields, and combines the correction mechanism of humidity on convective heat transfer coefficient to construct a heat transfer equation with the synergistic effect of multi-dimensional environmental parameters, which significantly improves 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.

[0069] 3. Different from the traditional data-driven method, the present invention adopts a mechanism modeling path based on the heat conduction equation and fluid dynamics principles, by constructing a three-dimensional steady-state heat transfer differential equation , and , realizing a theoretical calculation framework that is independent of historical data. 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. It fundamentally solves the problem of inaccurate prediction under extreme meteorological conditions and realizes higher-precision prediction of photovoltaic module temperature distribution.

[0070] 4. Aiming at the nonlinear coupling problem existing in the heat balance equation, an adaptive iterative algorithm is used to construct an initial assumption matrix of the temperature field, assign an initial preset temperature to each grid, and establish an error feedback adjustment mechanism. When the results of continuous iterations meet the preset termination conditions (the temperature difference between two iterations is lower than the set value or reaches the maximum iteration step), the calculation process is terminated. The algorithm achieves an optimal balance between calculation efficiency and accuracy. The present invention realizes temperature distribution prediction that traditional models cannot do, lays the foundation for further high-precision power prediction, and can also play an important role in the maintenance and repair of photovoltaic modules.

[0071] Embodiment 2: like Figure 8 As shown, the present invention provides a photovoltaic module temperature distribution prediction device, which is used to implement the steps of the method in the above embodiment 1, and the device specifically includes: The gridding module is used to grid the photovoltaic modules 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 is used to obtain the surface wind speed distribution of the photovoltaic module through wind speed field simulation based on the ambient wind speed; The first construction module is used to calculate the heat transfer coefficients of each grid by combining air humidity, temperature, surface wind speed and preset temperature to construct the heat loss equation of the front and back surfaces of each grid. ; The second building block is used to construct the absorbed heat equation of each grid based on irradiance and photovoltaic conversion efficiency. ; The third building block is used to construct the side heat transfer equation of each grid based on Fourier's law. ; Iterative solution module, used to establish , 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 further 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.

[0072] The photovoltaic module temperature distribution prediction device provided in the embodiment of the present invention and the photovoltaic module temperature distribution prediction method provided in Embodiment 1 are based on the same technical concept, and can produce beneficial effects as described in Embodiment 1. The content not described in detail in this embodiment can be referred to in Embodiment 1.

[0073] Embodiment 3: A terminal provided in an embodiment of the present invention includes a processor and a storage medium; The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps of the method according to any one of Embodiment 1.

[0074] Embodiment 4: A computer-readable storage medium provided in an embodiment of the present invention stores a computer program, and when the program is executed by a processor, the steps of the method according to any one of Embodiment 1 are implemented.

[0075] 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.

[0076] The computer-readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device. The computer-readable storage medium may be, for example, but 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 (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 mechanical encoding device, such as a punched card or raised structures in a groove having instructions stored thereon, and any suitable combination of the foregoing. The computer-readable storage medium 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 propagated 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.

[0077] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, 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 copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The 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 the computer-readable storage medium in each computing / processing device.

[0078] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent 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 "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, as a separate software package, partially on the user's computer, partially on a remote computer, or entirely on a 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., using an Internet service provider to connect through the Internet). In some embodiments, by using the state information of the computer-readable program instructions to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit may execute the computer-readable program instructions, thereby implementing various aspects of the present disclosure.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for predicting temperature distribution of photovoltaic modules, characterized in that: The method comprises the following steps: Step 1: Grid the PV panels to be predicted. Each grid is considered to have 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 by combining air humidity, temperature, surface wind speed and preset temperature to construct the heat loss equation 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: Construct the side heat transfer equation for each grid based on Fourier's law ; Step 6: Build based , and The heat balance equation is used to solve the temperature of each grid, and the temperature solution of each grid is used as its corresponding preset temperature for iterative calculation until the preset termination condition is met. The final solved temperature of each grid is output, that is, the temperature distribution of the photovoltaic module is obtained.

2. The photovoltaic module temperature distribution prediction method according to claim 1, characterized in that: In step 3, the steps of calculating the heat transfer coefficients of each grid respectively in combination with air humidity, temperature, surface wind speed and preset temperature include: Step 3.1: Calculate air density, viscosity and thermal conductivity based on air humidity and temperature; Step 3.2: Calculate the front / back natural convection heat transfer coefficient of each grid according to the preset temperature, air temperature, viscosity and thermal conductivity of each grid; Step 3.3: Calculate the positive / back forced convection heat transfer coefficient of each grid according to the surface wind speed, air density and viscosity of each grid; Step 3.4: Calculate the front / back radiation heat transfer coefficient of each grid based on the air temperature and the preset temperature.

3. The photovoltaic module temperature distribution prediction method according to claim 2, characterized in that: The calculation formulas for the air density, viscosity and thermal conductivity are as follows: In the formula, , and They represent the density, viscosity and thermal conductivity of air respectively; is the temperature of the air; is the relative humidity of the air; and denote the molar masses of dry air and water vapor, respectively; and They represent the atmospheric pressure and the pressure of saturated water vapor respectively; and represent the viscosity and thermal conductivity of dry air respectively; and represent the viscosity and thermal conductivity of water vapor respectively; and They are the correction factor from ideal gas to real gas and the compressibility factor of air respectively; Indicates the correction parameter from dry air to water vapor; Represents and correction parameter for water vapor to dry air.

4. The photovoltaic module temperature distribution prediction method according to claim 2, characterized in that: In step 3.3, the steps to calculate the front / back forced convection heat transfer coefficient of each grid include: Calculate the critical length of the front / back sides of each mesh based on the density and viscosity of the air and characteristic length The ratio of is used to determine the air flow types on the front / back sides of each grid, including turbulent flow, mixed flow, and laminar flow. The forced convection heat transfer coefficients on the front / back sides of each grid are calculated by the following formula: In the formula, is the forced convection heat transfer coefficient, The coordinates are Surface wind speed on the front / back side of the grid.

5. The photovoltaic module temperature distribution prediction method according to claim 4, characterized in that: The critical length of the front / back sides of each grid and characteristic length The calculation formula of the ratio is as follows: In the formula, Refers to the critical Reynolds number; and are the viscosity and density of air respectively.

6. The photovoltaic module temperature distribution prediction method according to claim 4, characterized in that: The step of determining the air flow type on the front / back side of each grid comprises: When the ratio is less than 0.05, the air flow type is turbulent; 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.

7. The photovoltaic module temperature distribution prediction method according to claim 2, characterized in that: In step 3.2, the expressions for calculating the natural convection heat transfer coefficient of the front / back sides of each grid are as follows: 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; is the characteristic length of the grid; is the Grashof number; is the acceleration due to gravity; is the volume variation coefficient; The coordinates are The preset temperature of the grid, is the temperature of the air; and are the viscosity and thermal conductivity of air, is the angle between the grid and the horizontal plane.

8. The photovoltaic module temperature distribution prediction method according to claim 1 or 7, characterized in that: In step 3, the heat loss equation for the front and back surfaces of each grid is constructed as follows: In the formula, The coordinates are The heat dissipation power of the front and back surfaces of the grid; and are the natural convection heat transfer coefficients on the front and back sides of the grid, respectively; and are the forced convection heat transfer coefficients on the front and back sides of the grid, respectively; and are the radiation heat transfer coefficients on the front and back sides of the grid, respectively; is the power sum coefficient; The coordinates are The temperature of the grid to be solved; is the temperature of the air; is the area of ​​a single grid.

9. The photovoltaic module temperature distribution prediction method according to claim 1, characterized in that: In step 4, construct the heat absorption equation for each grid as follows: In the formula, The coordinates are The heat absorption power of the grid that converts radiation into heat energy; is the transmittance of the illuminated glass; is the light coefficient absorption ratio; is the irradiance; The photoelectric conversion efficiency of photovoltaic modules under standard test conditions; and are the air temperature and irradiance under the standard test environment; is the power temperature coefficient of the photovoltaic module; The coordinates are The temperature of the grid to be solved; is the light radiation coefficient; is the area of ​​a single grid.

10. The photovoltaic module temperature distribution prediction method according to claim 1, characterized in that: In step 5, the side heat transfer equation of each grid is constructed as follows: In the formula, The coordinates are The heat transfer power of the sides of the grid to transfer heat; , and Respectively represent the coordinates , and The preset temperature of the grid, =-1, 0, 1; and The adjacent grids are Axis direction and Distance in a plane; The power of heat dissipated by 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.

11. The photovoltaic module temperature distribution prediction method according to claim 1, characterized in that: In step 6, the heat balance equation is as follows: In the formula, , and Respectively represent the coordinates The heat dissipation power of the front and back surfaces of the grid, the heat absorption power that converts radiation into thermal energy, and the heat transfer power that transfers heat from its sides.

12. A photovoltaic module temperature distribution prediction device, running the photovoltaic module temperature distribution prediction method according to any one of claims 1 to 11, characterized in that: The device includes: The gridding module is used to grid the photovoltaic modules 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 is used to obtain the surface wind speed distribution of the photovoltaic module through wind speed field simulation based on the ambient wind speed; The first construction module is used to calculate the heat transfer coefficients of each grid by combining air humidity, temperature, surface wind speed and preset temperature to construct the heat loss equation of the front and back surfaces of each grid. ; The second building block is used to construct the absorbed heat equation of each grid based on irradiance and photovoltaic conversion efficiency. ; The third building block is used to construct the side heat transfer equation of each grid based on Fourier's law. ; Iterative solution module, used to establish , and The heat balance equation is used to solve the temperature of each grid, and the temperature solution of each grid is used as its corresponding preset temperature for iterative calculation until the preset termination condition is met. The final solved temperature of each grid is output, that is, the temperature distribution of the photovoltaic module is obtained.

13. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-11.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 11 are implemented.

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