Method for evaluating fatigue life of photovoltaic module in desert region

Abstract

The present application belongs to the technical field of fatigue damage analysis, and relates to a fatigue life evaluation method for a photovoltaic module in a desert area. The fatigue life evaluation method for the photovoltaic module in the desert area comprises the following steps: obtaining a wind speed value and a sand particle diameter, determining a wind-sand load of the wind-sand on a cell based on the wind speed value and the sand particle diameter, obtaining a wind speed probability density, determining a first damage value of the wind-sand on the cell based on the wind speed probability density and the wind-sand load, obtaining a temperature value of the cell, determining a second damage value of the temperature on the cell based on the temperature value, and evaluating the fatigue life of the cell based on the first damage value and the second damage value. The present application can provide a theoretical basis and technical support for the structural optimization and reliability evaluation of the photovoltaic module in a wind-sand environment.

Description

Technical Field

[0001] This invention relates to the field of fatigue damage analysis technology, and more specifically, to a method for assessing the fatigue life of photovoltaic modules in desert regions. Background Technology

[0002] Solar photovoltaic (PV) technology is rapidly developing and widely used globally. During production and transportation, PV modules are prone to various initial damages, including inherent internal defects in silicon solar cells (cells), residual stress formed during high-temperature sintering, and microcracks. Research on the mechanical performance degradation of PV modules during long-term operation mainly focuses on two aspects: the initiation and propagation of cell cracks; and fatigue damage and life prediction of key components of PV modules under cyclic loading. Fatigue damage primarily refers to the gradual accumulation of damage to structural materials under cyclic stress loading. Its main mechanisms include stress concentration caused by cyclic loading, material defects, friction, corrosion, and other factors leading to material damage. In recent years, PV modules have been applied in various scenarios, with increasingly widespread use in desert regions. With the widespread application of PV modules in desert areas, research on cell fatigue damage has become increasingly important.

[0003] Fatigue analysis of photovoltaic (PV) modules helps engineers predict and assess the lifespan of structural components and take appropriate measures to improve their reliability and durability. While significant progress has been made in fatigue research on key PV module components under specific loads, including ongoing research on fatigue damage in offshore PV systems, research on fatigue damage in desert PV systems remains largely unexplored.

[0004] Therefore, in order to address the lack of relevant technologies for fatigue damage analysis of photovoltaic module cells in desert areas, this application is hereby submitted. Summary of the Invention

[0005] The purpose of this invention is to provide a fatigue life assessment method for photovoltaic modules in desert areas, thereby overcoming, to some extent, the problem that it is impossible to analyze the fatigue damage of photovoltaic module cells in desert areas or predict the fatigue life due to the limitations and defects of related technologies.

[0006] To solve the above-mentioned technical problems, this application is implemented as follows: A method for fatigue life assessment of photovoltaic modules in desert areas, wherein the photovoltaic module includes a backsheet, solar cells, and a panel arranged sequentially from bottom to top, wherein the panel and the solar cells, and the solar cells and the backsheet, are bonded together by adhesive layers; the method includes: Obtain wind speed and sand particle diameter, and determine the wind and sand load on the battery cells based on the wind speed and sand particle diameter. Obtain the wind speed probability density, and based on the wind speed probability density and the wind and sand load, determine the first damage value of wind and sand to the battery cell; The temperature value of the solar cell is obtained, and based on the temperature value, a second damage value of temperature to the solar cell is determined; The fatigue life of the solar cell is evaluated based on the first damage value and the second damage value.

[0007] Optionally, determining the wind and sand load on the battery cells based on the wind speed value and the sand grain diameter includes: Based on the wind speed value and the sand grain diameter, the sand transport rate is determined; wherein the sand transport rate is positively correlated with the wind speed value and negatively correlated with the sand grain diameter; Based on the wind speed value and the sand transport rate, the sand concentration value is determined; wherein, the sand concentration value is positively correlated with the sand transport rate; Based on the wind speed value and the sand concentration value, the sand load is determined; the sand load is positively correlated with the wind speed and positively correlated with the sand concentration value.

[0008] Optionally, the sediment transport rate at a height z above the ground The calculation model is as follows: ;in, The concentration of windblown sand at a height z above the ground. Let z be the wind speed at a height z above the ground. The calculation model for the sandstorm concentration value is as follows: Where h is the preset height above the ground, and it is assumed that the sand concentration is uniformly distributed and the wind speed is constant within the preset height range; the height z is not greater than the preset height. The calculation model for the wind and sand load is as follows: ;in, This refers to the wind load shape coefficient of the solar cell. Let g be the weight per unit volume of air, g be the acceleration due to gravity, and G be the gust safety factor. The angle between the solar cell and the horizontal plane.

[0009] Optionally, the wind speed includes average wind speed and fluctuating wind speed, and the calculation model for the wind speed is as follows: ; The average wind speed was provided by the ERA5 reanalysis dataset and subjected to a power-law exponential transformation to obtain the average wind speed. The calculation model is as follows: ;in, The wind speed profile index. Let be the average wind speed at a height of 10m, and b be the correction term in the power-law exponent formula; The fluctuating wind speed was obtained through Davenport power spectrum simulation and autoregressive model in linear filtering. The calculation model is as follows: ;in, Let be the pulsating wind speed at time t; K is the autoregressive coefficient, where K v = 1,2,…,L; L is the order of the autoregressive model; K before time t v The pulsating wind speed at any given moment; The time step of the pulsating wind speed; It has a mean of 0 and a variance of An independent random process.

[0010] Optionally, determining the first damage value of wind and sand to the solar cells based on the wind speed probability density and the wind and sand load includes: Based on the aforementioned wind and sand load and the mechanical model of the multilayer photovoltaic module, the first equivalent stress is determined; Based on the wind speed probability density and the first equivalent stress, the first damage value of wind and sand to the battery cells is determined.

[0011] Optionally, the wind speed probability density satisfies the probability density function of a gamma distribution, and its calculation model is as follows: ;in, For the whole year medium wind speed The probability of occurrence; The integral interval for wind speed; The calculation model for the first damage value is as follows: ;in, For a period of one year, Wind speed Duration; Wind speed Duration Damage values ​​caused by wind and sand to solar cells.

[0012] Optionally, determining the second damage value of the solar cell based on the temperature value includes: Based on the temperature value and the mechanical model of the multilayer photovoltaic module, the second equivalent stress is determined; Based on the second equivalent stress, a second damage value of temperature on the solar cell is determined; The calculation model for the temperature value is as follows: ;in, and These are the cell temperature and the ambient temperature, respectively. Solar radiation illuminance; This represents the ratio of heat flux from thermal radiation to heat flux from thermal convection. Solar energy absorption rate; The photoelectric conversion efficiency of the solar cell; The convective heat transfer coefficient is... .

[0013] Optionally, the mechanical model of the tandem photovoltaic module is: ;in, This represents the temperature change of the solar cell. The coefficient of thermal expansion of the battery cell material; the first k The stiffness coefficients of the slabs are as follows: , , ; For the first k Young's modulus of the lamellae For the first k Poisson's ratio of the shelf; The strain at any point in the solar cell is: ;in, ; ; ; ; Let be the thickness of the k-th layer. The distance from the center line of the k-th layer plate along the thickness direction to the mid-plane of the solar cell is the plane at the very center of the solar cell along the thickness direction. w The deflection of the solar cell along its thickness under wind and sand load. The calculation models for both the first and second equivalent stresses are: .

[0014] Optionally, the wind speed Duration Damage to solar cells caused by wind and sand, and temperature Damage values ​​to solar cells The calculation models are all: ; in, ; ; ; ; The equivalent stress when the solar cell suffers zero damage; E For Young's modulus, v Poisson's ratio; K is Cyclic strength coefficient; N This represents the number of loop iterations. n The cyclic strain hardening index; , These are material constants; To and and n Relevant material parameters; These are parameters related to the elastic parameters of the solar cell, the viscoelastic parameters of the adhesive layer, and the geometric parameters.

[0015] Optionally, evaluating the fatigue life of the solar cell based on the first damage value and the second damage value includes: Based on the first damage value and the second damage value, the total damage value of the battery cell under the influence of temperature and wind and sand is determined. The calculation model for the total damage value is as follows: ;in, The cumulative damage caused by wind and sand loads over a one-year period. The cumulative damage caused by a year-long temperature load; Based on the total damage value, the fatigue life of the battery cell is determined. The calculation model for the fatigue life is as follows: ;in, This represents the fatigue damage threshold.

[0016] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects: To analyze the fatigue damage of solar cells in photovoltaic modules in desert regions, this application proposes a fatigue life assessment method. Based on wind speed, sand particle diameter, and wind speed probability density, the wind and sand load and its damage value to the solar cells are determined. Based on the temperature of the solar cells, the temperature-related damage value is determined. These two damage values ​​are combined to comprehensively evaluate the fatigue life of the solar cells. Considering the environmental characteristics of desert regions, wind and sand are a significant factor causing solar cell damage. Compared with existing technologies, this application takes into account this important factor and the impact of wind and sand load damage on the fatigue life of the solar cells, thus more accurately assessing the fatigue damage and fatigue life of the solar cells. This provides a theoretical basis and technical support for the structural optimization and reliability assessment of photovoltaic modules in windy and sandy environments.

[0017] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.

[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 Scanning electron microscope (SEM) images of surface cracks in silicon solar cells provided for related technologies; Figure 1 (a) is a schematic diagram of micro-cracks on the surface of a solar cell. Figure 1 (b) is a schematic diagram of macroscopic cracks on the surface of a solar cell; Figure 2 A flowchart illustrating the fatigue life assessment method for photovoltaic modules in desert areas provided in this embodiment of the invention; Figure 3 A schematic diagram (coordinate system diagram) of the structure of a (double-glass) photovoltaic module provided in an embodiment of the present invention. Figure 4 This is a schematic diagram of the fatigue damage calculation framework for photovoltaic module cells provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the installation tilt angle of a photovoltaic module provided in an embodiment of the present invention; Figure 6 The wind speed, load, and stress time history curves provided for embodiments of the present invention under an average wind speed of 8.5 m / s. Figure 6 (a) is a time history curve of wind speed. Figure 6 (b) is the load time history curve. Figure 6 (c) is the stress-time history curve; Figure 7 Damage variables provided for embodiments of the present invention D With the number of loops N Evolution curves and schematic diagram of fitting results; Figure 8 This is a schematic diagram comparing the simulated power spectrum and the Davenport spectrum provided in an embodiment of the present invention; Figure 9 This is a schematic diagram of the time history simulation results of fluctuating wind speed under different average wind speeds provided in the embodiments of the present invention; Figure 9 (a) is a schematic diagram with an average wind speed of 5 m / s. Figure 9 (b) is a schematic diagram with an average wind speed of 10 m / s. Figure 9 (c) is a schematic diagram of an average wind speed of 15 m / s; Figure 10 A comparison chart of theoretical cumulative probability and observed cumulative probability (PP comparison chart) provided for embodiments of the present invention. Figure 10 (a) is the Weiber distribution plot. Figure 10 (b) is a gamma distribution map; Figure 11 A schematic diagram of the cumulative distribution function provided in an embodiment of the present invention; Figure 12 A schematic diagram of the probability mass function of wind speed provided in an embodiment of the present invention; Figure 13 This is a schematic diagram of the fatigue life of the battery cell under different installation tilt angles, provided in an embodiment of the present invention.

[0021] Explanation of reference numerals in the attached figures: 10-Glass back panel; 20-Adhesive layer (EVA layer); 30-cell layer; 40-Glass panel; 100 - Photovoltaic modules. Detailed Implementation

[0022] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings.

[0024] refer to Figure 1 As shown, Figure 1 (a) is a schematic diagram of micro-cracks on the surface of a solar cell. Figure 1 (b) is a schematic diagram of macroscopic cracks on the surface of solar cells. During operation in desert regions, photovoltaic modules are frequently subjected to cyclic loads such as wind and sand, high and low temperatures, and solar radiation. This causes microcracks in the cells to continuously evolve, gradually leading to macroscopic cracks and irreversible damage, which has become a significant failure mode for photovoltaic modules. This damage not only affects the power generation efficiency of the modules but also requires regular maintenance and repair during operation, increasing energy costs. With the widespread application of photovoltaic modules in desert regions, research on cell fatigue damage has become increasingly important.

[0025] Significant progress has been made in the research on the initiation and damage mechanisms of cracks in photovoltaic module cells. However, there is a lack of research that can characterize the damage evolution relationship between the failure area of ​​cracks in silicon solar cells and macroscopic damage variables.

[0026] Continuum Damage Mechanics (CDM) describes the mechanical behavior of microscopic defects inside materials as a continuous field by introducing damage variables. It is consistent with physical fields such as stress field and strain field, and can completely describe the damage evolution process of solar cells under wind and sand cycle loads and temperature loads from a thermodynamic perspective.

[0027] Furthermore, in the research of related technologies, the focus of photovoltaic module fatigue life studies is on the fatigue characteristics of circuit interconnects and key structures in the cells under cyclic thermomechanical stress. Although significant progress has been made in fatigue research on key components of photovoltaic modules under specific loads, including the gradual development of fatigue research on offshore photovoltaic systems, research on fatigue damage in desert photovoltaic systems remains in its infancy.

[0028] In view of this, the technical solution of the present invention provides a fatigue life assessment method for photovoltaic modules in desert areas, which fills the gap in related technologies for the analysis or prediction of fatigue damage of photovoltaic modules (especially photovoltaic module cells) in desert areas, thereby meeting practical needs. Specific technical solutions are described below.

[0029] refer to Figure 2 and Figure 3 This application provides a method for evaluating the fatigue life of photovoltaic module cells in desert areas. The photovoltaic module includes a backsheet, cells, and a panel arranged sequentially from bottom to top. The panel and cells, as well as the cells and backsheet, are bonded together with adhesive layers to form a stacked photovoltaic module. The evaluation method includes the following steps: S100: Obtain wind speed and sand particle diameter, and determine the wind and sand load on the battery cells based on the wind speed and sand particle diameter. S200, obtain the wind speed probability density, and determine the first damage value of wind and sand to the battery cell based on the wind speed probability density and wind and sand load. S300: Obtain the temperature value of the solar cell and, based on the temperature value, determine the second damage value of the solar cell caused by temperature. S400 evaluates the fatigue life of solar cells based on a first damage value and a second damage value.

[0030] In this embodiment, the wind and sand load and its damage to the solar cells are determined based on wind speed, sand particle diameter, and wind speed probability density. The temperature-related damage to the solar cells is determined based on the cell temperature. The fatigue life of the solar cells is comprehensively evaluated by combining these two damage values. Considering the environmental characteristics of desert areas, wind and sand are one of the important factors causing damage to solar cells. Compared with the prior art, this application takes into account this important factor and the impact of wind and sand load damage on the fatigue life of solar cells. This allows for a more accurate assessment of the fatigue damage and fatigue life of solar cells, providing a theoretical basis and technical support for the structural optimization and reliability assessment of photovoltaic modules in windy and sandy environments.

[0031] It should be understood that the fatigue life assessment method for photovoltaic modules in desert areas in this embodiment can be mainly used to assess the fatigue damage and fatigue life of the cells in photovoltaic modules in desert areas.

[0032] refer to Figure 3 The structure of photovoltaic modules and their coordinate system are as follows: Figure 3 As shown, the photovoltaic module can be a double-glass photovoltaic module, where both the top and bottom surfaces are glass layers, meaning both the backsheet and the front panel are glass sheets, and the middle layers are an adhesive layer and a silicon cell layer, respectively. Therefore, the photovoltaic module includes, from bottom to top, a glass backsheet 10, an adhesive layer 20 (such as an EVA layer), a cell layer 30 (such as a silicon cell layer), another adhesive layer 20 (such as an EVA layer), and a glass front panel 40. Figure 3 In the diagram, the x, y, and z axes are along the longitudinal, transverse, and plate thickness directions, respectively.

[0033] In this embodiment, to evaluate the fatigue life of the battery cell, the average wind speed is first obtained based on the ERA5 reanalysis dataset and the wind speed power law exponential model, and the fluctuating wind speed is obtained based on the power spectrum model and the linear filtered autoregressive model. The wind speed value is obtained through the average wind speed value and the fluctuating wind speed value. Then, based on the wind speed value and the sand particle diameter, the sand transport rate is determined. Based on the sand transport rate and the wind speed value, the sand concentration is determined. Based on the sand concentration and the wind speed value, the sand load on the battery cell is determined. Then, based on the sand load, the deflection / displacement is determined. Based on the deflection / displacement, the first equivalent stress is determined. Based on the first equivalent stress and the wind speed probability density, the damage model (sand damage model) is determined, that is, the first damage value of the sand on the battery cell is determined. Furthermore, based on the battery temperature value, the second equivalent stress is determined. Based on the second equivalent stress, the damage model (temperature damage model) is determined, that is, the second damage value of the sand on the battery cell is determined. Thus, the first damage value and the second damage value can be used to evaluate the fatigue life of the battery cell.

[0034] In other words, this embodiment primarily establishes a pulsating wind field model, combined with the sand grain characteristics of desert regions, to realistically reflect the true characteristics of wind and sand in desert areas. The wind and sand load on the components is then solved using an equivalent wind and sand model. Based on the ERA5 meteorological data platform, a wind speed probability model is established to predict the temperature change history of the solar cells. The established mechanical model is applied to solve for the mechanical stress time history and thermal stress time history of the solar cells. The stress amplitude and cycle number are statistically obtained using the rainflow counting method. Then, based on continuum damage mechanics (CDM), a damage evolution equation that characterizes micro-cracks and macro-variables is constructed, thereby achieving a quantitative description of the damage process. Finally, the stress amplitude and cycle number are substituted into the damage evolution equation to assess the fatigue life and construct a complete fatigue damage analysis framework for desert photovoltaic modules.

[0035] In some specific implementation methods, refer to Figure 4 As shown, Figure 4 This invention illustrates a calculation framework for fatigue damage of silicon solar cells provided in an embodiment of the present invention. To analyze the fatigue damage of silicon solar cells in photovoltaic modules located in desert regions, this invention provides a calculation framework that includes pulsating wind field simulation, wind speed probability modeling, wind and sand impact load calculation, and a cell temperature prediction model. Furthermore, based on continuum damage mechanics, a damage evolution equation for the cell is established, and the fatigue life of photovoltaic module cells in desert regions is analyzed.

[0036] Specifically, firstly, a pulsating wind field was established based on the Davenport power spectrum, and the wind and sand loads acting on the photovoltaic module cells were obtained by combining the characteristics of sand particles in the study area. The temperature of the cells was predicted based on meteorological data provided by ERA5. Subsequently, the temperature and wind and sand loads were substituted into the mechanical model to obtain stress-time history curves. The damage evolution equation of the cells was established based on continuum damage mechanics (CDM), and the damage evolution parameters were obtained by fitting the data from the four-point bending experiment. Furthermore, the stress amplitude and cycle number were extracted using the rainflow counting method, substituted into the damage evolution equation, and the cumulative damage of the silicon solar cells was calculated according to the Miner criterion to predict the fatigue life of the cells.

[0037] Furthermore, in some embodiments, the present invention also analyzed the influence of photovoltaic module geometric parameters on the fatigue life of solar cells. The results show that when the aspect ratio is greater than 2, the solar cells exhibit higher fatigue life; under the combined conditions of a cover glass thickness of 5 mm and a back glass thickness of 2.4 mm, the solar cells have optimal fatigue resistance and minimal cumulative damage; the fatigue life of the solar cells reaches its maximum when the installation tilt angle is 12°.

[0038] To make the solution of this application clearer and more complete, the method of the present invention will be further described in detail below with reference to the accompanying drawings.

[0039] In step S100, based on the wind speed value and sand particle diameter, the wind and sand load on the solar cells is determined, including the following steps: S101, based on wind speed and sand grain diameter, determine the sand transport rate; whereby the sand transport rate is positively correlated with wind speed and negatively correlated with sand grain diameter; Specifically, the sediment transport rate at a height z above the ground. The calculation model is as follows: ;in, The concentration of windblown sand at a height z above the ground. Let z be the wind speed at a height z above the ground.

[0040] S102, based on wind speed and sediment transport rate, determine the sand concentration value; among which, the sand concentration value is positively correlated with the sediment transport rate; Specifically, the calculation model for windblown sand concentration is as follows: Where h is the preset height above the ground, and it is assumed that the sand concentration is uniformly distributed and the wind speed is constant within the preset height range; the height z is not greater than the preset height.

[0041] S103, Based on wind speed and sand concentration, determine the sand load; the sand load is positively correlated with wind speed and sand concentration. Specifically, the calculation model for wind and sand load is as follows: ;in, This refers to the wind load shape coefficient of the solar cell. Let g be the weight per unit volume of air, g be the acceleration due to gravity, and G be the gust safety factor. The angle between the solar cell and the horizontal plane.

[0042] In step S100, wind speed includes average wind speed and fluctuating wind speed. The calculation model for wind speed is as follows: ; The mean wind speed was obtained from the ERA5 reanalysis dataset and subjected to a power-law exponential transformation. The calculation model is as follows: ;in, The wind speed profile index. Let be the average wind speed at a height of 10m, and b be the correction term in the power-law exponent formula; The fluctuating wind speed was obtained through Davenport power spectrum simulation and autoregressive model in linear filtering. The calculation model is as follows: ;in, Let be the pulsating wind speed at time t; K is the autoregressive coefficient, where K v = 1,2,…,L; L is the order of the autoregressive model; K before time t v The pulsating wind speed at any given moment; The time step of the pulsating wind speed; It has a mean of 0 and a variance of An independent random process.

[0043] It should be noted that the meteorological data used in this embodiment of the invention comes from the fifth-generation atmospheric reanalysis dataset of the European Centre for Medium-Range Weather Forecasts (ERA5). ERA5 provides multiple meteorological elements such as solar radiation, heat, temperature, and wind speed in a grid format, with a spatial resolution of 0.25° × 0.25° and a temporal resolution of 1 hour. Typically, but not limitingly, to assess the fatigue damage value of photovoltaic modules in desert areas, this embodiment of the invention mainly uses the Minfeng Photovoltaic Power Station in Xinjiang, China, as the research object. Optionally, the photovoltaic module is installed at a height of approximately 2 m above the ground surface. The near-surface meteorological parameters selected in the ERA5 reanalysis data in this embodiment of the invention include net solar radiation at the ground surface, 2-meter temperature, and instantaneous 10-meter gusts. These parameters directly reflect the meteorological conditions acting on the surface of the photovoltaic module, and the specific parameter information is shown in Table 1 below.

[0044] Table 1 Main meteorological parameters

[0045] It should be understood that air motion in the near-surface layer is a random turbulent motion, and wind speed exhibits complex variations with time and space. In this embodiment, Reynolds decomposition is used to decompose the instantaneous wind speed at any location into average wind speed. Pulsating wind speed Mean wind speed reflects the overall flow trend of the wind field, and its data are provided by the ERA5 reanalysis dataset. Fluctuating wind speed represents the random fluctuations caused by turbulent motion and is usually approximated as a zero-mean stationary Gaussian random process. Therefore, the wind speed calculation model can be expressed as: .

[0046] Average wind speed, as a crucial component of wind speed, can be obtained at a preset height of the photovoltaic module using a power-law exponential formula. To further enhance the reliability of wind speed data, and considering the geographical characteristics of desert regions, this embodiment transforms the wind speed power-law exponential formula as follows to obtain the average wind speed. The calculation model is as follows: .

[0047] It should be noted that different terrains have different roughness, resulting in different wind speed profile indices 'a' and 'correction term b' in the power law exponent formula. In this embodiment, the power law exponent and correction term for desert regions are taken as 0.1 and 0.15, respectively.

[0048] In constructing the fluctuating wind field model, based on the turbulent properties of near-surface wind speeds, the Davenport power spectral density function and linear filtering method were used to construct a fluctuating wind field model that incorporates the characteristics of desert sand grains; thus, the obtained fluctuating wind speeds... The calculation model is as follows: .

[0049] The Davenport power spectrum is a mathematical model used to describe the energy distribution of the pulsating component (i.e., the part of wind speed that fluctuates randomly over time) at different frequencies in natural wind. In this embodiment, the Davenport power spectrum is chosen to simulate pulsating wind, and its mathematical expression is: ;in, S ( n 1) is the power spectrum of fluctuating wind speed. , k For surface roughness coefficient, n 1 represents the frequency of the pulsating wind.

[0050] The autoregressive (AR) model in linear filtering is a forecasting method based on time series analysis. It achieves forecasting of time series data by establishing a linear regression relationship between the current observation and the observations of the previous p time steps. For details on the simulation principle of linear filtering, please refer to related technologies.

[0051] Optionally, the specific process of solving the fluctuating wind speed using the linear filtering AR model is as follows: First, substitute the Davenport target power spectrum into the formula. First, determine the correlation function R(τ); then, substitute the calculated correlation function into the formula. Calculate the autoregressive coefficient φk; then, substitute the calculated autoregressive coefficient into the formula. Then you can obtain it. Finally, the obtained sub-regression coefficients φk and Substitute into the formula By assuming that the fluctuating wind speed before the initial moment is 0, the time history of the fluctuating wind speed can be calculated. .

[0052] Based on the above solution process, a simulation program for pulsating wind fields was developed using MATLAB software. The specific parameter settings involved in the simulation process are shown in Table 2 below.

[0053] Table 2. Main parameters of the time history of simulating fluctuating wind speed using the linear filtering method.

[0054] Therefore, based on the above, the average wind speed and the fluctuating wind speed can be obtained respectively, and the total wind speed can be obtained based on the average wind speed and the fluctuating wind speed.

[0055] Furthermore, in step S101, the sediment transport rate can be determined based on the acquired wind speed value and sand grain diameter. The calculation model is as follows: In determining the sediment transport rate In this embodiment, the sediment transport rate is defined as the sediment transport flux per unit time and unit width, which increases with increasing wind speed and decreases with increasing sand grain diameter. Through research on the threshold velocity, threshold shear velocity, and sediment transport rate of sand grains with different diameters under different wind speed conditions, and based on experimental results, the O'Brien–Rindlaub sediment transport rate model was modified. The modified sediment transport rate model expression is as follows: ;in, Q ( z The sediment transport rate is expressed in kg / (ms). ρ The density of air is 1.25 kg / m³. 3 ; g This is the acceleration due to gravity, with a value of 9.81 m / s². 2 V(z) is the wind speed; V t The threshold wind speed.

[0056] f 1 ( d () represents the proportionality coefficient that varies with particle size. f 1 ( d The expression for ) is as follows: ;in, d The diameter of the sand grain. D d The reference particle size is 0.25 mm.

[0057] In this embodiment, the threshold wind speed V t It is the minimum wind speed required to start moving sand grains on the ground, and its expression is as follows: ;in, The density of the sand particles is taken as 2660 kg / m³. 3 .

[0058] It should be understood that the higher the sediment transport rate, the more intense the wind and sand activity, the more sand particles enter the air, the greater the intensity of the wind and sand, and the higher the concentration of wind and sand in the air. Therefore, the quantitative relationship between the sediment transport rate Q(z) and the wind and sand concentration C(z) can be established as follows: .

[0059] Furthermore, in step S102, based on the quantitative relationship between the aforementioned sand transport rate Q(z) and the wind-blown sand concentration C(z), and assuming that the wind-blown sand concentration is uniformly distributed and the wind speed is constant within the preset height range, the calculation model for the wind-blown sand concentration value is obtained as follows: .

[0060] Furthermore, in step S103, during the process of determining the calculation model for wind and sand loads, considering that the wind and sand loads do not act perpendicularly on the surface of the photovoltaic module, it is necessary to introduce the installation tilt angle of the photovoltaic module 100. θ , that is, This is the angle between the photovoltaic module 100 and the horizontal plane. For example... Figure 5 As shown, Figure 5 A schematic diagram showing the installation tilt angle of the photovoltaic module 100 is displayed.

[0061] Specifically, in step S103, the wind and sand load P generated by the windy and sandy weather acting on the surface of the photovoltaic module consists of the net wind pressure P1 and the sand particle impact pressure P2, that is... Where P is the wind and sand load, P1 is the net wind pressure, and P2 is the sand impact pressure.

[0062] According to Bernoulli's equation, the pressure generated by the net airflow on the photovoltaic panel is: ;in, This refers to the wind load shape coefficient of the solar cell. Let g be the weight of air per unit volume, and g be the acceleration due to gravity. p This is the net air velocity. Under standard atmospheric pressure, , , .

[0063] Assuming the collision of sand grains is an elastic collision, with the grains having the same velocity before and after the collision, and treating all sand particles as spheres, based on the momentum conservation equation, the force acting on the surface of the photovoltaic module is: ;in, This refers to the velocity of the sand grains, and S is the area of ​​the photovoltaic panel's windward side. Therefore, the pressure P2 acting on the surface of the photovoltaic module is: .

[0064] Therefore, from the above, we can conclude that .

[0065] Generally, the velocity of sand particles is 0.125-0.5 times the airflow velocity. When calculating wind and sand loads, the average velocity of sand particles during their flight is taken as 0.5 times the wind speed. v s =0.5 vIntroducing a gust safety factor (G=3), which is considered relatively safe for engineering purposes, the calculation model for wind and sand loads can be obtained as follows: .

[0066] In step S200, based on wind speed probability density and wind-blown sand load, the first damage value of wind-blown sand to the solar cells is determined, including the following steps: S201, Based on wind and sand loads and the mechanical model of the tandem photovoltaic module, the first equivalent stress is determined. This embodiment uses tandem theory to establish a mechanical model of the photovoltaic module. The established mechanical model of the photovoltaic module considers the influence of temperature changes and introduces the thermal strain effect into the physical equations. This model can solve for the thermal and mechanical stresses of the silicon solar cells. For details on the establishment of the mechanical model, please refer to the description below.

[0067] S202, based on wind speed probability density and first equivalent stress, determines the first damage value of wind and sand to the battery cells.

[0068] Specifically, the wind speed probability density follows the probability density function of the gamma distribution, and its calculation model is as follows: ;in, For the whole year medium wind speed The probability of occurrence; The integral interval is the wind speed.

[0069] In step S202, the gamma distribution is used to characterize the wind speed probability density, i.e., the wind speed probability model. The expression for the probability density function of the gamma distribution is shown below: The probability density function of the gamma distribution is expressed as follows: in, f Gamma ( x ) represents the gamma distribution x The probability of occurrence, α x and β x The shape and scale parameters of the gamma distribution are given.

[0070] Using the wind speed probability density function f V ( V The probability mass function for different wind speeds (e.g., V = 0.5, 1.5, 2.5, 3.5…, 18.5, 19.5 m / s) is obtained through integral calculation, which is also the calculation model for the wind speed probability density. .

[0071] Therefore, in this embodiment, meteorological data of the target area is extracted using the ERA5 reanalysis dataset; based on the extracted meteorological data and probability density function of the target area, the wind speed probability mass function under different wind speeds is derived. That is, based on the above, the wind speed probability mass function is determined, and the wind speed probability density is established.

[0072] In this embodiment of the application, the mechanical model of the tandem photovoltaic module is as follows: ;in, This represents the temperature change of the solar cell. The coefficient of thermal expansion of the battery cell material; the first k The stiffness coefficients of the slabs are as follows: , , ; For the first k Young's modulus of the lamellae For the first k Poisson's ratio of the shelf; as an example, k =1, 2, 3, 4, 5.

[0073] The strain at any point in the solar cell is: ;in, ; ; ; ; Let be the thickness of the k-th layer. The distance from the center line of the k-th layer plate along the thickness direction to the mid-plane of the solar cell is the distance from the center line of the solar cell along the thickness direction. w This represents the deflection of the solar cell along its thickness direction under wind and sand load.

[0074] Therefore, the calculation model for the first equivalent stress is as follows: In other words, the stress at a point within a photovoltaic module can be measured based on the Von Mises equivalent stress. The calculation model for the equivalent stress at any point in a photovoltaic module is as follows: .

[0075] Furthermore, in this embodiment, the calculation models for both the first and second equivalent stresses are: .

[0076] It should also be noted that photovoltaic (PV) modules are typically mounted on support structures, which provide structural support. When subjected to external forces such as wind loads, the PV modules can rotate slightly to some extent. This is because the connection between the module and the support structure is not perfectly rigid, allowing for a degree of rotational freedom. Treating the PV module as a simply supported boundary condition is a reasonable engineering simplification when performing mechanical analysis. The boundary condition for the PV module is simply supported on four sides (in...). x The length on the axis is A, y If the length on the axis is B, then the following formula should be satisfied.

[0077] On the side ( x =0) and ( x =A) satisfies: ; On the side ( y =0) and ( y =B) satisfies: .

[0078] in, w For deflection, deflection w Represented as the following double Fourier series: ; ; ;in, W mn These are coefficients to be determined.

[0079] Expanding the wind and sand load into a double Fourier series: .

[0080] Any lateral wind and sand load can be expressed by the following formula: .

[0081] Under uniform load, i.e., when the wind and sand load p is constant, it can be expressed by the following formula: .

[0082] From the above formula, we can obtain: ; .

[0083] Therefore, the deflection of the solar cell along its thickness direction under wind and sand load. w The calculation model is as follows: Among them, P mn For arbitrary lateral wind and sand loads.

[0084] In this embodiment, based on the mechanical model of the aforementioned multilayer photovoltaic module, the mechanical stress time history and thermal stress time history of the photovoltaic module cells are solved to obtain stress time history curves. Furthermore, during the solution process, the wind and sand load acting on the surface of the photovoltaic module or the photovoltaic module cell temperature assessment model are incorporated into the mechanical model for solution.

[0085] Based on the established pulsating wind field model and the characteristics of sand particles in the sand source area, this embodiment calculates the wind and sand load acting on the photovoltaic panel using the above expression. Substituting this wind and sand load into the aforementioned mechanical model, the mechanical stress spectrum of the silicon solar cell is obtained by solving. Figure 6 The wind speed, load, and stress time history curves are shown under the condition of an average wind speed of 8.5 m / s. Figure 6 (a) is a time history curve of wind speed. Figure 6 (b) is the load time history curve. Figure 6 (c) is the stress-time history curve. The results show that the wind speed fluctuates randomly within the range of 6-12 m / s, the wind and sand impact load varies between 50-300 Pa, and the equivalent stress acting on the silicon solar cell ranges from 0.4-1.5 MPa. The three curves show the same trend, thus this embodiment reveals the transmission relationship from wind speed to load and then to stress.

[0086] Therefore, based on the above mechanical model of wind and sand load and tandem photovoltaic modules, the first equivalent stress was determined; in addition, the wind speed probability density was obtained. Furthermore, based on this wind speed probability density and the first equivalent stress, the first damage value of wind and sand to the solar cells can be determined.

[0087] In this embodiment, the calculation model for the first damage value is as follows: ;in, For a period of one year, Wind speed Duration; Wind speed Duration Damage values ​​caused by wind and sand to solar cells.

[0088] In this embodiment, wind speed Duration Damage to solar cells caused by wind and sand and temperature Damage values ​​to solar cells The calculation models are all: ; in, ; ; ; ; The equivalent stress when the solar cell suffers zero damage; E For Young's modulus, v Poisson's ratio; K is Cyclic strength coefficient; N This represents the number of loop iterations. n The cyclic strain hardening index; , These are material constants; To and and n Relevant material parameters; These are parameters related to the elastic parameters of the solar cell, the viscoelastic parameters of the adhesive layer, and the geometric parameters.

[0089] In this embodiment, meteorological data of the study area is extracted using the ERA5 reanalysis dataset to construct a wind speed probability function. Then, based on the turbulent characteristics of near-surface wind speed, a pulsating wind field model is constructed using linear filtering and the Davenport power spectrum. Combined with the sand grain characteristics of the study area, an equivalent wind and sand load model is used to obtain the wind and sand load acting on the photovoltaic module surface. Furthermore, considering that the temperature of the photovoltaic module cells is mainly affected by solar radiation, ambient temperature, and wind speed, a cell temperature assessment model is established. This provides load input for subsequent fatigue damage research; that is, it provides load condition input and basic data support that conforms to real environmental conditions for subsequent fatigue damage research and life prediction.

[0090] In practical applications, photovoltaic modules are affected by various factors during operation, resulting in uneven surface temperature distribution. In this embodiment, given the small temperature difference between different areas of the photovoltaic module surface, the impact of thermal stress on the photovoltaic module can be ignored. Therefore, this embodiment treats the surface temperature distribution of the photovoltaic module as uniform.

[0091] In step S300, based on the temperature value, a second damage value of temperature to the solar cell is determined; this includes the following steps: S301, based on temperature values ​​and the mechanical model of the multilayer photovoltaic module, determine the second equivalent stress; S302, based on the second equivalent stress, determine the second damage value of temperature on the solar cell; Specifically, the model for calculating temperature values ​​is as follows: ;in, and These are the cell temperature and the ambient temperature, respectively. Solar radiation illuminance; This represents the ratio of heat flux from thermal radiation to heat flux from thermal convection. Solar energy absorption rate; The photoelectric conversion efficiency of the solar cell; The convective heat transfer coefficient is... .

[0092] In establishing the temperature calculation model, a temperature prediction model is first established based on the unsteady-state heat conduction equation: ;in, and These are the cell temperature and the ambient temperature, respectively. Solar irradiance (W / m²) 2 ); Response time (the time it takes for the photovoltaic panel temperature to stabilize). The solar energy absorption rate is 1; The photovoltaic module's photoelectric conversion efficiency is taken as 0.12. The heat flux ratio of thermal radiation to thermal convection (0.62 in summer, 0.42 in winter). , , These are the density, heat capacity, and thickness of the Si layer, respectively. The convective heat transfer coefficient is... .

[0093] The initial response time is defined as the time it takes for the battery temperature to reach 99% of its steady-state temperature. Specifically, it is described as follows: .

[0094] Therefore, based on the above, the calculation model for the unsteady-state temperature value of a photovoltaic module can be obtained as follows: .

[0095] In this embodiment of the application, the process of determining the second equivalent stress in step S300 is basically the same as the process of determining the first equivalent stress in step S200, that is, the calculation models for both the first and second equivalent stresses are: .

[0096] The mechanical model of the tandem photovoltaic module and the deflection of the solar cells along the thickness direction under wind and sand loads are included. w The calculations for these parameters can be performed with reference to the description in step S200 above.

[0097] Furthermore, in determining the second damage value of temperature to the solar cell based on the second equivalent stress, as mentioned earlier, temperature... Damage values ​​to solar cells The calculation model (and the aforementioned wind speed) Duration Damage to solar cells caused by wind and sand Di All of them are: ; in, ; ; ; ; The equivalent stress when the solar cell suffers zero damage; E For Young's modulus, v Poisson's ratio; K is Cyclic strength coefficient; N This represents the number of loop iterations. n The cyclic strain hardening index; , These are material constants; To and and n Relevant material parameters; These are parameters related to the elastic parameters of the solar cell, the viscoelastic parameters of the adhesive layer, and the geometric parameters.

[0098] Among them, in building the model In the process, firstly, based on the Clausius-Duhem inequality of the second law of thermodynamics, the following inequality for the generation of local entropy of materials is derived: ;in, , These are the Cauchy stress and the infinitesimal strain tensor, respectively. This represents the Helmholtz free energy per unit volume. The dot above the variable indicates the material derivative, and the subscript follows the summation convention.

[0099] Assuming the material is elastic and isotropic during the damage process, a scalar damage value (first damage value or second damage value) defined by mesoscopic principles is used. D Helmholtz specific free energy is used to describe the mesoscopic damage state of isotropic materials. With strain and damage value D Related, that is Through transformation operations such as differentiation, we can obtain: ; .

[0100] Furthermore, for For any value of to hold true, the following relationship must be satisfied: .

[0101] definition ,Will Substitution ,available: ;in, Y Is related to damage value D The generalized thermodynamic force of the coupling is called the damage strain energy release rate.

[0102] The damage process is an energy dissipation process, and the dissipation characteristics can be expressed using another thermodynamic potential. This is described as the dissipative potential or damage flow potential, which relates to the damage driving force. Y The convex function. Furthermore, according to the positive alternating current law of internal variables, the following expression can be derived: Therefore, by giving a specific dissipation potential The damage evolution model can be derived from the above formula, and the damage value can then be further derived. D。

[0103] Will Expressed as the equivalent stress of the damaged state, then It can be represented as: .

[0104] thermodynamic potential This can be represented by the following expression: ;in, To observe the equivalent strain rate of microplasticity; , is a material constant.

[0105] For damage to isotropic materials, the damage driving force Y It can be represented by the following expression: ; .

[0106] Therefore, the following expression can be obtained from the above expression:

[0107] Assumption , Let these be the maximum and minimum equivalent damage stresses in a cycle, respectively, and then express them in the form of... Integrating within the loop yields the rate of change of the damage value D with respect to the number of loops N: ； To and and n Relevant material parameters.

[0108] Furthermore, the relationship between the VonMises equivalent stress of the solar cell and the damage variable D is expressed as follows: ; These are parameters related to the elastic parameters of the solar cell, the viscoelastic parameters of the adhesive layer, and the geometric parameters. This represents the equivalent stress when the battery cell suffers zero damage.

[0109] Therefore, the expression Substitution ,available: Integrating it, we get: Thus, the expression for the constructed damage evolution equation can be obtained.

[0110] Considering the cyclic strength coefficient in the above formula K Cyclic strain hardening index n、 Material constants , This led to the simplified damage value model: .

[0111] In some embodiments, after constructing the damage evolution equation (the model for calculating damage values), damage parameter fitting is required. In this embodiment, after constructing the damage evolution equation, the fatigue performance of the photovoltaic module is tested using a four-point bending test method to determine the damage evolution parameters in the damage evolution equation and verify the applicability of the damage evolution equation.

[0112] Optionally, the fatigue performance of the photovoltaic module was systematically tested using a four-point bending test method. A photovoltaic module with dimensions of 1580mm × 808mm was selected as the research object, and an application of 0.64 kN / m was applied. 2 Wind pressure load was applied, at which point the equivalent stress at the center of the solar cell reached 29.8 MPa. The ambient temperature was T = 303 K, the stress ratio R = 0, and the number of cyclic loading cycles was 1000. To determine the material parameters in the damage evolution equation, this embodiment used the least squares method to fit and analyze the experimental data. The fitting results are as follows: Figure 7 As shown in Table 3, the damage evolution parameter values ​​were finally obtained through this fitting analysis.

[0113] Table 3 Damage evolution material parameters

[0114] Furthermore, to verify the accuracy of the model constructed in this invention and improve the accuracy of fatigue life assessment of photovoltaic module cells in desert areas, some experimental verifications were also conducted in the embodiments of this invention. As an example, in some embodiments, the constructed pulsating wind field model was verified, including: A simulation program for fluctuating wind speed was developed using a linear filtering method to simulate the wind speed at a depth of 2m near the ground in the study area. To verify the accuracy of the simulated fluctuating wind speed, the simulation results were converted to the frequency domain and compared with the Davenport target power spectrum in a double logarithmic coordinate system. Figure 8 The simulated wind speed power spectrum at a depth of 2 meters near the ground from a photovoltaic power plant is displayed. Figure 8As can be seen, the simulated power spectrum matches the Davenport spectrum well, maintaining good consistency across the entire frequency range. The deviation is defined as the metric for measuring the degree of agreement between the simulated and target power spectra, expressed by the following formula: ;in, To simulate the power spectrum, The target power spectrum.

[0115] In this embodiment, the deviation between the simulated power spectrum and the target power spectrum is less than 10%. This demonstrates the feasibility and accuracy of using the linear filtering method to simulate the Davenport wind speed spectrum, thus providing a reliable load input for fatigue damage analysis of photovoltaic modules.

[0116] also, Figure 9 Wind speed time-history images were simulated for average wind speeds of 5 m / s, 10 m / s, and 15 m / s, respectively. Figure 9 (a) is a schematic diagram with an average wind speed of 5 m / s. Figure 9 (b) is a schematic diagram with an average wind speed of 10 m / s. Figure 9 (c) is a schematic diagram of an average wind speed of 15 m / s; observe... Figure 9 It can be seen that during the 600s simulation period, the fluctuating wind speeds all fluctuated around their respective average wind speed values, and the fluctuation amplitude increased with the increase of the average wind speed, which truly reflects the random fluctuation characteristics of the near-surface wind speed in the study area.

[0117] As an example, in some embodiments, the constructed wind speed probability density is validated, including: Based on the hourly average wind speed data of the study area from 2000 to 2024 provided by the ERA5 dataset, this embodiment uses the maximum likelihood estimation method to obtain the overall wind speed probability distribution of the region. The original wind speed data is corrected by combining the photovoltaic module installation height and wind speed meteorological data. Subsequently, based on the aforementioned probability density function model, the theoretical cumulative distributions of the Weiber and Gamma distributions are derived respectively, and a PP plot (theoretical cumulative probability - observed cumulative probability) is plotted between them and the observed cumulative distribution, as shown below. Figure 10 As shown. From Figure 10 As can be observed, when comparing the fitting effect of the two distributions on wind speed data, the theoretical cumulative probability point of the gamma distribution is closer to the 45° baseline in the figure, indicating that the consistency between its predicted value and the actual observed value is better than that of the Weiber distribution.

[0118] further, Figure 11 The cumulative distribution function plot is shown, by Figure 11It can be seen that the cumulative distribution function of the gamma distribution closely matches the cumulative distribution function of the original wind speed data. However, the cumulative distribution function of the Weiber distribution shows a significant deviation from the original wind speed data in the 0-15 m / s wind speed range. Intuitively, the gamma distribution provides a better fit to the wind speed data. Furthermore, the root mean square error (RMSE) of the cumulative distribution function was calculated in this embodiment of the invention. The results show that the RMS error of the cumulative distribution function is 0.0031 m / s for the gamma distribution and 0.0204 m / s for the Weiber distribution. Therefore, the gamma distribution has a smaller error, further demonstrating its better fitting accuracy and applicability in wind speed probability modeling.

[0119] Furthermore, the gamma distribution parameters are obtained using the maximum likelihood estimation method. =4.1136 and =1.3694. Based on the wind speed probability density function... ,by V =0.5, 1.5, 2.5, 3.5, ….17.5, 18.5, 19.5 m / s as the midpoint of the integration interval with wind speed. =1m / s, the wind speed in the study area is calculated by integral according to equation (12). V i The probability mass function for values ​​of 0.5, 1.5, 2.5, 3.5, ..., 17.5, 18.5, 19.5 m / s, as shown below. Figure 12 As shown.

[0120] Rainflow counting can decompose complex random load time histories into a series of complete stress cycles. This method extracts peak and valley load data, identifies and closes hysteresis loops according to specific rules, and statistically analyzes all stress fluctuations. This process allows fatigue damage assessment to account for the contribution of each load cycle, resulting in accurate lifetime predictions. Based on this, this embodiment uses rainflow counting to process the stress time histories generated by wind and sand loads and temperature loads, statistically analyzing the stress amplitudes of these two types of loads acting on the solar cells and their corresponding cycle counts. Finally, the statistical results are substituted into the damage evolution model to obtain damage values, namely, the first damage value and the second damage value.

[0121] In step S400, the fatigue life of the solar cell is evaluated based on the first damage value and the second damage value, including the following steps: S401, based on the first damage value and the second damage value, determine the total damage value of the solar cell under the action of temperature and wind and sand; Specifically, the calculation model for the total damage value is as follows: ;in, The cumulative damage caused by wind and sand loads over a one-year period. The cumulative damage caused by a year-long temperature load; S402, determine the fatigue life of the solar cell based on the total damage value; Specifically, the fatigue life calculation model is as follows: ;in, This represents the fatigue damage threshold.

[0122] Optionally, a failure is defined as a crack in the solar cell causing a failure area exceeding 10% of the module's circuitry. In this case, significant cracks appear on the surface of the silicon solar cell, and the fatigue damage threshold is accordingly set to... D c =0.1. Based on this threshold, the fatigue life of the structure can be calculated.

[0123] Therefore, according to the method provided in the embodiments of the present invention, the failure time of the photovoltaic modules in the study area is predicted to be approximately the 9th year of operation. Related technologies indicate that after 11 years of external load application, visual inspection of photovoltaic modules in desert areas revealed obvious cracks in the cells, resulting in the effective circuit area of ​​the cells exceeding 10%. The similarity between the two results fully verifies the rationality and engineering application value of the calculation framework and various models or expressions provided in the embodiments of the present invention.

[0124] Optionally, embodiments of the present invention also investigated the influence of some photovoltaic module geometric parameters on cell fatigue damage. As an example, in some embodiments, the influence of aspect ratio on cell fatigue damage was investigated. The results showed that, under the same wind speed conditions, cell fatigue life is positively correlated with the aspect ratio of the photovoltaic module; that is, the larger the aspect ratio, the higher the fatigue life. For example, cells with aspect ratios of 3, 4, and 5 exhibited relatively high fatigue lives, while cells with aspect ratios of 1 and 2 had relatively low fatigue lives. Furthermore, the inventors of this application also discovered a significant interaction effect between wind speed and aspect ratio. In low-wind-speed areas, the difference in fatigue life between photovoltaic module cells with different aspect ratios was more significant. As wind speed increased, this difference gradually decreased, indicating that the influence of aspect ratio was relatively weakened in high-wind-speed environments.

[0125] As an example, in some embodiments, the effect of the cover glass thickness on the fatigue damage of the solar cell was also investigated. The results show that the cover glass thickness has a certain impact on the fatigue life of the solar cell; for example, from a cover glass thickness of 2 mm to 5 mm, the damage accumulation rate significantly decreases, and the fatigue life is greatly improved. In contrast, the change in back glass thickness has a relatively small impact on fatigue performance. Specifically, the combination of a 5 mm cover glass thickness and a 2.4 mm back glass thickness exhibits the best fatigue resistance performance, resulting in the least cumulative damage.

[0126] As an example, in some embodiments, the effects of different wind speeds and installation tilt angles on cell fatigue life analysis were also examined.

[0127] The installation tilt angle of photovoltaic modules affects the wind load shape factor of photovoltaic modules. As an example, the wind load shape factor of photovoltaic modules in the installation tilt angle range of 0-60° is shown in Table 4 below.

[0128] Table 4. Shape coefficient of photovoltaic modules under different installation tilt angles due to wind load

[0129] Typically, but not limitingly, in this embodiment, the fatigue life of the solar cells was studied at installation tilt angles of 12°, 20°, 30°, 40°, 50°, and 60°. The results show that the correlation between the fatigue life of the photovoltaic module cells and the installation tilt angle is constrained by wind speed conditions. Figure 13 The fatigue life of the solar cells under different installation tilt angles was shown, and observations were made. Figure 13 It can be seen that under wind speeds of 10 m / s or less, the fatigue life of solar cells decreases sharply with increasing wind speed, and the difference in fatigue life among photovoltaic modules with different installation tilt angles is significant. However, under wind speeds greater than 10 m / s, the decreasing trend of fatigue life with increasing wind speed slows down significantly, and the difference in fatigue life among photovoltaic modules with different installation tilt angles gradually narrows. Notably, under a wind speed of 2.5 m / s, the 12° tilt angle photovoltaic module exhibits the highest fatigue life; however, as wind speed increases, the fatigue lives of the other tilt angle photovoltaic modules become similar.

[0130] Therefore, based on the above, it can be seen that the damage evolution equation established in this invention can accurately describe the damage evolution process of the solar cells under wind and sand loads and temperature loads. Furthermore, this invention combines a four-point bending experiment to fit the damage evolution parameters and conducts fatigue life assessment of photovoltaic module solar cells in desert areas. The results are basically consistent with field observations, verifying the engineering applicability of the computational framework. In addition, this invention also studies the influence of the aspect ratio, installation tilt angle, and glass thickness of the photovoltaic module on the fatigue life of the solar cells. This invention provides a theoretical basis and technical support for the structural optimization and reliability assessment of photovoltaic modules under wind and sand conditions.

[0131] 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 foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for assessing the fatigue life of photovoltaic modules in desert areas, characterized in that, The photovoltaic module includes a backsheet, solar cells, and a panel arranged sequentially from bottom to top, wherein the panel and the solar cells, and the solar cells and the backsheet, are bonded together by an adhesive layer; the method includes: Obtain wind speed and sand particle diameter, and determine the wind and sand load on the battery cells based on the wind speed and sand particle diameter. Obtain the wind speed probability density, and based on the wind speed probability density and the wind and sand load, determine the first damage value of wind and sand to the battery cell; The temperature value of the solar cell is obtained, and based on the temperature value, a second damage value of temperature to the solar cell is determined; The fatigue life of the solar cell is evaluated based on the first damage value and the second damage value.

2. The method according to claim 1, characterized in that, The determination of the wind and sand load on the battery cells based on the wind speed value and the sand particle diameter includes: Based on the wind speed value and the sand grain diameter, the sand transport rate is determined; wherein the sand transport rate is positively correlated with the wind speed value and negatively correlated with the sand grain diameter; Based on the wind speed value and the sand transport rate, the sand concentration value is determined; wherein, the sand concentration value is positively correlated with the sand transport rate; Based on the wind speed value and the sand concentration value, the sand load is determined; the sand load is positively correlated with the wind speed and positively correlated with the sand concentration value.

3. The method according to claim 2, characterized in that, The sediment transport rate at a height z above the ground The calculation model is as follows: ;in, The concentration of windblown sand at a height z above the ground. Let z be the wind speed at a height z above the ground. The calculation model for the sandstorm concentration value is as follows: Where h is the preset height above the ground, and it is assumed that the sand concentration is uniformly distributed and the wind speed is constant within the preset height range; the height z is not greater than the preset height. The calculation model for the wind and sand load is as follows: ;in, This refers to the wind load shape coefficient of the solar cell. Let g be the weight per unit volume of air, g be the acceleration due to gravity, and G be the gust safety factor. The angle between the solar cell and the horizontal plane.

4. The method according to claim 3, characterized in that, The wind speed includes average wind speed and fluctuating wind speed, and the calculation model for the wind speed is as follows: ； The average wind speed was provided by the ERA5 reanalysis dataset and subjected to a power-law exponential transformation to obtain the average wind speed. The calculation model is as follows: ;in, The wind speed profile index. Let be the average wind speed at a height of 10m, and b be the correction term in the power-law exponent formula; The fluctuating wind speed was obtained through Davenport power spectrum simulation and autoregressive model in linear filtering. The calculation model is as follows: ;in, Let be the pulsating wind speed at time t; K is the autoregressive coefficient, where K v = 1,2,…,L; L is the order of the autoregressive model; K before time t v The pulsating wind speed at any given moment; The time step of the pulsating wind speed; It has a mean of 0 and a variance of An independent random process.

5. The method according to claim 1, characterized in that, The determination of the first damage value of wind and sand to the battery cells based on the wind speed probability density and the wind and sand load includes: Based on the aforementioned wind and sand load and the mechanical model of the multilayer photovoltaic module, the first equivalent stress is determined; Based on the wind speed probability density and the first equivalent stress, the first damage value of wind and sand to the battery cells is determined.

6. The method according to claim 5, characterized in that, The wind speed probability density satisfies the probability density function of a gamma distribution, and its calculation model is as follows: ;in, For the whole year medium wind speed The probability of occurrence; The integral interval for wind speed; The calculation model for the first damage value is as follows: ;in, For a period of one year, Wind speed Duration; Wind speed Duration Damage values ​​caused by wind and sand to solar cells.

7. The method according to claim 6, characterized in that, The determination of the second damage value of the battery cell based on the temperature value includes: Based on the temperature value and the mechanical model of the multilayer photovoltaic module, the second equivalent stress is determined; Based on the second equivalent stress, a second damage value of temperature on the solar cell is determined; The calculation model for the temperature value is as follows: ;in, and These are the cell temperature and the ambient temperature, respectively. Solar irradiance; This represents the ratio of heat flux from thermal radiation to heat flux from thermal convection. Solar energy absorption rate; The photoelectric conversion efficiency of the solar cell; The convective heat transfer coefficient is... .

8. The method according to claim 7, characterized in that, The mechanical model of the tandem photovoltaic module is as follows: ;in, This represents the temperature change of the solar cell. The coefficient of thermal expansion of the battery cell material; the first k The stiffness coefficients of the slabs are as follows: , , ; For the first k Young's modulus of the lamellae For the first k Poisson's ratio of the shelf; The strain at any point in the solar cell is: ;in, ； ； ； ； Let be the thickness of the k-th layer. The distance from the center line of the k-th layer plate along the thickness direction to the mid-plane of the solar cell is the plane at the very center of the solar cell along the thickness direction. w The deflection of the solar cell along its thickness under wind and sand load. The calculation models for both the first and second equivalent stresses are: 。 9. The method according to claim 8, characterized in that, The wind speed Duration Damage to solar cells caused by wind and sand, and temperature Damage values ​​to solar cells The calculation models are all: ； in, ; ； ； ; The equivalent stress when the solar cell suffers zero damage; E For Young's modulus, v Poisson's ratio; K is Cyclic strength coefficient; N This represents the number of loop iterations. n The cyclic strain hardening index; , These are material constants; To and and n Relevant material parameters; These are parameters related to the elastic parameters of the solar cell, the viscoelastic parameters of the adhesive layer, and the geometric parameters.

10. The method according to any one of claims 7-9, characterized in that, The evaluation of the fatigue life of the solar cell based on the first damage value and the second damage value includes: Based on the first damage value and the second damage value, the total damage value of the battery cell under the influence of temperature and wind and sand is determined. The calculation model for the total damage value is as follows: ;in, The cumulative damage caused by wind and sand loads over a one-year period. The cumulative damage caused by a year-long temperature load; Based on the total damage value, the fatigue life of the battery cell is determined. The calculation model for the fatigue life is as follows: ;in, This represents the fatigue damage threshold.

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