Photovoltaic panel thermal stress prediction method based on thermal load

Abstract

The invention relates to the technical field of photovoltaic panels, in particular to a photovoltaic panel thermal stress prediction method based on thermal load, and the method comprises the steps: collecting the thickness parameter of each layer of a photovoltaic panel, calculating the temperature change coefficient of the photovoltaic panel, and setting the inherent attribute of each layer of material of the photovoltaic panel; and constructing a thermal stress prediction model of each layer of the photovoltaic panel, and predicting the thermal stress of each layer of the photovoltaic panel by using the temperature change coefficient, the elastic modulus, the Poisson's ratio, the thermal expansion coefficient, the neutral layer curvature and the lower surface strain of each layer of the photovoltaic panel. The problems of complex structure and poor compatibility when an existing photovoltaic panel thermal stress prediction model needs to collect a large amount of data and then is trained are solved.

Description

Technical Field

[0001] The invention relates to the technical field of photovoltaic panels, and in particular to a method for predicting thermal stress of photovoltaic panels based on heat load. Background Art

[0002] Stress can be used to evaluate the quality of photovoltaic panels during the production process. By analyzing the performance of photovoltaic panels under different stress conditions, potential quality problems can be discovered and resolved in a timely manner, helping to ensure that photovoltaic panels comply with relevant standards and specifications, thereby improving the overall quality of the product.

[0003] The patent with publication number CN118886254A proposes a method for assessing the hidden crack risk of photovoltaic panels for pastoral-photovoltaic complementary projects based on finite element analysis, fatigue analysis and machine learning. It predicts the hidden crack risk of photovoltaic panels in actual pasture environments. However, this method requires the collection of a large amount of data and model training. When the photovoltaic panels change, the data needs to be collected again and the model needs to be trained, which results in poor compatibility.

[0004] The patent with publication number CN119167681A derives the temperature values ​​of the photovoltaic panel at various heights along the thickness direction through the temperature field model, obtains the temperature value at any position along the thickness direction and the temperature value of the lower surface, and thus obtains the temperature difference △T; the photoelectric conversion efficiency can be evaluated later based on the predicted temperature, but the stress distribution of each layer cannot be predicted, and the risk of hidden cracks in the photovoltaic panel cannot be evaluated later. Summary of the invention

[0005] In view of the shortcomings of the existing methods, the present invention constructs an expansion strain model based on temperature difference and thermal expansion coefficient, obtains the strain at any point from the lower surface strain and the neutral axis curvature, and combines the relationship between strain, free expansion strain and thermal strain to obtain the thermal stress caused by temperature gradient.

[0006] The technical solution adopted by the present invention is: a method for predicting thermal stress of photovoltaic panels based on heat load comprises the following steps:

[0007] Step 1: Collect the thickness parameters of each layer of the photovoltaic panel, calculate the temperature variation coefficient of the photovoltaic panel, and set the inherent properties of each layer of the photovoltaic panel material;

[0008] Step 2: Construct a thermal stress prediction model for each layer of the photovoltaic panel, and use the temperature variation coefficient, elastic modulus, Poisson's ratio, thermal expansion coefficient, neutral layer curvature and lower surface strain of each layer of the photovoltaic panel to predict the thermal stress of each layer of the photovoltaic panel;

[0009] As a preferred embodiment of the present invention, the formula of the thermal stress prediction model is:

[0010]

[0011] Among them, σ 热i is the thermal stress distribution of the ith photovoltaic panel along the z direction; E i is the elastic modulus of the i-th layer material; μ i Poisson's ratio of the i-th layer material; ε0 is the strain at the lower surface of the photovoltaic panel under the action of thermal expansion and thermal stress due to temperature difference; κ is the curvature of the neutral layer of the photovoltaic panel under the action of thermal expansion and thermal stress due to temperature difference; is the thermal expansion coefficient of the i-th layer material; ΔT j is the temperature variation coefficient of the jth layer material; h j is the thickness of the j-th layer of material; Z i are the thickness coordinates of the i-th layer of material respectively.

[0012] As a preferred embodiment of the present invention, the strain at the lower surface of the photovoltaic panel = (second coefficient * sixth coefficient - third coefficient * fifth coefficient) / (second coefficient * fourth coefficient - first coefficient * fifth coefficient).

[0013] As a preferred embodiment of the present invention, the curvature of the neutral layer of the photovoltaic panel = (third coefficient*fourth coefficient-first coefficient*sixth coefficient) / (second coefficient*fourth coefficient-first coefficient*fifth coefficient).

[0014] As a preferred embodiment of the present invention, the first coefficient is calculated using the elastic modulus, Poisson's ratio and the thickness difference between two adjacent layers of material.

[0015] As a preferred embodiment of the present invention, the second coefficient is calculated using the elastic modulus, Poisson's ratio and the thickness difference between two adjacent layers of material.

[0016] As a preferred embodiment of the present invention, the third coefficient is calculated using the elastic modulus, Poisson's ratio, the thickness difference between two adjacent layers of material, the temperature variation coefficient, the thickness of the material and the thermal expansion coefficient.

[0017] As a preferred embodiment of the present invention, the fourth coefficient is calculated using the elastic modulus, Poisson's ratio and the thickness difference between two adjacent layers of material.

[0018] As a preferred embodiment of the present invention, the fifth coefficient is calculated using the elastic modulus, Poisson's ratio and the thickness difference between two adjacent layers of material.

[0019] As a preferred embodiment of the present invention, the sixth coefficient is calculated using the elastic modulus, Poisson's ratio, the thickness difference between two adjacent layers of material, the temperature variation coefficient, the thickness of the material and the thermal expansion coefficient.

[0020] Beneficial effects of the present invention:

[0021] 1. By precisely controlling and optimizing temperature distribution and reducing thermal stress, the service life of photovoltaic panels can be effectively extended, ensuring their reliability and stability in practical applications;

[0022] 2. The thermal stress prediction model of each layer of the photovoltaic panel constructed by the present invention is simple. By measuring the upper and lower surface temperatures, elastic modulus, Poisson's ratio, thermal expansion coefficient and thickness parameters of the photovoltaic panel, and combining the thermal conductivity of each layer material, the thermal stress value corresponding to the coordinates of each layer can be accurately predicted;

[0023] 3. The predicted thermal stress value can be used to evaluate indicators such as the risk of hidden cracks during the service life of photovoltaic panels. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a flow chart of a method for predicting thermal stress of a photovoltaic panel based on heat load of the present invention;

[0025] Figure 2 It is a schematic diagram of the temperature difference and coordinates of each layer of the photovoltaic panel of the present invention;

[0026] Figure 3 It is a numerical simulation photovoltaic panel thermal stress cloud map of the present invention;

[0027] Figure 4 It is a comparison chart of the numerical simulation and model analytical calculation results of the present invention. DETAILED DESCRIPTION

[0028] The present invention is further described below in conjunction with the accompanying drawings and embodiments. This figure is a simplified schematic diagram, which only illustrates the basic structure of the present invention in a schematic manner, and therefore it only shows the components related to the present invention.

[0029] like Figure 1 As shown, a method for predicting thermal stress of photovoltaic panels based on heat load includes the following steps:

[0030] It is crucial to study the impact of temperature and thermal stress on photovoltaic panels, which not only helps to optimize the design and material selection of photovoltaic panels, improve their performance and lifespan, but also provides an important basis for subsequent service life prediction.

[0031] Step 1: Measure the thickness parameters of each layer of the photovoltaic panel, calculate the temperature variation coefficient, and set the inherent properties of each layer of the photovoltaic panel material;

[0032] Intrinsic material properties include: elastic modulus E, Poisson's ratio μ, thermal expansion coefficient Thermal conductivity λ.

[0033] The layers of the photovoltaic panel include: a back panel layer, an EVA layer, a silicon wafer layer and a glass layer. Please refer to the description of the photovoltaic panel in the publication number CN119167681A.

[0034] Step 2: Construct a thermal stress prediction model for each layer of the photovoltaic panel, and use the material temperature variation coefficient, elastic modulus, Poisson's ratio, thermal expansion coefficient, neutral layer curvature and lower surface strain of each layer of the photovoltaic panel to predict the thermal stress of each layer of the photovoltaic panel;

[0035] The thermal stress prediction model formula for each layer of the photovoltaic panel is:

[0036]

[0037] Among them, σ 热i is the thermal stress distribution of the ith photovoltaic panel along the z direction, Figure 2 is a schematic diagram of the coordinate system; E i is the elastic modulus of the i-th layer material; μ i Poisson's ratio of the i-th layer material; ε0 is the strain at the lower surface of the photovoltaic panel under the action of thermal expansion and thermal stress due to temperature difference; κ is the curvature of the neutral layer of the photovoltaic panel under the action of thermal expansion and thermal stress due to temperature difference; is the thermal expansion coefficient of the i-th layer material; ΔT j is the temperature variation coefficient of the jth layer material; h j is the thickness of the j-th layer of material; Z i are the thickness coordinates of the i-th layer of material respectively.

[0038] Among them, the temperature variation coefficient ΔT i Using the formula (2) in CN119167681A, -A i ;

[0039] The formula for the strain ε0 at the bottom surface of the photovoltaic panel is:

[0040]

[0041] The formula for the curvature κ of the neutral layer of a photovoltaic panel is:

[0042]

[0043] Among them, a, b, c, d, e, and f are the first to sixth coefficients respectively;

[0044] The formulas for the first coefficient a, the second coefficient b, the third coefficient c, the fourth coefficient d, the fifth coefficient e, and the sixth coefficient f are:

[0045]

[0046] Among them, λ i is the thermal conductivity of the i-th layer material; E i is the elastic modulus of the i-th layer material; μ i Poisson's ratio of the i-th layer of material; Thermal expansion coefficient of the jth layer material; h jis the thickness of the j-th layer of material; h j =Z j+1 -Z j , Z j-1 is the z-direction coordinate value of the j-1th layer of material.

[0047] The thermal conductivity is determined by the material of each layer. Table 1 shows the parameter values ​​of thermal conductivity, elastic modulus, Poisson's ratio and thermal expansion coefficient of a certain type of glass plate, a certain material EVA, a certain type of silicon wafer and a certain type of backplane. The thermal conductivity, elastic modulus, Poisson's ratio and thermal expansion coefficient are determined by the model or material.

[0048] Table 1 Intrinsic property values ​​of materials in each layer of a certain type of photovoltaic panel

[0049]

[0050]

[0051] The measured values ​​of the thickness of each layer of a certain type of single-layer photovoltaic panel can be found in Table 2 in CN119167681A.

[0052] Specific example:

[0053] Through Matlab, the key data are calculated as follows:

[0054] The strain at the lower surface of the photovoltaic panel is ε0 = -2.6690e-04;

[0055] The curvature of the neutral layer of the photovoltaic panel κ = 3.6022e-05;

[0056] The expressions of thermal stress in each layer are as follows:

[0057]

[0058] In the above-mentioned thermal stress expressions of each layer, when the multilayer board expands freely, it cannot expand freely to the ideal value due to the interlayer constraints. The cumulative calculation method will cause errors compared with the actual situation. Therefore, a free expansion adjustment coefficient is added. When the cumulative number of free expansion layers of the composite material is less than 4, the adjustment coefficient is 1. When the cumulative number of free expansion layers of the composite material is greater than or equal to 4, the adjustment coefficient is 0.6, so as to correct the actual situation.

[0059] The expressions of the corrected thermal stresses of each layer are as follows:

[0060] σ 热1 =0.01656*z-0.01521,0≤z≤0.17;

[0061] σ 热2 =0.009445*z-0.006592,0.17≤z≤0.57;

[0062] σ 热3 =0.1859*z-0.3716,0.57≤z≤0.87;

[0063] σ 热4 =0.009445*z-0.009172,0.87≤z≤1.27;

[0064] σ 热5 =0.07904*z+0.1316,1.27≤z≤4.47;

[0065] Since stress changes suddenly at the material interface, in fact, stress is discontinuous at the material interface. By using the above-mentioned model of thermal stress distribution of each layer of material and bringing in the specific thickness value z, the thermal stress of each interface layer of the photovoltaic panel can be further obtained, as shown below:

[0066] z=0 (at the backplane layer), that is, the lower surface of the backplane, thermal stress: σ 热1 =-0.0152MPa;

[0067] z=0.17 (in the back sheet layer), that is, the thermal stress at the interface between the back sheet and the first EVA layer: σ 热1 =-0.0124MPa;

[0068] z=0.17 (in EVA1 layer), that is, thermal stress at the interface between the back sheet and the first EVA layer: σ 热2 =-0.0050MPa;

[0069] z=0.57 (in EVA1 layer), that is, thermal stress at the interface between the first EVA layer and the silicon wafer layer: σ 热2 =-0.0012MPa; z = 0.57 (at the silicon wafer layer), that is, the thermal stress at the interface between the first EVA layer and the silicon wafer layer: σ 热3 =-0.2657MPa; z = 0.87 (in the silicon wafer layer), that is, the thermal stress at the interface between the silicon wafer layer and the second EVA layer: σ 热3 =-0.2099MPa; z = 0.87 (in EVA2 layer), that is, the thermal stress at the interface between the silicon wafer layer and the second EVA layer: σ 热4 =-9.5478e-04MPa; z = 1.27 (in EVA2 layer), that is, the thermal stress at the interface between the second EVA layer and the glass layer: σ 热4 =0.0028MPa;

[0070] z=1.27 (in the glass layer), that is, the thermal stress at the interface between the second EVA layer and the glass layer: σ 热5 =-0.0313MPa; z = 4.47 (in the glass layer), that is, the upper surface of the glass layer, thermal stress: σ 热5=0.2217MPa.

[0071] Since the internal thermal stress of photovoltaic panels cannot be non-destructively detected during the actual manufacturing or service process, that is, detection after destruction is meaningless, or additional hot spots may be generated to affect and change the performance of the photovoltaic panels; this verification scheme adopts a numerical simulation method to directly extract the thermal stress of the observation point through a software platform, and uses the same condition parameters as the numerical simulation to substitute into the stress prediction model of the present invention for analytical calculation, and then compares the simulation with the analytical calculation results to verify the effectiveness of the method of the present invention.

[0072] Numerical simulation: Assuming that the upper surface temperature of the photovoltaic panel is maintained at 65°C and the lower surface temperature is maintained at 25°C, the parameters in Table 1 are used to simulate the thermal stress field using Abaqus finite element software. The thermal stress field distribution cloud diagram is shown in Figure 3 As shown in Table 2, the software extracts the thermal stress of the upper and lower surfaces of the entire photovoltaic panel and the interfaces of each layer of material, with the horizontal black color being the interface of each layer.

[0073] The interface temperature calculated by the analytical model of the present invention is compared with the corresponding layer temperature calculated by numerical simulation through automatic calculation by simple software programming, as shown in Table 2 and Figure 4 As shown:

[0074] Table 2 Comparison of thermal stress predicted by numerical simulation and model of the present invention

[0075] Thickness(mm) Numerical calculation value (MPa) Model predicted value (MPa) z=0 (at the backplane layer) -0.0152 -0.0039 z=0.17(in the backplane layer) -0.0124 -0.003693 z=0.17 (in EVA1 layer) -0.0050 -0.001644 z=0.57 (in EVA1 layer) -0.0012 -0.00129684 z=0.57(at silicon wafer layer) -0.2657 -0.259682 z=0.87(at silicon wafer layer) -0.2099 -0.208832 z=0.87(in EVA2 layer) -9.5478e-04 -0.000834229 z=1.27(in EVA2 layer) 0.0028 -0.000488823 z=1.27(in the glass layer) -0.0313 -0.0185988 z=3.51(in the glass layer) 0.1458 0.162675 z=4.15(in the glass layer) 0.1964 0.204506 z=4.15(in the glass layer) 0.1964 -0.613762 z=4.47(in the glass layer) 0.2217 -0.613758

[0076] Abnormal explanation: The abnormal data in the glass layer (4.15≤z≤4.47) appears because the thermal stress model adopted in the present invention is only applicable to the calculation of thermal stress within the board. Due to various reasons such as boundary effects, geometric shapes and stress concentration, the thermal stress model of the present invention is not applicable to the ends or edges of the board.

[0077] Figure 4 It can be seen that the thermal stress prediction method provided by the present invention has sufficient accuracy.

[0078] The thermal stress prediction model for each layer of the photovoltaic panel constructed by the present invention is simple. By measuring the upper and lower surface temperatures and thickness parameters of the photovoltaic panel and combining the thermal conductivity, elastic modulus, Poisson's ratio and thermal expansion coefficient of each layer of material, the thermal stress corresponding to the coordinates of each layer can be accurately predicted; the predicted thermal stress value can be used to evaluate indicators such as the risk of hidden cracks during the service of the photovoltaic panel.

[0079] Based on the above ideal embodiments of the present invention, the relevant staff can make various changes and modifications without departing from the technical concept of the present invention through the above description. The technical scope of the present invention is not limited to the contents of the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A method for predicting thermal stress of photovoltaic panels based on heat load, characterized in that: The following steps are involved: Step 1: Collect the thickness parameters of each layer of the photovoltaic panel, calculate the temperature variation coefficient of the photovoltaic panel, and set the inherent properties of each layer of the photovoltaic panel material; Step 2: Construct a thermal stress prediction model for each layer of the photovoltaic panel, and use the temperature variation coefficient, elastic modulus, Poisson's ratio, thermal expansion coefficient, neutral layer curvature and lower surface strain of each layer of the photovoltaic panel to predict the thermal stress of each layer of the photovoltaic panel.

2. The method for predicting thermal stress of photovoltaic panels based on heat load according to claim 1, characterized in that: The formula of the thermal stress prediction model is: Among them, σ 热i is the thermal stress distribution of the ith photovoltaic panel along the z direction; E i is the elastic modulus of the i-th layer material; μ i Poisson's ratio of the i-th layer material; ε0 is the strain at the lower surface of the photovoltaic panel under the action of thermal expansion and thermal stress due to temperature difference; κ is the curvature of the neutral layer of the photovoltaic panel under the action of thermal expansion and thermal stress due to temperature difference; is the thermal expansion coefficient of the i-th layer material; ΔT j is the temperature variation coefficient of the jth layer material; h j is the thickness of the j-th layer of material; Z i are the thickness coordinates of the i-th layer of material respectively.

3. The method for predicting thermal stress of photovoltaic panels based on heat load according to claim 2, characterized in that: The strain at the lower surface of the photovoltaic panel = (second coefficient * sixth coefficient - third coefficient * fifth coefficient) / (second coefficient * fourth coefficient - first coefficient * fifth coefficient).

4. The method for predicting thermal stress of photovoltaic panels based on heat load according to claim 2, characterized in that: The curvature of the neutral layer of the photovoltaic panel = (the third coefficient * the fourth coefficient - the first coefficient * the sixth coefficient) / (the second coefficient * the fourth coefficient - the first coefficient * the fifth coefficient).

5. The method for predicting thermal stress of photovoltaic panels based on heat load according to claims 3 and 4 is characterized in that: The first coefficient is calculated using the elastic modulus, Poisson's ratio and the thickness difference between two adjacent layers of material.

6. The method for predicting thermal stress of photovoltaic panels based on heat load according to claims 3 and 4, characterized in that: The second coefficient is calculated using the elastic modulus, Poisson's ratio and the thickness difference between two adjacent layers of material.

7. The method for predicting thermal stress of photovoltaic panels based on heat load according to claims 3 and 4, characterized in that: The third coefficient is calculated using the elastic modulus, Poisson's ratio, the thickness difference between two adjacent layers of material, the temperature variation coefficient, the thickness of the material and the thermal expansion coefficient.

8. The method for predicting thermal stress of photovoltaic panels based on heat load according to claims 3 and 4, characterized in that: The fourth coefficient is calculated using the elastic modulus, Poisson's ratio and the thickness difference between two adjacent layers of material.

9. The method for predicting thermal stress of photovoltaic panels based on heat load according to claims 3 and 4, characterized in that: The fifth coefficient is calculated using the elastic modulus, Poisson's ratio and the thickness difference between two adjacent layers of material.

10. The method for predicting thermal stress of photovoltaic panels based on heat load according to claims 3 and 4, characterized in that: The sixth coefficient is calculated using the elastic modulus, Poisson's ratio, the thickness difference between two adjacent layers of material, the temperature variation coefficient, the thickness of the material and the thermal expansion coefficient.

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