Photovoltaic module service life prediction method, device and medium

Abstract

The invention relates to a photovoltaic module life prediction method and device and a medium, and the method comprises the steps: calculating the activation energy of a photovoltaic module through employing an Arrhenus formula; performing an ultraviolet accelerated aging test on the photovoltaic module to obtain characteristic data of the photovoltaic module after ultraviolet aging; introducing the ultraviolet irradiance into a Peck model, and calculating an influence factor associated with a damp and hot condition and an influence factor associated with ultraviolet aging in the Peck model according to the activation energy and the characteristic data of the photovoltaic module after ultraviolet aging to obtain a Peck equation; and constructing a Wiener attenuation model based on the Peck equation, and predicting the service life of the photovoltaic module by using the Wiener attenuation model. The method can better conform to the environmental characteristics of outdoor operation of the photovoltaic module, and multiple environmental physical parameters are considered at the same time.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic component reliability analysis, and in particular to a photovoltaic component life prediction method, equipment and medium. Background Art

[0002] With the increasing depletion of traditional fossil energy, clean and environmentally friendly solar energy has become an important part of the energy field. The reliability of solar photovoltaic modules is easily affected by uncontrolled environmental factors such as temperature, humidity, and sunshine, leading to systemic risks and waste of photovoltaic resources. As a new technology photovoltaic module, the reliability of crystalline silicon heterojunction photovoltaic modules has not been fully studied, especially its reliability under high ultraviolet radiation and high humidity conditions. This is because the hydrogenated amorphous silicon at the interface of crystalline silicon heterojunction solar cells is easily damaged by ultraviolet rays. Therefore, it is of great significance to study the reliability of photovoltaic modules using accelerated experiments, simulations, theoretical analysis and outdoor operation data. The research results can be used to evaluate the safety of photovoltaic resources, reconstruct distributed energy storage, and solve the problem of photovoltaic grid connection at a low cost. At present, the reliability research of photovoltaic modules based on accelerated aging experiments often only has a single environmental variable, such as temperature or cumulative ultraviolet irradiance, which cannot meet the needs of simulating real outdoor environments. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a photovoltaic module life prediction method, device and medium, which can better meet the environmental characteristics of outdoor operation of photovoltaic modules and take into account multiple environmental physical parameters.

[0004] The technical solution adopted by the present invention to solve the technical problem is: to provide a photovoltaic module life prediction method, comprising the following steps:

[0005] The activation energy of photovoltaic modules is calculated using the Arenus formula;

[0006] Performing an ultraviolet accelerated aging test on the photovoltaic module to obtain characteristic data of the photovoltaic module after ultraviolet aging;

[0007] Incorporating ultraviolet irradiance into the Peck model, and calculating the influencing factors associated with humid heat conditions and ultraviolet aging in the Peck model based on the activation energy and characteristic data of the photovoltaic module after ultraviolet aging, to obtain the Peck equation;

[0008] A Wiener attenuation model is constructed based on the Peck equation, and the Wiener attenuation model is used to predict the life of the photovoltaic module.

[0009] The Arenus formula is used to calculate the activation energy of the photovoltaic module, specifically including:

[0010] Conduct multiple sets of wet heat aging tests on photovoltaic modules with varying temperatures while maintaining constant humidity to obtain the relationship between the life of the photovoltaic modules and temperature;

[0011] According to the relationship between the life of photovoltaic modules and temperature, the activation energy of photovoltaic modules is obtained using the Arenus formula.

[0012] The Arenus formula is: ln(L(T))=E A (1 / kT)+ln(C), where L(T) is the lifetime of the PV module, T is the temperature, k is the Boltzmann constant, and E A is the activation energy of the photovoltaic module, and C is the parameter of the Arenius formula.

[0013] When the photovoltaic module is subjected to the ultraviolet accelerated aging test to obtain characteristic data of the photovoltaic module after ultraviolet aging, a series of stricter ultraviolet accelerated aging tests are performed according to the IEC standard to obtain output electrical parameters of the same module under different irradiances.

[0014] The Peck model incorporating UV irradiance is expressed as: Among them, R D is the failure time of photovoltaic modules, A is the material process parameter of photovoltaic modules, E A is the activation energy of the photovoltaic module, T is the temperature, k is the Boltzmann constant, UV is the ultraviolet irradiance, RH is the relative humidity, n is the influencing factor associated with ultraviolet aging, and m is the influencing factor associated with humid and hot conditions.

[0015] When calculating the influencing factors associated with damp heat conditions and the influencing factors associated with ultraviolet aging in the Peck model based on the activation energy and the characteristic data of the photovoltaic components after ultraviolet aging, the residual method is used to optimize the influencing factors associated with damp heat conditions and the influencing factors associated with ultraviolet aging in the Peck model, and a double calibration-verification procedure is used to determine the parameter combination that provides the best solution.

[0016] The Wiener attenuation model is expressed as: Among them, X(t) is the performance attenuation, σ is the diffusion coefficient, W(t) is the standard Wiener process, and t represents the accelerated aging test time.

[0017] The method of predicting the life of the photovoltaic module by using the Wiener attenuation model specifically includes:

[0018] Determine the likelihood function of the Wiener attenuation model using attenuation data obtained from a damp heat aging accelerated test and an ultraviolet aging accelerated test;

[0019] Setting the likelihood function of the Wiener attenuation model equal to zero, respectively calculating the partial derivatives of the drift coefficient and the diffusion coefficient with respect to the likelihood function, and obtaining a plurality of parameters under a preset accelerated aging condition;

[0020] Substituting multiple parameters under preset accelerated aging conditions into the likelihood function of the Wiener attenuation model to obtain a drift coefficient and a diffusion coefficient;

[0021] Bringing the drift coefficient and the diffusion coefficient into the Wiener decay model, and based on the activation energy, obtaining the operating time probability density function under the preset accelerated aging conditions by using the nonlinear least squares method;

[0022] The horizontal axis value at the highest point of the operating time probability density function under the preset accelerated aging conditions is taken as the most likely operating time length of the photovoltaic module.

[0023] The technical solution adopted by the present invention to solve its technical problem is: to provide an electronic device, including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned photovoltaic module life prediction method when executing the computer program.

[0024] The technical solution adopted by the present invention to solve its technical problem is: providing a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned photovoltaic component life prediction method are implemented.

[0025] Beneficial Effects

[0026] Due to the adoption of the above technical solution, the present invention has the following advantages and positive effects compared with the prior art: the present invention combines the Peck model with the Wiener process, first calculates the activation energy of the photovoltaic module, then calculates the influence factor of the Peck model according to the ultraviolet acceleration experiment, obtains the power attenuation Peck model of the photovoltaic module, and finally uses the Wiener process combined with the Peck model to obtain the life span of the photovoltaic module, which greatly improves the efficiency of the reliability research of the photovoltaic power generation system, and finally achieves the purpose of predicting the new high-efficiency crystalline silicon heterojunction photovoltaic module. The present invention is suitable for life prediction in different climate zones and can be used under any temperature, humidity and irradiance conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a flow chart of a photovoltaic module life prediction method according to a first embodiment of the present invention;

[0028] Figure 2 is the Ahrendts diagram obtained in the first embodiment of the present invention;

[0029] Figure 3is a schematic diagram of the life of a photovoltaic module calculated by the Peck model in the first embodiment of the present invention;

[0030] Figure 4 is a schematic diagram of a probability density function of running time in the first embodiment of the present invention;

[0031] Figure 5 Schematic diagram of the lifetime distribution function and reliability function of photovoltaic modules in the first embodiment of the present invention

[0032] Figure 6 This is a graph estimating the service life of photovoltaic modules at any temperature and humidity in the first embodiment of the present invention. DETAILED DESCRIPTION

[0033] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0034] The first embodiment of the present invention relates to a photovoltaic module life prediction method, which can be applied to crystalline silicon heterojunction HJT photovoltaic modules. First, the activation energy of the HJT photovoltaic module is calculated, and then the influence factor of the Peck model is calculated according to the ultraviolet accelerated aging experiment, thereby obtaining the power attenuation Peck model. Finally, the Wiener process is combined with the Peck model to obtain the life range of the HJT photovoltaic module, which greatly improves the efficiency of photovoltaic power generation reliability research. Figure 1 As shown, the specific steps include:

[0035] Step 1, using the Arrhenius formula to calculate the activation energy of the HJT photovoltaic module. In this step, multiple groups of heat and humidity aging experiments are performed on the HJT photovoltaic module with varying temperatures under constant humidity to obtain the relationship between the life of the photovoltaic module and the temperature; based on the relationship between the life of the photovoltaic module and the temperature, the Arrhenius formula is used to obtain the activation energy of the photovoltaic module.

[0036] According to the Arrhenius formula, higher temperature will accelerate the power degradation of heterojunction modules as shown below:

[0037]

[0038] Where, L(T) is the life of the photovoltaic module, T is the temperature, k is the Boltzmann constant, and E A is the activation energy of the photovoltaic module, and C is the parameter of the Arenius formula.

[0039] In order to obtain the Arenus formula parameter C in formula (1) and the activation energy E of the photovoltaic module A , the equation can be represented by a straight line on a logarithmic scale using the following equation:

[0040] ln(L(T))=E A ·(1 / kT)+ln(C) (2)

[0041] When the power ratio (P i / P 0 ) is less than 80%, Figure 2 Set (L(T)) in the Arrhenius plot shown. ln(L(T)) vs. 1 / T gives an Arrhenius plot with a slope of E A / k, the intercept is ln(C). The activation energy E is calculated A The E of polycrystalline silicon photovoltaic modules is 0.43eV, slightly higher than that of A .

[0042] Step 2: Perform an ultraviolet accelerated aging test on the photovoltaic module to obtain characteristic data of the photovoltaic module after ultraviolet aging. In this step, at least two groups of photovoltaic modules of the same specification are prepared for indoor standard testing according to IEC standards, wherein the number of photovoltaic modules in each group is not less than 5. During the experiment, a standard AM1.5 light source is used to test each group of photovoltaic modules to obtain the power P of the same module under different irradiances. max , short circuit current I sc and open circuit voltage V oc Output electrical parameters.

[0043] Step 3, incorporating ultraviolet irradiance into the Peck model, and calculating the influencing factors associated with humid heat conditions and ultraviolet aging in the Peck model based on the activation energy and characteristic data of the photovoltaic components after ultraviolet aging, to obtain the Peck equation.

[0044] The Peck model is widely used in reliability research and is a mathematical model based on the Arrhenius formula. It describes the attenuation of materials and equipment over time, taking into account the dependence on temperature and humidity. The model helps to evaluate the operating life and is of great value in evaluating the impact of environmental factors on long-term performance and helping to optimize technology to improve reliability. The existing Peck model is shown below:

[0045]

[0046] In order to obtain a more accurate degradation rate of HJT modules, this step incorporates ultraviolet irradiance to improve the Peck model, thereby realizing the prediction of photovoltaic module life associated with multiple physical factors such as temperature, humidity, and ultraviolet irradiation. The improved model is expressed as follows:

[0047]

[0048] Among them, R D is the failure time of the photovoltaic module, that is, the module life, A is the material process parameter of the photovoltaic module, E A is the activation energy of the photovoltaic module, T is the temperature, k is the Boltzmann constant, UV is the ultraviolet irradiance, RH is the relative humidity, n is the influencing factor associated with ultraviolet aging, and m is the influencing factor associated with humid and hot conditions.

[0049] The calculation of the influence factor n associated with UV aging and the influence factor m associated with humid and hot conditions in the improved Peck model involves optimizing the parameters of the nonlinear model by minimizing the residual sum of squares. The initial parameters of the influence factor n associated with UV aging and the influence factor m associated with humid and hot conditions can be selected arbitrarily, with different convergence speeds, and are set to [1,10] in this embodiment. The residual is calculated as follows:

[0050]

[0051] The goal is to minimize the sum of squares of these residuals, which is calculated as follows:

[0052]

[0053] Where S is the observed value of the dependent variable μ i and the predictions from the estimated regression equation The difference reflects the use of the estimated regression equation to predict μ i The residual of the i-th observation is

[0054] To determine the combination of parameters that provides the best solution, a double calibration-verification procedure can be used. max , short circuit current I sc and open circuit voltage V oc and the corresponding accumulated UV radiation values ​​are used to calculate the parameters of the improved Peck model and obtain multiple numerical solutions. The power P obtained from the UV accelerated aging experiment of another group of photovoltaic modules with the same process is then used to calculate the power P max , short circuit current I sc and open circuit voltage V o , the parameters of the improved Peck model are determined to be n = 1.675, m = 1.402. The experimental data used to calculate the parameters in this embodiment are from the 60°C ultraviolet accelerated test. Finally, combined with UV and DH conditions, the model is used to calculate the life of the crystalline silicon heterojunction photovoltaic module in a more realistic outdoor environment, such as Figure 3shown.

[0055] Step 4: construct a Wiener attenuation model based on the Peck equation, and use the Wiener attenuation model to predict the life of the photovoltaic module.

[0056] Usually the attenuation model based on the Wiener process can be expressed as:

[0057] X(t)=μt+σW(t) (7)

[0058] Where X(t) represents the attenuation of performance; μ represents the drift coefficient; σ represents the diffusion coefficient; W(t) represents the standard Wiener process, and t represents the accelerated aging test time. In this embodiment, the normal distribution random variable obeyed by W(t) is N(0,3000), and 3000 hours is the wet heat test time of the photovoltaic module. If this method is used to perform other wet heat accelerated tests, the wet heat accelerated test of that time shall prevail.

[0059] Combining the Wiener attenuation model and the Peck model, we can define:

[0060]

[0061] Right now:

[0062]

[0063] W(t) is a standard Wiener process, satisfying that for any α>β, W(α)-W(β)~N(0,α-β), N(μ′,σ′ 2 ) indicates that the expectation is μ′ and the variance is σ′ 2 Normally distributed random variables. In this embodiment, the attenuation model variables of the Wiener process include damp heat aging and ultraviolet aging, but are not limited to damp heat aging and ultraviolet aging. The variables can also be different spatial environmental variables such as electron irradiation. The way of introducing different environmental variables into the Wiener model is the same as the way of introducing ultraviolet aging changes.

[0064] Assume that l is the normalized maximum power attenuation of the photovoltaic module output. When l = 0.2, that is, the output power is 80% of the initial power, the photovoltaic module is considered to be failed. The life of the photovoltaic module can be expressed as:

[0065] T=inf{t|X(t)≥l} (10)

[0066] In order to conform to the actual situation, assuming that the drift parameter μ>0, the analytical expressions of the probability density function f and the cumulative distribution function F of the PV module life can be expressed as:

[0067]

[0068] The corresponding reliability analytical expression is:

[0069]

[0070] For the decay process {X(t), t≥0} that follows the Wiener process, the decay increment follows the normal distribution, that is: ΔX(t)~N(μΔt,σ 2 Δt),μ>0, the corresponding probability density function is:

[0071]

[0072] X ik ={x i1 ,x i2 ,…,x ik},i=1,2,…,u,k=1,2,…,v,X ik is the power attenuation data of the photovoltaic module under the damp heat condition (85℃, 85%RH), and follows the Wiener attenuation process, v is the number of detections of the photovoltaic module damp heat aging, and u is the number of photovoltaic modules in the same accelerated aging environment. In order to test the validity of this theory, the power attenuation data under the conditions of (85℃, 85%RH) of the 3000-hour damp heat experiment is used to construct the corresponding Wiener attenuation model. It should be noted that in order to reduce the amount of calculation, the UV variable is not added to the model construction in this embodiment, but it is still convincing because the accuracy after adding it will be higher than the existing example, and the likelihood function is obtained as follows:

[0073]

[0074] Let the likelihood function be equal to zero, and then calculate σ i , μ i Partial derivatives with respect to L:

[0075]

[0076] Calculated Multiple parameters under accelerated aging conditions (85°C, 85% RH) Substitute (Formula (16)) into Formula (15), and we get μ = 0.000255, σ = 0.043449. Then substitute the values ​​of μ and σ into Formula (9). Based on the known E A =0.43eV, by using the nonlinear least squares method, we get A=42, m=1.675. Therefore, the operating time probability density function of the photovoltaic module under (85℃, 85%RH) conditions is plotted according to formula (14) as follows: Figure 4 shown.

[0077] According to the properties of the probability density function, the horizontal axis value at the highest point of the probability density function is the most likely operating time of the HJT photovoltaic module, that is, the mode of all operating times. It is found that the mode of the operating time of the HJT photovoltaic module at (85℃ / 85%RH) is 3137 hours. This is very close to the normal operating time of the photovoltaic module directly measured by the accelerated test at (85℃ / 85%RH).

[0078] Known reliability function:

[0079] R(t)=1-F(t) (17)

[0080] The lifetime distribution function and reliability function of photovoltaic modules can be obtained as follows: Figure 5 As shown, the reliability function curve is analyzed, and the mean μΔt and standard deviation of formula (14) are calculated. The estimated service life of HJT photovoltaic modules at (85℃ / 85%RH) with a confidence interval of 95% is [3131,3143] hours, which is close to the measured service life of HJT photovoltaic modules, further proving the correctness and effectiveness of the proposed model and algorithm.

[0081] Based on the parameters of the Wiener process and Peck equation calculated above, A = 42, m = 1.675, E A =0.43eV, K = 8.617×10-5eV / K, σ = 0.043449, the Wiener process description unique to HJT photovoltaic modules can be obtained:

[0082]

[0083] Wherein, RH and T are unknown variables. In this embodiment, the normal distribution random variable that W(t) obeys is N(0,3000).

[0084] When the output power is 80% of the initial power, the HJT photovoltaic module is considered to be ineffective, and the product life can be calculated by T = inf{t|X(t)>l}, l = 0.2. Finally, the estimated service life of the photovoltaic module under any temperature and humidity can be obtained, such as Figure 6 As shown. It can be seen that different temperature and humidity levels correspond to different service life estimates, and as the temperature and humidity levels increase, the service life of the components decreases rapidly. It is not difficult to find that when the temperature and humidity inside the photovoltaic components are operated in outdoor environmental conditions for a long time (50℃ / 45%RH), it can be found that the average service life of the photovoltaic components under this temperature and humidity level is estimated to be 24.95 years, which is closer to the 25-year average service life promised by the current photovoltaic component manufacturers. In addition, from Figure 6It was found that under ideal conditions, that is, when the ambient humidity is 30% RH and the temperature is 17°C, the predicted life of HJT components can be as high as 89 years, which means that HJT components have a longer service life and extremely low cost per kilowatt-hour in low temperature and low humidity environments.

[0085] It can be seen that the present invention combines the Peck model with the Wiener process, first calculates the activation energy of the photovoltaic module, then calculates the influence factor of the Peck model according to the ultraviolet acceleration experiment, obtains the power attenuation Peck model of the photovoltaic module, and finally uses the Wiener process combined with the Peck model to obtain the life span of the photovoltaic module, which greatly improves the efficiency of the reliability research of the photovoltaic power generation system, and finally achieves the purpose of predicting the new high-efficiency crystalline silicon heterojunction photovoltaic module. The present invention is suitable for life prediction in different climate zones and can be used under any temperature, humidity and irradiance conditions.

[0086] The second embodiment of the present invention relates to an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the photovoltaic component life prediction method of the first embodiment when executing the computer program.

[0087] The third embodiment of the present invention relates to a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the photovoltaic component life prediction method of the first embodiment.

[0088] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0089] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0090] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction method, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0091] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0092] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A photovoltaic module life prediction method, characterized in that: The following steps are involved: The activation energy of photovoltaic modules is calculated using the Arenus formula; Performing an ultraviolet accelerated aging test on the photovoltaic module to obtain characteristic data of the photovoltaic module after ultraviolet aging; Incorporating ultraviolet irradiance into the Peck model, and calculating the influencing factors associated with humid heat conditions and ultraviolet aging in the Peck model based on the activation energy and characteristic data of the photovoltaic module after ultraviolet aging, to obtain the Peck equation; A Wiener attenuation model is constructed based on the Peck equation, and the Wiener attenuation model is used to predict the life of the photovoltaic module.

2. The photovoltaic module life prediction method according to claim 1, characterized in that: The Arenus formula is used to calculate the activation energy of the photovoltaic module, specifically including: Conduct multiple sets of wet heat aging tests on photovoltaic modules with varying temperatures while maintaining constant humidity to obtain the relationship between the life of the photovoltaic modules and temperature; According to the relationship between the life of photovoltaic modules and temperature, the activation energy of photovoltaic modules is obtained using the Arenus formula.

3. The photovoltaic module life prediction method according to claim 2, characterized in that: The Arenus formula is: ln(L(T))=E A (1 / kT)+ln(C), where L(T) is the lifetime of the PV module, T is the temperature, k is the Boltzmann constant, and E A is the activation energy of the photovoltaic module, and C is the parameter of the Arenius formula.

4. The photovoltaic module life prediction method according to claim 1, characterized in that: When the photovoltaic module is subjected to the ultraviolet accelerated aging test to obtain characteristic data of the photovoltaic module after ultraviolet aging, a series of stricter ultraviolet accelerated aging tests are performed according to the IEC standard to obtain output electrical parameters of the same module under different irradiances.

5. The photovoltaic module life prediction method according to claim 1, characterized in that: The Peck model incorporating UV irradiance is expressed as: Among them, R D is the failure time of photovoltaic modules, A is the material process parameter of photovoltaic modules, E A is the activation energy of the photovoltaic module, T is the temperature, k is the Boltzmann constant, UV is the ultraviolet irradiance, RH is the relative humidity, n is the influencing factor associated with ultraviolet aging, and m is the influencing factor associated with humid and hot conditions.

6. The photovoltaic module life prediction method according to claim 1, characterized in that: When calculating the influencing factors associated with damp heat conditions and the influencing factors associated with ultraviolet aging in the Peck model based on the activation energy and the characteristic data of the photovoltaic components after ultraviolet aging, the residual method is used to optimize the influencing factors associated with damp heat conditions and the influencing factors associated with ultraviolet aging in the Peck model, and a double calibration-verification procedure is used to determine the parameter combination that provides the best solution.

7. The photovoltaic module life prediction method according to claim 5, characterized in that: The Wiener attenuation model is expressed as: Among them, X(t) is the performance attenuation, σ is the diffusion coefficient, W(t) is the standard Wiener process, and t represents the accelerated aging test time.

8. The photovoltaic module life prediction method according to claim 7, characterized in that: The method of predicting the life of the photovoltaic module by using the Wiener attenuation model specifically includes: Determine the likelihood function of the Wiener attenuation model using attenuation data obtained from a damp heat aging accelerated test and an ultraviolet aging accelerated test; Setting the likelihood function of the Wiener attenuation model equal to zero, respectively calculating the partial derivatives of the drift coefficient and the diffusion coefficient with respect to the likelihood function, and obtaining a plurality of parameters under a preset accelerated aging condition; Substituting multiple parameters under preset accelerated aging conditions into the likelihood function of the Wiener attenuation model to obtain a drift coefficient and a diffusion coefficient; Bringing the drift coefficient and the diffusion coefficient into the Wiener decay model, and based on the activation energy, obtaining the operating time probability density function under the preset accelerated aging conditions by using the nonlinear least squares method; The horizontal axis value at the highest point of the operating time probability density function under the preset accelerated aging conditions is taken as the most likely operating time length of the photovoltaic module.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the photovoltaic component life prediction method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the photovoltaic component life prediction method according to any one of claims 1 to 8 are implemented.

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