Photovoltaic module reliability detection method, system and device and readable storage medium

Abstract

The invention discloses a photovoltaic module reliability detection method, system and device and a readable storage medium, and the method comprises the steps: obtaining the corrosion reaction rate of a to-be-detected photovoltaic module under a preset environment stress condition; constructing an accelerated degradation model based on the preset environmental stress condition, wherein the accelerated degradation model is used for outputting a power attenuation curve of the photovoltaic module based on the preset environmental stress condition; based on the preset environmental stress condition and the corresponding corrosion reaction rate, solving model parameters of the accelerated degradation model; and acquiring environmental stress data of a target scene, inputting the environmental stress data of the target scene into the accelerated degradation model, and outputting a power attenuation curve of the to-be-detected photovoltaic module in the target scene. Compared with the prior art, the test time is shortened, the test efficiency is improved, and the test cost is reduced. Various influence factors such as material mechanisms and environmental meteorological conditions are organically combined, so that accelerated degradation data are more accurate and comprehensive.

Description

Technical Field

[0001] This invention belongs to the field of photovoltaic module prediction technology, specifically relating to a photovoltaic module reliability testing method, system, equipment, and readable storage medium. Background Technology

[0002] As a core component of photovoltaic (PV) systems, the quality and reliability of PV modules directly affect the power generation efficiency and lifespan of PV power plants. Research indicates that alternating high and low temperatures and humidity intrusion are the main factors affecting the reliability of PV modules. Therefore, selecting superior PV modules and conducting research on reliability assessment and lifespan prediction methods for PV module products is crucial and an urgent issue for ensuring the healthy development of this strategic emerging industry.

[0003] The long-term reliability and durability of photovoltaic (PV) modules, especially under harsh environmental conditions, still lack sufficient verification. Lifespan prediction of PV modules is one of the crucial tasks for ensuring the long-term stable operation of PV systems. While the lifespan of PV modules is increasing with technological advancements, actual lifespan is influenced by various factors such as environmental conditions, material quality, and manufacturing processes. Currently, assessments of PV module lifespan primarily rely on long-term monitoring results from application sites, leading to a significant degree of error between the assessment results and actual outcomes.

[0004] Therefore, in order to address the aforementioned technical issues, it is necessary to provide a reliability testing strategy for photovoltaic modules.

[0005] The information disclosed in this background section is intended only to enhance the understanding of the overall background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a method, system, device, and readable storage medium for testing the reliability of photovoltaic modules. These methods can provide accurate predictions of the lifespan of photovoltaic modules, effectively assess the long-term degradation characteristics of modules under various environmental conditions, and thus provide a scientific basis for project reliability assessment and module selection.

[0007] To achieve the above objectives, a specific embodiment of the present invention provides the following technical solution:

[0008] In a first aspect, the present invention provides a method for testing the reliability of photovoltaic modules, comprising:

[0009] Under preset environmental stress conditions, the corrosion reaction rate of the photovoltaic module under test was obtained;

[0010] An accelerated degradation model is constructed based on the preset environmental stress conditions. The accelerated degradation model is used to output the power degradation curve of the photovoltaic module based on the preset environmental stress conditions.

[0011] Based on the preset environmental stress conditions and the corresponding corrosion reaction rate, the model parameters of the accelerated degradation model are solved.

[0012] The environmental stress data of the target scenario is obtained, and the environmental stress data of the target scenario is input into the accelerated degradation model to output the power degradation curve of the photovoltaic module under test in the target scenario.

[0013] In one or more embodiments of the present invention, the model function of the accelerated degradation model is:

[0014]

[0015]

[0016] Where A is the pre-exponential factor constant; RH m The humidity value of the photovoltaic module under test is denoted as ; n is the failure mode constant; E is the value of the photovoltaic module under test. a k is the activation energy value of the encapsulating film in a photovoltaic module. b Boltzmann constant; T is the temperature of the photovoltaic module under test; B is the shape parameter; R d P(t) represents the corrosion reaction rate of the photovoltaic module under test; P(t) is a function of the power of the photovoltaic module under test as a function of time.

[0017] In one or more embodiments of the present invention, the humidity value of the photovoltaic module under test is:

[0018] in,

[0019] In the formula, T is the temperature value, in degrees Celsius; RH amb T represents the relative humidity in the environment. amb For ambient temperature, T m For component temperature, P sat (T) represents the saturated water vapor pressure at temperature T.

[0020] In one or more embodiments of the present invention, solving the accelerated degradation model parameters includes:

[0021] Based on the preset environmental stress conditions and the corrosion reaction rate corresponding to the environmental stress conditions, the initial parameter values ​​of the accelerated degradation model are obtained.

[0022] Traverse each preset particle, obtain the fitness of each preset particle based on the particle position vector and the accelerated degradation model function, and record the global optimal fitness value and the optimal fitness value of each particle itself; the position vector is a vector composed of the accelerated degradation model parameter values;

[0023] Based on the global optimal fitness value and the optimal fitness value of each particle, update the velocity and position vectors of each preset particle;

[0024] Based on the updated position vector, the fitness values ​​of each preset particle are obtained again and the global optimal fitness value and the optimal fitness value of each particle are updated until a preset iteration round is reached or the difference between the global optimal fitness of the current round and the global optimal fitness of the previous round is less than a preset threshold.

[0025] The parameter values ​​of the accelerated degradation model are updated based on the position vector of the particle corresponding to the global optimal fitness value.

[0026] In one or more embodiments of the present invention, the method further includes:

[0027] Set corresponding threshold ranges for each parameter value and particle velocity value of the accelerated degradation model;

[0028] If the updated parameter values ​​and / or particle velocity values ​​of the accelerated degradation model are less than the minimum value of the corresponding threshold interval, then the parameter values ​​of the accelerated degradation model are set to the minimum value of the corresponding threshold interval.

[0029] If the updated parameter values ​​and / or particle velocity values ​​of the accelerated degradation model are greater than the maximum value of the corresponding threshold interval, then the parameter values ​​of the accelerated degradation model are set to the maximum value of the corresponding threshold interval.

[0030] In one or more embodiments of the present invention, the velocity and position vectors of the preset particles are updated as follows:

[0031] v i (t+1)=ω×v i (t)+c1r1(t)[p i,best (t)-θ i (t)]+c2r2(t)[g best (t)-θ i (t)]

[0032] θ i (t+1)=θ i (t)+v i (t+1)

[0033] Among them, θ(t)={A(t), n(t), E a(t), B(t)

[0034] In the formula, ω is the inertia coefficient, A is the pre-exponential factor constant, n is the failure mode constant, and E is the failure mode constant. a B represents the activation energy of the encapsulating film for photovoltaic modules; B represents the shape parameter; v i (t) represents the velocity value of particle i in round t; r1(t) and r2(t) represent uniformly distributed random numbers in the range [0, 1]; c1 and c2 are preset learning factors; p i，best (t) represents the optimal fitness value of particle i up to the t-th iteration, gbes t θ(t) represents the globally optimal fitness value up to the t-th iteration. i (t) is the position vector of particle i in the t-th round.

[0035] In one or more embodiments of the present invention, the fitness of each preset particle is obtained as follows:

[0036] F(θ) = minS(θ), where,

[0037] In the formula, F(θ) represents the fitness of the preset particles; h represents the number of experimental samples; y i f(x) represents the power attenuation of the photovoltaic module under test in the i-th experiment. i θ) represents the power degradation of the photovoltaic module under test, as output by the accelerated degradation model, under the same conditions as the i-th experiment; x i This includes the temperature and humidity of the photovoltaic module under test in the i-th experiment, as well as the experimental time.

[0038] Secondly, the present invention provides a photovoltaic module reliability testing system, comprising:

[0039] The acquisition module is used to acquire the corrosion reaction rate of the photovoltaic module under test under preset environmental stress conditions.

[0040] A construction module is used to construct an accelerated degradation model, which is used to output the power degradation curve of the photovoltaic module based on environmental stress conditions;

[0041] The solution module is used to solve the model parameters of the accelerated degradation model based on the multiple sets of environmental stress conditions and the corrosion reaction rates corresponding to the environmental stress conditions.

[0042] The application module is used to acquire environmental stress data of the target scene, input the environmental stress data of the target scene into the accelerated degradation model, and output the power degradation curve of the photovoltaic module under test in the target scene.

[0043] Thirdly, the present invention provides a computer device comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the photovoltaic module reliability testing method by executing the computer instructions.

[0044] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the photovoltaic module reliability testing method.

[0045] Compared with existing technologies, the photovoltaic module reliability testing method provided by this invention establishes an accelerated degradation model, combines it with test data of the photovoltaic module under artificial high stress conditions, improves the model parameters, and then extrapolates the reliability data of the corresponding photovoltaic module under normal use conditions. Since the failure mechanism of the photovoltaic module remains unchanged, the reliability under actual use conditions can be estimated through data processing and data conversion, which can accelerate life testing, shorten the test time, improve test efficiency, and reduce test costs.

[0046] On the other hand, this invention organically combines material mechanisms with various influencing factors such as environmental and meteorological conditions, making accelerated degradation data more accurate and comprehensive, which can provide an important basis for component design optimization and component installation area adaptability assessment. Furthermore, this invention initializes multiple sets of particles and further optimizes and updates the model parameters using a preset objective function. The iteratively output model parameters further improve the accuracy of the power decay curve output by the accelerated degradation model. Attached Figure Description

[0047] 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 some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a schematic diagram of a photovoltaic module reliability testing scenario according to one embodiment of the present invention;

[0049] Figure 2 This is a flowchart illustrating a photovoltaic module reliability testing method according to one embodiment of the present invention;

[0050] Figure 3 This is a structural block diagram of a photovoltaic module reliability testing system according to one embodiment of the present invention;

[0051] Figure 4 This is a structural block diagram of an electronic device according to one embodiment of the present invention;

[0052] Figure 5 This is a schematic diagram of accelerated testing data of photovoltaic modules in a laboratory according to a specific embodiment of the present invention;

[0053] Figure 6 This is a schematic diagram of the environmental temperature curve of Hainan over one year in a specific embodiment of the present invention;

[0054] Figure 7 This is a schematic diagram of the relative humidity curve of Hainan over a year in a specific embodiment of the present invention;

[0055] Figure 8 This is a power attenuation prediction diagram of the photovoltaic module under test in Hainan Province in a specific embodiment of the present invention. Detailed Implementation

[0056] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.

[0057] Unless otherwise expressly stated, throughout the specification and claims, the term "comprising" or its variations such as "including" or "comprises" shall be understood to include the stated elements or components without excluding other elements or other components.

[0058] Existing technologies include methods for testing the reliability of photovoltaic (PV) modules. These methods typically involve configuring PV modules in a desired area, performing long-term power degradation testing on the modules under test, and generating a power degradation curve for each module. It's understandable that as material quality and manufacturing processes improve, product lifespans become longer. While this approach can maximize the accuracy and reliability of data, it is time-consuming, requires separate measurements for different environments, and is costly with limited adaptability and flexibility.

[0059] The inventors of this invention identified the main shortcomings of existing technologies and proposed a new technical approach based on these shortcomings: Based on the material mechanism of photovoltaic modules, key factors affecting module performance were analyzed and identified. Based on these factors, an accelerated degradation model describing power decay was constructed. Using indoor environmental meteorological parameters of the module's location and indoor accelerated aging test data, the accelerated model's parameters were solved, and the model parameters were iteratively optimized to obtain the key parameters of the indoor accelerated model and the degradation trajectory of the photovoltaic module. The outdoor environmental meteorological parameters of the module's location and the solved model parameters were substituted into the model to predict the power decay of the photovoltaic module in an outdoor environment, and further, the module's lifespan in an outdoor environment was obtained. Since the failure mechanism of the photovoltaic module remains unchanged, and reliability under actual usage conditions can be estimated through data processing and data conversion, lifespan testing can be accelerated, shortening test time, improving test efficiency, and reducing test costs. Furthermore, optimizing and updating the model parameters further improves the accuracy of the output model parameters after iteration. In different usage environments, only the stress index of the corresponding scenario needs to be input, eliminating the need for repeated testing and making it more adaptable.

[0060] Please refer to Figure 1 The diagram shows an application scenario of the method provided by the present invention under one embodiment, which specifically includes: a test module 101, a calculation module 102, and a user terminal 103.

[0061] It should be noted that communication connections are established between the test module 101, the computing module 102, and the user terminal 103. The communication network derived from these connections can include various connection types, including but not limited to wired connections, wireless connections, or fiber optic cable connections. Furthermore, this communication network can be a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), or any combination of these three.

[0062] The testing module 101 is used to conduct accelerated aging tests on the photovoltaic module under test under various stress scenarios indoors. It acquires data on the main influencing factors defined by the user terminal 103 under these experimental scenarios, as well as the power degradation data of the photovoltaic module under test. The data is then transmitted to the calculation module 102 to optimize the preset accelerated degradation model.

[0063] The calculation module 102 is equipped with an accelerated degradation model function and a particle swarm optimization algorithm. Based on the test data from the test module 101, the parameters in the accelerated degradation model function can be solved. The parameters are updated by the particle swarm optimization algorithm so that they can accurately fit the power decay curve of the corresponding photovoltaic module based on the input influence factor data, thereby realizing the detection of the reliability of the photovoltaic module.

[0064] User terminal 103 is used to respond to user configuration requests and configure the accelerated degradation model function, parameter threshold range, relative humidity update algorithm, etc., necessary for implementing the photovoltaic module reliability testing method provided by this invention. Simultaneously, user terminal 103 is equipped with computer software programs that match the photovoltaic module reliability testing method provided by this invention. User terminal 103 may be, but is not limited to, portable electronic devices or wearable electronic devices such as desktop computers (PCs), smartphones, handheld computers, tablet computers, personal digital assistants (PDAs), etc., and this embodiment of the invention does not impose any limitations on the above.

[0065] It should also be noted that the photovoltaic module reliability testing method of this embodiment can be applied to the photovoltaic module reliability testing system of this embodiment. This photovoltaic module reliability testing system can be configured on a terminal. The terminal may include, but is not limited to, a PC (Personal Computer), a PDA (Tablet PC), a smartphone, a smart wearable device, etc.

[0066] Please refer to Figure 2 The diagram shown is a flowchart of a photovoltaic module reliability testing method according to an embodiment of the present invention. This photovoltaic module reliability testing method specifically includes the following steps:

[0067] S201: Under preset environmental stress conditions, obtain the corrosion reaction rate of the photovoltaic module under test;

[0068] It should be noted that the structure of a crystalline silicon photovoltaic module mainly includes a cover glass, solar cells, backsheet material, EVA film, aluminum frame, connecting wires, and junction box. Different structures use different materials, and the corresponding material mechanisms differ. The material mechanism refers to the material properties of the module and its aging behavior. For example, in practical applications, the surface of the solar panel oxidizes due to ultraviolet radiation; the glass encapsulation layer develops stress cracks due to temperature changes; and the backsheet material corrodes due to high humidity environments.

[0069] Based on the material aging mechanisms of different photovoltaic (PV) module structures, the main influencing factors of PV module power degradation can be determined. Utilizing these factors to predict the reliability of PV modules can lead to more accurate predictions. In one embodiment of this invention, the main influencing factors affecting PV module power degradation may include, but are not limited to, temperature, humidity, chemical corrosion state, ultraviolet radiation intensity, and various combinations of these factors.

[0070] The aforementioned main influencing factors are also the environmental stresses preset in this invention. While the choice of environmental stress is not limited in this embodiment, it is understood that in actual experiments, temperature fluctuations and extreme high temperatures can trigger thermal cycling, generating mechanical stress on photovoltaic module materials; high temperatures can accelerate the degradation of encapsulation and backsheet materials, leading to decreased module performance; moisture intrusion can cause corrosion of internal metal components and delamination at interlayer interfaces; and the hydrolysis of EVA encapsulation material in high humidity environments can produce acetic acid, further corroding the cell grid lines. Therefore, in this embodiment, temperature and humidity are preferably used as the main influencing factors.

[0071] Similarly, it is understandable that when materials are exposed to corrosive media, chemical or electrochemical reactions occur, leading to gradual material degradation. This degradation typically manifests as a gradual thinning of the material's surface. The corrosion reaction rate is an indicator used to evaluate a material's corrosion resistance, characterizing the average depth of material loss due to corrosion per unit time, usually measured in mm / a (millimeters per year). The aforementioned temperature and humidity, as key influencing factors, are precisely those that corrode photovoltaic modules, reducing their reliability and lifespan. Therefore, the reliability of photovoltaic modules can be quantified by establishing a functional relationship between the degree of corrosion and temperature and humidity.

[0072] In embodiments of the present invention, the detection methods for the corrosion reaction rate of photovoltaic modules may include, but are not limited to, polarization resistance method, AC impedance method, electrochemical noise method, and electrochemical tomography, etc., and the embodiments of the present invention do not limit this.

[0073] It should also be noted that the purpose of conducting experiments under multiple environmental stress conditions and obtaining corrosion reaction rates is to solve for the initial parameter values ​​in the accelerated degradation model. Therefore, the number of experiments conducted based on multiple environmental stress types should be no less than the number of parameters to be determined, to ensure that the initial values ​​of all parameters are available.

[0074] S202: Construct an accelerated degradation model, which is used to output the power degradation curve of photovoltaic modules based on environmental stress conditions;

[0075] It should be noted that accelerated degradation models can quantify the accelerating effect of environmental factors on the aging of photovoltaic modules. Depending on the different influencing factors selected above, the acceleration models that can be used include, but are not limited to, physical acceleration models, empirical acceleration models, and statistical acceleration models. Physical acceleration models, such as the Arrhenius model, are used to describe the effect of temperature changes on chemical reaction rates; empirical acceleration models, such as the inverse power-law model and the Eyring model, are used to describe the degradation law based on empirical data; and statistical acceleration models reveal the relationship between influencing factors and module lifespan through the analysis of large amounts of experimental data.

[0076] In one embodiment of the present invention, following the above-described embodiments, since temperature and humidity are selected as the main influencing factors, the corresponding accelerated degradation model function is established as follows:

[0077]

[0078]

[0079] Where A is the pre-exponential factor constant; RH m The humidity value of the photovoltaic module under test is denoted as ; n is the failure mode constant; E is the value of the photovoltaic module under test. a The activation energy value of the encapsulating film for photovoltaic modules; k b Boltzmann constant; T is the temperature of the photovoltaic module under test; B is the shape parameter; R d P(t) represents the corrosion reaction rate of the photovoltaic module under test; P(t) is a function of the power of the photovoltaic module under test as a function of time.

[0080] It should be noted that failure mode refers to corrosion of photovoltaic modules caused by humid and hot conditions. Different failure mode constants correspond to different predicted power degradation values. Since temperature and humidity primarily cause corrosion of the encapsulating film, the E value can be directly... a Defined as the activation energy value of the encapsulating film for photovoltaic modules. The unknown parameters in the constructed accelerated degradation model can be obtained through three or more sets of damp heat experiments with different stress values, and the above equation R... d Taking the logarithm of both sides of the function expression, we can obtain the relative humidity (RH) in three different experiments during the damp heat experiment. m Temperature T, decay rate R d The magnitude of the unknown parameters A, Ea, and n can be obtained from the above formula.

[0081] It should also be noted that, under normal circumstances, the ambient humidity obtained by the sensor can be directly used as the humidity value of the photovoltaic module for calculation. However, in reality, the actual temperature and humidity values ​​of the photovoltaic module differ somewhat from the data obtained by the instrument. Therefore, in this embodiment of the invention, the method of obtaining the humidity of the photovoltaic module is optimized, and the corrected module humidity calculation formula is as follows:

[0082]

[0083]

[0084] Where T is the temperature value, in degrees Celsius; RH amb T represents the relative humidity in the environment. amb For ambient temperature, T m For component temperature, P sat (T) represents the saturated water vapor pressure at temperature T.

[0085] It should be noted that since the actual water vapor pressure remains constant when the water vapor content is constant, the saturated water vapor pressure also increases as the temperature rises, resulting in a decrease in relative humidity. Therefore, under the premise of constant water vapor content, relative humidity is negatively correlated with temperature. During equipment operation, the temperature of the photovoltaic module will be higher than the ambient temperature. Therefore, directly using the ambient relative humidity as the relative humidity of the photovoltaic module will cause errors. One embodiment of the present invention uses the above formula to calculate the relative humidity of the photovoltaic module. The relative humidity of the photovoltaic module is adjusted based on the ratio of the saturated water vapor pressure at the ambient temperature to the saturated water vapor pressure at the actual temperature of the photovoltaic module, thereby calculating the accurate relative humidity of the photovoltaic module.

[0086] It should also be noted that, in the embodiments of the present invention, the measurement of relative humidity can be captured based on a preset sensing device and / or a weather station; parameters such as component temperature can be measured based on sensors. The embodiments of the present invention do not limit the specific measurement methods of the above-mentioned types of parameters.

[0087] S203: Based on the preset environmental stress conditions and the corresponding corrosion reaction rate, solve for the model parameters of the accelerated degradation model;

[0088] In an exemplary embodiment of the present invention, solving the accelerated degradation model parameters includes: traversing each preset particle, calculating the fitness of each preset particle based on the particle position vector and the accelerated degradation model function, and recording the global optimal fitness value and the optimal fitness value of each particle itself; updating the velocity and position vector of each preset particle based on the global optimal fitness value and the optimal fitness value of each particle itself; calculating the fitness value of each preset particle again based on the updated position vector and updating the global optimal fitness value and the optimal fitness value of each particle; after the iteration is completed, updating the initial parameter values ​​of the accelerated degradation model based on the position vector of the particle corresponding to the global optimal fitness value.

[0089] In this invention, a particle can be abstracted as a point without mass or volume, which can extend to N-dimensional space. A particle possesses position and velocity. Velocity, representing the change in the particle's position, is a vector of the same dimension as the particle's position. In this embodiment, the particle's position is an N-dimensional vector, where each element represents a parameter value to be solved in the accelerated degradation model.

[0090] The fitness of each particle is determined by the objective function. Using the accelerated degradation model function from the previous embodiment, the objective function in this example can be:

[0091] F(θ)=minS(θ)

[0092]

[0093] Where F(θ) is the formula for calculating the fitness of the preset particles; h is the number of experimental samples; y i f(x) represents the power attenuation of the photovoltaic module under test in the i-th experiment. i θ) represents the power degradation of the photovoltaic module under test, as output by the accelerated degradation model, under the same conditions as the i-th experiment; x i This includes the temperature and humidity of the photovoltaic module under test in the i-th experiment, as well as the experimental time.

[0094] It is understandable that the objective function used to calculate particle fitness is, in fact, the difference between the actual experimental value and the model's inferred value. The smaller this difference, the higher the model's inference accuracy. In this embodiment, f(x) i θ) is in fact the functional expression of the inference model, that is:

[0095]

[0096] It should also be noted that, to avoid extreme values ​​in particle position or velocity during iteration, threshold ranges need to be set for each parameter value of the accelerated degradation model and the particle velocity value before starting the iteration. Specifically, if the updated parameter value and / or particle velocity value of the accelerated degradation model is less than the minimum value of the corresponding threshold range, then the parameter value of the accelerated degradation model is set to the minimum value of the corresponding threshold range; if the updated parameter value and / or particle velocity value of the accelerated degradation model is greater than the maximum value of the corresponding threshold range, then the parameter value of the accelerated degradation model is set to the maximum value of the corresponding threshold range.

[0097] Furthermore, typically, each particle initializes its position and velocity vectors randomly, which reduces iteration efficiency and increases iteration time. Therefore, in one embodiment of the present invention, the initial parameter values ​​of the accelerated degradation model can be obtained based on the multiple sets of environmental stress conditions and the corresponding corrosion reaction rates. The initial values ​​are not necessarily optimal solutions, but a rough solution can be obtained with a small amount of computation. The initial values ​​of each parameter can serve as a reference for the particle swarm optimization algorithm. For example, the initial positions of each particle can be defined based on the initial values ​​of each parameter, reducing the number of iterations and improving solution efficiency.

[0098] It is understood that the global optimal fitness value refers to the optimal fitness value found by all particles during the iteration process, while the individual optimal fitness value of each particle refers to the optimal fitness value found by that single particle during the iteration process. All particles follow the particle with the current optimal fitness value, updating their own velocity and position. In each iteration, the optimal particle changes, and other particles follow the new optimal particle to update their own values, thus repeating the iteration process.

[0099] The formulas for updating the velocity and position vectors of the preset particles are as follows:

[0100] v i (t+1)=ω×v i (t)+c1r1(t)[p i,best (t)-θ i (t)]+c2r2(t)[g best (t)-θ i (t)]

[0101] θ i (t+1)=θ i (t)+v i (t+1)

[0102] θ(t) = {A(t), n(t), E} a (t), B(t)

[0103] Where ω is the inertia coefficient, A is the pre-exponential factor constant, n is the failure mode constant, and E is the failure mode constant. a B represents the activation energy of the encapsulating film for photovoltaic modules; B represents the shape parameter; v i (t) represents the velocity value of particle i in round t; r1(t) and r2(t) represent uniformly distributed random numbers in the range [0, 1]; c1 and c2 are preset learning factors; p i,best (t) represents the optimal fitness value of particle i up to the t-th iteration, gbes t θ(t) represents the globally optimal fitness value up to the t-th iteration. i (t) is the position vector of particle i in the t-th round.

[0104] It should be noted that the iteration process can terminate after reaching a preset condition and output the position vector of the particle corresponding to the optimal fitness, that is, output the optimal parameter values ​​of the accelerated degradation model obtained by the iteration. This embodiment of the invention does not impose restrictions on the conditions for terminating the iteration. In one embodiment, the iteration can end when the training rounds reach a preset number of rounds, or when the difference between the global optimal fitness of the current round and the global optimal fitness of the previous round is less than a preset threshold.

[0105] S204: Obtain environmental stress data of the target scenario, input the environmental stress data of the target scenario into the accelerated degradation model, and output the power degradation curve of the photovoltaic module under test in the target scenario.

[0106] Understandably, the iteratively updated accelerated degradation model can now simulate the power degradation of photovoltaic modules under different environments based on varying input conditions. Therefore, by inputting the stress conditions of the desired operating scenario for the photovoltaic module under test, the model can output the power degradation curve of the photovoltaic module over time.

[0107] For example, such as Figure 5 The image shown is a graph illustrating the accelerated degradation model fitted to a photovoltaic module of the present invention, obtained through accelerated testing in the laboratory. At this point, the accelerated degradation model has met the predetermined conditions. Taking Hainan as an example... Figures 6-7 Using annual temperature and humidity data from Hainan, this data is imported into a pre-trained accelerated degradation model to obtain a power degradation prediction map for the photovoltaic module in Hainan. Figure 8 As shown.

[0108] Please refer to Figure 3 As shown, based on the same inventive concept as the aforementioned photovoltaic module reliability testing method, one embodiment of the present invention provides a photovoltaic module reliability testing system 300, including: an acquisition module 301, a construction module 302, a solution module 303, and an application module 304.

[0109] Specifically, the acquisition module 301 is used to acquire the corrosion reaction rate of the photovoltaic module under test under preset environmental stress conditions; the construction module 302 is used to construct an accelerated degradation model, which is used to output the power decay curve of the photovoltaic module based on the environmental stress conditions; the solution module 303 is used to solve the model parameters of the accelerated degradation model based on the multiple sets of environmental stress conditions and the corrosion reaction rate corresponding to the environmental stress conditions; and the application module 304 is used to acquire environmental stress data of the target scene, input the environmental stress data of the target scene into the accelerated degradation model, and output the power decay curve of the photovoltaic module under test under the target scene.

[0110] Please refer to Figure 4As shown, embodiments of the present invention also provide an electronic device 400, which includes at least one processor 401, a memory 402 (e.g., non-volatile memory), a memory 403, and a communication interface 404, wherein the at least one processor 401, the memory 402, the memory 403, and the communication interface 404 are connected together via an internal bus 405. The at least one processor 401 is used to invoke at least one program instruction stored or encoded in the memory 402, so that the at least one processor 401 performs various operations and functions of the photovoltaic module reliability testing method described in the various embodiments of this specification.

[0111] In the embodiments of this specification, electronic device 400 may include, but is not limited to: personal computer, server computer, workstation, desktop computer, laptop computer, notebook computer, mobile electronic device, smartphone, tablet computer, cellular phone, personal digital assistant (PDA), handheld device, messaging device, wearable electronic device, consumer electronic device, etc.

[0112] This invention also provides a computer-readable medium carrying computer-executable instructions. When executed by a processor, these instructions can be used to implement various operations and functions of the photovoltaic module reliability testing method described in the various embodiments of this specification.

[0113] The computer-readable medium in this invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0114] In this invention, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. This propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wireline, optical fiber, RF, etc., or any suitable combination thereof.

[0115] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0116] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, systems, and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0117] The foregoing description of specific exemplary embodiments of the invention is for illustrative and explanatory purposes. These descriptions are not intended to limit the invention to the precise forms disclosed, and it will be apparent that many changes and variations can be made in accordance with the foregoing teachings. The exemplary embodiments were chosen and described in order to explain the specific principles of the invention and its practical application, thereby enabling those skilled in the art to implement and utilize various different exemplary embodiments of the invention, as well as various different choices and variations. The scope of the invention is intended to be defined by the claims and their equivalents.

[0118] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0119] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for testing the reliability of photovoltaic modules, characterized in that, include: Under preset environmental stress conditions, the corrosion reaction rate of the photovoltaic module under test was obtained; An accelerated degradation model is constructed based on the preset environmental stress conditions. The accelerated degradation model is used to output the power degradation curve of the photovoltaic module based on the preset environmental stress conditions. Based on the preset environmental stress conditions and the corresponding corrosion reaction rate, the model parameters of the accelerated degradation model are solved. The environmental stress data of the target scenario is obtained, and the environmental stress data of the target scenario is input into the accelerated degradation model to output the power degradation curve of the photovoltaic module under test in the target scenario.

2. The photovoltaic module reliability testing method according to claim 1, characterized in that, The model function of the accelerated degradation model is: Where A is the pre-exponential factor constant; RH m The humidity value of the photovoltaic module under test is denoted as ; n is the failure mode constant; E is the value of the photovoltaic module under test. a k is the activation energy value of the encapsulating film in a photovoltaic module. b Boltzmann constant; T is the temperature of the photovoltaic module under test; B is the shape parameter; R d P(t) represents the corrosion reaction rate of the photovoltaic module under test; P(t) is a function of the power of the photovoltaic module under test as a function of time.

3. The photovoltaic module reliability testing method according to claim 2, characterized in that, The humidity value of the photovoltaic module under test is: in, In the formula, T is the temperature value, in degrees Celsius; RH amb T represents the relative humidity in the environment. amb For ambient temperature, T m For component temperature, P sat (T) represents the saturated water vapor pressure at temperature T.

4. The photovoltaic module reliability testing method according to claim 2, characterized in that, Solving for the parameters of the accelerated degradation model includes: Based on the preset environmental stress conditions and the corrosion reaction rate corresponding to the environmental stress conditions, the initial parameter values ​​of the accelerated degradation model are obtained. Traverse each preset particle, obtain the fitness of each preset particle based on the particle position vector and the accelerated degradation model function, and record the global optimal fitness value and the optimal fitness value of each particle itself; the position vector is a vector composed of the accelerated degradation model parameter values; Based on the global optimal fitness value and the optimal fitness value of each particle, update the velocity and position vectors of each preset particle; Based on the updated position vector, the fitness values ​​of each preset particle are obtained again and the global optimal fitness value and the optimal fitness value of each particle are updated until a preset iteration round is reached or the difference between the global optimal fitness of the current round and the global optimal fitness of the previous round is less than a preset threshold. The parameter values ​​of the accelerated degradation model are updated based on the position vector of the particle corresponding to the global optimal fitness value.

5. The photovoltaic module reliability testing method according to claim 4, characterized in that, The method further includes: Set corresponding threshold ranges for each parameter value and particle velocity value of the accelerated degradation model; If the updated parameter values ​​and / or particle velocity values ​​of the accelerated degradation model are less than the minimum value of the corresponding threshold interval, then the parameter values ​​of the accelerated degradation model are set to the minimum value of the corresponding threshold interval. If the updated parameter values ​​and / or particle velocity values ​​of the accelerated degradation model are greater than the maximum value of the corresponding threshold interval, then the parameter values ​​of the accelerated degradation model are set to the maximum value of the corresponding threshold interval.

6. The photovoltaic module reliability testing method according to claim 4, characterized in that, The preset particle velocity and position vectors are updated as follows: v i (t+1)=ω×v i (t)+c1r1(t)[p i,best (t)-θ i (t)]+c2r2(t)[g best (t)-θ i (t)] θ i (t+1)=θ i (t)+v i (t+1) Where, θ(t)={A(t),n(t),E a (t),B(t)} In the formula, ω is the inertia coefficient, A is the pre-exponential factor constant, n is the failure mode constant, and E is the failure mode constant. a B represents the activation energy of the encapsulating film for photovoltaic modules; B represents the shape parameter; v i (t) represents the velocity value of particle i in round t; r1(t) and r2(t) represent uniformly distributed random numbers in the range [0, 1]; c1 and c2 are preset learning factors; p i,best (t) represents the optimal fitness value of particle i up to the t-th iteration, g best θ(t) represents the globally optimal fitness value up to the t-th iteration. i (t) is the position vector of particle i in the t-th round.

7. The photovoltaic module reliability testing method according to claim 5, characterized in that, The fitness of each preset particle is obtained as follows: F(θ)=minS(θ), where, In the formula, F(θ) represents the fitness of the preset particles; h represents the number of experimental samples; y i f(x) represents the power attenuation of the photovoltaic module under test in the i-th experiment. i (x, θ) represents the power degradation of the photovoltaic module under test, output by the accelerated degradation model, under the same conditions as the i-th experiment; i This includes the temperature and humidity of the photovoltaic module under test in the i-th experiment, as well as the experimental time.

8. A photovoltaic module reliability testing system, characterized in that, include: The acquisition module is used to acquire the corrosion reaction rate of the photovoltaic module under test under preset environmental stress conditions. A construction module is used to construct an accelerated degradation model, which is used to output the power degradation curve of the photovoltaic module based on environmental stress conditions; The solution module is used to solve the model parameters of the accelerated degradation model based on the multiple sets of environmental stress conditions and the corrosion reaction rates corresponding to the environmental stress conditions. The application module is used to acquire environmental stress data of the target scene, input the environmental stress data of the target scene into the accelerated degradation model, and output the power degradation curve of the photovoltaic module under test in the target scene.

9. A computer device, characterized in that, include: A memory and a processor are interconnected, the memory stores computer instructions, and the processor executes the computer instructions to perform the photovoltaic module reliability testing method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to execute the photovoltaic module reliability testing method according to any one of claims 1-7.

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