CN118395869A - Photovoltaic module life prediction method, storage medium and electronic equipment - Google Patents
Photovoltaic module life prediction method, storage medium and electronic equipment Download PDFInfo
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Abstract
The application provides a life prediction method of a photovoltaic module, a storage medium and electronic equipment, comprising the following steps: determining an influence factor of the photovoltaic module based on a material mechanism of the photovoltaic module; establishing an acceleration model based on the influence factors; calculating the acceleration model based on indoor environmental weather parameters and outdoor environmental weather parameters of the region where the photovoltaic module is located, and obtaining acceleration factors of indoor and outdoor performance attenuation of the photovoltaic module; and determining test parameters of a photovoltaic module reliability evaluation test based on the acceleration factors, and performing the test based on the test parameters to simulate and acquire the attenuation condition of the photovoltaic module. The method combines the indoor accelerated aging performance and the short-term outdoor aging performance of the photovoltaic module, can more accurately predict the long-term attenuation characteristics of the lower photovoltaic module in different climatic regions, and effectively improves the accuracy of life prediction of the photovoltaic module.
Description
Technical Field
The application belongs to the technical field of solar power generation equipment, and particularly relates to the technical field of photovoltaic modules.
Background
Photovoltaic (PV) modules are the core components of photovoltaic power generation systems, and life prediction research of photovoltaic modules is an important field of renewable energy research, with emphasis on understanding and improving the life and efficiency of solar panels. Degradation of photovoltaic modules over time affects their performance, energy yield, and economic value, and life prediction is therefore an important aspect of the overall sustainability and feasibility of solar energy systems.
The field life of a photovoltaic module is affected by a number of factors including the time of day, the amount of solar radiation, the amount of precipitation, wind speed, temperature, relative humidity, PM10 concentration, and SO2/NO2 concentration in the geographical area in which it is located. Because the geographical area environment of the photovoltaic module is complex and various, the influence factors of different geographical areas are different, and the field life of the photovoltaic module shows obvious area difference. The current evaluation of the field life of the photovoltaic module is mainly based on the results obtained by laboratory acceleration tests or the long-term monitoring results of the application field, and the evaluation results and the actual results have a considerable error due to limited influence factors.
Disclosure of Invention
The application provides a life prediction method of a photovoltaic module, a storage medium and electronic equipment, which are used for improving the accuracy of life prediction of the photovoltaic module.
In a first aspect, an embodiment of the present application provides a method for predicting a lifetime of a photovoltaic module, including: determining an influence factor of the photovoltaic module based on a material mechanism of the photovoltaic module; establishing an acceleration model based on the influence factors; calculating the acceleration model based on indoor environmental weather parameters and outdoor environmental weather parameters of the region where the photovoltaic module is located, and obtaining acceleration factors of indoor and outdoor performance attenuation of the photovoltaic module; and determining test parameters of a photovoltaic module reliability evaluation test based on the acceleration factors, and performing the test based on the test parameters to simulate and acquire the attenuation condition of the photovoltaic module.
In one implementation manner of the first aspect, the influence factor of the photovoltaic module includes: temperature, humidity, ultraviolet radiation, mechanical damage conditions, and chemical corrosion conditions.
In one implementation manner of the first aspect, the acceleration model includes one or more of an Arrhenius model, an inverse power law model, a Peck model, and a Ai Lin model.
In an implementation manner of the first aspect, the obtaining an acceleration factor of indoor and outdoor performance attenuation of the photovoltaic module includes: calculating the acceleration model based on indoor environment meteorological parameters to obtain the indoor attenuation rate of the photovoltaic module in the indoor environment; calculating the acceleration model based on outdoor environment meteorological parameters to obtain the outdoor attenuation rate of the photovoltaic module in the outdoor environment; and acquiring an acceleration factor of the indoor and outdoor performance attenuation of the photovoltaic module based on the ratio of the indoor attenuation rate to the outdoor attenuation rate.
In an implementation manner of the first aspect, the determining, based on the acceleration factor, a test parameter of a photovoltaic module reliability evaluation test includes: and calculating the test time and the test cycle times of the simulated outdoor photovoltaic module in a preset time period based on the acceleration factor.
In an implementation manner of the first aspect, the performing an experiment based on the test parameter, and simulating to obtain the attenuation condition of the photovoltaic module includes: and inputting the test parameters into a life prediction model, and acquiring the attenuation condition of the photovoltaic module through the life prediction model.
In an implementation manner of the first aspect, the method further includes: training the life prediction model, comprising: acquiring first characteristic data based on one or more data of an environmental variable, a component material, a manufacturing process and/or a component structure of the photovoltaic component; acquiring second characteristic data based on the IV characteristic and the impedance characteristic of the photovoltaic module; constructing a training data set based on the first feature data and the second feature data; and training a machine learning model through the training data set to form the life prediction model.
In one implementation manner of the first aspect, the attenuation condition of the photovoltaic module includes attenuation trend of current, voltage and impedance, and distribution condition of failure probability.
In a second aspect, an embodiment of the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the photovoltaic module lifetime prediction method of any one of the first aspects of the present application.
In a third aspect, an embodiment of the present application provides an electronic device, including: a memory storing a computer program; and the processor is in communication connection with the memory and executes the photovoltaic module service life prediction method according to any one of the first aspect of the application when the computer program is called.
According to the photovoltaic module life prediction method provided by the embodiment of the application, the indoor accelerated aging and the short-term outdoor aging performance of the photovoltaic module are combined, so that the long-term attenuation characteristics of the lower photovoltaic module in different climatic regions can be predicted more accurately, and the accuracy of the life prediction of the photovoltaic module is effectively improved.
Drawings
Fig. 1 is a schematic diagram of a life prediction method of a photovoltaic module according to an embodiment of the present application.
Fig. 2 is a flowchart of a method for predicting lifetime of a photovoltaic module according to an embodiment of the application.
FIG. 3 is a schematic diagram showing an acceleration factor obtaining process in a method for predicting lifetime of a photovoltaic module according to an embodiment of the application;
FIG. 4 is a schematic diagram showing a training process of a life prediction model in a life prediction method of a photovoltaic module according to an embodiment of the present application;
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the application.
Description of element reference numerals
100. Electronic equipment
101. Memory device
102. Processor and method for controlling the same
103. Display device
S100 to S400 steps
S101-S104 steps
Detailed Description
Other advantages and effects of the present application will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present application with reference to specific examples. The application may be practiced or carried out in other embodiments and details within the scope and range of equivalents of the specific embodiments and ranges of equivalents, and modifications and variations may be made in the practice of the application without departing from the spirit or scope of the application. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
The embodiment of the application provides a life prediction method of a photovoltaic module, a storage medium and electronic equipment, which are used for improving the accuracy of life prediction of the photovoltaic module.
The following describes the technical solution in the embodiment of the present application in detail with reference to fig. 1 to 5 in the embodiment of the present application. The photovoltaic module life prediction method of the embodiment can be understood and implemented by a person skilled in the art without creative labor.
In this embodiment, fig. 1 is a schematic diagram of a life prediction method of a photovoltaic module according to an embodiment of the present application. As shown in figure 1, the application analyzes the performance change trend curves of all parts on the assembly from the aspects of assembly performance and material physicochemical properties by analyzing the performance change mechanisms of all parts of the photovoltaic assembly under three single stresses of irradiation, moisture and high temperature respectively and performing damp heat, thermal cycle, damp freeze and ultraviolet tests. Based on the attenuation mechanism of each part of the component under the environmental test, the physical model is selected and the relevant constant is determined by the design test in combination with the physical and chemical properties of the polymer on the component and the environmental conditions of the actual outdoor operation of the component, and the parameters such as the temperature, the actual humidity and the like on the component are optimized in consideration of the difference between the microclimate of the component and the climate of the environment, so that the modified and optimized physical acceleration model of the photovoltaic component is finally obtained. And further, the Chinese climate characteristics are analyzed based on the meteorological data collected and arranged in the national meteorological bureau in one year, and the acceleration rate effects of different typical climates and cities are compared. And the indoor accelerated aging performance and the short-term outdoor aging performance of the assembly are combined, the long-term attenuation characteristics of the lower photovoltaic assembly in different climatic regions are predicted, and the power attenuation threshold (usually the attenuation rate is 20%) when the service life of the photovoltaic assembly reaches is defined according to market demands, so that the service life of the assembly is predicted.
Fig. 2 is a flowchart of a method for predicting lifetime of a photovoltaic module according to an embodiment of the application. Specifically, as shown in fig. 2, the method for predicting the lifetime of a photovoltaic module according to the embodiment of the present application includes the following steps S100 to S400.
Step S100, determining an influence factor of the photovoltaic module based on a material mechanism of the photovoltaic module;
Step S200, establishing an acceleration model based on the influence factors;
step S300, calculating the acceleration model based on indoor environment weather parameters and outdoor environment weather parameters of the region where the photovoltaic module is located, and obtaining acceleration factors of indoor and outdoor performance attenuation of the photovoltaic module;
and step S400, determining test parameters of a photovoltaic module reliability evaluation test based on the acceleration factors, and performing the test based on the test parameters to simulate and acquire the attenuation condition of the photovoltaic module.
The above steps S100 to S3400 in the photovoltaic module lifetime prediction method of the present embodiment are described in detail below.
And step S100, determining an influence factor of the photovoltaic module based on a material mechanism of the photovoltaic module.
The crystalline silicon photovoltaic module structure mainly comprises: cover glass, battery pieces, backboard materials, EVA adhesive films, metal aluminum frames, connecting wires, junction boxes and the like. The material mechanism of the photovoltaic module is the material characteristic mechanism of cover glass, a battery piece, a back plate material, an EVA adhesive film, a metal aluminum frame, a connecting wire, a junction box and the like.
In practical applications, for example, the surface of a solar panel may be oxidized due to ultraviolet irradiation, a glass sealing layer may be cracked by stress due to temperature change, and a back sheet may be corroded due to humidity, etc. The corresponding impact factors for each component are determined based on each material mechanism. And the aging mechanism based on the materials is helpful for predicting the service life of the component, and corresponding preventive and repair measures are taken to prolong the service life of the component.
In one implementation manner of this embodiment, the influence factors of the photovoltaic module include: temperature, humidity, ultraviolet radiation, mechanical damage conditions, and chemical corrosion conditions. Namely, external environmental factors affecting the life of the photovoltaic module include:
1) Temperature: temperature fluctuations and extreme temperatures can cause thermal cycling, thereby causing mechanical stresses to the materials within the photovoltaic module. The high temperature accelerates degradation of the encapsulant and backsheet materials, resulting in reduced performance:
2) Humidity: the ingress of moisture can lead to corrosion of the metal parts within the assembly and delamination of the layer interfaces;
3) Ultraviolet (UV) radiation: ultraviolet radiation can cause the polymers and other materials used in the photovoltaic module to decompose, affecting its optical properties and mechanical integrity, thereby reducing the module's ability to efficiently convert sunlight into electrical energy. The ultraviolet radiation has the most obvious effect on the aging of the photovoltaic module, and causes the EVA of the bonding material to yellow, so that the light transmittance is reduced. The ultraviolet energy in the light radiation is higher than the breaking energy of EVA chain to degrade EVA main chain;
4) Mechanical damage, chemical corrosion, etc.: the wind, hail and other impacts in the outdoor environment and the corrosion of various ions in the environment can accelerate the aging of the photovoltaic module, but the photovoltaic module has the accidents and the burst performance and cannot be well predicted.
And step S200, establishing an acceleration model based on the influence factors.
In one implementation of this embodiment, the acceleration model includes an Arrhenius model, an inverse power law model, a Peck model, and/or a Ai Lin model.
In this embodiment, acceleration models are built for different influence factors, and mainly include a physical acceleration model, an empirical acceleration model, a statistical acceleration model, and the like. Corresponding indoor aging environment parameters are established by the acceleration model. The physical acceleration model includes an Arrhenius model, an empirical acceleration model mainly includes an inverse power law model and Eyring (Ai Lin) model. The main acceleration model is exemplified below.
1) Arrhenius model
For describing a degradation mechanism of an insulating material, a semiconductor, a battery, a printed board, and the like caused by temperature. The Arrhenius (Arrhenius) model expression is as follows:
Wherein M is the degradation amount of a certain characteristic value of the product; the degradation rate is a linear function of time t; k is a Boltzmann constant, and the numerical value is 8.617385 X10 -5; t is Kelvin temperature in K; a 0 is a constant; t is the reaction time; Δe is failure mechanism activation energy in eV and is constant for the same failure mode for the same class of product.
2) Peck model
Suitable for non-sealing electronic or electromechanical devices with failure caused by temperature and humidity.
The decay rate (R (D,Peck)) based on the Peck model is calculated as follows:
wherein A is a pre-finger factor and is a constant; rh—relative humidity (%); n is a constant that depends on the failure mode; e a -the activation energy of the material;
The unknown constant in the Peck model can be obtained through three groups of damp-heat experiments with different stress values, and the logarithm of the two ends of the (2) model can be obtained
In the damp-heat experiment, the magnitudes of relative humidity rh, temperature T and attenuation rate R (D,Peck) in three different experiments are tested, and unknown parameters A, E a and n are obtained through fitting by the above formula, wherein the unknown parameters are 13.042,0.47 and 4.3 respectively.
Taking into consideration that the actual temperature and humidity values of the photovoltaic module have certain difference with the data acquired by the instrument, optimizing the Peck model, wherein the corrected model is
3) Generalized Eyring model
One or more non-thermal acceleration variables (e.g., humidity or voltage) are allowed. The model can be written as
And step S300, calculating the acceleration model based on indoor environment weather parameters and outdoor environment weather parameters of the region where the photovoltaic module is located, and obtaining acceleration factors of the indoor and outdoor performance attenuation of the photovoltaic module.
FIG. 3 is a schematic diagram showing an acceleration factor obtaining process in a method for predicting lifetime of a photovoltaic module according to an embodiment of the application; as shown in fig. 3, in an implementation manner of the present embodiment, the obtaining the acceleration factor of the indoor and outdoor performance attenuation of the photovoltaic module includes:
calculating the acceleration model based on indoor environment weather parameters to obtain the indoor attenuation rate of the photovoltaic module in the indoor environment, and calculating the acceleration model based on outdoor environment weather parameters to obtain the outdoor attenuation rate of the photovoltaic module in the outdoor environment; and then acquiring an acceleration factor of the indoor and outdoor performance attenuation of the photovoltaic module based on the ratio of the indoor attenuation rate to the outdoor attenuation rate.
Different parameters which are respectively brought into the indoor and the outdoor in the decay rate formula calculated by the Peck model, wherein the ratio of the parameters is the acceleration rate of the performance decay of the indoor and the outdoor components, and the expression is that
When parameters such as activation energy are known, it can be written as
Similarly, the acceleration rate or the acceleration factor calculated by using the generalized Eyring model is
And step S400, determining test parameters of a photovoltaic module reliability evaluation test based on the acceleration factors, and performing the test based on the test parameters to simulate and acquire the attenuation condition of the photovoltaic module.
In one implementation manner of this embodiment, the determining, based on the acceleration factor, the test parameter of the photovoltaic module reliability evaluation test includes: and calculating the test time and the test cycle times of the simulated outdoor photovoltaic module in a preset time period based on the acceleration factor.
In an implementation manner of this embodiment, the performing an experiment based on the test parameter, and simulating to obtain the attenuation condition of the photovoltaic module includes: and inputting the test parameters into a life prediction model, and acquiring the attenuation condition of the photovoltaic module through the life prediction model.
And combining the physical model with the machine learning model to improve the accuracy of life prediction of the photovoltaic module. First, acceleration factors such as light, humidity, heat and the like are introduced from the aspects of components and material mechanisms, and the influence of the factors on the service life of the components is well defined in a physical model. And secondly, training by using a large amount of data through a machine learning model, extracting characteristic parameters and the like, and constructing a model capable of accurately predicting the service life of the component.
Based on the life prediction model, the embodiment can improve an indoor accelerated aging test method, obtain corresponding attenuation rate by inputting indoor experimental environment parameters into the model, and compare the attenuation rate obtained by outdoor actual operation environment parameters to reveal indoor and outdoor aging differences or multiplying power. In addition, the influence of different areas on the attenuation of the photovoltaic module is compared, so that the model is further verified, and the different influences of environmental factors such as irradiation, humidity and temperature in different areas are considered.
The aging attenuation characteristics of the photovoltaic module are extracted by means of impedance and IV, and experimental verification schemes of indoor and outdoor attenuation models can be better compared. In general, this study provides a new and effective method for photovoltaic module life prediction. The method is not only helpful for improving the indoor accelerated aging test method, but also is helpful for better understanding the influence of environmental factors in different areas on the attenuation of the photovoltaic module.
In one implementation of this embodiment, the method further includes: and training the life prediction model. FIG. 4 is a schematic diagram showing a training process of a life prediction model in a life prediction method of a photovoltaic module according to an embodiment of the present application; as shown in fig. 4, training the life prediction model includes the following steps S101 to S104.
Step S101, acquiring first characteristic data based on one or more data of environmental variables, component materials, manufacturing processes and/or component structures of the photovoltaic component;
Step S102, acquiring second characteristic data based on IV characteristics and impedance characteristics of the photovoltaic module;
Step S103, constructing a training data set based on the first characteristic data and the second characteristic data;
and step S104, training a machine learning model through the training data set to form the life prediction model.
In one implementation of this embodiment, the attenuation conditions of the photovoltaic module include, but are not limited to, attenuation trends of current, voltage, impedance, and distribution conditions of failure probabilities.
The use of Machine Learning (ML) in photovoltaic module lifetime prediction provides an innovative approach to analyze and predict the degradation and performance of solar panels over time. In this embodiment, machine learning overview in photovoltaic module life prediction machine learning models are trained on data sets that include variables such as temperature, humidity, ultraviolet (UV) radiation level, and other environmental factors, as well as the corresponding degradation conditions observed in photovoltaic modules. These models may discover complex relationships and patterns that may not be apparent through traditional statistical methods or direct observation: this method involves training a model on the labeled dataset, mapping the input features (environmental conditions) to the output (degradation level or performance index).
In order to build a training data set for training machine learning, the present embodiment first processes the training data set using an information entropy method to extract effective features including component environment variables, component materials, manufacturing processes, component structures, and the like. The selection of these features is based on their potential impact and importance on the performance of the photovoltaic module. And secondly, obtaining more characteristic information from aspects of IV characteristics, impedance characteristics and the like of the component, so as to be used for training a model. In this embodiment, the machine learning model is a BP neural network model or an ensemble learning model, and these models can effectively capture transient characteristics of component output and make corresponding predictions.
Thereafter, the present embodiment trains the model using the already established training data set. By inputting a large amount of data and making appropriate model adjustments, the decay-over-time characteristics of the component can be more accurately predicted. The predicted results include the decay trend of the current, voltage, impedance of the component, and the distribution of failure probabilities. The information provides understanding and prediction of the long-term evolution of the performance of the photovoltaic module, and lays a foundation for building a correlation system of photovoltaic materials, devices, modules and machine learning training data sets.
The power of the photovoltaic module follows a normal distribution, and the related probability density function can be expressed as
Where μ is the mean power of the component and σ is the standard deviation. Assuming that the component performance decay decreases linearly with increasing time, i.e., the normal distribution average varies with time, there are
μ(t)＝P0-At
Assuming that the standard deviation increases linearly with time, there are
σ(t)＝σ0+Bt
A is the annual decrease in power, B is the annual increase in standard deviation, and the distribution of component performance decay at different times can be deduced by substituting the above formula (9)
And (3) establishing an aging acceleration rate model of the photovoltaic module according to the ratio of the power P (t) to the initial power P 0 at different using times, and extracting the degradation rate and the service life t f of the module from the model. The main models are the linear model, the exponential model, the Pan model and the Kaaya model, as shown in table 1.
TABLE 1 aging acceleration rate model
Notably, the parameter k in the Kaaya model is jointly affected by the temperature Tmod, humidity RH and UV, and is illustratively calculated specifically as follows:
kT＝AN·(1+kH)(1+kP)(1+kTm)-1
the life expectancy of photovoltaic modules is greatly affected by the climate and environmental conditions of the installation area. Differences in temperature, humidity, solar irradiance levels, and other environmental pressure factors can lead to regional differences in the degradation rate of the photovoltaic system, which in turn can affect its life expectancy.
In areas with higher average temperature or larger temperature fluctuation, larger stress can be caused to the photovoltaic module due to thermal expansion and contraction. Such stresses can lead to microcracking or delamination of the solar cell, accelerating aging; areas with high humidity or frequent precipitation can increase the risk of the photovoltaic module getting wet, leading to corrosion of the metal module and electrical insulation failure. Coastal areas may also expose photovoltaic modules to salt spray, further exacerbating corrosion problems; high levels of solar radiation have a positive effect on energy production, but also accelerate material aging, especially components susceptible to uv-induced degradation. Areas near the equator are typically exposed to more intense sunlight, which affects the useful life of the solar panel.
Because the climatic conditions of different cities are different, the test scheme for evaluating the reliability of the photovoltaic module is suitable for local conditions, and the scheme for evaluating the reliability of the photovoltaic module based on a certain region is as follows:
the first step: calculating an acceleration factor AF according to the climate data of the selected city and the set test parameters;
and a second step of: then, the test time of simulating the outdoor photovoltaic module for one year is as follows:
The number of test cycles is the test time divided by the time of operation in one cycle:
and a third step of: and (3) performing a test according to the calculation result to obtain the attenuation condition of the selected city for one year in outdoor operation.
The following describes a process for predicting the attenuation of a photovoltaic module using the climate conditions of the Shanghai region as an example.
The latitude 31 DEG, longitude 121 DEG, altitude 5.5m, the annual average temperature of the Shanghai region from section 5.4 is 17.2 ℃, the average humidity is 72.8%, the average wind speed is 2.4m/s, the total solar radiation is 1270.38kWh/m 2, the sunshine duration is 4.07h/d, the solar radiation intensity is 854.9W/m 2, the surface temperature of the component is 310.2K, the actual humidity of the component is 90.45%, and the calculated acceleration factor AF is 12.6, then the test time t Total (S) is:
The number of cycles in the accelerated ageing oven N is then:
The component decay rate for one year of operation outdoors is equivalent to the component decay rate of about 29 days in an accelerated aging environment box, and in order to verify this conclusion, the following test was designed to verify the feasibility of the theory.
The group a device measured the average of the electrical performance parameters before and after exposure to obtain the data shown in table 2 below:
TABLE 2 Electrical performance parameters of component A before and after one year of outdoor exposure
The attenuation rate R D of the component after exposure for one year is 1.02 percent;
The other group of components B is subjected to an accelerated aging test for 29 days according to the parameter setting, in the test process, a thermocouple is attached to the back surface of the component B, the surface temperature of the component is monitored in real time, the relative humidity of the component is detected in real time by a hygrometer, and the test scheme is as follows:
The average values of the electrical performance parameters before and after the test of the group B four components are shown in the following table 3:
TABLE 3 Electrical performance parameters of component B before and after accelerated aging test
I.e., the decay rate R D of the assembly after the test was 1.15% over a common decay interval of one year.
Comparing the two groups of test results with the outdoor component electrical performance data of the roof of the Shanghai building, the component environment test can be carried out in the accelerated aging environment box for 29 days to obtain better consistency, and the time for examining the reliability of the components is greatly shortened. The feasibility of the optimized accelerated aging model and the formulated test scheme and the implementation effect of the method of the embodiment are basically verified.
The protection scope of the photovoltaic module life prediction method according to the embodiment of the application is not limited to the execution sequence of the steps listed in the embodiment, and all the schemes of step increase and decrease and step replacement in the prior art according to the principles of the application are included in the protection scope of the application.
The embodiment of the application also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the photovoltaic module lifetime prediction method provided by any embodiment of the application.
Any combination of one or more storage media may be employed in embodiments of the present application. The storage medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a RAM, a ROM, an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The embodiment of the application also provides electronic equipment. Fig. 5 is a schematic structural diagram of an electronic device 100 according to an embodiment of the application. In some embodiments, the electronic device may be a mobile phone, a tablet computer, a wearable device, an in-vehicle device, an Augmented Reality (AR)/Virtual Reality (VR) device, a notebook computer, an Ultra-Mobile Personal Computer (UMPC), a netbook, a Personal digital assistant (Personal DIGITAL ASSISTANT, PDA), or the like. In addition, the photovoltaic module life prediction method provided by the application can be applied to a training data set, a server and a service response system according to terminal artificial intelligence. The embodiment of the application does not limit the specific application scene of the life prediction method of the photovoltaic module.
As shown in fig. 5, an electronic device 100 provided in an embodiment of the present application includes a memory 101 and a processor 102.
The memory 101 is for storing a computer program; preferably, the memory 101 includes: various media capable of storing program codes, such as ROM, RAM, magnetic disk, U-disk, memory card, or optical disk.
In particular, memory 101 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) and/or cache memory. Electronic device 100 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. Memory 101 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of embodiments of the application.
The processor 102 is connected to the memory 101 and is configured to execute a computer program stored in the memory 101, so that the electronic device 100 executes the photovoltaic module lifetime prediction method provided in any embodiment of the present application.
Alternatively, the processor 102 may be a general-purpose processor, including a central processor (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but may also be a digital signal Processor (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field programmable gate array (Field Programmable GATE ARRAY, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components.
Optionally, the electronic device 100 in this embodiment may further include a display 103. The display 103 is communicatively coupled to the memory 101 and the processor 102 for displaying a GUI interactive interface associated with the photovoltaic module life prediction method.
In conclusion, the method combines the indoor accelerated aging performance and the short-term outdoor aging performance of the photovoltaic module, can more accurately predict the long-term attenuation characteristics of the lower photovoltaic module in different climatic regions, and effectively improves the accuracy of life prediction of the photovoltaic module. Therefore, the application effectively overcomes various defects in the prior art and has high industrial utilization value.
The above embodiments are merely illustrative of the principles of the present application and its effectiveness, and are not intended to limit the application. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the application. Accordingly, it is intended that all equivalent modifications and variations of the application be covered by the claims, which are within the ordinary skill of the art, be within the spirit and scope of the present disclosure.
Claims (10)
1. The life prediction method of the photovoltaic module is characterized by comprising the following steps of:
determining an influence factor of the photovoltaic module based on a material mechanism of the photovoltaic module;
Establishing an acceleration model based on the influence factors;
calculating the acceleration model based on indoor environmental weather parameters and outdoor environmental weather parameters of the region where the photovoltaic module is located, and obtaining acceleration factors of indoor and outdoor performance attenuation of the photovoltaic module;
and determining test parameters of a photovoltaic module reliability evaluation test based on the acceleration factors, and performing the test based on the test parameters to simulate and acquire the attenuation condition of the photovoltaic module.
2. The method for predicting the life of a photovoltaic module according to claim 1, wherein the influencing factors of the photovoltaic module include: temperature, humidity, ultraviolet radiation, mechanical damage conditions, and chemical corrosion conditions.
3. The method of claim 1, wherein the acceleration model comprises one or more of an alennius model, an inverse power law model, a Peck model, and a Ai Lin model.
4. The method for predicting the lifetime of a photovoltaic module according to claim 1 or 3, wherein the step of obtaining an acceleration factor for the indoor and outdoor performance degradation of the photovoltaic module comprises:
Calculating the acceleration model based on indoor environment meteorological parameters to obtain the indoor attenuation rate of the photovoltaic module in the indoor environment;
calculating the acceleration model based on outdoor environment meteorological parameters to obtain the outdoor attenuation rate of the photovoltaic module in the outdoor environment;
And acquiring an acceleration factor of the indoor and outdoor performance attenuation of the photovoltaic module based on the ratio of the indoor attenuation rate to the outdoor attenuation rate.
5. The method according to claim 1, wherein the determining test parameters of the photovoltaic module reliability evaluation test based on the acceleration factor comprises:
and calculating the test time and the test cycle times of the simulated outdoor photovoltaic module in a preset time period based on the acceleration factor.
6. The method for predicting the lifetime of a photovoltaic module according to claim 1, wherein the performing an experiment based on the test parameters, and simulating to obtain the attenuation condition of the photovoltaic module comprises:
And inputting the test parameters into a life prediction model, and acquiring the attenuation condition of the photovoltaic module through the life prediction model.
7. The method of claim 6, further comprising: training the life prediction model, comprising:
acquiring first characteristic data based on one or more data of an environmental variable, a component material, a manufacturing process and/or a component structure of the photovoltaic component;
acquiring second characteristic data based on the IV characteristic and the impedance characteristic of the photovoltaic module;
Constructing a training data set based on the first feature data and the second feature data;
and training a machine learning model through the training data set to form the life prediction model.
8. The method for predicting the life of a photovoltaic module according to claim 1, 6 or 7, wherein the attenuation conditions of the photovoltaic module include attenuation trends of current, voltage and impedance, and distribution conditions of failure probability.
9. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the photovoltaic module lifetime prediction method of any one of claims 1 to 8.
10. An electronic device, the electronic device comprising:
A memory storing a computer program;
A processor, in communication with the memory, for executing the photovoltaic module life prediction method of any one of claims 1 to 8 when the computer program is invoked.
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