A method and related apparatus for photovoltaic array lifetime prediction

Abstract

The application provides a photovoltaic array life prediction method and related device, and belongs to the technical field of new energy, and steps include: obtaining historical electrical and meteorological data of a photovoltaic array; inputting the historical electrical and meteorological data of the photovoltaic array into a photovoltaic array life prediction model corresponding to a photovoltaic power station to obtain failure time and residual life of the photovoltaic array; the photovoltaic array life prediction model includes a historical degradation trend analysis module, a fitting model screening module and a residual life prediction module, and the method of the application improves the accuracy and reliability of photovoltaic array life prediction, and provides a scientific basis for long-term operation and maintenance of a photovoltaic power station.

Description

Technical Field

[0001] This invention belongs to the field of new energy technology, specifically to a method and related device for predicting the lifetime of photovoltaic arrays in a photovoltaic power station. Background Technology

[0002] With increasing emphasis on renewable energy, photovoltaic (PV) power plants, as an important clean energy generation method, have seen their operational efficiency and lifespan prediction become a key focus of the industry. As the core component of a PV power plant, the performance degradation of the PV array directly impacts the overall power generation capacity and operation and maintenance costs. Therefore, accurately predicting the lifespan of the PV array is crucial for the long-term operation and maintenance of the power plant.

[0003] Currently, photovoltaic (PV) array lifetime prediction methods largely rely on physics-based degradation models and data-driven artificial intelligence (AI) algorithms. However, both methods face significant challenges in practical applications. While physics-based degradation models can theoretically describe the degradation process of PV arrays relatively accurately, calibrating the model parameters is an extremely complex and difficult task. Although data-driven AI algorithms can leverage the advantages of big data for prediction, their accuracy is often affected by factors such as data quality and algorithm selection. Furthermore, PV arrays are also affected by various factors during actual operation, such as environment, climate, and maintenance, all of which directly or indirectly impact their lifetime. Therefore, developing a comprehensive and efficient method for predicting PV array lifetime that considers all these factors has become a hot topic and a difficult challenge in the current PV industry research. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a method and related apparatus for predicting the lifetime of photovoltaic arrays, which improves the accuracy and reliability of photovoltaic array lifetime prediction and provides a scientific basis for the long-term operation and maintenance of photovoltaic power plants.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for predicting the lifetime of a photovoltaic array, the specific steps of which are as follows: Acquire historical electrical and meteorological data for the photovoltaic array; By inputting historical electrical and meteorological data of the photovoltaic array into the photovoltaic array lifetime prediction model of the corresponding photovoltaic power station, the failure time and remaining lifetime of the photovoltaic array can be obtained. The photovoltaic array lifetime prediction model includes a historical degradation trend analysis module, a fitting model screening module, and a remaining lifetime prediction module. The historical degradation trend analysis module obtains the degradation rate using historical electrical and meteorological data of the photovoltaic array; the fitting model screening module obtains the degradation rate and performs fitting, construction, and screening of the photovoltaic array lifetime prediction model; the remaining lifetime prediction module predicts the failure time and remaining lifetime of the photovoltaic array based on the degradation rate setpoint and the degradation rate obtained by the historical degradation trend analysis module.

[0006] Furthermore, the historical degradation trend analysis module preprocesses the historical electrical and meteorological data of the photovoltaic array to obtain the historical degradation trend, and uses the historical degradation trend to calculate the efficiency of the photovoltaic array system to obtain the degradation rate.

[0007] Furthermore, the degradation rate is obtained by comparing the estimation results of the photovoltaic array model with the historical electrical and meteorological data of the photovoltaic array monitored by the photovoltaic equipment.

[0008] Furthermore, the photovoltaic array model uses a single diode model to describe the nonlinear IV characteristics of the photovoltaic array, and the Stephenson iteration method is used to obtain the theoretical IV curves of the photovoltaic array under different environmental conditions. The theoretical IV curves of the photovoltaic array under different environmental conditions are superimposed to obtain the IV curve of the entire photovoltaic array.

[0009] Furthermore, the fitting model screening module uses data-driven artificial intelligence algorithms to correct and update the key model parameters in the existing physics-based degradation model, and uses the degradation rate to fit and construct a photovoltaic array lifetime prediction model to obtain a personalized photovoltaic array lifetime prediction model for a specific photovoltaic power station.

[0010] Furthermore, multiple photovoltaic array lifetime prediction models were constructed using the degradation rate. The optimal photovoltaic array lifetime prediction model was selected based on the model accuracy evaluation index and used as a personalized photovoltaic array lifetime prediction model to predict the failure time and remaining lifetime of the photovoltaic arrays in the corresponding photovoltaic power station.

[0011] The present invention also provides a photovoltaic array lifetime prediction system, comprising: The data processing module is used to acquire historical electrical and meteorological data of the photovoltaic array; The lifespan prediction module is used to input the historical electrical and meteorological data of the photovoltaic array into the photovoltaic array lifespan prediction model of the corresponding photovoltaic power station to obtain the failure time and remaining lifespan of the photovoltaic array. The photovoltaic array lifetime prediction model includes a historical degradation trend analysis module, a fitting model screening module, and a remaining lifetime prediction module. The historical degradation trend analysis module preprocesses the historical electrical and meteorological data of the photovoltaic array to obtain the degradation rate. The fitting model screening module obtains the degradation rate and fits and constructs the photovoltaic array lifetime prediction model. The remaining lifetime prediction module predicts the failure time and remaining lifetime of the photovoltaic array based on the degradation rate setpoint and the degradation rate obtained by the historical degradation trend analysis module.

[0012] The present invention also provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the photovoltaic array lifetime prediction method described above.

[0013] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the photovoltaic array lifetime prediction method described above.

[0014] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described photovoltaic array lifetime prediction method.

[0015] Compared with the prior art, the present invention has at least the following beneficial effects: This invention provides a method for predicting the lifespan of photovoltaic (PV) arrays. By comprehensively considering historical electrical and meteorological data of the PV array, a PV array lifespan prediction model can more accurately predict the lifespan of the PV array. Compared to traditional single-factor prediction methods, this comprehensive analysis approach can more fully reflect the actual operating status of the PV array, thereby improving the accuracy and reliability of the prediction. Accurate PV array lifespan prediction can provide important decision support for the long-term operation and maintenance of PV power plants. Power plant operators can rationally arrange maintenance plans based on the prediction results, promptly replace aging PV modules, and ensure the stable operation and efficient power generation of the power plant. This helps reduce operation and maintenance costs and improve the economic benefits of the power plant.

[0016] The photovoltaic array lifetime prediction model of this invention, through a historical degradation trend analysis module, can monitor the performance degradation trend of the photovoltaic array in real time. Once signs of performance decline are detected, intervention measures can be taken immediately to prevent the problem from worsening. Simultaneously, through the remaining lifetime prediction module, the failure time and remaining lifetime of the photovoltaic array can be predicted, providing a time reference for power plant maintenance planning.

[0017] The photovoltaic array lifetime prediction model of this invention employs a data-driven artificial intelligence algorithm to correct and update key model parameters in existing physics-based degradation models, enabling it to obtain models applicable to different types and regions of photovoltaic arrays. This is thanks to its flexible photovoltaic array model and artificial intelligence algorithm, which allows for personalized prediction and analysis based on specific photovoltaic array types and operating environments. This makes this method widely applicable in the field of photovoltaic power plant lifetime prediction. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the photovoltaic array lifetime prediction model process; Figure 2 The results are from the preprocessing of the original data and the fitting of the system degradation rate. Figure 3 Comparison of different types of performance degradation models; Figure 4 This is the prediction result for the performance degradation trajectory. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0021] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0022] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0023] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0024] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0025] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0026] This invention provides a method for predicting the lifetime of a photovoltaic array, the specific steps of which are as follows: (1) Establishing a photovoltaic array model The realization of photovoltaic power plant equipment life prediction depends on the accurate establishment of corresponding mathematical models, among which the photovoltaic array modeling steps mainly include: 1) Based on the theoretical foundation and analysis of the electrical physics of photovoltaic arrays, a single diode model is used to describe the nonlinear IV characteristics of photovoltaic arrays: To describe the nonlinear IV characteristics of a photovoltaic array, it is necessary to analyze the theoretical model of the photovoltaic array. Currently, single-diode models are commonly used for modeling solar cells. The output current of the photovoltaic module is: (1) In the formula, Iph is the photocurrent; I0 ​​is the reverse saturation current of the PN junction in the photovoltaic module; q is the charge constant; Rs is the equivalent series resistance; Rsh is the equivalent parallel resistance; a is the diode ideality coefficient; K is the Boltzmann constant; T is the photovoltaic module temperature; I and V are the output current and voltage of the photovoltaic module, respectively; the value of Iph is: (2) In the formula, I SC,STCT represents the short-circuit current under standard test conditions (STC), α is the temperature coefficient at the short-circuit current point, and G is the coplanar irradiance. STC This is the reference temperature under STC, which is 25℃. G STC The reference irradiance at STC is 1000 W / m². The value of I0 is: (3) Where Eg is the bandgap of the semiconductor material, and I 0,STC This represents the equivalent reverse saturation current of the PN junction in a photovoltaic module under STC (Solution-Turned Current) configuration. Its value is: (4) In the formula, VOC and STC are the open-circuit voltages under STC.

[0027] 2) The mathematical model of the photovoltaic array was established, and the theoretical IV curves of the photovoltaic array under different environmental conditions were quickly calculated using the Stephenson iterative method: To establish a mathematical model of a photovoltaic array, the computational speed and accuracy of the modeling should be ensured first. Since equation (1) is an implicit equation, iterative calculation is required to obtain an approximate solution. Commonly used iterative algorithms include Newton's iteration method, parabolic method, Stephenson method, etc. However, different iterative methods have different convergence speeds when solving the above equation. Usually, the photovoltaic module is the smallest unit analyzed in the photovoltaic array. Considering that the photovoltaic array may be affected by local shading, the equivalent absorbed irradiance of each photovoltaic module may be different. Therefore, it is necessary to calculate the output characteristics of the photovoltaic module under different environmental conditions, and finally obtain the IV curve of the entire photovoltaic array by superimposing the IV curves of each photovoltaic module. By analyzing the IV curve of the photovoltaic array, for example, when a photovoltaic module is partially shaded, the short-circuit current of the abnormal photovoltaic module is less than the short-circuit current of other normal photovoltaic modules. Because the photovoltaic modules in the string are connected in series, when the current of the normal photovoltaic module in the string is greater than the short-circuit current of the partially shaded photovoltaic module, the shaded photovoltaic module will become a load, causing a negative bias voltage. At this time, the bypass diode of the photovoltaic module with local shading will be turned on. By combining the aforementioned mathematical models of photovoltaic modules and modeling methods for photovoltaic arrays, the Stephenson iterative method can be used to quickly calculate the theoretical IV curves of photovoltaic arrays under different environmental conditions.

[0028] 3) Model parameter optimization and correction: The parameters are corrected based on environmental irradiance and temperature to obtain corrected parameters relative to the environmental parameters. Using equations (1)-(4), the series resistance Rs, parallel resistance Rsh, and ideality factor a of the photovoltaic array model can be extracted. Based on these three parameters, as well as environmental irradiance and temperature, the model parameters Iph and I0 can be further estimated. However, these model parameters change with the degradation of the photovoltaic array; therefore, they need to be corrected and preprocessed before analysis. The output IV curve of the photovoltaic array is also affected by environmental irradiance and temperature. Therefore, it is necessary to correct the parameters under different environmental conditions to obtain the corrected parameters relative to the environmental parameters. After establishing the above mathematical model of the photovoltaic array, the photovoltaic equipment monitoring data can be compared with the estimation results of the above theoretical model. Combined with the application of artificial intelligence algorithms, the lifespan of the photovoltaic equipment can be predicted.

[0029] (2) Constructing a photovoltaic array lifetime prediction model Photovoltaic array lifetime prediction models are key tools for predicting the performance degradation and failure of photovoltaic arrays in photovoltaic power plants. By predicting their performance failure trends, these models can accurately estimate the remaining lifetime and service life of photovoltaic arrays. Figure 1 As shown, the photovoltaic array lifetime prediction model includes a historical degradation trend analysis module, a fitting model selection module, and a remaining lifetime prediction module. The historical degradation trend analysis module uses historical electrical and meteorological data of the photovoltaic array for preprocessing to obtain historical degradation trends and calculates the degradation rate by measuring the photovoltaic array system efficiency. The fitting model selection module uses data-driven artificial intelligence algorithms to correct and update key model parameters in existing physics-based degradation models, obtains the degradation rate, and fits the photovoltaic array lifetime prediction model to construct a personalized photovoltaic array lifetime prediction model suitable for specific photovoltaic sites. This personalized photovoltaic array lifetime prediction model is the optimal fitting model selected based on model accuracy evaluation indicators. The remaining lifetime prediction module uses the input degradation rate setpoint and the historical degradation trend calculated by the historical degradation trend analysis module to solve for the remaining lifetime, predicting the failure time and remaining lifetime of the photovoltaic array. This process provides a scientific and accurate method for photovoltaic array lifetime prediction.

[0030] Specifically, different photovoltaic (PV) array lifetime prediction models can be trained for different models and even those deployed in different regions. In the model selection module, a training task for the PV array lifetime prediction model is created in the data mining toolbox. Data for the corresponding PV array is selected or accessed, and appropriate data preprocessing operators are chosen. PV array lifetime prediction operators are selected, and the parameters of each operator are configured to generate the training task. The model generated by the corresponding task is found in the model list, and an API is generated to deploy the PV array lifetime prediction model for the corresponding model or region of PV array.

[0031] Example 1 Taking data from a photovoltaic power station as an example, this paper studies the preprocessing method for raw data, specifically as follows: Figure 2 As shown in the figure, part (a) displays the raw data, which fluctuates significantly and reflects historical electrical and meteorological data. Preprocessing allows for the acquisition of historical degradation trends. Part (b) shows the preprocessed data, where data points are more concentrated and evenly distributed, facilitating subsequent fitting. Data-driven artificial intelligence algorithms can fit the preprocessed data to construct a personalized photovoltaic array lifetime prediction model suitable for specific photovoltaic power plants. Parts (c) and (d) show the fitting results using polynomials and parabolic curves, respectively. Comparing first-order fitting (Figure c) and second-order fitting (Figure d) helps in selecting the optimal fitting model. In practical applications, the most suitable fitting method is selected based on model accuracy evaluation indicators. The remaining lifetime of the photovoltaic array is predicted using the degradation rate obtained from the fitting. The remaining lifetime prediction module uses the input degradation rate setpoint and the historical degradation trend calculated by the historical degradation trend analysis module to solve for the failure time and remaining lifetime of the photovoltaic array.

[0032] Furthermore, such as Figure 3 As shown in the figure, the accuracy of several different types of photovoltaic array performance degradation models was compared and analyzed, specifically including linear functions, exponential functions, logarithmic functions, and quadratic functions. The graph shows that the fitted lines behave differently for different types of functions. For some functions, such as quadratic functions, the fitted lines may accurately represent the overall trend of the function; however, for other types of functions, such as exponential and logarithmic functions, the fitted lines may only reflect the trend to a certain extent. Figure 4 As shown, the quadratic function can fit the performance degradation trajectory well, so the quadratic function model is selected as the photovoltaic array lifetime prediction model for this power station.

[0033] The following are embodiments of the apparatus of the present invention, which can be used to execute embodiments of the method of the present invention. For details not omitted in the apparatus embodiments, please refer to the embodiments of the method of the present invention.

[0034] In another embodiment of the present invention, a photovoltaic array lifetime prediction system is also provided. This system can implement the aforementioned photovoltaic array lifetime prediction method. The photovoltaic array lifetime prediction system includes: a data acquisition module, a data processing module, a model building module, a model training module, and a prediction output module. Data acquisition module: Used to collect historical electrical and meteorological data of the photovoltaic array.

[0035] Data processing module: Used to preprocess the collected data, including data cleaning and feature extraction.

[0036] Model building module: Used to build mathematical models of photovoltaic arrays and photovoltaic array lifetime prediction models.

[0037] Model training module: Used for training and selecting life prediction models, including data input, parameter configuration, model generation, etc.

[0038] Specifically, in the task list module of the data mining toolbox, you can create a training task for a photovoltaic array lifetime prediction model. This process requires selecting or accessing data from the corresponding photovoltaic array and choosing an appropriate data preprocessing operator. Next, select the photovoltaic array lifetime prediction operator and configure the parameters for each operator. After completing these steps, click "Complete Creation" to generate the training task. In the model list, you can find the model generated by the corresponding task and generate an API. This allows you to deploy a photovoltaic array lifetime prediction model for a specific model or region of photovoltaic array. Application systems can call the corresponding API and input the latest photovoltaic array data to obtain the remaining lifetime of the photovoltaic array.

[0039] Prediction output module: Based on the selected and trained model, input photovoltaic array data, perform lifetime prediction and output the results.

[0040] In another embodiment of the present invention, a terminal device is also provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used in the operation of an internet advertising fake traffic identification method.

[0041] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the method for identifying fake internet advertising traffic in the above embodiments.

[0042] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application 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.

[0043] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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.

[0044] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The function specified in one or more boxes.

[0045] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0046] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for predicting the lifetime of a photovoltaic array, characterized in that, The specific steps are as follows: Acquire historical electrical and meteorological data for the photovoltaic array; By inputting historical electrical and meteorological data of the photovoltaic array into the photovoltaic array lifetime prediction model of the corresponding photovoltaic power station, the failure time and remaining lifetime of the photovoltaic array can be obtained. The photovoltaic array lifetime prediction model includes a historical degradation trend analysis module, a fitting model screening module, and a remaining lifetime prediction module. The historical degradation trend analysis module obtains the degradation rate using historical electrical and meteorological data of the photovoltaic array; the fitting model screening module obtains the degradation rate and performs fitting, construction, and screening of the photovoltaic array lifetime prediction model; the remaining lifetime prediction module predicts the failure time and remaining lifetime of the photovoltaic array based on the degradation rate setpoint and the degradation rate obtained by the historical degradation trend analysis module.

2. The photovoltaic array lifetime prediction method according to claim 1, characterized in that, The historical degradation trend analysis module preprocesses the historical electrical and meteorological data of the photovoltaic array to obtain the historical degradation trend, and uses the historical degradation trend to calculate the efficiency of the photovoltaic array system to obtain the degradation rate.

3. The photovoltaic array lifetime prediction method according to claim 2, characterized in that, The degradation rate is obtained by comparing the estimation results of the photovoltaic array model with the historical electrical and meteorological data of the photovoltaic array monitored by the photovoltaic equipment.

4. The photovoltaic array lifetime prediction method according to claim 3, characterized in that, The photovoltaic array model uses a single diode model to describe the nonlinear IV characteristics of the photovoltaic array. The Stephenson iteration method is used to obtain the theoretical IV curves of the photovoltaic array under different environmental conditions. The theoretical IV curves of the photovoltaic array under different environmental conditions are superimposed to obtain the IV curve of the entire photovoltaic array.

5. The photovoltaic array lifetime prediction method according to claim 1, characterized in that, The fitting model screening module uses data-driven artificial intelligence algorithms to correct and update key model parameters in existing physics-based degradation models, and uses the degradation rate to fit and construct a photovoltaic array lifetime prediction model, thereby obtaining a personalized photovoltaic array lifetime prediction model for a specific photovoltaic power station.

6. The photovoltaic array lifetime prediction method according to claim 1, characterized in that, Multiple photovoltaic array lifetime prediction models were constructed using degradation rates. The optimal photovoltaic array lifetime prediction model was selected based on the model accuracy evaluation index and used as a personalized photovoltaic array lifetime prediction model to predict the failure time and remaining lifetime of the photovoltaic arrays in the corresponding photovoltaic power plants.

7. A photovoltaic array lifetime prediction system, characterized in that, include: The data processing module is used to acquire historical electrical and meteorological data of the photovoltaic array; The lifespan prediction module is used to input the historical electrical and meteorological data of the photovoltaic array into the photovoltaic array lifespan prediction model of the corresponding photovoltaic power station to obtain the failure time and remaining lifespan of the photovoltaic array. The photovoltaic array lifetime prediction model includes a historical degradation trend analysis module, a fitting model screening module, and a remaining lifetime prediction module. The historical degradation trend analysis module preprocesses the historical electrical and meteorological data of the photovoltaic array to obtain the degradation rate. The fitting model screening module obtains the degradation rate and fits and constructs the photovoltaic array lifetime prediction model. The remaining lifetime prediction module predicts the failure time and remaining lifetime of the photovoltaic array based on the degradation rate setpoint and the degradation rate obtained by the historical degradation trend analysis module.

8. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of a photovoltaic array lifetime prediction method as described in any one of claims 1 to 7.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of a photovoltaic array lifetime prediction method as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the photovoltaic array lifetime prediction method according to any one of claims 1 to 7.

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