Method, system, device, medium and program for predicting photovoltaic life

Abstract

The invention discloses a method, a system, equipment, a medium and a program for predicting photovoltaic life, and belongs to the technical field of photovoltaic energy. The method comprises the steps of collecting historical electrical data and meteorological data of a photovoltaic array in a photovoltaic station, and preprocessing the historical electrical data and the meteorological data to obtain a historical degradation trend analysis result of the photovoltaic array; constructing a physical degradation model of the photovoltaic station based on the historical degradation trend analysis result of the photovoltaic array; and predicting the failure time and the residual life of the photovoltaic array by using the physical degradation model of the photovoltaic station. According to the method, the problem of difficulty in photovoltaic array life prediction is solved, the stability and reliability of a photovoltaic power generation system are improved, and the operation and maintenance cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic energy technology, and in particular to a method, system, device, medium and program for predicting photovoltaic life. Background Art

[0002] As a clean and renewable form of energy, photovoltaic power generation plays an increasingly important role in the global energy structure. With the continuous advancement of technology and the gradual reduction of costs, photovoltaic power generation has become a key force in promoting global energy transformation and achieving sustainable development goals. Photovoltaic arrays, as the core components of photovoltaic power generation systems, are responsible for converting solar energy into electrical energy. However, photovoltaic arrays are affected by a variety of complex factors during operation, which not only lead to the gradual degradation of their performance, but also seriously affect their service life, and thus have a profound impact on the power generation efficiency and economic benefits of the entire photovoltaic power generation system.

[0003] Environmental factors are one of the main reasons for the degradation of photovoltaic array performance. Environmental factors such as temperature, humidity and irradiance will have a significant impact on the performance of photovoltaic arrays. For example, high temperature will increase the heat loss of photovoltaic cells, thereby reducing the photoelectric conversion efficiency; excessive humidity may cause corrosion and short circuit problems inside photovoltaic modules; and changes in irradiance directly affect the power generation capacity of photovoltaic arrays. These factors will continue to accumulate during the long-term operation of photovoltaic arrays, resulting in a gradual decline in performance.

[0004] Photovoltaic arrays are usually installed in outdoor environments and need to withstand natural forces such as wind pressure and snow pressure. These mechanical stresses may cause the packaging materials of photovoltaic modules to crack and the cells to break, which in turn affects the power generation efficiency and life of the photovoltaic array. In addition, photovoltaic arrays may also be damaged by human factors during installation and maintenance, such as improper installation and excessive bending, which will accelerate their performance degradation. The photovoltaic cells, packaging materials, connectors and other components in the photovoltaic array will gradually age over time. The aging of the cells may lead to a decrease in their photoelectric conversion efficiency, and the aging of the packaging materials may cause water seepage, air leakage and other problems, which in turn damage the internal structure of the photovoltaic module. These aging phenomena will not only reduce the power generation efficiency of the photovoltaic array, but may also cause safety hazards.

[0005] What is more complicated is that these factors often interact and influence each other. For example, high temperature and high humidity environments may accelerate the corrosion and aging process of materials inside photovoltaic modules; mechanical stress may cause cracking and water seepage of packaging materials, which in turn causes aging and damage of solar cells. These interactions make the performance degradation and life shortening of photovoltaic arrays more complicated and difficult to predict. In addition, photovoltaic arrays of different models and manufacturers also have significant differences in performance degradation and life. This is mainly due to the differences in material selection, process manufacturing, structural design, etc. of different photovoltaic modules, which lead to different performances when facing the same environmental factors and mechanical stress. This difference increases the difficulty of predicting the performance degradation and life of photovoltaic arrays. Summary of the invention

[0006] Aiming at the problem that the photovoltaic array in the prior art is difficult to predict due to the combined influence of multiple complex factors such as environmental factors, material aging and pollutant accumulation during operation, the life of the photovoltaic array is difficult to predict. The present invention provides a method for predicting the photovoltaic life, which solves the problem of the difficulty in predicting the life of the photovoltaic array, improves the stability and reliability of the photovoltaic power generation system, and reduces the operation and maintenance cost.

[0007] In order to achieve the above object, the present invention provides the following technical solutions.

[0008] In a first aspect, the present invention provides a method for predicting photovoltaic lifetime, comprising: Collect historical electrical data and meteorological data of photovoltaic arrays in photovoltaic stations, and pre-process the historical electrical data and meteorological data to obtain historical degradation trend analysis results of photovoltaic arrays; Based on the historical degradation trend analysis results of the photovoltaic array, a physical degradation model of the photovoltaic station is constructed; The physical degradation model of photovoltaic stations is used to predict the failure time and remaining life of photovoltaic arrays.

[0009] As a further improvement of the present invention, the collecting of historical electrical data and meteorological data of photovoltaic arrays in a photovoltaic station and preprocessing of the historical electrical data and meteorological data include: Collect historical electrical and meteorological data of photovoltaic arrays in photovoltaic stations; Perform data cleaning, data transformation and data reduction on historical electrical data and meteorological data.

[0010] As a further improvement of the present invention, the physical degradation model of the photovoltaic station is constructed based on the historical degradation trend analysis results of the photovoltaic array, including: Based on the historical degradation trend analysis results of PV arrays, a data-driven algorithm is used to correct and update the key model parameters in the existing physics-based degradation model, and to construct a personalized physical degradation model suitable for specific PV sites.

[0011] As a further improvement of the present invention, the data-driven algorithm includes a machine learning algorithm, a deep learning algorithm and a neural network algorithm.

[0012] As a further improvement of the present invention, the method of using the physical degradation model of the photovoltaic station to predict the failure time and the remaining life of the photovoltaic array includes: Calibrate the parameters of the physical degradation model using historical electrical data; Inputting PV array data into the calibrated and updated physical degradation model; Physical degradation model calculates failure time of PV array based on PV array data The remaining life of the PV array is calculated based on the current operating time and failure time of the PV array.

[0013] As a further improvement of the present invention, the physical degradation model of the photovoltaic station is obtained by training based on historical fault data and treatment measures.

[0014] In a second aspect, the present invention provides a system for predicting photovoltaic life, comprising: Analysis result module: used to collect historical electrical data and meteorological data of photovoltaic arrays in photovoltaic stations, and pre-process the historical electrical data and meteorological data to obtain historical degradation trend analysis results of photovoltaic arrays; Model building module: used to build a physical degradation model of the photovoltaic station based on the historical degradation trend analysis results of the photovoltaic array; Life prediction module: used to predict the failure time and remaining life of the photovoltaic array by using the physical degradation model of the photovoltaic station.

[0015] In a third aspect, the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for predicting photovoltaic life when executing the computer program.

[0016] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for predicting photovoltaic life are implemented.

[0017] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the steps of the method for predicting photovoltaic lifetime.

[0018] Compared with the prior art, the present invention has the following beneficial effects: The present invention significantly improves the accuracy of photovoltaic array life prediction. Traditional methods often rely on empirical formulas or statistical models, and it is difficult to fully consider the impact of multiple complex factors on photovoltaic arrays during operation. The present invention can more deeply reveal the degradation law of photovoltaic arrays by collecting and analyzing historical electrical data and meteorological data, thereby constructing a more accurate physical degradation model. This model can more realistically reflect the performance changes of photovoltaic arrays in actual operation, making the prediction results more reliable. Secondly, the present invention helps to improve the stability and reliability of photovoltaic power generation systems. As the core component of photovoltaic power generation systems, the stability of the performance of photovoltaic arrays is directly related to the power generation capacity and power supply quality of the entire system. By accurately predicting the life of photovoltaic arrays, operation and maintenance personnel can promptly discover and solve potential performance problems to ensure that the photovoltaic power generation system can operate continuously and stably. This not only improves the power supply reliability of the system, but also provides users with a more stable and reliable power supply, which helps to improve users' satisfaction and trust in photovoltaic power generation systems. Furthermore, the present invention reduces the operation and maintenance costs of photovoltaic power generation systems. Since the failure time and remaining life of photovoltaic arrays can be accurately predicted, operation and maintenance personnel can formulate operation and maintenance plans and maintenance strategies more scientifically. Before the performance of the photovoltaic array has not declined significantly, timely maintenance measures, such as cleaning and replacing damaged parts, can extend the service life of the photovoltaic array and reduce the losses caused by downtime due to faults. At the same time, by predicting the remaining life, operation and maintenance personnel can also reasonably arrange the replacement plan of the photovoltaic array to avoid waste of resources caused by excessive maintenance or premature replacement. This method not only improves the operation and maintenance efficiency, but also reduces the operation and maintenance costs, providing a strong guarantee for the long-term stable operation of the photovoltaic power generation system. This method is not only applicable to photovoltaic arrays of different types and specifications, but can also be expanded and optimized according to actual needs. For example, more influencing factors and data sources can be introduced to improve the accuracy and generalization ability of the prediction model; this method can also be combined with other prediction methods to form a comprehensive prediction system to further improve the accuracy and reliability of the prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are for illustrative purposes only and are not intended to limit the scope of the present disclosure in any way. In the drawings: Figure 1 A schematic flow chart of a method for predicting photovoltaic life in the present invention; Figure 2It is a schematic diagram of the photovoltaic string intelligent monitoring and fault diagnosis algorithm based on the maximum power point on the DC side of the present invention; Figure 3 This is a schematic diagram of the photovoltaic inverter fault diagnosis process of the present invention; Figure 4 This is a schematic diagram of the IGBT life prediction process of the present invention; Figure 5 A schematic diagram of the transformer fault diagnosis technology flow chart of the present invention; Figure 6 A schematic diagram of the structure of a system for predicting photovoltaic life in the present invention; Figure 7 It is a schematic diagram of the electronic device of the present invention. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. The described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present invention.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed inventions.

[0022] In view of the problem that the photovoltaic array in the prior art is difficult to predict due to the combined influence of multiple complex factors such as environmental factors, material aging and pollutant accumulation during operation, the life of the photovoltaic array is difficult to predict. The present invention provides a method for predicting the life of photovoltaic arrays, such as Figure 1 As shown, the method includes: Collect historical electrical data and meteorological data of photovoltaic arrays in photovoltaic stations, and pre-process the historical electrical data and meteorological data to obtain historical degradation trend analysis results of photovoltaic arrays; Based on the historical degradation trend analysis results of the photovoltaic array, a physical degradation model of the photovoltaic station is constructed; The physical degradation model of photovoltaic stations is used to predict the failure time and remaining life of photovoltaic arrays.

[0023] The present invention solves the problem of difficult life prediction of photovoltaic arrays, improves the stability and reliability of photovoltaic power generation systems, and reduces operation and maintenance costs.

[0024] The present invention is further explained below in conjunction with the specific drawings.

[0025] like Figure 2 As shown in Figure 1, the photovoltaic life prediction model is the core of predicting the degradation and failure of the working performance of the photovoltaic array in the photovoltaic station. By predicting its performance failure trend, the remaining life and service life of the photovoltaic array can be accurately estimated. The photovoltaic life prediction model to be used in the present invention is as follows: Figure 2 As shown. Based on the historical electrical and meteorological data of the photovoltaic array, the historical degradation trend analysis results are obtained by preprocessing the historical data; and based on the historical data, the key model parameters in the existing physical-based degradation model are corrected and updated by using data-driven artificial intelligence algorithms, and a personalized physical degradation model suitable for a specific photovoltaic site is constructed. The degradation model is used to predict the failure time and remaining life of the photovoltaic array.

[0026] Different photovoltaic life prediction models can be trained for photovoltaic arrays of different models or even deployed in different regions. For photovoltaic arrays of a certain model or in a specific region, create a photovoltaic life prediction model training task in the task list module of the data mining toolbox, select or access the data of the corresponding photovoltaic array, select the appropriate data preprocessing operator, and finally select the photovoltaic array life prediction operator. Configure the parameters of each operator, click Finish to create, and the training task can be created. Find the model generated by the corresponding task in the model list, generate an API, and then implement the deployment of the photovoltaic life prediction model for the photovoltaic array of the corresponding model or region. The application system calls the corresponding API and passes in the latest photovoltaic array data to obtain the remaining life of the photovoltaic array.

[0027] The safe and stable operation of photovoltaic inverters has an important impact on improving the reliability of the entire photovoltaic station. The inverter is always subjected to high electrical and thermal stress during operation. The influence of disturbances on the AC and DC sides will increase the failure rate of the inverter system. After a fault occurs, if the fault location and cause cannot be diagnosed in time, the fault will persist and induce a chain reaction, ultimately causing adverse effects on personnel health and the economic benefits of the photovoltaic station.

[0028] The schematic diagram of the photovoltaic inverter fault diagnosis process to be adopted in the present invention is as follows Figure 3 As shown, first, based on the DC voltage and current on the input side of the inverter, the three-phase AC voltage and current parameters on the output side, and the temperature parameters in the inverter, it is planned to use variational mode decomposition, wavelet analysis, FFT analysis and other signal processing methods to extract the fault characteristics, and simultaneously save the corresponding fault characteristics in the historical database. The artificial intelligence system then identifies the current fault characteristics and obtains the current fault characteristic diagnosis results, thereby diagnosing the inverter fault, and can diagnose the typical faults or abnormalities of the inverter.

[0029] In addition, to address the IGBT aging failure problem, it is planned to adopt Figure 4 The process shown in the figure predicts the remaining life of the IGBT. First, based on the measured IGBT electrical data in the inverter and its internal temperature, ambient temperature and other thermal parameters, the IGBT junction temperature is estimated to obtain the IGBT junction temperature curve, and then the IGBT average junction temperature and the amplitude of each thermal cycle are estimated based on the rain flow counting method, and then input into the IGBT life prediction model to finally obtain the predicted remaining life of the IGBT.

[0030] Therefore, on this basis, for each inverter of the photovoltaic array, a personalized inverter anomaly recognition and fault diagnosis model can be trained. Create an inverter fault diagnosis model training task in the task list module of the data mining toolbox, select or access the data of the corresponding inverter, select the appropriate data preprocessing operator, and finally select the inverter fault diagnosis model operator, configure the parameters of each operator, and click Finish to create the training task. Find the model generated by the corresponding task in the model list, generate an API, and then the deployment of the corresponding inverter fault diagnosis model can be realized. The application system calls the corresponding API, passes in the latest inverter data, and returns whether the inverter has a fault and what fault has occurred through model diagnosis. If the inverter fails, the application system will call the photovoltaic equipment fault measure recommendation model API to obtain the disposal measures of the corresponding model. At the same time, the alarm information and disposal measures will be displayed or notified to the relevant person in charge through the system interface, email, SMS, etc., to realize real-time monitoring and alarm of the inverter.

[0031] Similar to the inverter, for each transformer, a personalized transformer anomaly recognition and fault diagnosis model can be trained, such as Figure 5 As shown. The present invention intends to design a complete transformer multi-source data acquisition scheme, build a database of typical transformer operating status data including current, voltage, power, vibration, etc.; establish a transformer simulation model, perform circuit, length measurement, structural force field and flow field coupling calculation, and analyze the voltage, current curves and magnetic field distribution of the transformer under various states through electromagnetic simulation; analyze the winding deformation trend, vibration and reactance characteristic change law of the transformer under various states through structural force field and flow field coupling calculation; establish an electrical parameter and mechanical feature extraction model based on data-driven methods such as time domain signal adaptive decomposition, blind source separation and dynamic load source identification, and form vibration and reactance characteristic maps corresponding to different states of the transformer under various test conditions; construct a transformer loading comprehensive evaluation method with an evaluation layer (evaluation index system) + a diagnosis layer (data-driven method) based on mechanical and electrical parameters to determine the current operating status of the transformer, whether a fault occurs and what kind of fault occurs; support different fault solutions based on different faults and causes.

[0032] At the same time, create a transformer fault diagnosis model training task in the task list module of the data mining toolbox, select or access the data of the corresponding transformer, select the appropriate data preprocessing operator, and finally select the transformer fault diagnosis model operator, configure the parameters of each operator, and click Finish to create the training task. Find the model generated by the corresponding task in the model list, generate an API, and then deploy the corresponding transformer fault diagnosis model. The application system calls the corresponding API, passes in the latest transformer data, and returns whether the transformer has a fault and what fault has occurred through model diagnosis. If the transformer fails, the application system will call the photovoltaic equipment fault measure recommendation model API to obtain the disposal measures of the corresponding model, and at the same time display or notify the relevant person in charge through the system interface, email, SMS, etc., to achieve real-time monitoring and alarm.

[0033] The second object of the present invention is to provide a system for predicting photovoltaic life. Figure 6 As shown, including: Analysis result module: used to collect historical electrical data and meteorological data of photovoltaic arrays in photovoltaic stations, and pre-process the historical electrical data and meteorological data to obtain historical degradation trend analysis results of photovoltaic arrays; Model building module: used to build a physical degradation model of the photovoltaic station based on the historical degradation trend analysis results of the photovoltaic array; Life prediction module: used to predict the failure time and remaining life of the photovoltaic array by using the physical degradation model of the photovoltaic station.

[0034] like Figure 7 As shown, the third object of the present invention is to provide an electronic device, the electronic device comprising: a processor 301, a memory 302 and a display screen 303. Among them, the memory 302 and the display screen 303 are connected to the processor 301, such as through a bus 304. Optionally, the electronic device may also include a transceiver 305. It should be noted that in actual applications, the transceiver 305 is not limited to one, and the structure of the electronic device does not constitute a limitation on the embodiments of the present application.

[0035] The processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0036] The bus 304 may include a path to transmit information between the above components. The bus 304 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus 304 may be divided into an address bus, a data bus, a control bus, etc.

[0037] The memory 302 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0038] The memory 302 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 302 to implement the contents shown in the above method embodiment.

[0039] Figure 7 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0040] The fourth object of the present invention is to provide a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program is stored thereon, and when the program is executed by a processor, the aforementioned Figure 1 The various processes of the method embodiment shown in the figure include, for example, a memory including instructions, and the instructions can be executed by a processor of an electronic device to complete the method.

[0041] A computer-readable storage medium may be a tangible device that holds and stores instructions used by an instruction execution device. A computer-readable storage medium may be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, a computer-readable storage medium may be a portable computer disk, a hard disk, a USB flash drive, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a lectern random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, an optical disk, a magnetic disk, a mechanical encoding device, and any combination thereof.

[0042] A fifth object of the present invention is to provide a computer program product, comprising computer instructions, which, when executed by a processor, implement the above Figure 1 The various processes of the method embodiment shown can achieve the same technical effect, and will not be described again here to avoid repetition.

[0043] Many embodiments and many applications beyond the examples provided will be apparent to those skilled in the art upon reading the above description. Therefore, the scope of the present teachings should not be determined with reference to the above description, but rather with reference to the preceding claims and the full scope of equivalents to which such claims are entitled. For the purpose of comprehensiveness, all articles and references, including disclosures of patent applications and publications, are incorporated herein by reference. The omission of any aspect of the subject matter disclosed herein in the preceding claims is not intended to be a disclaimer of such subject matter, nor should it be considered that the applicants did not consider such subject matter to be part of the disclosed inventive subject matter.

[0044] The above content is a further detailed description of the present invention. It cannot be determined that the specific implementation methods of the present invention are limited to these. For ordinary technicians in the technical field to which the present invention belongs, without departing from the concept of the present invention, they can also make several simple deductions or substitutions, which should be regarded as belonging to the protection scope of the present invention determined by the submitted claims.

Claims

1. A method for predicting photovoltaic lifetime, characterized in that: include: Collect historical electrical data and meteorological data of photovoltaic arrays in photovoltaic stations, and pre-process the historical electrical data and meteorological data to obtain historical degradation trend analysis results of photovoltaic arrays; Based on the historical degradation trend analysis results of the photovoltaic array, a physical degradation model of the photovoltaic station is constructed; The physical degradation model of photovoltaic stations is used to predict the failure time and remaining life of photovoltaic arrays.

2. A method for predicting photovoltaic life according to claim 1, characterized in that: The collecting of historical electrical data and meteorological data of photovoltaic arrays in the photovoltaic station and preprocessing of the historical electrical data and meteorological data include: Collect historical electrical and meteorological data of photovoltaic arrays in photovoltaic stations; Perform data cleaning, data transformation and data reduction on historical electrical data and meteorological data.

3. A method for predicting photovoltaic life according to claim 1, characterized in that: The physical degradation model of the photovoltaic station is constructed based on the historical degradation trend analysis results of the photovoltaic array, including: Based on the historical degradation trend analysis results of PV arrays, a data-driven algorithm is used to correct and update the key model parameters in the existing physics-based degradation model, and to construct a personalized physical degradation model suitable for specific PV sites.

4. A method for predicting photovoltaic life according to claim 3, characterized in that: The data-driven algorithms include machine learning algorithms, deep learning algorithms and neural network algorithms.

5. The method for predicting photovoltaic life according to claim 1, characterized in that: The method of using the physical degradation model of the photovoltaic station to predict the failure time and the remaining life of the photovoltaic array includes: Calibrate the parameters of the physical degradation model using historical electrical data; Inputting PV array data into the calibrated and updated physical degradation model; Physical degradation model calculates failure time of PV array based on PV array data The remaining life of the PV array is calculated based on the current operating time and failure time of the PV array.

6. A method for predicting photovoltaic life according to claim 1, characterized in that: The physical degradation model of the photovoltaic station is obtained by training based on historical fault data and disposal measures.

7. A system for predicting photovoltaic lifetime, characterized in that: include: Analysis result module: used to collect historical electrical data and meteorological data of photovoltaic arrays in photovoltaic stations, and pre-process the historical electrical data and meteorological data to obtain historical degradation trend analysis results of photovoltaic arrays; Model building module: used to build a physical degradation model of the photovoltaic station based on the historical degradation trend analysis results of the photovoltaic array; Life prediction module: used to predict the failure time and remaining life of the photovoltaic array by using the physical degradation model of the photovoltaic station.

8. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of a method for predicting photovoltaic life as described in any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for predicting photovoltaic life described in any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The method comprises computer instructions, which, when executed by a processor, implement the steps of a method for predicting photovoltaic life as claimed in any one of claims 1 to 6.

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