Photovoltaic module control method and device

Abstract

The present invention relates to the technical field of photovoltaic modules, and in particular to a photovoltaic module control method and device. The method comprises the following steps: obtaining an installation location based on the design data of the photovoltaic module, measuring changes in light intensity, and further evaluating changes in the luminous flux of the photovoltaic module; calculating the fluctuation in the power generation of the photovoltaic module based on the luminous flux change data, and then evaluating power quality degradation, and extracting grid transmission voltage fluctuation data; combining grid voltage fluctuation and power quality degradation data to evaluate battery overcharge and battery life degradation, further calculating battery capacity degradation, and evaluating photovoltaic module system efficiency degradation; and performing a photovoltaic module degradation risk warning by analyzing the system efficiency degradation data, and feeding back the risk warning information to a terminal in real time. The present invention achieves more accurate prediction of photovoltaic module degradation by providing a warning of photovoltaic module power generation.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic modules, and in particular to a photovoltaic module control method and device. Background Art

[0002] Photovoltaic power generation systems convert solar energy into electrical energy through photovoltaic modules. Using inverters and other devices, the DC power is converted into AC power for load consumption or grid connection. PV modules, the core components of a photovoltaic power generation system, are primarily composed of multiple photovoltaic cells (also known as solar cells). Each cell is typically made of silicon and directly converts sunlight into electricity. However, because the power generation capacity of photovoltaic cells is affected by factors such as sunlight intensity, angle of illumination, temperature, and shadows, the output power of photovoltaic modules often fluctuates. This results in significant instability in the output power of photovoltaic power generation systems over time and under different environmental conditions. The output power of photovoltaic modules is also affected by factors such as differences in module compatibility and cell aging. To improve the efficiency, stability, and predictability of photovoltaic power generation systems, traditional photovoltaic module technology suffers from inaccurate calculations of module power generation efficiency and inaccurate predictions of the aging of the photovoltaic module's energy storage cells. Summary of the Invention

[0003] Based on this, it is necessary to provide a photovoltaic module control method and device to solve at least one of the above technical problems.

[0004] To achieve the above object, a photovoltaic module control method includes the following steps:

[0005] Step S1: Acquire photovoltaic module design data; collect photovoltaic module installation positions according to the photovoltaic module design data to obtain photovoltaic module installation position data; measure light intensity changes according to the photovoltaic module installation position data to obtain light intensity change data; evaluate photovoltaic module luminous flux changes according to the light intensity change data to obtain photovoltaic module luminous flux change data;

[0006] Step S2: Calculating the photovoltaic module power generation fluctuation based on the photovoltaic module luminous flux change data to obtain photovoltaic module power generation fluctuation data; evaluating power quality attenuation based on the photovoltaic module power generation fluctuation data to obtain photovoltaic module power quality attenuation data; extracting the grid transmission voltage fluctuation based on the photovoltaic module power quality attenuation data to obtain grid transmission voltage fluctuation data;

[0007] Step S3: performing an overcharge assessment of the photovoltaic energy storage battery based on the grid transmission voltage fluctuation data and the photovoltaic module power quality attenuation data to obtain photovoltaic energy storage battery overcharge data; performing a battery life attenuation estimation based on the photovoltaic energy storage battery overcharge data and the photovoltaic module power quality attenuation data to obtain photovoltaic storage battery life attenuation data;

[0008] Step S4: Calculate the storage battery capacity attenuation based on the photovoltaic storage battery life attenuation data to obtain the storage battery capacity attenuation data; evaluate the photovoltaic module system efficiency attenuation based on the storage battery capacity attenuation data to obtain the photovoltaic module system efficiency attenuation data; perform photovoltaic module degradation risk warning based on the photovoltaic module system efficiency attenuation data to obtain photovoltaic module degradation risk warning data, and feed back the photovoltaic module risk warning data to the terminal.

[0009] By collecting and analyzing detailed photovoltaic module design data, the system can accurately assess the module's installation location and the actual operating lighting conditions, ensuring efficient operation of the photovoltaic system. Further light intensity variation measurement and luminous flux assessment can promptly capture the impact of lighting conditions on the photovoltaic module's power generation capacity, providing the system with key data support regarding power generation fluctuations. By accurately calculating photovoltaic module power generation fluctuations and assessing power quality degradation, the system can monitor grid load fluctuations and power quality changes, proactively identifying potential grid voltage instability issues and thus providing more stable voltage conditions for subsequent power transmission. By extracting grid transmission voltage fluctuations, the system can analyze the performance of photovoltaic power generation in the grid and, based on this data, assess overcharge of photovoltaic energy storage batteries, promptly detecting overcharge conditions that could lead to battery damage. By combining photovoltaic module power quality degradation data with battery life degradation prediction, battery aging and performance degradation trends can be identified in advance, thereby preventing excessive battery loss and extending battery life. Further calculation of storage battery capacity degradation and system efficiency assessment can reveal the performance degradation of photovoltaic modules over long-term use and provide data for optimizing system design. By analyzing these attenuation data, timely warnings can be provided for the degradation risks of photovoltaic modules, ensuring the safety and stability of the system in long-term operation, and avoiding sudden failure of the system or serious performance degradation. It can monitor each link of the photovoltaic system in real time, accurately identify potential performance degradation and equipment risks, and effectively avoid losses caused by equipment failure and reduced power generation efficiency. Through comprehensive evaluation of multiple data such as batteries, voltage, and light intensity, the system can provide optimized scheduling solutions to ensure the stability of photovoltaic power generation and compatibility with the power grid, which helps to improve the overall operating efficiency and reliability of the system, reduce energy waste, extend equipment life, and perform maintenance and adjustments when necessary. Therefore, the present invention is an optimization process made to a traditional photovoltaic module control method and device, which solves the traditional problems of inaccurate calculation of photovoltaic module power generation efficiency and inaccurate prediction of photovoltaic module power storage battery aging, and improves the accuracy of photovoltaic module power generation efficiency calculation and photovoltaic module power storage battery aging prediction.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Acquire photovoltaic module design data;

[0012] Step S12: collecting the photovoltaic module installation position according to the photovoltaic module design data to obtain the photovoltaic module installation position data;

[0013] Step S13: measuring the solar radiation variation according to the photovoltaic module installation position data to obtain solar radiation variation data;

[0014] Step S14: evaluating the luminous flux variation of the photovoltaic module according to the sunlight variation data and the photovoltaic module design data to obtain the luminous flux variation data of the photovoltaic module.

[0015] The present invention ensures that the system can make reasonable configurations based on the geographical location and installation environment by collecting the design data and installation locations of photovoltaic modules. Then, based on the measurement of changes in solar illumination, the fluctuations in light intensity can be captured in real time, providing an important basis for the subsequent prediction of power generation performance. By evaluating the changes in luminous flux in combination with the design data of photovoltaic modules, the fluctuations in the power generation efficiency of photovoltaic modules under different lighting conditions can be accurately predicted, ensuring that the system can still maintain efficient operation under changing lighting conditions. This series of steps not only optimizes the design and configuration of photovoltaic modules, but also provides data support for the efficient and stable operation of the system, improves the stability of power generation, and reduces energy losses caused by changes in illumination.

[0016] Preferably, step S14 includes the following steps:

[0017] Step S141: Calculating the sunlight angle according to the sunlight variation data to obtain sunlight angle data;

[0018] Step S142: performing a solar direct intensity detection on the solar illumination change data to obtain solar direct intensity data;

[0019] Step S143: Calculating the solar panel tilt angle based on the photovoltaic module design data to obtain solar panel tilt angle data;

[0020] Step S144: Calculating the change in the light incident angle based on the solar panel tilt angle data and the sunlight angle data to obtain sunlight incident angle change data;

[0021] Step S145: performing statistics on changes in radiation received by the solar panel based on the sunlight incident angle change data and the sunlight direct intensity data to obtain the radiation received by the solar panel change data;

[0022] Step S146: performing photovoltaic module luminous flux variation evaluation on the solar panel received radiation variation data to obtain photovoltaic module luminous flux variation data.

[0023] The present invention calculates the sunlight angle and direct intensity to accurately capture the changing trend of light, help identify the fluctuation of light intensity under different time and weather conditions, and thus provide important data support for the subsequent energy conversion efficiency evaluation of photovoltaic modules. Secondly, through a comprehensive analysis of the design data and installation position of the photovoltaic modules, the tilt angle of the solar panel is calculated to ensure the optimal incident angle of light and the photovoltaic panel, thereby improving the photoelectric conversion efficiency. Further, combined with the calculation of the change in the incident angle of light, the impact of light on the radiation received by the module can be accurately grasped, helping the system to optimize the design and layout of the photovoltaic module and reduce unnecessary energy loss. Finally, through the statistics and evaluation of the change data of the radiation received by the solar panel, the system can more accurately predict the change in the luminous flux of the photovoltaic module, identify the fluctuation in power generation in advance, and ensure that the system can operate stably under different lighting conditions. This series of steps improves the efficiency and adaptability of the photovoltaic system, and provides solid technical support for the long-term stable operation of photovoltaic modules in complex environments.

[0024] Preferably, step S2 includes the following steps:

[0025] Step S21: performing a photovoltaic module power generation fluctuation analysis based on the photovoltaic module luminous flux change data to obtain photovoltaic module power generation fluctuation data;

[0026] Step S22: performing power output variability assessment on the photovoltaic module power generation fluctuation data to obtain photovoltaic module power output variability data;

[0027] Step S23: performing power quality attenuation assessment based on the PV module power output variability data to obtain PV module power quality attenuation data;

[0028] Step S24: extracting grid transmission voltage fluctuations based on the photovoltaic module power quality attenuation data to obtain grid transmission voltage fluctuation data.

[0029] By analyzing the power generation fluctuations of the photovoltaic module luminous flux change data, the present invention can accurately grasp the power generation fluctuations of the photovoltaic system under different lighting conditions, and provide basic data for the subsequent power output variability assessment. Next, the power output variability assessment is performed on the power generation fluctuation data, which can further reveal the power generation stability of the photovoltaic system in actual operation, predict the power generation fluctuation range under different climatic and environmental conditions, and effectively identify the factors that lead to power grid instability. Based on the power output variability data, the power quality attenuation assessment is performed to determine the impact of large fluctuations in power generation on the grid voltage quality and promptly discover the trend of power quality degradation. Finally, by extracting the power quality attenuation data, the grid transmission voltage fluctuation is assessed, and an early warning signal is provided for the stable operation of the grid, reducing equipment damage or power loss caused by voltage fluctuations. Overall, the system can identify and respond to volatility problems in photovoltaic power generation in advance, optimize grid load scheduling and power quality, and improve the reliability of photovoltaic power generation and the stability of the grid.

[0030] Preferably, step S21 includes the following steps:

[0031] Step S211: dividing the photovoltaic module luminous flux change data into luminous flux size to obtain photovoltaic module strong luminous flux data and photovoltaic module weak luminous flux data;

[0032] Step S212: performing peak radiation calculation based on the strong luminous flux data of the photovoltaic module to obtain luminous flux peak radiation data;

[0033] Step S213: performing power generation sudden increase trend analysis on the luminous flux peak radiation data to obtain photovoltaic module power generation sudden increase trend data;

[0034] Step S214: performing component over-illumination processing according to the luminous flux peak radiation data to obtain photovoltaic component over-illumination data;

[0035] Step S216: performing photovoltaic effect attenuation evaluation on the weak light flux data of the photovoltaic module to obtain photovoltaic effect attenuation data of the photovoltaic module;

[0036] Step S217: performing photovoltaic module power generation fluctuation analysis based on photovoltaic module photovoltaic effect attenuation data, photovoltaic module over-illumination data, and photovoltaic module power generation sudden increase trend data to obtain photovoltaic module power generation fluctuation data.

[0037] By classifying luminous flux variation data, the present invention can clearly distinguish power generation performance under strong and weak light conditions, helping to identify the system's operating status under extreme lighting conditions. Then, peak radiation calculations based on the strong luminous flux data can determine the maximum radiant intensity of the photovoltaic module under strong light, further analyzing trends in power generation surges. This process helps identify power generation fluctuations under high radiation conditions and predict overload risks in advance. For excessive light exposure under strong light conditions, the system handles this to prevent damage to the module. Simultaneously, by assessing photovoltaic effect degradation under weak light conditions, it can promptly detect reduced power generation efficiency under low light conditions and help optimize power generation strategies during inefficient operation. Finally, by comprehensively analyzing photovoltaic effect degradation, excessive light treatment, and power generation surge trends, it can accurately assess module power generation fluctuations under different lighting conditions, ensuring the stability and efficiency of the photovoltaic system in various environments. Overall, the system effectively addresses the challenges posed by varying light exposure to photovoltaic power generation, improving module stability and power output consistency.

[0038] Preferably, step S217 includes the following steps:

[0039] Performing thermal cumulative overload detection on photovoltaic modules based on excessive illumination data of photovoltaic modules to obtain thermal cumulative overload data of photovoltaic modules;

[0040] Evaluate the reduction in photoelectric conversion efficiency based on the accumulated thermal overload data of the photovoltaic modules to obtain the reduction in photoelectric conversion efficiency of the photovoltaic modules;

[0041] The performance degradation of photovoltaic modules is estimated based on the reduction data of photovoltaic module photoelectric conversion efficiency and the cumulative thermal overload data of photovoltaic modules, and the performance degradation data of photovoltaic modules is obtained;

[0042] PV module load growth detection is performed based on PV module performance attenuation data and PV module power generation surge trend data to obtain PV module load growth data;

[0043] Performing photovoltaic module power generation instability detection on photovoltaic module load growth data to obtain photovoltaic module power generation instability data;

[0044] The photovoltaic module power generation fluctuation analysis is performed based on the photovoltaic module power generation instability data and the photovoltaic effect attenuation data of the photovoltaic module to obtain the photovoltaic module power generation fluctuation data.

[0045] The excessive illumination data of the photovoltaic modules of the present invention is used for thermal cumulative overload detection, which helps to identify the potential overload risk of the modules under high temperature conditions in advance, thereby avoiding performance loss due to heat accumulation. By analyzing the overload data, it is possible to evaluate whether the photoelectric conversion efficiency of the module has decreased, timely detect the attenuation of the photovoltaic effect, and ensure that the module is always in the best operating state. Based on the changes in the photoelectric conversion efficiency, further performance attenuation estimates are made to effectively predict the performance changes of photovoltaic modules after long-term use, helping to optimize maintenance and replacement strategies. In addition, load growth detection can monitor in real time whether the module can still maintain a stable power generation capacity when the load increases, and prevent system instability problems caused by excessive load. Power generation instability detection helps identify potential operational anomalies, provides necessary warnings, and avoids equipment failures under high loads. Combined with power generation fluctuation analysis, it can provide a basis for the optimization of the photovoltaic system, reduce power generation fluctuations, and improve the stability and predictability of power output, thereby extending the service life of the system.

[0046] Preferably, step S23 includes the following steps:

[0047] Step S231: Acquire the photovoltaic module grid transmission capacity data;

[0048] Step S232: performing statistics on instantaneous surges in power output on the PV module power output variability data to obtain instantaneous surge data in power output;

[0049] Step S233: performing power transmission capacity overload detection on the photovoltaic module grid transmission capacity data according to the power output instantaneous surge data to obtain power transmission capacity overload data;

[0050] Step S234: performing a grid frequency imbalance risk assessment on the power transmission capacity overload data and the power output instantaneous surge data to obtain grid frequency imbalance risk data;

[0051] Step S235: performing grid harmonic imbalance state detection according to the grid frequency imbalance risk data to obtain grid harmonic imbalance state data;

[0052] Step S236: Perform power quality degradation assessment based on the grid harmonic imbalance state data and the grid frequency imbalance risk data to obtain photovoltaic module power quality degradation data.

[0053] The present invention comprehensively assesses the impact of the photovoltaic system's power output on grid stability, ensuring the efficient and safe operation of the power system. By obtaining grid transmission capacity data from photovoltaic modules, it can provide basic data for subsequent power output assessments, ensuring that the system has sufficient transmission capacity to cope with the volatility of photovoltaic power generation. Statistics on instantaneous surges in power output variability help identify sudden power fluctuations in a short period of time and prepare for timely response. Power transmission capacity overload detection can prevent system overload caused by exceeding the grid transmission capacity when power output fluctuates violently, thereby avoiding equipment damage or shutdown. Combined with grid frequency imbalance risk assessment, it can identify frequency fluctuation problems, provide early warning, and avoid frequency imbalance in the grid caused by fluctuations in the photovoltaic system. By detecting harmonic imbalance status, current distortion in the grid can be monitored in real time to ensure the stable operation of the grid. In addition, power quality degradation assessment based on frequency imbalance and harmonic status data helps to accurately determine the impact of the photovoltaic system on power quality, ensuring that the grid can maintain a high-quality power supply, thereby optimizing system efficiency and extending the service life of photovoltaic equipment.

[0054] Preferably, step S3 includes the following steps:

[0055] Step S31: performing overcharge assessment of the photovoltaic energy storage battery based on the grid transmission voltage fluctuation data and the photovoltaic module power quality attenuation data to obtain photovoltaic energy storage battery overcharge data;

[0056] Step S32: performing an energy storage battery overheat detection on the photovoltaic energy storage battery overcharge data to obtain the energy storage battery overheat data;

[0057] Step S33: Calculating the battery overheating expansion probability based on the electric energy storage battery overheating data and the photovoltaic electric energy storage battery overcharge data to obtain battery overheating expansion probability data;

[0058] Step S34: performing battery life attenuation estimation on the battery overheating expansion probability data and the photovoltaic module power quality attenuation data to obtain photovoltaic storage battery life attenuation data.

[0059] The present invention performs storage battery overcharge assessment based on grid transmission voltage fluctuations and photovoltaic module power quality attenuation data, and can monitor in real time whether the battery is in an overcharge state, thereby avoiding safety hazards caused by overcharging, such as battery performance degradation or failure. Further overheat detection of battery overcharge data helps to promptly identify abnormal battery temperatures and prevent battery overheating from causing fires or safety problems. The calculation of the probability of battery overheating expansion further optimizes battery management, helps to assess the risk of battery expansion in overheating conditions, and thus take preventive measures to reduce damage. Finally, by combining battery overheating expansion probability data and power quality attenuation data to estimate battery life attenuation, it is possible to accurately predict the battery's service life, provide a basis for optimized maintenance, thereby extending the battery's service life and improving the overall efficiency of the photovoltaic system. The implementation of these steps helps to ensure the long-term stable operation of the photovoltaic battery storage system, reduce the failure rate, and ensure the efficient contribution of the photovoltaic system to the power grid.

[0060] Preferably, step S4 includes the following steps:

[0061] Step S41: performing storage battery capacity attenuation calculation based on photovoltaic storage battery life attenuation data to obtain storage battery capacity attenuation data;

[0062] Step S42: Calculating the battery storage capacity degradation based on the stored battery capacity degradation data to obtain battery storage capacity degradation data;

[0063] Step S43: performing a photovoltaic module system efficiency attenuation evaluation on the battery storage capacity degradation data to obtain photovoltaic module system efficiency attenuation data;

[0064] Step S44: performing photovoltaic module degradation risk warning on the photovoltaic module system efficiency attenuation data to obtain photovoltaic module degradation risk warning data, and feeding back the photovoltaic module risk warning data to the terminal.

[0065] The present invention can evaluate the health status of the battery in real time by calculating the storage battery life attenuation data, and predict the battery's future storage capacity through capacity attenuation data, thereby discovering potential problems in advance and preventing battery performance degradation from affecting the operation of the entire system. Next, based on the battery storage capacity degradation data, the efficiency attenuation of the photovoltaic module system is evaluated, which can accurately reflect the impact of battery performance changes on the entire photovoltaic system, and help operators to adjust and optimize the system operation strategy in a timely manner. Finally, through the analysis of system efficiency attenuation, photovoltaic module degradation risk warning is implemented, and potential system degradation risks are identified in advance and fed back to the terminal, providing users with real-time alerts, promoting timely maintenance and repair, thereby extending the overall life of the system and reducing maintenance costs. The comprehensive application of these steps ensures the efficient and stable operation of the photovoltaic system during use and improves the safety of the system.

[0066] The present invention further provides a photovoltaic assembly device, comprising a photovoltaic assembly device body, a power supply unit, and an electrical control unit, wherein the power supply unit is installed inside the photovoltaic assembly device body, the electrical control unit is electrically connected to the power supply unit, and the electrical control unit is used to charge the photovoltaic assembly device body and control the photovoltaic assembly device body, wherein the electrical control unit is used to execute the photovoltaic assembly control method described above, and the electrical control unit includes:

[0067] Luminous flux change assessment module: obtains photovoltaic module design data; collects photovoltaic module installation positions based on the photovoltaic module design data to obtain photovoltaic module installation position data; measures light intensity changes based on the photovoltaic module installation position data to obtain light intensity change data; assesses photovoltaic module luminous flux changes based on the light intensity change data to obtain photovoltaic module luminous flux change data;

[0068] Voltage fluctuation extraction module: Calculates the fluctuation of photovoltaic module power generation based on the PV module luminous flux change data to obtain the PV module power generation fluctuation data; evaluates the power quality attenuation based on the PV module power generation fluctuation data to obtain the PV module power quality attenuation data; extracts the grid transmission voltage fluctuation based on the PV module power quality attenuation data to obtain the grid transmission voltage fluctuation data;

[0069] Battery life attenuation estimation module: Based on the grid transmission voltage fluctuation data and the photovoltaic module power quality attenuation data, the overcharge evaluation of the photovoltaic energy storage battery is performed to obtain the photovoltaic energy storage battery overcharge data; based on the photovoltaic energy storage battery overcharge data and the photovoltaic module power quality attenuation data, the battery life attenuation is estimated to obtain the photovoltaic storage battery life attenuation data;

[0070] Degradation risk warning module: Calculate the storage battery capacity attenuation based on the photovoltaic storage battery life attenuation data to obtain the storage battery capacity attenuation data; evaluate the photovoltaic module system efficiency attenuation based on the storage battery capacity attenuation data to obtain the photovoltaic module system efficiency attenuation data; perform photovoltaic module degradation risk warning based on the photovoltaic module system efficiency attenuation data to obtain photovoltaic module degradation risk warning data, and feed back the photovoltaic module risk warning data to the terminal.

[0071] The present invention provides a system that, through detailed collection and analysis of photovoltaic module design data, accurately assesses the module installation location and actual operating lighting conditions, ensuring efficient operation of the photovoltaic system. Further light intensity variation measurement and luminous flux assessment can promptly capture the impact of lighting conditions on the photovoltaic module's power generation capacity, providing the system with key data support regarding power generation fluctuations. By accurately calculating photovoltaic module power generation fluctuations and assessing power quality degradation, the system can monitor grid load fluctuations and power quality changes, proactively identifying potential grid voltage instability issues and thus providing more stable voltage conditions for subsequent power transmission. By extracting grid transmission voltage fluctuations, the system can analyze the performance of photovoltaic power generation within the grid and, based on this data, assess overcharge of photovoltaic energy storage batteries, promptly detecting overcharge conditions that could lead to battery damage. Battery life degradation prediction, combined with photovoltaic module power quality degradation data, can proactively identify trends in battery aging and performance degradation, thereby preventing excessive battery loss and extending battery life. Further calculation of storage battery capacity degradation and system efficiency assessment can reveal the performance degradation of photovoltaic modules over long-term use and provide data for optimizing system design. By analyzing these attenuation data, timely warnings can be provided for the degradation risks of photovoltaic modules, ensuring the safety and stability of the system in long-term operation, and avoiding sudden failure of the system or serious performance degradation. It can monitor each link of the photovoltaic system in real time, accurately identify potential performance degradation and equipment risks, and effectively avoid losses caused by equipment failure and reduced power generation efficiency. Through comprehensive evaluation of multiple data such as batteries, voltage, and light intensity, the system can provide optimized scheduling solutions to ensure the stability of photovoltaic power generation and compatibility with the power grid, which helps to improve the overall operating efficiency and reliability of the system, reduce energy waste, extend equipment life, and perform maintenance and adjustments when necessary. Therefore, the present invention is an optimization process made to a traditional photovoltaic module control method and device, which solves the traditional problems of inaccurate calculation of photovoltaic module power generation efficiency and inaccurate prediction of photovoltaic module power storage battery aging, and improves the accuracy of photovoltaic module power generation efficiency calculation and photovoltaic module power storage battery aging prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 A schematic flow chart of the steps of a photovoltaic module control method and device;

[0073] Figure 2 for Figure 1 Detailed implementation steps of step S2 in FIG.

[0074] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0075] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0076] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0077] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0078] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0079] To achieve this, please refer to Figures 1 to 3 , a photovoltaic module control method and device, comprising the following steps:

[0080] Step S1: Acquire photovoltaic module design data; collect photovoltaic module installation positions according to the photovoltaic module design data to obtain photovoltaic module installation position data; measure light intensity changes according to the photovoltaic module installation position data to obtain light intensity change data; evaluate photovoltaic module luminous flux changes according to the light intensity change data to obtain photovoltaic module luminous flux change data;

[0081] Step S2: Calculating the photovoltaic module power generation fluctuation based on the photovoltaic module luminous flux change data to obtain photovoltaic module power generation fluctuation data; evaluating power quality attenuation based on the photovoltaic module power generation fluctuation data to obtain photovoltaic module power quality attenuation data; extracting the grid transmission voltage fluctuation based on the photovoltaic module power quality attenuation data to obtain grid transmission voltage fluctuation data;

[0082] Step S3: performing an overcharge assessment of the photovoltaic energy storage battery based on the grid transmission voltage fluctuation data and the photovoltaic module power quality attenuation data to obtain photovoltaic energy storage battery overcharge data; performing a battery life attenuation estimation based on the photovoltaic energy storage battery overcharge data and the photovoltaic module power quality attenuation data to obtain photovoltaic storage battery life attenuation data;

[0083] Step S4: Calculate the storage battery capacity attenuation based on the photovoltaic storage battery life attenuation data to obtain the storage battery capacity attenuation data; evaluate the photovoltaic module system efficiency attenuation based on the storage battery capacity attenuation data to obtain the photovoltaic module system efficiency attenuation data; perform photovoltaic module degradation risk warning based on the photovoltaic module system efficiency attenuation data to obtain photovoltaic module degradation risk warning data, and feed back the photovoltaic module risk warning data to the terminal.

[0084] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a flow chart of steps of a photovoltaic module control method and device according to the present invention. In this example, the photovoltaic module control method and device include the following steps:

[0085] Step S1: Acquire photovoltaic module design data; collect photovoltaic module installation positions according to the photovoltaic module design data to obtain photovoltaic module installation position data; measure light intensity changes according to the photovoltaic module installation position data to obtain light intensity change data; evaluate photovoltaic module luminous flux changes according to the light intensity change data to obtain photovoltaic module luminous flux change data;

[0086] In an embodiment of the present invention, design data for a photovoltaic module is obtained. This data includes the module model, specifications, power output, efficiency, and applicable environmental parameters, such as temperature and humidity. Design data can be obtained from technical manuals or databases provided by the photovoltaic module manufacturer. Next, based on the design data for the photovoltaic module, its installation location is collected. Specifically, Geographic Information System (GIS) technology combined with Global Positioning System (GPS) equipment is used to determine the longitude and latitude of the photovoltaic module installation location, its altitude, and any surrounding obstructions, such as buildings, trees, or other obstacles that affect light. Obtaining this installation location data ensures the accuracy of subsequent measurement data. Then, based on this installation location data, light intensity variation measurements are performed. This step uses a light intensity sensor (such as a pyranometer) to conduct on-site measurements, recording light intensity variation data at different locations over different time periods. The light intensity sensor performs measurements at multiple time periods (such as early morning, midday, and dusk) to ensure that all-weather light intensity data is collected. This data is used to evaluate the luminous flux variation of the photovoltaic module. Finally, based on the light intensity variation data, the luminous flux variation is evaluated using the photovoltaic module's photoelectric conversion efficiency. The specific method is to calculate the luminous flux change of the photovoltaic module under different lighting conditions based on the numerical value of the light intensity change, combined with the technical parameters of the photovoltaic module (such as maximum power point, open circuit voltage and short circuit current), and obtain the luminous flux change data of the photovoltaic module under different light intensity conditions.

[0087] Step S2: Calculating the photovoltaic module power generation fluctuation based on the photovoltaic module luminous flux change data to obtain photovoltaic module power generation fluctuation data; evaluating power quality attenuation based on the photovoltaic module power generation fluctuation data to obtain photovoltaic module power quality attenuation data; extracting the grid transmission voltage fluctuation based on the photovoltaic module power quality attenuation data to obtain grid transmission voltage fluctuation data;

[0088] In this embodiment of the present invention, the PV module power generation fluctuation is calculated based on the PV module luminous flux variation data acquired in step S1. This process processes the time series of luminous flux variation data and uses mathematical modeling to calculate the impact of changes in light intensity on power generation, thereby obtaining power generation fluctuation data for the PV modules over different time periods. Signal processing methods such as linear regression or Fourier transform can be used to analyze the relationship between luminous flux changes and power generation, thereby deriving power generation fluctuations. Next, power quality degradation is assessed based on the PV module power generation fluctuation data. Specifically, the power generation fluctuation data is compared with grid standards, and the impact of fluctuations on grid power quality is assessed by analyzing factors such as the amplitude, frequency, and duration of PV module output power fluctuations. The calculation of power quality degradation data involves evaluating grid stability indicators such as power frequency offset and voltage fluctuation. Finally, the power quality degradation data is used to extract grid transmission voltage fluctuations. By analyzing the relationship between power quality degradation and grid voltage, voltage fluctuation data during grid transmission is extracted. This can be achieved by obtaining real-time voltage data from voltage monitoring devices installed in the grid and performing fluctuation analysis to obtain grid transmission voltage fluctuation data.

[0089] Step S3: performing an overcharge assessment of the photovoltaic energy storage battery based on the grid transmission voltage fluctuation data and the photovoltaic module power quality attenuation data to obtain photovoltaic energy storage battery overcharge data; performing a battery life attenuation estimation based on the photovoltaic energy storage battery overcharge data and the photovoltaic module power quality attenuation data to obtain photovoltaic storage battery life attenuation data;

[0090] In an embodiment of the present invention, an overcharge assessment of photovoltaic energy storage batteries is performed based on grid transmission voltage fluctuation data and photovoltaic module power quality degradation data. This process relies on monitoring the battery's charge and discharge status and, combined with grid transmission voltage fluctuations and photovoltaic system output data, calculates whether the battery is at risk of overcharging. When grid voltage fluctuations are large or photovoltaic module power generation fluctuates significantly, the battery is prone to overcharging. Therefore, it is necessary to monitor changes in battery voltage and current, combined with the battery's rated charge capacity, to assess whether the battery will experience performance degradation due to overcharging. Next, based on the battery overcharge data and photovoltaic module power quality degradation data, the battery life degradation is estimated. This step uses a battery life degradation model to estimate the battery's remaining service life by analyzing the battery's charge and discharge cycles, charging status, and the instability of the photovoltaic system output. Based on the negative impact of overcharging and power quality instability, the extent of battery performance degradation over a period of time is estimated, thereby deriving the photovoltaic cell's life degradation data.

[0091] Step S4: Calculate the storage battery capacity attenuation based on the photovoltaic storage battery life attenuation data to obtain the storage battery capacity attenuation data; evaluate the photovoltaic module system efficiency attenuation based on the storage battery capacity attenuation data to obtain the photovoltaic module system efficiency attenuation data; perform photovoltaic module degradation risk warning based on the photovoltaic module system efficiency attenuation data to obtain photovoltaic module degradation risk warning data, and feed back the photovoltaic module risk warning data to the terminal.

[0092] In this embodiment of the present invention, the capacity decay of photovoltaic storage batteries is calculated based on the lifetime decay data of photovoltaic storage batteries. This process analyzes the number of charge cycles, internal voltage decay, and capacity loss during long-term use to calculate the remaining effective capacity of the storage batteries after different years of use. Generally, battery capacity decay accelerates with age. Therefore, it is necessary to estimate the remaining capacity based on actual battery operating data and a degradation model provided by the manufacturer. Next, the stored battery capacity decay data is used to assess the efficiency decay of the photovoltaic module system. The efficiency of the photovoltaic system is affected by battery performance degradation. Therefore, it is necessary to evaluate the overall efficiency change of the photovoltaic system based on the extent of battery capacity decay and the actual power generation data of the photovoltaic modules. This process calculates the overall power generation capacity of the photovoltaic modules at different levels of battery degradation to assess the degree of system efficiency decay. Finally, based on the system efficiency decay data, a photovoltaic module degradation risk warning is issued. In this step, the potential failure risk of the photovoltaic modules is analyzed by combining the performance decay of the photovoltaic modules with the changes in system efficiency. Specifically, a threshold is set so that when the system efficiency drops to a certain level, a degradation risk warning is triggered, and the alarm information is fed back to the photovoltaic system management platform.

[0093] Preferably, step S1 includes the following steps:

[0094] Step S11: Acquire photovoltaic module design data;

[0095] Step S12: collecting the photovoltaic module installation position according to the photovoltaic module design data to obtain the photovoltaic module installation position data;

[0096] Step S13: measuring the solar radiation variation according to the photovoltaic module installation position data to obtain solar radiation variation data;

[0097] Step S14: evaluating the luminous flux variation of the photovoltaic module according to the sunlight variation data and the photovoltaic module design data to obtain the luminous flux variation data of the photovoltaic module.

[0098] In embodiments of the present invention, design data for photovoltaic modules is obtained. This data is typically provided by the module manufacturer and includes all module technical parameters, such as rated power, open-circuit voltage, short-circuit current, maximum power point voltage, and maximum power point current. This design data can be obtained from the module's technical manual, product specification sheet, or equipment information management system. Furthermore, the module's environmental adaptability parameters should be obtained, including operating temperature range, wind resistance, water resistance, and other requirements related to the installation environment, such as tilt angle and orientation. This design data will provide an important reference for subsequent installation location selection and light intensity analysis. During operation, ensure that the latest and accurate design data is obtained from the module manufacturer or certified supplier. Based on this design data, the module installation location is collected to obtain installation location data. The key to this step is to accurately determine the installation location of the photovoltaic modules. Using a geographic information system (GIS) combined with global positioning system (GPS) technology, the precise geographic coordinates (latitude and longitude) and altitude of the installation location should be obtained. The selection of the installation location should not only consider the geographical location, but also factors such as shading, sunshine duration, and ambient temperature. High-resolution satellite images or ground laser scanning technology (LIDAR) can be used to perform occlusion analysis of the surrounding environment to ensure that the installation location will not be blocked by high-rise buildings, trees and other objects. When collecting installation location data, the inclination angle and azimuth of the installation location must also be recorded so that the influence of these factors can be considered in subsequent light evaluations. After obtaining this information, a database management system can be used to store and analyze it. According to the installation location data obtained in step S12, a light intensity sensor (such as a radiation meter) is deployed near the installation location of the photovoltaic module. The sensor should have high sensitivity and be able to perform accurate measurements under different weather conditions. The sensor needs to monitor the intensity of sunlight around the clock, especially in different time periods (such as morning, noon, and evening) and different weather conditions (such as sunny, cloudy, and rainy days). Light intensity data usually includes solar radiation intensity (in W / m 2) and the angle of solar radiation. This data can be directly obtained through the real-time output of the sensor. During the measurement process, the sensor should be connected to a data acquisition system to transmit the measurement data in real time and record it in a data storage and management system for subsequent analysis. The resulting solar radiation variation data should cover the changes in sunlight over multiple time periods. Using the radiation intensity and angle information recorded in the sunlight variation data, combined with the design data of the PV module (such as maximum power point voltage and short-circuit current), specific mathematical formulas or standards are used to evaluate the change in luminous flux. This process does not rely on complex models but directly utilizes the electrical characteristics of the PV module and solar radiation data for calculation. Specifically, based on the solar radiation intensity and incident angle, the radiant power under illumination conditions is calculated. Combined with the efficiency of the PV module (usually obtained from the PV module's technical manual), the luminous flux change of the PV module under different illumination conditions is inferred. At this point, it is necessary to ensure that the time stamps in the sunlight variation data are consistent with the technical parameters recorded in the module design data to accurately evaluate the trend of luminous flux changes. The results of the luminous flux variation evaluation provide the output power changes of the PV module under different illumination environments, ultimately generating the luminous flux variation data of the PV module.

[0099] Preferably, step S14 includes the following steps:

[0100] Step S141: Calculating the sunlight angle according to the sunlight variation data to obtain sunlight angle data;

[0101] Step S142: performing a solar direct intensity detection on the solar illumination change data to obtain solar direct intensity data;

[0102] Step S143: Calculating the solar panel tilt angle based on the photovoltaic module design data to obtain solar panel tilt angle data;

[0103] Step S144: Calculating the change in the light incident angle based on the solar panel tilt angle data and the sunlight angle data to obtain sunlight incident angle change data;

[0104] Step S145: performing statistics on changes in radiation received by the solar panel based on the sunlight incident angle change data and the sunlight direct intensity data to obtain the radiation received by the solar panel change data;

[0105] Step S146: performing photovoltaic module luminous flux variation evaluation on the solar panel received radiation variation data to obtain photovoltaic module luminous flux variation data.

[0106] In an embodiment of the present invention, the solar angle is calculated based on the solar radiation change data. This step calculates the solar angle data by recording the changes in solar radiation intensity over time. In specific implementation, a geographic information system (GIS) is used in combination with a solar position algorithm (such as the SPA algorithm) to calculate the azimuth and elevation of the sun at a specific moment based on the known time, date, latitude, longitude and geographical coordinates of the installation site. The azimuth describes the direction of the sun relative to the north, while the elevation is the angle between the sun and the ground. With the help of an accurate solar radiation model, the change in the incident angle of the solar radiation is calculated. A radiometer (such as a light intensity sensor) is used to monitor the light intensity of the installation site for a long time, and the solar intensity data of different time periods is recorded in real time. This data record includes direct light intensity, that is, the light intensity directly radiated by the sun to the ground. To ensure the accuracy of the detection, a high-precision sensor is selected and continuous measurement is performed under different climatic conditions (such as sunny, cloudy, rainy, etc.). The sensor measures and outputs the light intensity per unit area (W / m2) in real time. 2), this data is used to calculate the direct sunlight intensity. By integrating multi-point data from multiple sensors, the changes in the direct sunlight intensity are detected, and then the direct sunlight intensity data is obtained. The optimal tilt angle of the photovoltaic panel is calculated using the design parameters of the photovoltaic module (such as the recommended tilt angle) and the geographical coordinates of the installation site (such as latitude and longitude). The ideal tilt angle of the installation site can be obtained through a standard formula (for example, tilt angle = installation latitude ± 10°, adjusted according to the target season). Taking into account seasonal light changes, multiple calculations will be performed to determine the optimal angle of the photovoltaic panel to ensure maximum utilization of light. Accurate calculation of the tilt angle is crucial for photovoltaic panels to receive radiation in different seasons and time periods. The output result is a dynamic angle value, which combines the sunlight angle (including the sun's azimuth and elevation) obtained in step S141 with the photovoltaic module tilt angle calculated in step S143, and uses a geometric formula to calculate the incident angle of sunlight on the photovoltaic panel. The incident angle of sunlight is the angle at which solar radiation reaches the surface of a photovoltaic panel. It is typically calculated using the formula: Incident angle = cos-1[(cos(sun elevation angle) * cos(PV panel tilt angle)) + (sin(sun elevation angle) * sin(PV panel tilt angle) * cos(sun azimuth angle - PV panel azimuth angle))]. This calculation takes into account the orientation of the photovoltaic panel and seasonal variations. The obtained data on the variation in the incident angle of sunlight is combined with the direct sunlight intensity data and the variation in the incident angle to calculate the actual radiation energy received by the photovoltaic panel surface using a radiation transfer model. The amount of radiation received is closely related to the incident angle, as the angle of incidence of sunlight affects the distribution of radiation on the photovoltaic panel. The intensity of direct radiation is proportional to the incident angle, reaching its maximum intensity when the angle of incidence is close to vertical. By statistically analyzing the incident angle and radiation intensity over different time periods, the variation in radiation received by the solar panel can be analyzed. Combined with the solar panel received radiation change data obtained in step S145, and the design parameters of the photovoltaic module (such as maximum power, conversion efficiency, etc.), the actual electrical energy output by the photovoltaic panel is calculated. The change in the luminous flux of the photovoltaic panel is usually closely related to the amount of solar radiation received and the photoelectric conversion efficiency of the photovoltaic panel. By combining the amount of radiation received with the maximum power output and conversion efficiency of the photovoltaic module, the luminous flux change of the module is calculated using a formula. This calculation process needs to take into account various factors, such as the temperature effect and efficiency attenuation of the photovoltaic module. The obtained photovoltaic module luminous flux change data will describe the change in the power output of the photovoltaic panel under different time and environmental conditions.

[0107] Preferably, step S2 includes the following steps:

[0108] Step S21: performing a photovoltaic module power generation fluctuation analysis based on the photovoltaic module luminous flux change data to obtain photovoltaic module power generation fluctuation data;

[0109] Step S22: performing power output variability assessment on the photovoltaic module power generation fluctuation data to obtain photovoltaic module power output variability data;

[0110] Step S23: performing power quality attenuation assessment based on the photovoltaic module power output variability data to obtain photovoltaic module power quality attenuation data;

[0111] Step S24: extracting grid transmission voltage fluctuations based on the photovoltaic module power quality attenuation data to obtain grid transmission voltage fluctuation data.

[0112] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0113] Step S21: performing a photovoltaic module power generation fluctuation analysis based on the photovoltaic module luminous flux change data to obtain photovoltaic module power generation fluctuation data;

[0114] In an embodiment of the present invention, a data statistical analysis method is used to process the luminous flux change data obtained from the photovoltaic module. By recording the luminous flux data (in lumens) within a specific time period and combining it with the photoelectric conversion efficiency of the photovoltaic module, the power generation of the photovoltaic module in the corresponding time period is calculated. The fluctuation of power generation is mainly affected by factors such as sunlight intensity, weather conditions, and angle changes of the photovoltaic panel. In specific implementation, a data acquisition system is used to monitor the light intensity in real time, and a time series analysis method is used to analyze the fluctuation of power generation in combination with the design efficiency and conversion coefficient of the photovoltaic module. The analysis results will reveal the fluctuation trend of the power generation of the photovoltaic module in different time periods, and organize the data into statistical results of the frequency and amplitude of fluctuations.

[0115] Step S22: performing power output variability assessment on the photovoltaic module power generation fluctuation data to obtain photovoltaic module power output variability data;

[0116] In an embodiment of the present invention, by collecting the power generation fluctuation data obtained in step S21, a fluctuation analysis is performed on the time series of power generation. Statistical methods (such as standard deviation, root mean square error, coefficient of variation, etc.) are used to quantitatively evaluate the power generation fluctuation and evaluate the stability and fluctuation degree of power generation output. For example, the adaptability of the system under load changes and light fluctuations can be evaluated by calculating the maximum fluctuation amplitude of power generation per unit time. In addition, the frequency of power generation fluctuations can be analyzed to determine the periodicity of power generation fluctuations and their impact on power grid supply. By long-term monitoring of data, the variability of power output of photovoltaic modules under different environmental conditions can be identified, thereby providing a basis for power quality attenuation assessment.

[0117] Step S23: performing power quality attenuation assessment based on the photovoltaic module power output variability data to obtain photovoltaic module power quality attenuation data;

[0118] In an embodiment of the present invention, the power output quality of the photovoltaic system is analyzed based on the power output variability data in step S22, mainly considering factors such as the frequency fluctuation of the power output, the instantaneous power change and the influence of the grid load. A power quality monitoring device (such as a power quality analyzer) is used to monitor the power output of the photovoltaic module in real time to capture power quality fluctuation data. Power quality assessment usually includes analysis of power parameters such as voltage deviation, frequency fluctuation and harmonics. By filtering and statistically analyzing the high-frequency data of the output power fluctuation, the degree of power quality attenuation is evaluated, especially during the long-term operation of the photovoltaic module, the influence of weather changes, insufficient light or excessive light on the power output quality is evaluated. By comparing and analyzing these data, the power quality attenuation data is calculated, and the quality attenuation trend of the power output of the photovoltaic module is clarified.

[0119] Step S24: extracting grid transmission voltage fluctuations based on the photovoltaic module power quality attenuation data to obtain grid transmission voltage fluctuation data.

[0120] In an embodiment of the present invention, the impact of photovoltaic modules on the grid transmission voltage is analyzed based on the power quality degradation data obtained in step S23, combined with real-time monitoring data from the power transmission system. In specific implementations, a power grid monitoring system is used to collect grid voltage data, monitor instantaneous changes in grid voltage, and compare and analyze these changes with changes in the power output of the photovoltaic modules. By correlating the photovoltaic module power quality degradation data with grid transmission voltage fluctuation data, the main causes of voltage fluctuations, such as sudden changes in photovoltaic power generation or sharp fluctuations in grid load, are identified. A voltage fluctuation analyzer is used to detect unstable or frequent voltage fluctuations in the grid, thereby extracting grid transmission voltage fluctuation data.

[0121] Preferably, step S21 includes the following steps:

[0122] Step S211: dividing the photovoltaic module luminous flux change data into luminous flux size to obtain photovoltaic module strong luminous flux data and photovoltaic module weak luminous flux data;

[0123] Step S212: performing peak radiation calculation based on the strong luminous flux data of the photovoltaic module to obtain luminous flux peak radiation data;

[0124] Step S213: performing power generation surge trend analysis on the luminous flux peak radiation data to obtain photovoltaic module power generation surge trend data;

[0125] Step S214: performing component over-illumination processing according to the luminous flux peak radiation data to obtain photovoltaic component over-illumination data;

[0126] Step S216: performing photovoltaic effect attenuation evaluation on the weak light flux data of the photovoltaic module to obtain photovoltaic effect attenuation data of the photovoltaic module;

[0127] Step S217: performing photovoltaic module power generation fluctuation analysis based on photovoltaic module photovoltaic effect attenuation data, photovoltaic module over-illumination data, and photovoltaic module power generation sudden increase trend data to obtain photovoltaic module power generation fluctuation data.

[0128] In an embodiment of the present invention, a luminous flux threshold is set based on the luminous flux data collected by the photovoltaic module to distinguish between strong and weak light ranges. Specifically, a critical value (e.g., 1000 lx) is set. When the luminous flux exceeds this threshold, it is classified as strong luminous flux data; when the luminous flux is less than this threshold, it is classified as weak luminous flux data. A flux meter (or light sensor) is used to measure the light intensity, ensuring high accuracy and real-time data collection. For each collection time period, the luminous flux value is recorded and classified according to the set threshold to obtain luminous flux data of different intensities. This step can stratify the luminous flux changes of the photovoltaic module under different lighting conditions, providing detailed light intensity data for subsequent power generation analysis. The highest light intensity value in the strong light flux data is selected as the peak light data for that time period. The light intensity at each time point is accurately measured using a radiation intensity meter (e.g., a radiometer). The radiant power at each moment is calculated by combining the photovoltaic module's photoelectric conversion efficiency and radiation intensity. The formula for calculating radiant power is: Power = Light Intensity × Photovoltaic Panel Area × Photovoltaic Conversion Efficiency. This method is used to calculate the peak radiant power at each moment under strong light conditions and compile it into peak luminous flux radiation data. The key to this step is to accurately assess the maximum amount of radiation received by the photovoltaic module under strong light conditions by monitoring light intensity and radiant power in real time. Using the photovoltaic module's power generation efficiency model, the peak radiation data obtained in step S212 is combined with the module's electrical performance characteristics (such as maximum power point and conversion efficiency) to calculate the power generation at each moment. Next, a time series analysis method is used to analyze short-term sudden increases in power generation. By setting an appropriate fluctuation detection threshold (e.g., a sudden increase in power generation exceeding 5%), time periods of significant fluctuations (sudden increases) in power generation over a short period of time are identified. For each sudden increase event, the lighting conditions and related environmental factors are recorded, and their short-term impact on the photovoltaic module's power generation is analyzed. Trend data for sudden increases in photovoltaic module power generation is derived and presented graphically or using statistical methods. This step analyzes trends in rapid increases in power generation to assess the operational stability of PV modules under strong sunlight and predict future power generation fluctuations. By comparing the operating voltage and current of the PV module with the actual radiation intensity, it is determined whether there is excessive sunlight exposure. Excessive sunlight exposure is defined as light intensity exceeding the designed operating range of the PV module, which typically causes the module to overheat and affect the photoelectric conversion efficiency. Use an infrared thermal imager or temperature sensor to monitor the surface temperature of the PV panel to determine whether thermal runaway is occurring. If the radiation intensity exceeds the designed load and the temperature of the PV module continues to rise to a critical value, it can be determined to be excessive sunlight. At this point, the changing trends of light intensity, module temperature, and conversion efficiency over this period are calculated to generate PV module excessive sunlight data.The luminous flux data of the photovoltaic module under weak light conditions is selected, and the voltage-current (IV) characteristic curve model of the photovoltaic module is used to calculate the output power at each moment. Under weak light conditions, due to the low photoelectric conversion efficiency of the photovoltaic module, the attenuation of the output power is more significant. Therefore, the focus is on monitoring the attenuation of the conversion efficiency under weak light conditions. By statistically analyzing the photovoltaic effect attenuation rate of different time periods (for example, the proportion of the reduction in photoelectric conversion efficiency under weak light conditions), the attenuation data of the photovoltaic module under weak light conditions is obtained. Combined with the data in step S216 and step S214, the joint impact of photovoltaic effect attenuation and excessive light on the power generation of the photovoltaic module is analyzed. Using the weighted average method, each data (photovoltaic effect attenuation, excessive light, sudden increase in power generation) is weighted and calculated to obtain a comprehensive power generation fluctuation index. By establishing a mathematical model, the impact of different light intensities on power generation fluctuations is comprehensively considered to obtain the power generation fluctuation data of the photovoltaic module.

[0129] Preferably, step S217 includes the following steps:

[0130] Performing thermal cumulative overload detection on photovoltaic modules based on excessive illumination data of photovoltaic modules to obtain thermal cumulative overload data of photovoltaic modules;

[0131] Evaluate the reduction in photoelectric conversion efficiency based on the accumulated thermal overload data of the photovoltaic modules to obtain the reduction in photoelectric conversion efficiency of the photovoltaic modules;

[0132] The performance degradation of photovoltaic modules is estimated based on the reduction data of photovoltaic module photoelectric conversion efficiency and the cumulative thermal overload data of photovoltaic modules, and the performance degradation data of photovoltaic modules is obtained;

[0133] PV module load growth detection is performed based on PV module performance attenuation data and PV module power generation surge trend data to obtain PV module load growth data;

[0134] Performing photovoltaic module power generation instability detection on photovoltaic module load growth data to obtain photovoltaic module power generation instability data;

[0135] The photovoltaic module power generation fluctuation analysis is performed based on the photovoltaic module power generation instability data and the photovoltaic effect attenuation data of the photovoltaic module to obtain the photovoltaic module power generation fluctuation data.

[0136] In an embodiment of the present invention, a comprehensive assessment is performed based on data collected from photovoltaic modules under excessive light conditions, combining light intensity, module temperature, and the rated power of the photovoltaic panel. Infrared temperature sensors or thermal imaging equipment are used to monitor changes in the surface temperature of the photovoltaic panel in real time. The temperature value at each moment is recorded and compared with the module's designed operating temperature range. When the temperature exceeds the module's safe operating range, a thermal overload event is flagged. Furthermore, the duration of the temperature exceedance within each time period is recorded, and a thermal accumulation model is used to calculate the cumulative thermal load of the photovoltaic module, generating cumulative thermal overload data. Based on this cumulative thermal overload data, the degree of overload for each time period is determined. The degradation of the photovoltaic module's photoelectric conversion efficiency under excessive heat load is assessed by combining its operating characteristics (such as maximum operating temperature and conversion efficiency). There is a clear negative correlation between temperature and photoelectric conversion efficiency; as temperature increases, the module's conversion efficiency decreases significantly. The trend of conversion efficiency is calculated by measuring the ratio of the photovoltaic module's output power to the irradiated light intensity. When the temperature exceeds a set critical value, the efficiency decay rate increases rapidly, allowing the degree of degradation of the photoelectric conversion efficiency for each time period to be determined. This step generates data on the photovoltaic module's reduced photoelectric conversion efficiency by quantifying the relationship between excessive thermal load and efficiency. The reduced photoelectric conversion efficiency data obtained in step S222 is combined with the cumulative thermal overload data to analyze the impact of overload on the long-term performance of the photovoltaic modules. A mathematical modeling approach is used to simulate the performance changes of photovoltaic modules under prolonged excessive thermal load. Based on the measured duration of excessive light and overload, the cumulative degradation effect on the photovoltaic module's photoelectric efficiency is calculated. Furthermore, based on the module's long-term usage data, the degradation rate of the photovoltaic modules under different environments is evaluated, and the future performance degradation trend of the photovoltaic modules is estimated. The performance degradation data from step S223 is combined with the power generation surge trend data to analyze the probability of load increases during the photovoltaic module's operation. Power generation surges are typically caused by sudden increases in light intensity or temperature changes, which can lead to system overload. By combining power generation data with the performance degradation model, the load changes of the photovoltaic modules over a certain period of time can be predicted. A load analysis algorithm is used to calculate the load growth rate based on the trend changes in historical data, identify load growth patterns, and generate load growth data. Load growth data is used to analyze the power generation stability of PV modules under different load conditions. By monitoring the output power of PV modules in real time and combining it with load variation data, the power generation stability of the modules under different load conditions is assessed. Rapid load growth rates and large fluctuations in light intensity can lead to unstable output power of PV modules during certain time periods. By calculating the impact of load growth on power generation fluctuations and combining it with historical power generation data, a fluctuation detection algorithm is used to assess power generation instability. By statistically analyzing the power generation fluctuation amplitude within each time period, power generation instability is identified and generated as PV module power generation instability data.The power generation instability data from step S225 is combined with the photovoltaic effect attenuation data from step S216 to analyze their combined impact on power generation fluctuations. Power generation instability is often closely related to changes in lighting conditions and system load fluctuations, while photovoltaic effect attenuation directly affects the power generation capacity of photovoltaic modules. Through regression analysis of historical data, the magnitude of power generation fluctuations and their temporal trends are assessed. Furthermore, a volatility analysis method is used to calculate power generation fluctuation data for different time periods and visualize this data to facilitate identification of time periods with higher power generation instability. By comprehensively considering the impact of power generation instability and photovoltaic effect attenuation on power generation fluctuations, power generation fluctuation data for photovoltaic modules is generated.

[0137] Preferably, step S23 includes the following steps:

[0138] Step S231: Acquire the photovoltaic module grid transmission capacity data;

[0139] Step S232: performing statistics on instantaneous surges in power output on the PV module power output variability data to obtain instantaneous surge data in power output;

[0140] Step S233: performing power transmission capacity overload detection on the photovoltaic module grid transmission capacity data according to the power output instantaneous surge data to obtain power transmission capacity overload data;

[0141] Step S234: performing a grid frequency imbalance risk assessment on the power transmission capacity overload data and the power output instantaneous surge data to obtain grid frequency imbalance risk data;

[0142] Step S235: performing grid harmonic imbalance state detection according to the grid frequency imbalance risk data to obtain grid harmonic imbalance state data;

[0143] Step S236: Perform power quality degradation assessment based on the grid harmonic imbalance state data and the grid frequency imbalance risk data to obtain photovoltaic module power quality degradation data.

[0144] In an embodiment of the present invention, data on the grid transmission capacity of photovoltaic modules is obtained. This step relies on measuring the connection capability between the photovoltaic modules and the grid. Grid transmission capacity refers to the maximum power capacity that the grid can carry when receiving and transmitting electricity. Specifically, this method uses power monitoring equipment (such as power sensors or smart meters) to measure the power output of the photovoltaic system and record the data. This data, including current, voltage, and power factor, reflects the real-time transmission status of the grid. This real-time data can be used to analyze the grid transmission capacity and compare it with a predetermined standard capacity to obtain the power transmission capacity data for the photovoltaic system when connected to the grid. During operation, the output power of photovoltaic modules fluctuates due to changes in light intensity. In particular, during periods of cloud cover or significant weather changes, the power output of the photovoltaic system may experience momentary surges or decreases. This step monitors the power output of the photovoltaic modules in real time and records the changes in the power output of the photovoltaic modules using equipment that monitors transient power fluctuations (such as a high-frequency power meter or oscilloscope). Whenever the power output experiences a significant surge within a short period of time, it is determined to be a "transient surge event." Data acquisition equipment can record the timing and magnitude of these transient surges, and statistical analysis tools can be used to calculate the frequency, magnitude, and duration of these surge events, generating data on transient power output surges. This step requires analyzing the transient power output surge data acquired in step S232 to determine whether any transient surge magnitude exceeds the upper limit of the grid's transmission capacity. When the PV module's power output surges to a level exceeding the grid's transmission capacity, the grid transmission system becomes overloaded. Specifically, this involves real-time monitoring of the PV system's power output and the grid's transmission capacity. If the power output exceeds a predetermined threshold of the grid's transmission capacity (typically set based on the grid's rated power capacity), an overload event is considered to have occurred. At this point, an automated load detection system is used to record the overload of the power transmission capacity and generate power transmission capacity overload data. Grid frequency imbalance is often associated with power output overload or load variations. If the PV module's power output experiences a transient surge that exceeds the grid's transmission capacity, it can lead to fluctuations or imbalances in the grid frequency. At this time, frequency monitoring instruments (such as grid frequency meters) are used to measure the frequency fluctuations of the power grid in real time, and these data are combined with the power transmission capacity overload data. By analyzing the relationship between the load changes caused by the sudden increase in power and the fluctuation of the grid frequency, the risk of frequency imbalance can be assessed. If the frequency fluctuation exceeds the allowable range of the power grid system, it is determined that the power grid has a frequency imbalance. Harmonic imbalance is an important consequence of grid frequency imbalance, which is usually caused by unstable power output. In particular, when the power output suddenly increases, resulting in unstable load, the power grid will generate high-order harmonics, which will interfere with the normal operation of the power grid. In order to detect the harmonic imbalance state of the power grid, it is necessary to deploy harmonic analysis instruments to monitor the harmonic components of different frequencies in the power grid in real time.Frequency analysis identifies high-frequency and low-frequency harmonics in the power grid, along with parameters such as amplitude and phase. Based on grid frequency imbalance risk data and real-time harmonic data, the system assesses whether the power grid is experiencing harmonic imbalance. If the harmonic content exceeds standard limits, the grid is determined to have harmonic imbalance, and grid harmonic imbalance status data is generated. Grid frequency imbalance and harmonic imbalance not only affect grid stability but also directly impact the power generation quality of photovoltaic modules. When receiving grid power with significant frequency fluctuations, the output power of photovoltaic modules is attenuated. Based on the grid harmonic imbalance status data from step S235 and the grid frequency imbalance risk data from step S234, the impact of frequency fluctuations and harmonic imbalance on the power output of photovoltaic modules is analyzed. A power quality analyzer monitors the output of the photovoltaic system and calculates the degree of power quality degradation caused by frequency imbalance and harmonic imbalance. By comparing the power output waveforms under normal grid conditions with those under frequency imbalance and harmonic imbalance conditions, power quality degradation data for the photovoltaic modules can be obtained.

[0145] Preferably, step S3 includes the following steps:

[0146] Step S31: performing overcharge assessment of the photovoltaic energy storage battery based on the grid transmission voltage fluctuation data and the photovoltaic module power quality attenuation data to obtain photovoltaic energy storage battery overcharge data;

[0147] Step S32: performing an energy storage battery overheat detection on the photovoltaic energy storage battery overcharge data to obtain the energy storage battery overheat data;

[0148] Step S33: Calculating the battery overheating expansion probability based on the electric energy storage battery overheating data and the photovoltaic electric energy storage battery overcharge data to obtain the battery overheating expansion probability data;

[0149] Step S34: performing battery life attenuation estimation on the battery overheating expansion probability data and the photovoltaic module power quality attenuation data to obtain photovoltaic storage battery life attenuation data.

[0150] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0151] Step S31: performing overcharge assessment of the photovoltaic energy storage battery based on the grid transmission voltage fluctuation data and the photovoltaic module power quality attenuation data to obtain photovoltaic energy storage battery overcharge data;

[0152] In an embodiment of the present invention, grid voltage fluctuations and power quality attenuation of the photovoltaic system lead to instability in the charging process of the storage battery. During the specific implementation process, the voltage fluctuation data of the grid is monitored in real time, combined with the power quality attenuation data output by the photovoltaic modules, the relationship between the grid voltage fluctuation and the battery charging process is analyzed, and the grid voltage fluctuation is accurately measured using a voltage sensor and a power meter to obtain data on grid voltage changes. Then, the battery charging status is monitored by the battery management system (BMS), including information such as the battery voltage, current, and temperature, to determine whether overcharging has occurred. If the battery voltage reaches or exceeds the predetermined charging upper limit, and the grid voltage fluctuates greatly, resulting in an abnormal battery charging process, it can be determined that the battery is overcharged, and the overcharge data of the photovoltaic power storage battery is recorded.

[0153] Step S32: performing an energy storage battery overheat detection on the photovoltaic energy storage battery overcharge data to obtain the energy storage battery overheat data;

[0154] In an embodiment of the present invention, the battery may overheat during the overcharging process, affecting the service life and safety of the battery. During specific operations, the battery temperature is monitored in real time by the battery management system (BMS). When the charging current of the battery is too large or the battery charging state is abnormal, the battery will overheat. In order to accurately monitor the overheating of the battery, a temperature sensor (such as a thermocouple or NTC temperature sensor) is installed inside and around the battery to collect battery temperature data in real time. When the battery temperature exceeds the preset safety temperature threshold, the battery is determined to be overheated and battery overheating data is generated. At the same time, by comparing the battery overheating with the battery charging voltage, current and other data, it is further confirmed whether the overheating is caused by overcharging, and then the photovoltaic power storage battery overheating data is obtained.

[0155] Step S33: Calculating the battery overheating expansion probability based on the electric energy storage battery overheating data and the photovoltaic electric energy storage battery overcharge data to obtain the battery overheating expansion probability data;

[0156] In an embodiment of the present invention, a temperature sensor is used to obtain battery temperature data, which is then combined with overcharge data to determine whether the battery has experienced overheating or overcharging. Then, based on the battery's thermal expansion characteristics, a relationship model between battery temperature and expansion is established using experimental data. This model is typically provided by the battery manufacturer or calibrated through field testing. Based on the overcharge and overheat data, the model is used to calculate the battery's expansion probability at different temperatures and charge states. This expansion probability data reflects the probability of battery expansion under the current operating state and can provide a basis for subsequent battery life estimation and risk control. Ultimately, the battery overheating expansion probability data is obtained.

[0157] Step S34: performing battery life attenuation estimation on the battery overheating expansion probability data and the photovoltaic module power quality attenuation data to obtain photovoltaic storage battery life attenuation data.

[0158] In this embodiment of the present invention, the battery overheating and expansion probability data from step S33 and the photovoltaic module power quality degradation data are combined to analyze the battery degradation trend during long-term operation. The battery management system monitors parameters such as the number of battery cycles, charge and discharge depth, and charge voltage to establish a battery degradation model. This model considers the impact of factors such as battery overheating and overcharging on battery performance, while also incorporating the impact of power quality degradation on battery output voltage and current. This model can be used to predict the extent of battery life degradation under different operating environments. If a battery is frequently overcharged and overheated, its lifespan will be significantly shortened, ultimately resulting in the degradation data for the photovoltaic storage battery lifespan.

[0159] Preferably, step S4 includes the following steps:

[0160] Step S41: performing storage battery capacity attenuation calculation based on photovoltaic storage battery life attenuation data to obtain storage battery capacity attenuation data;

[0161] Step S42: Calculating the battery storage capacity degradation based on the stored battery capacity degradation data to obtain battery storage capacity degradation data;

[0162] Step S43: performing a photovoltaic module system efficiency attenuation evaluation on the battery storage capacity degradation data to obtain photovoltaic module system efficiency attenuation data;

[0163] Step S44: performing photovoltaic module degradation risk warning on the photovoltaic module system efficiency attenuation data to obtain photovoltaic module degradation risk warning data, and feeding back the photovoltaic module risk warning data to the terminal.

[0164] In an embodiment of the present invention, a battery management system (BMS) records in real time the number of charge and discharge cycles, the depth of each charge and discharge (i.e., the amount of charge discharged), and the battery's operating temperature. Combining this data, a battery performance degradation model is used to calculate the battery's actual capacity degradation. This model takes into account the difference between the battery's rated capacity and actual capacity and, in combination with historical battery charge and discharge data (such as the number and depth of cycles), derives the battery's capacity degradation under current usage conditions. If the battery's capacity decreases by more than a preset threshold at a certain stage, it is determined to be capacity degradation, and further storage battery capacity degradation data is generated. By accurately calculating and monitoring these parameters, the battery's performance under different conditions can be effectively determined, and battery performance degradation can be promptly identified. Based on the battery capacity degradation data obtained in step S41, combined with the battery's output voltage and current data, the battery's storage capacity degradation is calculated. During this process, the battery management system monitors the battery's voltage and current changes during each charge and discharge cycle to assess the battery's energy storage and release capabilities under different charge and discharge conditions. If the battery's capacity degradation reaches a certain level, causing the battery's output voltage to be unable to remain within the normal operating range, it is determined to be a storage capacity degradation. This data is calculated by integrating battery operating data to determine the degree of battery storage capacity degradation, which is recorded as battery storage capacity degradation data. A decline in battery storage capacity directly impacts the efficiency of the photovoltaic system, particularly after prolonged operation. Decreased energy storage device efficiency can reduce overall system power output. To assess this impact, the system monitoring platform collects capacity degradation data for the storage batteries and data on battery storage capacity degradation. This data is then combined with photovoltaic system parameters such as the photovoltaic module's power generation capacity and inverter efficiency to calculate the degree of degradation in the photovoltaic module system efficiency due to the decline in battery storage capacity. Specifically, the operating efficiency of the energy storage component of the system is inferred based on the reduction in battery stored energy, and its impact on power output stability is also assessed. This calculation accurately determines system efficiency degradation data, enabling a comprehensive analysis of the photovoltaic module system efficiency degradation data in step S43. This analysis takes into account multiple factors, including the long-term use of the photovoltaic modules, the status of the energy storage device, and the lighting conditions of the photovoltaic modules. Using the photovoltaic system monitoring platform and data analysis algorithms, the trend of system efficiency degradation is determined, and thresholds are set to determine whether there is a risk of degradation. When system efficiency degradation reaches a predetermined risk threshold, the system generates degradation risk warning data, indicating that the PV panels are deteriorating or about to deteriorate. This warning data is fed back to the terminal in real time via a wireless communication module (such as GPRS, Wi-Fi, or ZigBee).

[0165] The present invention further provides a photovoltaic assembly device, comprising a photovoltaic assembly device body, a power supply unit, and an electrical control unit, wherein the power supply unit is installed inside the photovoltaic assembly device body, the electrical control unit is electrically connected to the power supply unit, and the electrical control unit is used to charge the photovoltaic assembly device body and control the photovoltaic assembly device body, wherein the electrical control unit is used to execute the photovoltaic assembly control method described above, and the electrical control unit includes:

[0166] Luminous flux change assessment module: obtains photovoltaic module design data; collects photovoltaic module installation positions based on the photovoltaic module design data to obtain photovoltaic module installation position data; measures light intensity changes based on the photovoltaic module installation position data to obtain light intensity change data; assesses photovoltaic module luminous flux changes based on the light intensity change data to obtain photovoltaic module luminous flux change data;

[0167] Voltage fluctuation extraction module: Calculates the fluctuation of photovoltaic module power generation based on the PV module luminous flux change data to obtain the PV module power generation fluctuation data; evaluates the power quality attenuation based on the PV module power generation fluctuation data to obtain the PV module power quality attenuation data; extracts the grid transmission voltage fluctuation based on the PV module power quality attenuation data to obtain the grid transmission voltage fluctuation data;

[0168] Battery life attenuation estimation module: Based on the grid transmission voltage fluctuation data and the photovoltaic module power quality attenuation data, the overcharge evaluation of the photovoltaic energy storage battery is performed to obtain the photovoltaic energy storage battery overcharge data; based on the photovoltaic energy storage battery overcharge data and the photovoltaic module power quality attenuation data, the battery life attenuation is estimated to obtain the photovoltaic storage battery life attenuation data;

[0169] Degradation risk warning module: Calculate the storage battery capacity attenuation based on the photovoltaic storage battery life attenuation data to obtain the storage battery capacity attenuation data; evaluate the photovoltaic module system efficiency attenuation based on the storage battery capacity attenuation data to obtain the photovoltaic module system efficiency attenuation data; perform photovoltaic module degradation risk warning based on the photovoltaic module system efficiency attenuation data to obtain photovoltaic module degradation risk warning data, and feed back the photovoltaic module risk warning data to the terminal.

[0170] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A photovoltaic module control method, characterized in that: The following steps are involved: Step S1: Acquire photovoltaic module design data; collect photovoltaic module installation positions according to the photovoltaic module design data to obtain photovoltaic module installation position data; measure light intensity changes according to the photovoltaic module installation position data to obtain light intensity change data; evaluate photovoltaic module luminous flux changes according to the light intensity change data to obtain photovoltaic module luminous flux change data; Step S21: performing a photovoltaic module power generation fluctuation analysis based on the photovoltaic module luminous flux change data to obtain photovoltaic module power generation fluctuation data; wherein step S21 includes the following steps: Step S211: dividing the photovoltaic module luminous flux change data into luminous flux size to obtain photovoltaic module strong luminous flux data and photovoltaic module weak luminous flux data; Step S212: performing peak radiation calculation based on the strong luminous flux data of the photovoltaic module to obtain luminous flux peak radiation data; Step S213: performing power generation sudden increase trend analysis on the luminous flux peak radiation data to obtain photovoltaic module power generation sudden increase trend data; Step S214: performing component over-illumination processing according to the luminous flux peak radiation data to obtain photovoltaic component over-illumination data; Step S216: performing photovoltaic effect attenuation evaluation on the weak light flux data of the photovoltaic module to obtain photovoltaic effect attenuation data of the photovoltaic module; Step S217: performing a photovoltaic module power generation fluctuation analysis based on the photovoltaic module photovoltaic effect attenuation data, the photovoltaic module over-illumination data, and the photovoltaic module power generation sudden increase trend data to obtain photovoltaic module power generation fluctuation data; Step S217 includes the following steps: Performing thermal cumulative overload detection on photovoltaic modules based on excessive illumination data of photovoltaic modules to obtain thermal cumulative overload data of photovoltaic modules; Evaluate the reduction in photoelectric conversion efficiency based on the accumulated thermal overload data of the photovoltaic modules to obtain the reduction in photoelectric conversion efficiency of the photovoltaic modules; The performance degradation of photovoltaic modules is estimated based on the reduction data of photovoltaic module photoelectric conversion efficiency and the cumulative thermal overload data of photovoltaic modules, and the performance degradation data of photovoltaic modules is obtained; PV module load growth detection is performed based on PV module performance attenuation data and PV module power generation surge trend data to obtain PV module load growth data; Performing photovoltaic module power generation instability detection on photovoltaic module load growth data to obtain photovoltaic module power generation instability data; The photovoltaic module power generation fluctuation analysis is performed based on the photovoltaic module power generation instability data and the photovoltaic effect attenuation data of the photovoltaic module to obtain the photovoltaic module power generation fluctuation data; Step S22: performing power output variability assessment on the photovoltaic module power generation fluctuation data to obtain photovoltaic module power output variability data; Step S23: Performing a power quality attenuation assessment based on the PV module power output variability data to obtain PV module power quality attenuation data; Step S23 includes the following steps: Step S231: Acquire the photovoltaic module grid transmission capacity data; Step S232: performing statistics on instantaneous surges in power output on the PV module power output variability data to obtain instantaneous surge data in power output; Step S233: performing power transmission capacity overload detection on the photovoltaic module grid transmission capacity data according to the power output instantaneous surge data to obtain power transmission capacity overload data; Step S234: performing a grid frequency imbalance risk assessment on the power transmission capacity overload data and the power output instantaneous surge data to obtain grid frequency imbalance risk data; Step S235: performing grid harmonic imbalance state detection according to the grid frequency imbalance risk data to obtain grid harmonic imbalance state data; Step S236: Perform power quality degradation assessment based on the grid harmonic imbalance status data and the grid frequency imbalance risk data to obtain the photovoltaic module power quality degradation data. Step S24: extracting grid transmission voltage fluctuations based on the photovoltaic module power quality attenuation data to obtain grid transmission voltage fluctuation data; Step S3: performing an overcharge assessment of the photovoltaic energy storage battery based on the grid transmission voltage fluctuation data and the photovoltaic module power quality attenuation data to obtain photovoltaic energy storage battery overcharge data; and performing a battery life attenuation estimation based on the photovoltaic energy storage battery overcharge data and the photovoltaic module power quality attenuation data to obtain photovoltaic storage battery life attenuation data; wherein step S3 includes the following steps: Step S31: performing overcharge assessment of the photovoltaic energy storage battery based on the grid transmission voltage fluctuation data and the photovoltaic module power quality attenuation data to obtain photovoltaic energy storage battery overcharge data; Step S32: performing an energy storage battery overheat detection on the photovoltaic energy storage battery overcharge data to obtain the energy storage battery overheat data; Step S33: Calculating the battery overheating expansion probability based on the electric energy storage battery overheating data and the photovoltaic electric energy storage battery overcharge data to obtain the battery overheating expansion probability data; Step S34: estimating battery life attenuation based on the battery overheating expansion probability data and the photovoltaic module power quality attenuation data to obtain photovoltaic storage battery life attenuation data; Step S4: Calculate the storage battery capacity attenuation based on the photovoltaic storage battery life attenuation data to obtain the storage battery capacity attenuation data; evaluate the photovoltaic module system efficiency attenuation based on the storage battery capacity attenuation data to obtain the photovoltaic module system efficiency attenuation data; perform photovoltaic module degradation risk warning based on the photovoltaic module system efficiency attenuation data to obtain photovoltaic module degradation risk warning data, and feed back the photovoltaic module risk warning data to the terminal.

2. The photovoltaic module control method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire photovoltaic module design data; Step S12: collecting the photovoltaic module installation position according to the photovoltaic module design data to obtain the photovoltaic module installation position data; Step S13: measuring the solar radiation variation according to the photovoltaic module installation position data to obtain solar radiation variation data; Step S14: evaluating the luminous flux variation of the photovoltaic module according to the sunlight variation data and the photovoltaic module design data to obtain the luminous flux variation data of the photovoltaic module.

3. The photovoltaic module control method according to claim 2, characterized in that: Step S14 includes the following steps: Step S141: Calculating the sunlight angle according to the sunlight variation data to obtain sunlight angle data; Step S142: performing a solar direct intensity detection on the solar illumination change data to obtain solar direct intensity data; Step S143: Calculating the solar panel tilt angle based on the photovoltaic module design data to obtain solar panel tilt angle data; Step S144: Calculating the change in the light incident angle based on the solar panel tilt angle data and the sunlight angle data to obtain sunlight incident angle change data; Step S145: performing statistics on changes in radiation received by the solar panel based on the sunlight incident angle change data and the sunlight direct intensity data to obtain the radiation received by the solar panel change data; Step S146: performing photovoltaic module luminous flux variation evaluation on the solar panel received radiation variation data to obtain photovoltaic module luminous flux variation data.

4. The photovoltaic module control method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing storage battery capacity attenuation calculation based on photovoltaic storage battery life attenuation data to obtain storage battery capacity attenuation data; Step S42: Calculating the battery storage capacity degradation based on the stored battery capacity degradation data to obtain battery storage capacity degradation data; Step S43: performing a photovoltaic module system efficiency attenuation evaluation on the battery storage capacity degradation data to obtain photovoltaic module system efficiency attenuation data; Step S44: performing photovoltaic module degradation risk warning on the photovoltaic module system efficiency attenuation data to obtain photovoltaic module degradation risk warning data, and feeding back the photovoltaic module risk warning data to the terminal.

5. A photovoltaic module device, characterized in that: The photovoltaic module comprises a main body, a power supply, and an electrical control unit. The power supply is installed inside the main body of the photovoltaic module. The electrical control unit is electrically connected to the power supply. The electrical control unit is used to charge and control the main body of the photovoltaic module. The electrical control unit is used to execute the photovoltaic module control method according to claim 1. The electrical control unit includes: Luminous flux change assessment module: obtains photovoltaic module design data; collects photovoltaic module installation positions based on the photovoltaic module design data to obtain photovoltaic module installation position data; measures light intensity changes based on the photovoltaic module installation position data to obtain light intensity change data; assesses photovoltaic module luminous flux changes based on the light intensity change data to obtain photovoltaic module luminous flux change data; Voltage fluctuation extraction module: Calculates the fluctuation of photovoltaic module power generation based on the PV module luminous flux change data to obtain the PV module power generation fluctuation data; evaluates the power quality attenuation based on the PV module power generation fluctuation data to obtain the PV module power quality attenuation data; extracts the grid transmission voltage fluctuation based on the PV module power quality attenuation data to obtain the grid transmission voltage fluctuation data; Battery life attenuation estimation module: Based on the grid transmission voltage fluctuation data and the photovoltaic module power quality attenuation data, the overcharge evaluation of the photovoltaic energy storage battery is performed to obtain the photovoltaic energy storage battery overcharge data; based on the photovoltaic energy storage battery overcharge data and the photovoltaic module power quality attenuation data, the battery life attenuation is estimated to obtain the photovoltaic storage battery life attenuation data; Degradation risk warning module: Calculate the storage battery capacity attenuation based on the photovoltaic storage battery life attenuation data to obtain the storage battery capacity attenuation data; evaluate the photovoltaic module system efficiency attenuation based on the storage battery capacity attenuation data to obtain the photovoltaic module system efficiency attenuation data; perform photovoltaic module degradation risk warning based on the photovoltaic module system efficiency attenuation data to obtain photovoltaic module degradation risk warning data, and feed back the photovoltaic module risk warning data to the terminal.

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