Photovoltaic module control method and device

Abstract

The invention relates to the technical field of photovoltaic modules, in particular to a photovoltaic module control method and device. The method comprises the following steps: obtaining an installation position according to design data of the photovoltaic module, carrying out illumination intensity change measurement, and further evaluating the luminous flux change of the photovoltaic module; on the basis of the luminous flux change data, calculating the generating capacity fluctuation of the photovoltaic module, further performing power quality attenuation evaluation, and extracting power grid transmission voltage fluctuation data; according to the power grid voltage fluctuation and power quality attenuation data, the battery overcharge condition and the battery life attenuation are evaluated, the battery capacity attenuation is further calculated, and the photovoltaic module system efficiency attenuation is evaluated; by analyzing system efficiency attenuation data, photovoltaic module degradation risk early warning is carried out, and risk early warning information is fed back to the terminal in real time; according to the invention, through early warning of power generation of the photovoltaic module, the deterioration prediction of the photovoltaic module is more accurate.

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, and convert direct current into alternating current through devices such as inverters to supply loads or connect to the grid for transmission. Photovoltaic modules are the core equipment in photovoltaic power generation systems, which are mainly composed of multiple photovoltaic cell units (also known as solar cells). Each cell unit is usually made of silicon material and can directly convert light energy into electrical energy. However, since the power generation capacity of photovoltaic cells is affected by factors such as sunlight intensity, light angle, temperature, and shadow, the output power of photovoltaic modules often fluctuates, which leads to greater instability in the output power of photovoltaic power generation systems at different times and under different environmental conditions. The output power of photovoltaic modules is also affected by factors such as matching differences between modules and battery aging. In order to improve the efficiency, stability and predictability of photovoltaic power generation systems, however, the traditional photovoltaic module technology has problems such as inaccurate calculation of photovoltaic module power generation efficiency and inaccurate prediction of photovoltaic module power storage battery aging. 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 comprises 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: Calculate the fluctuation of photovoltaic module power generation according to the photovoltaic module luminous flux change data to obtain the photovoltaic module power generation fluctuation data; evaluate the power quality attenuation according to the photovoltaic module power generation fluctuation data to obtain the photovoltaic module power quality attenuation data; extract the power grid transmission voltage fluctuation according to the photovoltaic module power quality attenuation data to obtain the power grid transmission voltage fluctuation data;

[0007] Step S3: performing an overcharge assessment of the photovoltaic power storage battery according to the grid transmission voltage fluctuation data and the photovoltaic module power quality attenuation data to obtain the photovoltaic power storage battery overcharge data; performing a battery life attenuation estimation according to the photovoltaic power storage battery overcharge data and the photovoltaic module power quality attenuation data to obtain the 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 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] The present invention collects and analyzes the design data of photovoltaic modules in detail, and the system can accurately evaluate the installation position of photovoltaic modules and the lighting conditions in the actual working environment to ensure the efficient operation of the photovoltaic system. Further measurement of light intensity changes and evaluation of luminous flux can timely capture the impact of lighting conditions on the power generation capacity of photovoltaic modules, and provide the system with key data support for power generation fluctuations. Through the accurate calculation of the power generation fluctuations of photovoltaic modules and the evaluation of power quality attenuation, the system can monitor the load fluctuations and power quality changes of the power grid, and discover potential power grid voltage instability problems in advance, thereby providing more stable voltage conditions for subsequent power transmission. By extracting the power grid transmission voltage fluctuations, the system can analyze the performance of photovoltaic power generation in the power grid, and based on this data, conduct overcharge evaluation of photovoltaic power storage batteries, and timely discover overcharge conditions that cause battery damage. Combined with the power quality attenuation data of photovoltaic modules, the battery life attenuation is estimated, and the trend of battery aging and performance degradation can be identified in advance, thereby preventing excessive battery loss and extending the service life of the battery. Further calculation of the capacity attenuation of the storage battery and evaluation of the system efficiency can reveal the performance attenuation of photovoltaic modules in long-term use, and provide data basis for optimizing system design. By analyzing these attenuation data, timely warning can be provided for the degradation risk of photovoltaic components, 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 all aspects 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 various data such as batteries, voltage, and light intensity, the system can provide an optimized scheduling plan to ensure the stability of photovoltaic power generation and the compatibility of 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 component control method and device, which solves the traditional problem of inaccurate calculation of photovoltaic component power generation efficiency and inaccurate prediction of photovoltaic component power storage battery aging, and improves the accuracy of photovoltaic component power generation efficiency calculation and photovoltaic component power storage battery aging prediction.

[0010] Preferably, step S1 comprises 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 sunlight variation according to the photovoltaic module installation position data to obtain sunlight 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 according to the geographical location and installation environment by collecting the design data and installation location 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 subsequent predictions 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 comprises the following steps:

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

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

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

[0020] Step S144: Calculate the change of the light incident angle according to the solar panel tilt angle data and the sunlight angle data to obtain the sunlight incident angle change data;

[0021] Step S145: performing statistics on changes in radiation received by the solar panel according to 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: evaluating the change in luminous flux of photovoltaic modules based on the change in radiation received by the solar panel to obtain the change in luminous flux of photovoltaic modules.

[0023] The present invention calculates the sunlight angle and direct intensity to accurately capture the changing trend of illumination, and helps identify the fluctuation of illumination intensity under different time and weather conditions, thereby providing important data support for the subsequent evaluation of the energy conversion efficiency of photovoltaic modules. Secondly, through a comprehensive analysis of the design data and installation position of the photovoltaic module, the inclination angle of the solar panel is calculated to ensure the optimal incident angle of illumination and the photovoltaic panel, thereby improving the photoelectric conversion efficiency. Further, combined with the calculation of the change in the incident angle of illumination, the impact of illumination 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 comprises 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: evaluating the power output variability of the photovoltaic module power generation fluctuation data to obtain the power output variability data of the photovoltaic module;

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

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

[0029] The present invention can accurately grasp the power generation fluctuation of the photovoltaic system under different lighting conditions by analyzing the power generation fluctuation of the photovoltaic component luminous flux change data, and provide basic data for the subsequent power output variability assessment. Then, the power output variability assessment of the power generation fluctuation data 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 cause power grid instability. Based on the power output variability data, the power quality attenuation assessment can be performed to determine the impact of the power generation with large fluctuations on the power grid voltage quality, and timely discover the trend of power quality degradation. Finally, by extracting the power quality attenuation data, the power grid transmission voltage fluctuation is evaluated, and an early warning signal is provided for the stable operation of the power 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 power grid load scheduling and power quality, and improve the reliability of photovoltaic power generation and the stability of the power grid.

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

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

[0032] Step S212: performing peak radiation calculation according to 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 the power generation sudden increase trend data of the photovoltaic module;

[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 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 the photovoltaic module power generation fluctuation data.

[0037] The present invention can clearly distinguish the power generation performance under strong light and weak light conditions by dividing the luminous flux change data into large and small, and help identify the working state of the system under extreme lighting conditions. Then, based on the strong light flux data, the peak radiation calculation can be performed to obtain the maximum radiation intensity of the photovoltaic module under strong light irradiation, and further analyze the trend of sudden increase in power generation. This process helps to identify the fluctuation of power generation under high radiation and predict the overload risk in advance. For excessive illumination under strong light conditions, the system avoids component damage caused by excessive illumination through corresponding processing. At the same time, the attenuation of the photovoltaic effect under weak light conditions is evaluated, and the reduction of power generation efficiency under low light can be discovered in time, helping to optimize the power generation strategy during inefficient operation. Finally, the comprehensive analysis of the attenuation of the photovoltaic effect, excessive illumination processing and the sudden increase in power generation can accurately evaluate the fluctuation of power generation of the component under different lighting conditions, and ensure the stability and efficiency of the photovoltaic system in various environments. Overall, the system can effectively cope with the challenges brought by changes in illumination to photovoltaic power generation, and improve the stability of the component and the consistency of power output.

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

[0039] Perform thermal cumulative overload detection on photovoltaic modules according to the excessive illumination data of photovoltaic modules, and obtain thermal cumulative overload data of photovoltaic modules;

[0040] The photovoltaic module thermal cumulative overload data is evaluated for the reduction in photoelectric conversion efficiency to obtain the photovoltaic module photoelectric conversion efficiency reduction data;

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

[0042] Perform photovoltaic module load growth detection based on photovoltaic module performance attenuation data and photovoltaic module power generation sudden increase trend data to obtain photovoltaic 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 fluctuation of photovoltaic module power generation is analyzed based on the instability data of photovoltaic module power generation and the photovoltaic effect attenuation data of photovoltaic modules, and the fluctuation data of photovoltaic module power generation is obtained.

[0045] The excessive illumination data of the photovoltaic module of the present invention is used for thermal cumulative overload detection, which helps to identify the potential overload risk of the module under high temperature conditions in advance, thereby avoiding performance loss caused by heat accumulation. By analyzing the overload data, it is possible to evaluate whether the photoelectric conversion efficiency of the module has decreased, and timely discover the attenuation of the photovoltaic effect to ensure that the module is always in the best operating state. Based on the change in photoelectric conversion efficiency, further performance attenuation is estimated 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 caused by excessive load. Power generation instability detection helps identify potential operating 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 photovoltaic systems, 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 variability data of power output of the photovoltaic module 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 power grid harmonic imbalance state detection according to the power grid frequency imbalance risk data to obtain power grid harmonic imbalance state data;

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

[0053] The present invention comprehensively evaluates the impact of the power output of the photovoltaic system on the stability of the power grid, ensuring the efficient and safe operation of the power system. By obtaining the power grid transmission capacity data of the photovoltaic module, it can provide basic data for the subsequent power output evaluation, ensuring that the system has sufficient transmission capacity to cope with the volatility of photovoltaic power generation. The instantaneous surge statistics of the power output variability are helpful to identify sudden power fluctuations in a short period of time and prepare for timely response. The power transmission capacity overload detection can prevent the system from being overloaded due to exceeding the power grid transmission capacity when the power output fluctuates violently, thereby avoiding equipment damage or shutdown. Combined with the risk assessment of power grid frequency imbalance, the frequency fluctuation problem can be identified, and early warning can be given to avoid the frequency imbalance of the power grid due to the fluctuation of the photovoltaic system. By detecting the harmonic imbalance state, the current distortion occurring in the power grid can be monitored in real time to ensure the stable operation of the power grid. In addition, the power quality attenuation assessment based on the frequency imbalance and harmonic state data helps to accurately judge the impact of the photovoltaic system on the power quality, ensuring that the power grid can maintain high-quality power supply, thereby optimizing the system efficiency and extending the service life of the photovoltaic equipment.

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

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

[0058] Step S34: estimate the battery life decay based on the battery overheat expansion probability data and the photovoltaic module power quality decay data to obtain the photovoltaic storage battery life decay data.

[0059] The present invention can monitor in real time whether the battery is in an overcharged state by evaluating the overcharge of the storage battery according to the grid transmission voltage fluctuation and the photovoltaic module power quality attenuation data, thereby avoiding safety hazards caused by overcharging, such as battery performance degradation or failure. Further overheat detection of the battery overcharge data helps to timely identify abnormal battery temperature and prevent fire or safety problems caused by battery overheating. The calculation of the probability of battery overheat expansion further optimizes battery management and helps to evaluate the risk of battery expansion in overheating conditions, so as to take preventive measures and reduce damage. Finally, by combining the battery overheat expansion probability data and the power quality attenuation data to estimate the battery life attenuation, the battery service life can be accurately predicted, providing a basis for optimizing maintenance, thereby extending the battery life cycle 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 comprises the following steps:

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

[0062] Step S42: Calculating the battery storage capacity decline according to the stored battery capacity decline data to obtain battery storage capacity decline data;

[0063] Step S43: performing a photovoltaic module system efficiency attenuation evaluation on the battery storage capacity reduction 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 decay data, and predict the future storage capacity of the battery through the capacity decay data, so as to discover potential problems in advance and avoid the impact of battery performance degradation on the operation of the entire system. Next, the efficiency decay of the photovoltaic module system is evaluated based on the battery storage capacity degradation data, which can accurately reflect the impact of battery performance changes on the entire photovoltaic system and help the operator to adjust and optimize the system operation strategy in a timely manner. Finally, through the analysis of the system efficiency decay, the risk warning of photovoltaic module degradation is implemented, and the potential system degradation risk is identified in advance and fed back to the terminal, providing users with real-time alarms, 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 also provides a photovoltaic assembly device, including 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 as described above, and the electrical control unit includes:

[0067] Luminous flux change assessment module: obtain photovoltaic module design data; collect photovoltaic module installation positions according to photovoltaic module design data to obtain photovoltaic module installation position data; measure light intensity changes according to photovoltaic module installation position data to obtain light intensity change data; evaluate photovoltaic module luminous flux changes according to light intensity change data to obtain photovoltaic module luminous flux change data;

[0068] Voltage fluctuation extraction module: Calculate the fluctuation of photovoltaic module power generation according to the change data of photovoltaic module luminous flux, and obtain the fluctuation data of photovoltaic module power generation; evaluate the power quality attenuation according to the fluctuation data of photovoltaic module power generation, and obtain the power quality attenuation data of photovoltaic module; extract the power grid transmission voltage fluctuation according to the power quality attenuation data of photovoltaic module, and obtain the power grid transmission voltage fluctuation data;

[0069] Battery life attenuation estimation module: perform overcharge evaluation of photovoltaic energy storage batteries based on grid transmission voltage fluctuation data and photovoltaic module power quality attenuation data to obtain photovoltaic energy storage battery overcharge data; perform battery life attenuation estimation based on photovoltaic energy storage battery overcharge data and photovoltaic module power quality attenuation data to obtain photovoltaic storage battery life attenuation data;

[0070] Deterioration 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 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 is that by collecting and analyzing the design data of photovoltaic modules in detail, the system can accurately evaluate the installation position of photovoltaic modules and the lighting conditions in the actual working environment to ensure the efficient operation of the photovoltaic system. Further measurement of light intensity changes and evaluation of luminous flux can timely capture the impact of lighting conditions on the power generation capacity of photovoltaic modules, and provide the system with key data support for power generation fluctuations. Through the accurate calculation of the power generation fluctuations of photovoltaic modules and the evaluation of power quality attenuation, the system can monitor the load fluctuations and power quality changes of the power grid, and discover potential grid voltage instability problems in advance, thereby providing more stable voltage conditions for subsequent power transmission. By extracting the voltage fluctuations of power grid transmission, the system can analyze the performance of photovoltaic power generation in the power grid, and based on this data, conduct overcharge evaluation of photovoltaic power storage batteries, and timely discover overcharge conditions that cause battery damage. Combined with the power quality attenuation data of photovoltaic modules, the battery life attenuation is estimated, which can identify the trend of battery aging and performance degradation in advance, thereby preventing excessive battery loss and extending the service life of the battery. Further calculation of the capacity attenuation of the storage battery and evaluation of the system efficiency can reveal the performance attenuation of photovoltaic modules in long-term use, and provide data basis for optimizing system design. By analyzing these attenuation data, timely warning can be provided for the degradation risk of photovoltaic components, 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 all aspects 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 various data such as batteries, voltage, and light intensity, the system can provide an optimized scheduling plan to ensure the stability of photovoltaic power generation and the compatibility of 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 component control method and device, which solves the traditional problem of inaccurate calculation of photovoltaic component power generation efficiency and inaccurate prediction of photovoltaic component power storage battery aging, and improves the accuracy of photovoltaic component power generation efficiency calculation and photovoltaic component power storage battery aging prediction. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0073] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

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

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

[0076] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0077] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0078] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" 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: Calculate the fluctuation of photovoltaic module power generation according to the photovoltaic module luminous flux change data to obtain the photovoltaic module power generation fluctuation data; evaluate the power quality attenuation according to the photovoltaic module power generation fluctuation data to obtain the photovoltaic module power quality attenuation data; extract the power grid transmission voltage fluctuation according to the photovoltaic module power quality attenuation data to obtain the power grid transmission voltage fluctuation data;

[0082] Step S3: performing an overcharge assessment of the photovoltaic power storage battery according to the grid transmission voltage fluctuation data and the photovoltaic module power quality attenuation data to obtain the photovoltaic power storage battery overcharge data; performing a battery life attenuation estimation according to the photovoltaic power storage battery overcharge data and the photovoltaic module power quality attenuation data to obtain the 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 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 schematic diagram of a process flow of a photovoltaic module control method and device of 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, the design data of the photovoltaic module is obtained. These data include the model, specification, power output, efficiency and applicable environmental parameters of the photovoltaic module, such as temperature and humidity. The design data can be obtained through the technical manual or database provided by the photovoltaic module manufacturer. Then, according to the design data of the photovoltaic module, its installation location is collected. The specific operation is to use the geographic information system (GIS) technology combined with the global positioning system (GPS) device to determine the longitude and latitude of the photovoltaic module installation location, the altitude and the surrounding environment shielding, such as buildings, trees or obstacles affecting light. The acquisition of the installation location data ensures the accuracy of the subsequent measurement data. Then, based on the installation location data, the light intensity change measurement is performed. This step uses a light intensity sensor (such as a radiometer) for field measurement to record the light intensity change data at different locations in different time periods. The light intensity sensor is measured in multiple time periods (such as early morning, noon, dusk, etc.) to ensure that all-weather light intensity data is collected. These data will be used to evaluate the luminous flux change of the photovoltaic module. Finally, based on the light intensity change data, the photoelectric conversion efficiency of the photovoltaic module is used to evaluate the luminous flux change. 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: Calculate the fluctuation of photovoltaic module power generation according to the photovoltaic module luminous flux change data to obtain the photovoltaic module power generation fluctuation data; evaluate the power quality attenuation according to the photovoltaic module power generation fluctuation data to obtain the photovoltaic module power quality attenuation data; extract the power grid transmission voltage fluctuation according to the photovoltaic module power quality attenuation data to obtain the power grid transmission voltage fluctuation data;

[0088] In the embodiment of the present invention, the photovoltaic module power generation fluctuation calculation is performed according to the photovoltaic module luminous flux change data obtained in step S1. This process processes the time series of the luminous flux change data, uses a mathematical modeling method to calculate the influence of the change in light intensity on the power generation, and obtains the power generation fluctuation data of the photovoltaic module in different time periods. At this time, the relationship between the change in luminous flux and the power generation can be analyzed by using a signal processing method such as linear regression or Fourier transform, and the fluctuation of the power generation can be obtained accordingly. Next, the power quality attenuation evaluation is performed according to the photovoltaic module power generation fluctuation data. Specifically, the power generation fluctuation data is compared with the power grid standard, and the impact on the power quality of the power grid is evaluated by analyzing the fluctuation amplitude, frequency and duration of the output power of the photovoltaic module. The calculation of the power quality attenuation data involves evaluating the power grid stability indicators such as power frequency offset and voltage fluctuation. Finally, the power grid transmission voltage fluctuation extraction is performed according to the power quality attenuation data. At this time, the voltage fluctuation data in the power grid transmission process is extracted by analyzing the relationship between the power quality attenuation and the power grid voltage. This can obtain real-time voltage data through a voltage monitoring device installed in the power grid, and perform fluctuation analysis to obtain the power grid transmission voltage fluctuation data.

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

[0090] In an embodiment of the present invention, an overcharge assessment of a photovoltaic energy storage battery is performed based on the grid transmission voltage fluctuation data and the photovoltaic module power quality attenuation data. This process relies on monitoring the charge and discharge state of the battery, and combines the grid transmission voltage fluctuation and the output data of the photovoltaic system to calculate whether the battery is at risk of overcharging. When the grid voltage fluctuates greatly, or the photovoltaic module power generation fluctuates greatly, the battery is prone to overcharging. Therefore, it is necessary to monitor the changes in battery voltage and current, combined with the rated charging capacity of the battery, to assess whether the battery will cause performance degradation due to overcharging. Next, based on the battery overcharge data and the photovoltaic module power quality attenuation data, the battery life attenuation is estimated. This step estimates the remaining service life of the battery by analyzing the battery charge and discharge times, charging state, and the instability of the photovoltaic system output using a battery life attenuation model. Based on the negative effects of overcharging and power quality instability, the performance degradation of the battery in the future is estimated, thereby obtaining the life attenuation data of the photovoltaic cell.

[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 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 the embodiment of the present invention, according to the photovoltaic storage battery life decay data, the storage battery capacity decay calculation is performed. This process calculates the remaining effective capacity of the storage battery after different years of use by analyzing the number of charging cycles, internal voltage decay and battery capacity loss of the battery in long-term use. Generally, the capacity decay of the battery accelerates with the increase of the use time. Therefore, it is necessary to deduce the remaining capacity of the battery based on the actual operation data of the battery and the degradation model provided by the manufacturer. Next, the photovoltaic module system efficiency decay data is evaluated for the storage battery capacity decay data. The efficiency of the photovoltaic system will be affected by the battery performance decay. Therefore, it is necessary to evaluate the change in the overall efficiency of the photovoltaic system based on the amplitude of the battery capacity decay and the actual power generation data of the photovoltaic module. This process evaluates the degree of decay of the system efficiency by calculating the overall power generation capacity of the photovoltaic module at different battery decay levels. Finally, according to the system efficiency decay data, the photovoltaic module degradation risk warning is performed. In this link, the potential failure risk of the photovoltaic module is analyzed in combination with the performance decay of the photovoltaic module and the change of the system efficiency. Specifically, by setting a threshold, when the system efficiency drops to a certain level, the degradation risk warning is triggered, and the alarm information is fed back to the photovoltaic system management platform.

[0093] Preferably, step S1 comprises 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 sunlight variation according to the photovoltaic module installation position data to obtain sunlight 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 the embodiment of the present invention, the design data of the photovoltaic module is obtained. Usually, these data are provided by the manufacturer of the photovoltaic module and include all the technical parameters of the module, such as rated power, open circuit voltage, short circuit current, maximum power point voltage and maximum power point current. These design data can be obtained through the technical manual, product specification sheet, or equipment information management system of the photovoltaic module. In addition, the environmental adaptability parameters of the photovoltaic module should also be obtained, including the operating temperature range, wind resistance, water resistance, etc., as well as requirements related to the installation environment, such as tilt angle, orientation, etc. These design data will provide an important reference for the subsequent installation location selection and light intensity analysis. During operation, ensure that the latest and accurate design data is obtained from the manufacturer or certified supplier of the photovoltaic module, and the photovoltaic module installation location is collected according to the photovoltaic module design data to obtain the installation location data. The key to this step is to accurately determine the installation location of the photovoltaic module, and use the geographic information system (GIS) combined with the global positioning system (GPS) technology to obtain the precise geographic coordinates (latitude and longitude) and altitude of the installation location. The selection of the installation location should not only consider the geographical location, but also consider 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 evaluation. 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 information of solar radiation, which can be directly obtained through the real-time output of the sensor. During the measurement process, the sensor should be connected to the data acquisition system, transmit the measurement data in real time, and record it through the data storage and management system for subsequent analysis. The obtained solar illumination change data should cover the illumination changes in multiple time periods. The radiation intensity and angle information recorded in the illumination change data are combined with the design data of the photovoltaic module (such as maximum power point voltage, short-circuit current, etc.), and the change of luminous flux is evaluated using specific mathematical formulas or standards. This process does not rely on complex models, but directly uses the electrical characteristics of the photovoltaic module and solar radiation data for calculation. Specifically, based on the sunlight intensity and incident angle, the radiant power under illumination conditions is calculated, and combined with the efficiency of the photovoltaic module (usually obtained through the technical manual of the photovoltaic module), the change of luminous flux of the photovoltaic module under different illumination conditions is calculated. At this time, it is necessary to ensure that the time stamp in the sunlight change data is consistent with the technical parameters recorded in the module design data, so as to accurately evaluate the change trend of the luminous flux. The result of the luminous flux change evaluation will provide the output power change of the photovoltaic module under different illumination environments, and finally generate the luminous flux change data of the photovoltaic module.

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

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

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

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

[0103] Step S144: Calculate the change of the light incident angle according to the solar panel tilt angle data and the sunlight angle data to obtain the sunlight incident angle change data;

[0104] Step S145: performing statistics on changes in radiation received by the solar panel according to 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: evaluating the change in luminous flux of photovoltaic modules based on the change in radiation received by the solar panel to obtain the change in luminous flux of photovoltaic modules.

[0106] In the embodiment of the present invention, the solar illumination angle is calculated according to the solar illumination change data. This step calculates the solar illumination angle data by recording the change of solar radiation intensity over time. In the specific implementation, a geographic information system (GIS) is used in combination with a solar position algorithm (such as a SPA algorithm) to calculate the azimuth and elevation of the sun at a specific moment according to the known time, date, latitude, longitude and geographic 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 sun is calculated. A radiation meter (such as a light intensity sensor) is used to monitor the light intensity of the installation site for a long time, and the solar illumination 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. In order 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 / m 2), this data is used to calculate the direct light intensity. By integrating multi-point data from multiple sensors, the change in the direct intensity of sunlight is detected, and then the direct intensity data of solar radiation 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 azimuth and elevation of the sun) obtained in step S141 with the tilt angle of the photovoltaic module 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 the photovoltaic panel, which is usually calculated by the formula: incident angle = cos-1[(cos(sun elevation angle)*cos(photovoltaic panel tilt angle))+(sin(sun elevation angle)*sin(photovoltaic panel tilt angle)*cos(sun azimuth angle - photovoltaic panel azimuth angle))]. This calculation needs to take into account the orientation and seasonal changes of the photovoltaic panel. The obtained data on the change in the incident angle of sunlight is used to calculate the actual radiation energy received by the photovoltaic panel surface based on the direct sunlight intensity data and the change in the incident angle using the radiation transfer model. The amount of radiation received is closely related to the incident angle, because the incident angle of sunlight affects the distribution of radiation on the photovoltaic panel. The intensity of direct radiation is proportional to the incident angle, and the radiation intensity is maximum when the incident angle is close to vertical. By statistically analyzing the incident angle and radiation intensity in different time periods, the changes in the radiation received by the solar panel can be obtained. 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.), calculate the actual electrical energy output of the photovoltaic panel. The change in 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 received radiation 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 consider many 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 power output of the photovoltaic panel under different time and environmental conditions.

[0107] Preferably, step S2 comprises 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: evaluating the power output variability of the photovoltaic module power generation fluctuation data to obtain the power output variability data of the photovoltaic module;

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

[0111] Step S24: extracting grid transmission voltage fluctuations according to 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) in a specific time period and combining 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 sunshine intensity, weather conditions, and angle changes of photovoltaic panels. 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 conjunction with the design efficiency and conversion coefficient of the photovoltaic module. The analysis results will reveal the fluctuation trend of power generation of photovoltaic modules in different time periods, and organize the data into statistical results of fluctuation frequency and amplitude.

[0115] Step S22: evaluating the power output variability of the photovoltaic module power generation fluctuation data to obtain the power output variability data of the photovoltaic module;

[0116] In an embodiment of the present invention, by collecting the power generation fluctuation data obtained in step S21, a volatility 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 power supply. Through 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: Perform power quality attenuation assessment based on the variability data of power output of the photovoltaic module to obtain power quality attenuation data of the photovoltaic module;

[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 frequency fluctuations of power output, instantaneous power changes, and grid load influence. Use power quality monitoring equipment (such as a power quality analyzer) to monitor the power output of photovoltaic modules in real time and capture power quality fluctuation data. Power quality assessment usually includes analysis of power parameters such as voltage deviation, frequency fluctuations, and harmonics. By filtering and statistically analyzing the high-frequency data of output power fluctuations, the degree of power quality attenuation is evaluated, especially during the long-term operation of photovoltaic modules, due to the impact of weather changes, insufficient light or excessive light on the power output quality, by comparing and analyzing these data, the power quality attenuation data is calculated, and the quality attenuation trend of the power output of photovoltaic modules is clarified.

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

[0120] In an embodiment of the present invention, the influence of the photovoltaic module on the transmission voltage of the power grid is analyzed according to the power quality attenuation data obtained in step S23, combined with the real-time monitoring data of the power transmission system. In specific implementation, the voltage data of the power grid is collected by the power grid monitoring system, the instantaneous change of the power grid voltage is monitored, and the change is compared and analyzed with the power output change of the photovoltaic module. By correlating the power quality attenuation data of the photovoltaic module with the fluctuation data of the power grid transmission voltage, the main cause of the voltage fluctuation is identified, such as the sudden change of photovoltaic power generation or the drastic fluctuation of the power grid load. The voltage instability or frequent fluctuations in the power grid are detected by the voltage fluctuation analyzer, so as to extract the power grid transmission voltage fluctuation data.

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

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

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

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

[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 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 the photovoltaic module power generation fluctuation data.

[0128] In the embodiment of the present invention, a luminous flux threshold is set based on the luminous flux data collected by the photovoltaic module to distinguish the range of strong light and weak light. Specifically, a critical value (such as 1000lx) is set. When the luminous flux is greater than the threshold, it is classified as strong luminous flux data; when the luminous flux is less than the threshold, it is classified as weak luminous flux data. A flux meter (or light sensor) is used to measure the light intensity to ensure that the data collection is accurate and real-time. 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 and provide 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 in the time period. The light intensity at each time point is accurately measured by a radiation intensity measuring instrument (such as a radiometer). Combined with the photoelectric conversion efficiency and radiation intensity of the photovoltaic module, the radiation power at each moment is calculated. The formula for calculating the radiation power is: power = light intensity × photovoltaic panel area × photoelectric conversion efficiency. This method is used to calculate the peak radiation power at each moment under strong light conditions one by one, and organize it into luminous flux peak radiation data. The key to this step is to accurately evaluate the maximum received radiation of the photovoltaic module under strong light conditions by real-time monitoring of the light intensity and radiation power, and use the power generation efficiency model of the photovoltaic module to combine the peak radiation data obtained in step S212 with the electrical performance characteristics of the photovoltaic module (such as maximum power point, conversion efficiency) to calculate the power generation at each moment. Next, the time series analysis method is used to analyze the sudden increase trend of power generation in a short period of time. By setting a suitable fluctuation detection threshold (such as a sudden increase in power generation exceeding 5%), the time period of large fluctuations (sudden increase) in power generation in a short period of time is identified. For each sudden increase event, the light conditions and related environmental factors in which it occurs are recorded, and its short-term impact on the power generation of the photovoltaic module is analyzed to obtain the trend data of the sudden increase in power generation of the photovoltaic module, and these data are expressed in charts or statistical methods. This step provides a basis for evaluating the operating stability of photovoltaic modules under strong light and predicting future power generation fluctuations by analyzing the trend of a sharp increase in power generation. By comparing the operating voltage, current and actual radiation intensity of the photovoltaic module, it is determined whether there is excessive light. Excessive light is defined as light intensity exceeding the designed operating range of the photovoltaic module, which usually causes the module temperature to be too high, affecting the photoelectric conversion efficiency. Use an infrared thermal imager or temperature sensor to monitor the temperature of the photovoltaic panel surface to determine whether there is thermal runaway. If the radiation intensity exceeds the design load and the temperature of the photovoltaic module continues to rise to the critical value, it can be determined as excessive light. At this time, calculate the changing trend of light intensity, module temperature and conversion efficiency during this time period to generate excessive light data for the photovoltaic module.The luminous flux data of the photovoltaic module under weak light conditions is selected, and the output power at each moment is calculated using the voltage-current (IV) characteristic curve model of the photovoltaic module. 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 attenuation of the conversion efficiency under weak light conditions is monitored in particular. By statistically analyzing the photovoltaic effect attenuation rate in 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 effects of photovoltaic effect attenuation and excessive light on the power generation of the photovoltaic module are analyzed. The weighted average method is used to weight each data item (photovoltaic effect attenuation, excessive light, and sudden increase in power generation) to obtain a comprehensive power generation fluctuation index. By establishing a mathematical model, the effects of different light intensities on power generation fluctuations are comprehensively considered to obtain the power generation fluctuation data of the photovoltaic module.

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

[0130] Perform thermal cumulative overload detection on photovoltaic modules according to the excessive illumination data of photovoltaic modules, and obtain thermal cumulative overload data of photovoltaic modules;

[0131] The photovoltaic module thermal cumulative overload data is evaluated for the reduction in photoelectric conversion efficiency to obtain the photovoltaic module photoelectric conversion efficiency reduction data;

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

[0133] Perform photovoltaic module load growth detection based on photovoltaic module performance attenuation data and photovoltaic module power generation sudden increase trend data to obtain photovoltaic 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 fluctuation of photovoltaic module power generation is analyzed based on the instability data of photovoltaic module power generation and the photovoltaic effect attenuation data of photovoltaic modules, and the fluctuation data of photovoltaic module power generation is obtained.

[0136] In an embodiment of the present invention, a comprehensive evaluation is performed based on the data of the photovoltaic module under excessive light conditions, combined with the light intensity, the module temperature, and the rated power of the photovoltaic panel. The surface temperature change of the photovoltaic panel is monitored in real time by an infrared temperature sensor or a thermal imaging device, and the temperature value at each moment is recorded and compared with the designed operating temperature range of the module. When the temperature exceeds the safe operating range of the component, it is marked as a thermal overload event. Further, the temperature exceeding the standard duration in each time period is recorded, and the thermal cumulative load of the photovoltaic module is calculated using a thermal accumulation model to obtain the thermal cumulative overload data. Based on the thermal cumulative overload data, the overload degree of each time period is determined. Combined with the working characteristics of the photovoltaic module (such as the maximum operating temperature and conversion efficiency), the reduction in the photoelectric conversion efficiency of the module under excessive heat load is evaluated. There is a clear negative correlation between temperature and photoelectric conversion efficiency. As the temperature increases, the conversion efficiency of the module will decrease significantly. By measuring the ratio of the output power of the photovoltaic module to the irradiated light intensity, the change trend of the conversion efficiency is calculated. When the temperature exceeds the set critical value, the proportion of efficiency attenuation increases rapidly, and then the degree of reduction in the photoelectric conversion efficiency of each time period can be obtained. This step generates the photoelectric conversion efficiency reduction data of the photovoltaic module by quantitatively analyzing the relationship between excessive heat load and efficiency, and combines the photoelectric conversion efficiency reduction data obtained in step S222 with the thermal cumulative overload data to analyze the impact of overload on the long-term performance of the photovoltaic module. The mathematical modeling method is used to simulate the performance changes of the photovoltaic module under long-term excessive heat load. According to the actual measured excessive light and overload duration, the cumulative attenuation effect on the photoelectric efficiency of the photovoltaic module is calculated. Further, according to the long-term use data of the component, the attenuation rate of the photovoltaic module under different environments is evaluated, and the future performance attenuation trend of the photovoltaic module is calculated. Combined with the performance attenuation data in step S223 and the power generation surge trend data, the probability of load growth of the photovoltaic module during use is analyzed. The sudden increase in power generation is usually caused by a sudden increase in light intensity or temperature change, which will cause the system load to overload. By combining the power generation data with the performance attenuation model, the load changes of the photovoltaic module in a certain period of time in the future can be predicted. Using the load analysis algorithm, the load growth rate is calculated based on the trend changes of historical data, the load growth pattern is identified, and the load growth data is obtained. Use load growth data to analyze the power generation stability of photovoltaic modules under different load conditions. By real-time monitoring the output power of photovoltaic modules and combining load change data, evaluate the power generation stability of modules under different load conditions. If the load growth rate is fast and the light intensity fluctuates greatly, the output power of photovoltaic modules will be unstable in certain time periods. By calculating the impact of load growth on power generation fluctuations and combining historical power generation data, the fluctuation detection algorithm is used to evaluate the instability of power generation. By counting the power generation fluctuation amplitude in each time period, the instability of power generation is identified and the power generation instability data of photovoltaic modules is generated.Combine the power generation instability data in step S225 with the photovoltaic effect attenuation data in step S216 to analyze their joint impact on power generation fluctuations. Power generation instability is usually 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 amplitude of power generation fluctuations and their changing trends over time are evaluated. Furthermore, a volatility analysis method is used to calculate power generation fluctuation data in different time periods, and the power generation fluctuation data is visualized to facilitate identification of time periods with high power generation instability. By comprehensively considering the impact of power generation instability and photovoltaic effect attenuation on power generation fluctuations, power generation fluctuation data of photovoltaic modules are 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 variability data of power output of the photovoltaic module 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 power grid harmonic imbalance state detection according to the power grid frequency imbalance risk data to obtain power grid harmonic imbalance state data;

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

[0144] In an embodiment of the present invention, the transmission capacity data of the photovoltaic module grid is obtained. The implementation of this step depends on measuring the connection capability between the photovoltaic module and the grid. The transmission capacity of the grid refers to the maximum power capacity that the grid can carry when receiving and transmitting electricity. The specific method is to use power monitoring equipment (such as power sensors, smart meters, etc.) to measure the power output of the photovoltaic system and record the data. These data include current, voltage, power factor, etc., which reflect the real-time transmission status of the grid. Through these real-time data, the capacity of the grid transmission can be analyzed and compared with the predetermined standard capacity to obtain the power transmission capacity data when the photovoltaic system is connected to the grid. When the photovoltaic module is working, its output power will fluctuate due to changes in light intensity. In particular, when the cloud cover is blocked or the weather changes greatly, the power output of the photovoltaic system will increase or decrease suddenly. This step monitors the power output power of the photovoltaic module in real time, and records the changes in the power output of the photovoltaic module through a monitoring device for instantaneous power fluctuations (such as a high-frequency power meter or an oscilloscope). Whenever the power output increases significantly in a short period of time, it can be determined as a "transient surge event". By using data acquisition equipment, the time and amplitude of these instantaneous surges can be recorded, and the frequency, amplitude and duration of these surge events can be calculated through statistical analysis tools to obtain data such as the instantaneous surge in power output. This step requires analyzing the instantaneous surge data of power output obtained in step S232 to determine whether there is an instantaneous surge amplitude that exceeds the upper limit of the transmission capacity of the power grid. When the power output of the photovoltaic module surges to exceed the transmission capacity of the power grid, the power grid transmission system will be overloaded. The specific operation is to monitor the power output of the photovoltaic system and the transmission capacity of the power grid in real time. If the power output exceeds the predetermined threshold of the transmission capacity of the power grid (usually set according to the rated power capacity of the power grid), it is considered that an overload event has occurred. At this time, 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 usually related to overload or load change of power output. If the power output of the photovoltaic module surges instantly and exceeds the transmission capacity of the power grid, it will cause fluctuation or imbalance of the power grid frequency. At this time, frequency monitoring instruments (such as grid frequency meters) are used to measure the frequency fluctuations of the 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 instantaneous surge in power and the fluctuations in the grid frequency, the risk of frequency imbalance can be assessed. If the frequency fluctuation exceeds the allowable range of the grid system, it is determined that the 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 increases instantly, resulting in unstable load, the grid will generate high-order harmonics, which will interfere with the normal operation of the grid. In order to detect the harmonic imbalance state of the grid, it is necessary to deploy harmonic analysis instruments to monitor the harmonic components of different frequencies in the grid in real time.Through frequency analysis, high-frequency harmonics and low-frequency harmonics in the power grid, as well as parameters such as amplitude and phase, are identified. According to the power grid frequency imbalance risk data and real-time harmonic data, it is evaluated whether the power grid is in a harmonic imbalance state. If the harmonic component exceeds the standard limit, it is determined that the power grid has a harmonic imbalance problem, and the power grid harmonic imbalance state data is generated. The power grid frequency imbalance and harmonic imbalance not only affect the stability of the power grid, but also directly affect the power generation quality of the photovoltaic module. When the photovoltaic module receives power from the power grid with large frequency fluctuations, its output power is attenuated. According to the power grid harmonic imbalance state data in step S235 and the power grid frequency imbalance risk data in step S234, the influence of frequency fluctuations and harmonic imbalance on the power output of the photovoltaic module is analyzed. The output of the photovoltaic system is monitored by a power quality analyzer to calculate the degree of power quality degradation caused by frequency imbalance and harmonic imbalance. By comparing the power output waveforms under the normal state of the power grid and the state of frequency imbalance and harmonic imbalance, the power quality attenuation data of the photovoltaic module can be obtained.

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

[0146] Step S31: performing overcharge assessment of the photovoltaic power storage battery according to the grid transmission voltage fluctuation data and the photovoltaic module power quality attenuation data to obtain photovoltaic power 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 according to 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: estimate the battery life decay based on the battery overheat expansion probability data and the photovoltaic module power quality decay data to obtain the photovoltaic storage battery life decay 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 power storage battery according to the grid transmission voltage fluctuation data and the photovoltaic module power quality attenuation data to obtain photovoltaic power storage battery overcharge data;

[0152] In the embodiment of the present invention, the grid voltage fluctuation and the power quality attenuation of the photovoltaic system lead to instability in the charging process of the storage battery. In 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 module, 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 state 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 photovoltaic power storage battery overcharge data 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 the embodiment of the present invention, the battery may overheat during the overcharging process, affecting the service life and safety of the battery. During specific operation, the battery temperature is monitored in real time through 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 an 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 overheating data of the photovoltaic energy storage battery is obtained.

[0155] Step S33: Calculating the battery overheating expansion probability according to 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 the temperature data of the battery, and combined with the overcharge data to determine whether the battery has overheated and overcharged events. Then, based on the thermal expansion characteristics of the battery, a relationship model between the temperature and expansion of the battery is established through experimental data. The model is usually provided by the battery manufacturer or calibrated through field testing. Based on the overcharge data and overheat data, the model is used to calculate the expansion probability of the battery at different temperatures and charging states. The expansion probability data reflects the probability of the battery expanding under the current operating state, and can provide a basis for subsequent battery life estimation and risk control. Finally, the battery overheating expansion probability data is obtained.

[0157] Step S34: estimate the battery life decay based on the battery overheat expansion probability data and the photovoltaic module power quality decay data to obtain the photovoltaic storage battery life decay data.

[0158] In an embodiment of the present invention, by combining the battery overheating expansion probability data in step S33 and the photovoltaic module power quality attenuation data, the attenuation trend of the battery during long-term operation is analyzed, and the battery management system is used to monitor the battery cycle number, charge and discharge depth, charging voltage and other parameters to establish a battery attenuation model. The model takes into account the impact of factors such as battery overheating and overcharging on battery performance, and also combines the impact of power quality attenuation on battery output voltage and current. Through this model, the degree of battery life attenuation under different working environments can be predicted. If the battery is often in an overcharged and overheated state, its life will be significantly shortened, and ultimately the photovoltaic storage battery life attenuation data is obtained.

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

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

[0161] Step S42: Calculating the battery storage capacity decline according to the stored battery capacity decline data to obtain battery storage capacity decline data;

[0162] Step S43: performing a photovoltaic module system efficiency attenuation evaluation on the battery storage capacity reduction 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, the number of charge and discharge cycles of the battery, the depth of each charge and discharge (i.e., the amount of charge and discharge), and the operating temperature of the battery are recorded in real time by a battery management system (BMS). Combined with these data, the actual capacity decay of the battery is calculated by a battery performance decay model. The model takes into account the difference between the rated capacity and the actual capacity of the battery, and combines the historical data of battery charge and discharge (such as the number of cycles and depth) to obtain the capacity decay value of the battery under the current use conditions. If the capacity of the battery at a certain stage decreases by more than a preset threshold, it is determined to be capacity decay, and further storage battery capacity decay data is generated. By accurately calculating and monitoring these parameters, the working performance of the battery under different conditions can be effectively judged, and the performance degradation of the battery can be identified in time. According to the battery capacity decay data obtained in step S41, the battery storage capacity decrease is calculated by further combining the output voltage and current data of the battery. In this process, the battery management system monitors the voltage and current changes of the battery in each charge and discharge cycle, and evaluates the energy storage and release capacity of the battery under different charge and discharge states. If the capacity decay of the battery reaches a certain level, causing the output voltage of the battery to be unable to be maintained within the normal working range, it can be determined as a decrease in storage capacity. The data is calculated by integrating the operation data of the battery to obtain the degree of battery storage capacity decline, and recorded as battery storage capacity decline data. The decline in battery storage capacity will directly affect the efficiency of the photovoltaic system, especially after long-term operation, the decline in the efficiency of the energy storage device will reduce the power output of the overall system. In order to evaluate this impact, the capacity decay data of the storage battery and the data on the decline in battery storage capacity are obtained through the system monitoring platform. Then, these data are combined with photovoltaic system parameters such as the power generation capacity of the photovoltaic module and the inverter efficiency to calculate the degree of decline in the efficiency of the photovoltaic module system due to the decline in battery storage capacity. Specifically, according to the reduction in battery storage energy, the working efficiency of the energy storage part of the system is calculated, and its impact on the stability of power output is evaluated. Through this calculation, the system efficiency decay data can be accurately obtained, and the photovoltaic module system efficiency decay data in step S43 can be comprehensively analyzed. This analysis takes into account multiple factors such as the long-term use of photovoltaic modules, the state of energy storage equipment, and the lighting conditions of photovoltaic modules. The monitoring platform and data analysis algorithm of the photovoltaic system are used to determine the trend of system efficiency decay, and the threshold setting is used to determine whether there is a risk of degradation. When the system efficiency decay reaches a predetermined risk threshold, the system will generate degradation risk warning data, indicating that the photovoltaic module is in a degraded or about to degrade state. This warning data is fed back to the terminal in real time through a wireless communication module (such as GPRS, Wi-Fi or ZigBee).

[0165] The present invention also provides a photovoltaic assembly device, including 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 as described above, and the electrical control unit includes:

[0166] Luminous flux change assessment module: obtain photovoltaic module design data; collect photovoltaic module installation positions according to photovoltaic module design data to obtain photovoltaic module installation position data; measure light intensity changes according to photovoltaic module installation position data to obtain light intensity change data; evaluate photovoltaic module luminous flux changes according to light intensity change data to obtain photovoltaic module luminous flux change data;

[0167] Voltage fluctuation extraction module: Calculate the fluctuation of photovoltaic module power generation according to the change data of photovoltaic module luminous flux, and obtain the fluctuation data of photovoltaic module power generation; evaluate the power quality attenuation according to the fluctuation data of photovoltaic module power generation, and obtain the power quality attenuation data of photovoltaic module; extract the power grid transmission voltage fluctuation according to the power quality attenuation data of photovoltaic module, and obtain the power grid transmission voltage fluctuation data;

[0168] Battery life attenuation estimation module: perform overcharge evaluation of photovoltaic energy storage batteries based on grid transmission voltage fluctuation data and photovoltaic module power quality attenuation data to obtain photovoltaic energy storage battery overcharge data; perform battery life attenuation estimation based on photovoltaic energy storage battery overcharge data and photovoltaic module power quality attenuation data to obtain photovoltaic storage battery life attenuation data;

[0169] Deterioration 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 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 above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be 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 will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented 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 S2: Calculate the fluctuation of photovoltaic module power generation according to the photovoltaic module luminous flux change data to obtain the photovoltaic module power generation fluctuation data; evaluate the power quality attenuation according to the photovoltaic module power generation fluctuation data to obtain the photovoltaic module power quality attenuation data; extract the power grid transmission voltage fluctuation according to the photovoltaic module power quality attenuation data to obtain the power grid transmission voltage fluctuation data; Step S3: performing an overcharge assessment of the photovoltaic power storage battery according to the grid transmission voltage fluctuation data and the photovoltaic module power quality attenuation data to obtain the photovoltaic power storage battery overcharge data; performing a battery life attenuation estimation according to the photovoltaic power storage battery overcharge data and the photovoltaic module power quality attenuation data to obtain the 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 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 sunlight variation according to the photovoltaic module installation position data to obtain sunlight 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: Calculate the sunlight angle according to the sunlight variation data to obtain sunlight angle data; Step S142: performing a sunlight direct intensity detection on the sunlight change data to obtain sunlight direct intensity data; Step S143: Calculating the solar panel tilt angle based on the photovoltaic component design data to obtain solar panel tilt angle data; Step S144: Calculate the change of the light incident angle according to the solar panel tilt angle data and the sunlight angle data to obtain the sunlight incident angle change data; Step S145: performing statistics on changes in radiation received by the solar panel according to 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: evaluating the change in luminous flux of photovoltaic modules based on the change in radiation received by the solar panel to obtain the change in luminous flux of photovoltaic modules.

4. The photovoltaic module control method according to claim 1, characterized in that: Step S2 includes the following steps: 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; Step S22: evaluating the power output variability of the photovoltaic module power generation fluctuation data to obtain the power output variability data of the photovoltaic module; Step S23: Perform power quality attenuation assessment based on the variability data of power output of the photovoltaic module to obtain power quality attenuation data of the photovoltaic module; Step S24: extracting grid transmission voltage fluctuations according to the photovoltaic module power quality attenuation data to obtain grid transmission voltage fluctuation data.

5. The photovoltaic module control method according to claim 4, characterized in that: Step S21 includes the following steps: Step S211: dividing the photovoltaic module light flux change data into light flux size to obtain photovoltaic module strong light flux data and photovoltaic module weak light flux data; Step S212: performing peak radiation calculation according to 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 the power generation sudden increase trend data of the photovoltaic module; 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 the photovoltaic module power generation fluctuation data.

6. The photovoltaic module control method according to claim 5, characterized in that: Step S217 includes the following steps: Perform thermal cumulative overload detection on photovoltaic modules according to the excessive illumination data of photovoltaic modules, and obtain thermal cumulative overload data of photovoltaic modules; The photovoltaic module thermal cumulative overload data is evaluated for the reduction in photoelectric conversion efficiency to obtain the photovoltaic module photoelectric conversion efficiency reduction data; The performance attenuation of photovoltaic modules is estimated based on the photoelectric conversion efficiency reduction data of photovoltaic modules and the thermal cumulative overload data of photovoltaic modules, and the performance attenuation data of photovoltaic modules is obtained; Perform photovoltaic module load growth detection based on photovoltaic module performance attenuation data and photovoltaic module power generation sudden increase trend data to obtain photovoltaic 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 fluctuation of photovoltaic module power generation is analyzed based on the instability data of photovoltaic module power generation and the photovoltaic effect attenuation data of photovoltaic modules, and the fluctuation data of photovoltaic module power generation is obtained.

7. The photovoltaic assembly control method according to claim 4, characterized in that: 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 variability data of power output of the photovoltaic module 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 power grid harmonic imbalance state detection according to the power grid frequency imbalance risk data to obtain power grid harmonic imbalance state data; Step S236: Perform power quality attenuation assessment based on the power grid harmonic imbalance state data and the power grid frequency imbalance risk data to obtain photovoltaic module power quality attenuation data.

8. The photovoltaic module control method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing overcharge assessment of the photovoltaic power storage battery according to the grid transmission voltage fluctuation data and the photovoltaic module power quality attenuation data to obtain photovoltaic power 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 according to 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: estimate the battery life decay based on the battery overheat expansion probability data and the photovoltaic module power quality decay data to obtain the photovoltaic storage battery life decay data.

9. 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 according to photovoltaic storage battery life attenuation data to obtain storage battery capacity attenuation data; Step S42: Calculating the battery storage capacity decline according to the stored battery capacity decline data to obtain battery storage capacity decline data; Step S43: performing a photovoltaic module system efficiency attenuation evaluation on the battery storage capacity reduction 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.

10. A photovoltaic module device, characterized in that: The photovoltaic module device comprises a main body, a power supply unit and an electrical control unit, wherein the power supply unit is installed inside the main body of the photovoltaic module device, the electrical control unit is electrically connected to the power supply unit, and the electrical control unit is used to charge the main body of the photovoltaic module device and control the main body of the photovoltaic module device, wherein the electrical control unit is used to execute the photovoltaic module control method according to claim 1, and the electrical control unit comprises: Luminous flux change assessment module: obtain photovoltaic module design data; collect photovoltaic module installation positions according to photovoltaic module design data to obtain photovoltaic module installation position data; measure light intensity changes according to photovoltaic module installation position data to obtain light intensity change data; evaluate photovoltaic module luminous flux changes according to light intensity change data to obtain photovoltaic module luminous flux change data; Voltage fluctuation extraction module: Calculate the fluctuation of photovoltaic module power generation according to the change data of photovoltaic module luminous flux, and obtain the fluctuation data of photovoltaic module power generation; evaluate the power quality attenuation according to the fluctuation data of photovoltaic module power generation, and obtain the power quality attenuation data of photovoltaic module; extract the power grid transmission voltage fluctuation according to the power quality attenuation data of photovoltaic module, and obtain the power grid transmission voltage fluctuation data; Battery life attenuation estimation module: perform overcharge evaluation of photovoltaic energy storage batteries based on grid transmission voltage fluctuation data and photovoltaic module power quality attenuation data to obtain photovoltaic energy storage battery overcharge data; perform battery life attenuation estimation based on photovoltaic energy storage battery overcharge data and photovoltaic module power quality attenuation data to obtain photovoltaic storage battery life attenuation data; Deterioration 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 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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