Control method of solar photovoltaic equipment

Abstract

The invention relates to the technical field of solar photovoltaic, in particular to an electronic transaction management method and system based on digital authentication. The method comprises the following steps: obtaining solar photovoltaic equipment operation data, and carrying out illumination intensity feature extraction and photovoltaic equipment operation voltage feature extraction according to the solar photovoltaic equipment operation data so as to obtain illumination intensity data and photovoltaic equipment operation voltage data; performing maximum power point analysis according to the illumination intensity data and the operation voltage data of the photovoltaic equipment so as to obtain maximum power point data; the method comprises the following steps: acquiring solar photovoltaic equipment component data, and performing inverter component feature extraction according to the solar photovoltaic equipment component data so as to obtain inverter component data. Based on the solar photovoltaic technology, it is ensured that equipment rapidly positions the maximum power point, and the energy output conversion rate is increased.

Description

Technical Field

[0001] The invention relates to the technical field of solar photovoltaic technology, and in particular to a control method for solar photovoltaic equipment. Background Art

[0002] The basic principle of solar photovoltaic equipment is to convert light energy into electrical energy. Its core components include photovoltaic panels, inverters, charging controllers and energy storage devices. Traditional control methods show slow response problems in environments where light conditions change rapidly. For example, the perturbation observation method usually relies on periodic perturbations of the system output power to find the maximum power point. This method causes the system to be unable to quickly adjust to the maximum power point when the light changes frequently, resulting in energy loss. Traditional control methods lack real-time monitoring and feedback mechanisms for battery status and load demand when the load changes. Sudden changes in load make it impossible for the system to adjust operating parameters in a timely manner, reducing the stability and reliability of operation, and even causing damage to photovoltaic equipment. Summary of the invention

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

[0004] To achieve the above object, a control method for a solar photovoltaic device comprises the following steps:

[0005] Step S1: Acquire the operation data of the solar photovoltaic equipment, and perform light intensity feature extraction and photovoltaic equipment operation voltage feature extraction according to the operation data of the solar photovoltaic equipment, so as to obtain light intensity data and photovoltaic equipment operation voltage data; perform maximum power point analysis according to the light intensity data and photovoltaic equipment operation voltage data, so as to obtain maximum power point data;

[0006] Step S2: Acquire solar photovoltaic device component data, and perform inverter component feature extraction based on the solar photovoltaic device component data, thereby obtaining inverter component data; perform fault detection based on the inverter component data, thereby obtaining inverter fault data; perform electrical isolation based on the inverter fault data, thereby obtaining inverter isolation data;

[0007] Step S3: extracting energy storage device features according to the solar photovoltaic equipment component data, thereby obtaining energy storage device data; performing battery aging analysis according to the energy storage device data, thereby obtaining battery aging data;

[0008] Step S4: performing charging strategy analysis on the maximum power point data according to the battery aging data, thereby obtaining charging strategy data; performing battery operation mode switching on the battery aging data according to the inverter isolation data, thereby obtaining battery operation mode data;

[0009] Step S5: Perform battery protection control analysis according to the charging strategy data and the battery operation mode data to obtain battery protection control data, and upload the data to the solar photovoltaic equipment management cloud platform to execute the battery protection control task.

[0010] The present invention achieves a comprehensive understanding of environmental conditions and equipment status by acquiring the operating data of solar photovoltaic equipment and extracting light intensity and voltage characteristics. The real-time analysis of light intensity and voltage data makes the analysis of the maximum power point more accurate, which helps the equipment to quickly adjust to the optimal output state under different lighting conditions, maximizes energy conversion efficiency, and significantly reduces energy loss caused by light changes. Acquiring solar photovoltaic equipment component data and extracting inverter component characteristics helps to timely identify the operating status and potential faults of the inverter. The implementation of fault detection ensures the reliability and safety of system operation. At the same time, through electrical isolation, it can effectively prevent faults from spreading to other components, reduce the risk of equipment damage, improve the safety of the system, and reduce maintenance costs. Feature extraction and battery aging analysis of energy storage devices can monitor the health status of the battery in real time, timely identify the downward trend of battery performance, and provide data support for the subsequent formulation of charging strategies, ensuring that the optimal charging efficiency can be maintained under battery aging conditions, thereby extending the service life of the battery. The charging strategy analysis of the maximum power point data is carried out according to the battery aging data to ensure the efficiency and safety of the charging process. The inverter isolation data is combined with the battery aging data to realize the intelligent switching of the battery operation mode, which can dynamically adjust the working state of the battery to adapt to different environments and load requirements, thereby improving the flexibility and adaptability of the system. The battery protection control analysis is carried out on the basis of the charging strategy and battery operation mode data to ensure that the battery does not overcharge or over-discharge during the charging and discharging process, thereby protecting the safety and performance of the battery. By uploading the protection control data to the management cloud platform, remote monitoring and management are implemented to realize the intelligent management of the photovoltaic system. This mechanism not only improves the intelligence level of the system, but also provides data basis for subsequent maintenance and optimization, and improves the operating efficiency and reliability of the entire photovoltaic equipment.

[0011] Optionally, step S1 specifically includes:

[0012] Step S11: acquiring solar photovoltaic equipment operation data, and performing light intensity feature extraction and photovoltaic equipment operation voltage feature extraction according to the solar photovoltaic equipment operation data, thereby obtaining light intensity data and photovoltaic equipment operation voltage data;

[0013] Step S12: Acquiring photovoltaic equipment operating current data;

[0014] Step S13: Calculating the photovoltaic panel output power according to the photovoltaic device operating voltage data and the photovoltaic device operating current data, thereby obtaining the photovoltaic panel output power data;

[0015] Step S14: Plotting a photovoltaic panel characteristic curve for the photovoltaic panel output power data according to the light intensity data, thereby obtaining photovoltaic panel characteristic curve data;

[0016] Step S15: Perform maximum power point tracking according to the photovoltaic panel characteristic curve data to obtain maximum power point data.

[0017] The present invention obtains the operating data of the solar photovoltaic equipment and extracts the light intensity and voltage characteristics, so that the system can understand the external environment and the operating status of the equipment in real time. This comprehensive data collection lays a solid foundation for subsequent analysis. By accurately obtaining the light intensity and voltage data, it can ensure that the system responds quickly when the light conditions change and optimize the power conversion efficiency. Acquiring the operating current data of the photovoltaic equipment allows the system to fully understand the working status of the equipment. Real-time monitoring of the current data can help identify potential abnormal conditions, such as short circuits or other faults. The effective execution of this link can improve the safety of the system and reduce the risk of failure. By combining the operating voltage and current data of the photovoltaic equipment and calculating the output power of the photovoltaic panel, the working efficiency of the photovoltaic equipment can be accurately evaluated, ensuring that the system can evaluate the energy output in real time under different working conditions, optimize power management, and provide necessary data support for the subsequent characteristic curve drawing. Based on the light intensity and photovoltaic panel output power data, the characteristic curve is drawn, which helps the system analyze the performance of the photovoltaic panel under different lighting conditions. This curve provides a visual basis for the subsequent maximum power point tracking, so that the system can more intuitively understand the working characteristics of the photovoltaic panel, thereby improving the efficiency of energy conversion. By tracking the maximum power point based on the characteristic curve data of the photovoltaic panel, the optimal working state of the photovoltaic equipment can be found in real time. This process enables the equipment to dynamically adjust to cope with changes in lighting conditions and maximize energy output. By accurately tracking the maximum power point, energy losses are significantly reduced and the overall efficiency of the system is improved.

[0018] Optionally, step S15 is specifically:

[0019] Step S151: extracting power characteristics according to the photovoltaic panel characteristic curve data, thereby obtaining power data;

[0020] Step S152: performing differential calculation according to the power data to obtain power differential data;

[0021] Step S153: deriving the power increment derivative according to the power differential data, so as to obtain the maximum power point data.

[0022] The present invention extracts power features from the photovoltaic panel characteristic curve data, and the system can obtain power data related to light intensity and operating status, providing a basis for subsequent analysis, so that the device can accurately evaluate its energy output under different environmental conditions. Effective power feature extraction can help identify the optimal working point of the device under specific lighting conditions and improve the efficiency of converting light energy into electrical energy. Differential calculations are performed on the basis of obtaining power data, which can effectively analyze the rate of power change, reveal the dynamic characteristics of power output changing over time, and provide important information for the system to understand the response of photovoltaic panels under different conditions. By identifying the trend of power increase and decrease, the system can predict in advance whether adjustments are needed to better adapt to light changes. Derivation of power increment derivatives based on power differential data can help the system accurately find the maximum power point. This method is superior to the traditional perturbation observation method because it relies on the real-time power change rate and can respond quickly. By determining the maximum power point, the system can automatically adjust the operating parameters to ensure that the photovoltaic equipment always operates in the best working state, maximize power output, and significantly reduce energy loss.

[0023] Optionally, step S2 specifically includes:

[0024] Step S21: acquiring solar photovoltaic equipment component data, and extracting inverter component features based on the solar photovoltaic equipment component data, thereby obtaining inverter component data;

[0025] Step S22: performing harmonic distortion analysis according to the inverter component data, thereby obtaining the inverter component harmonic distortion data;

[0026] Step S23: performing temperature anomaly analysis according to the inverter component data, thereby obtaining inverter component temperature anomaly data;

[0027] Step S24: integrating fault data according to the harmonic distortion data of the inverter components and the abnormal temperature data of the inverter components, thereby obtaining inverter fault data;

[0028] Step S25: Perform electrical isolation according to the inverter fault data, thereby obtaining inverter isolation data.

[0029] By acquiring the inverter component data of the solar photovoltaic equipment, the system can fully understand the operating status and performance indicators of the inverter, laying the foundation for subsequent analysis, ensuring that the system can have sufficient information support during fault detection and performance optimization, thereby improving the working efficiency and reliability of the inverter. Performing harmonic distortion analysis on the inverter components can reveal the current or voltage harmonics generated by the inverter during the conversion process, which helps to identify power quality problems and promptly discover harmonic interference that causes equipment damage or system efficiency reduction. Through effective control of harmonic distortion, the performance of the inverter can be optimized and the overall operating efficiency of the power system can be improved. Temperature anomaly analysis can identify whether the inverter is overheating during operation. The inverter works at too high a temperature, causing damage or even failure. By real-time monitoring of temperature changes, the system can issue an early warning in time, thereby taking necessary cooling measures, effectively protecting the safety of the equipment and extending the service life of the inverter. Fault data integration based on harmonic distortion data and temperature anomaly data can comprehensively evaluate the operating status of the inverter. By correlating multiple factors, the system can accurately identify the root cause of potential faults, thereby improving the accuracy of fault diagnosis. Effective fault integration can not only reduce downtime, but also optimize maintenance plans and improve equipment availability. Electrical isolation based on inverter fault data helps protect other parts of the system from the impact of faults. By achieving rapid electrical isolation, the system can immediately cut off the power supply of the faulty component when an abnormality is detected, thereby preventing equipment damage in a wider range, enhancing the safety and stability of the system, and providing good conditions for subsequent troubleshooting and repairs.

[0030] Optionally, step S22 is specifically:

[0031] Step S221: collecting current waveform data according to inverter component data, thereby obtaining current waveform data;

[0032] Step S222: performing a discrete Fourier transform of the current signal according to the current waveform data, thereby obtaining current frequency domain data;

[0033] Step S223: performing current signal separation according to the current frequency domain data, thereby obtaining current fundamental wave data and current harmonic data;

[0034] Step S224: Calculate the total harmonic distortion rate according to the current fundamental wave data and the current harmonic data, so as to obtain the current harmonic distortion data;

[0035] Step S225: locating the harmonic distortion source of the inverter component data according to the current harmonic distortion data, thereby obtaining the inverter component harmonic distortion data.

[0036] The present invention collects current waveform data of the inverter component, and the system can obtain detailed real-time current information, providing basic data for subsequent analysis, ensuring accurate monitoring of the inverter working state, and the acquisition of such real-time data can timely reflect the operation of the equipment, which helps to find abnormal problems in time. The application of discrete Fourier transform to frequency domain analysis of current waveform data can convert time domain signals into frequency domain signals, so that the system can identify and analyze different frequency components, especially the existence of harmonic components. Through frequency domain data, users can more clearly understand the frequency distribution characteristics in the current signal, laying the foundation for subsequent harmonic analysis. According to the current frequency domain data, the current signal is separated, and the fundamental component and harmonic component of the current can be extracted. The fundamental data reflects the main characteristics of the current, while the harmonic data contains unnecessary frequency components in the current. Through this separation, the system can better identify the main characteristics of the current and harmonic pollution, providing a clear basis for harmonic distortion analysis. By calculating the total harmonic distortion (THD) of the current fundamental wave data and harmonic data, the harmonic distortion of the inverter output current can be quantified. The harmonic distortion rate is an important indicator for measuring the quality of the power system. High distortion rate leads to equipment damage and reduced system efficiency. Through this calculation, users can timely understand the current quality of the inverter and take corresponding corrective measures. Locating the harmonic distortion source based on the current harmonic distortion data helps to determine the specific cause of the harmonic generation and can identify key factors that affect the inverter performance, such as load changes and component aging. By effectively locating the harmonic source, the system can take corresponding optimization measures to reduce harmonic distortion, improve power quality, and ensure the stable operation of the photovoltaic system.

[0037] Optionally, step S23 is specifically:

[0038] Step S231: performing inverter power module temperature sensing extraction according to inverter component data, thereby obtaining inverter power module temperature sensing data;

[0039] Step S232: performing temperature fluctuation statistics on the temperature sensing data of the inverter power module, thereby obtaining frequent temperature fluctuation data of the power module;

[0040] Step S233: Acquire inverter power module safety temperature range data;

[0041] Step S234: performing abnormal high temperature comparison on the temperature sensing data of the inverter power module according to the safe temperature range data of the inverter power module, thereby obtaining abnormal high temperature data of the inverter power module;

[0042] Step S235: performing abnormal temperature intersection operation according to the abnormal high temperature data of the inverter power module and the data of frequent temperature fluctuation of the power module, so as to obtain the abnormal temperature data of the inverter component.

[0043] The present invention extracts the temperature sensor data of the inverter power module, and the system can monitor the working temperature of the inverter in real time, provide basic data for subsequent temperature analysis, ensure dynamic evaluation of the inverter performance, and this real-time data acquisition can help timely discover potential fault risks caused by overheating. The temperature fluctuation statistics of the inverter power module temperature sensor data can identify the temperature fluctuation frequency and amplitude, which is helpful to understand the temperature stability of the inverter under different working conditions, especially when the light changes or the load fluctuates. By statistically analyzing the temperature fluctuation data, the user can evaluate the heat dissipation performance of the inverter and the adaptability of the working environment. The safe temperature range of the inverter power module is defined to provide a benchmark for temperature monitoring, ensuring that the subsequent temperature anomaly detection can have a clear reference basis. Understanding the safe temperature range helps to formulate reasonable working conditions, ensure that the inverter operates within a safe range, and reduce the risk of failure. By comparing the temperature sensor data of the inverter power module with the safe temperature range, abnormal high temperature events can be identified, and the temperature exceeding the standard can be discovered in time to prevent equipment damage caused by high temperature. By timely identifying abnormal high temperature, the system can take early warning measures to ensure the safe operation of the inverter. By combining the abnormal high temperature data and frequent temperature fluctuation data of the inverter power module for intersection operations, we can analyze the causes of temperature abnormalities more deeply and identify which working conditions or load changes lead to frequent temperature fluctuations and abnormal high temperature phenomena. Through this intersection analysis, users can formulate targeted improvement measures, such as optimizing heat dissipation design or adjusting operation strategies, thereby improving the stability and reliability of the inverter.

[0044] Optionally, step S225 is specifically:

[0045] Perform harmonic component frequency statistics based on current harmonic distortion data to obtain current low-order harmonic data;

[0046] Extracting load voltage characteristics according to inverter component data, thereby obtaining inverter component load voltage data;

[0047] The voltage waveform is drawn according to the load voltage data of the inverter component, thereby obtaining the voltage waveform data of the inverter component;

[0048] Performing waveform distortion analysis based on the voltage waveform data of the inverter component to obtain nonlinear load data of the inverter component;

[0049] Time domain analysis is performed based on the nonlinear load data of the inverter components and the current low-order harmonic data to obtain the harmonic distortion data of the inverter components.

[0050] The present invention performs harmonic component frequency statistics on current harmonic distortion data, and the system can identify the frequency distribution of each harmonic component in the current, which helps to determine the influence of low-order harmonics and their contribution to total harmonic distortion, and facilitates the identification and resolution of harmonic problems that lead to reduced equipment efficiency. Load voltage feature extraction based on inverter component data can obtain the voltage performance of the inverter under different load conditions, provide an in-depth understanding of the working conditions of the inverter under various loads, and help optimize the inverter design and operation strategy to adapt to different application scenarios. Using the load voltage data of the inverter component to draw the voltage waveform can intuitively display the voltage change trend and its waveform characteristics. This visualization effect enables engineers to quickly identify abnormal conditions in the voltage waveform, such as waveform distortion or fluctuation, and thus provide a basis for fault diagnosis and system optimization. Waveform distortion analysis of the inverter component voltage waveform data can identify waveform distortion caused by nonlinear loads, which helps to judge the stability and reliability of the system under different load conditions, ensure that the quality of the voltage output meets the operating requirements of the equipment, and prevent equipment damage caused by waveform distortion. Combining the nonlinear load data of inverter components with the current low-order harmonic data for time domain analysis can comprehensively evaluate the overall performance of the inverter. This analysis provides a basis for identifying the source of harmonic distortion, thereby providing guidance for improving system design and operating strategies to ensure that the inverter can operate efficiently and stably.

[0051] Optionally, step S3 specifically includes:

[0052] Step S31: extracting energy storage device features according to solar photovoltaic equipment component data, thereby obtaining energy storage device data;

[0053] Step S32: performing battery gas evolution analysis according to the energy storage device data, thereby obtaining battery gas evolution data;

[0054] Step S33: performing battery internal resistance analysis according to the energy storage device data, thereby obtaining battery internal resistance data;

[0055] Step S34: performing a battery aging intersection operation according to the battery gas evolution data and the battery internal resistance data, thereby obtaining the battery aging data.

[0056] The present invention extracts the energy storage device features from the solar photovoltaic equipment component data, and the obtained energy storage device data can describe the performance indicators of the battery in detail, which is helpful for analyzing the capacity, efficiency and charge and discharge characteristics of the energy storage device, and providing basic data for subsequent battery management and optimization strategies. Battery gasification analysis based on the energy storage device data can evaluate the chemical reaction of the battery during the charging and discharging process. The appearance of battery gasification indicates that the battery has problems such as overcharging and excessive temperature, which helps to timely discover potential safety hazards and prevent battery damage or explosion risks caused by gasification. Battery internal resistance analysis can reflect the health status of the battery and its charge and discharge performance. Higher internal resistance usually means battery aging or failure. Through this analysis, the performance degradation of the battery can be identified in advance, thereby guiding maintenance and replacement strategies to ensure the efficient operation of the system. Combining battery gasification data with battery internal resistance data to perform battery aging intersection operations can comprehensively evaluate the aging status of the battery. By comprehensively analyzing different indicators, a more accurate battery health report is provided, which helps to optimize the battery management system and improve the reliability and stability of system operation.

[0057] Optionally, step S32 is specifically:

[0058] Step S321: extracting battery material features according to the energy storage device data, thereby obtaining battery material data;

[0059] Step S322: drawing a gas chromatogram of the battery electrolyte according to the battery material data, thereby obtaining gas chromatogram data of the battery electrolyte;

[0060] Step S323: dividing the gas components according to the gas chromatography data of the battery electrolyte, thereby obtaining the gas composition data of the battery electrolyte;

[0061] Step S324: performing battery gas evolution simulation according to the battery material data, thereby obtaining battery gas evolution simulation data;

[0062] Step S325: monitoring the concentration change of the battery gas evolution simulation data according to the battery electrolyte gas composition data, thereby obtaining gas evolution concentration change data;

[0063] Step S326: Obtain battery capacity performance attenuation data;

[0064] Step S327: performing gas concentration change analysis according to the battery capacity performance decay data, thereby obtaining battery capacity performance decay gas concentration change data;

[0065] Step S328: Perform concentration matching according to the gas evolution concentration change data and the battery capacity performance attenuation gas concentration change data, so as to obtain the battery gas evolution data.

[0066] The present invention extracts battery material characteristics from energy storage device data, and the obtained battery material data can describe the composition and performance characteristics of the battery in detail, which helps to evaluate the impact of different materials on the overall performance of the battery and provide a scientific basis for optimizing battery design and selection. The battery electrolyte gas chromatography is drawn according to the battery material data, and the composition and concentration changes in the electrolyte can be analyzed, which helps to identify the chemical stability of the electrolyte and provide important information for battery safety and efficiency. By dividing the gas components of the battery electrolyte gas chromatography data, the various gas components produced in the internal chemical reaction of the battery can be identified, which provides basic data for understanding the battery performance degradation mechanism and gas evolution process, and helps to discover potential problems in advance. Battery gas evolution simulation based on battery material data can predict the battery gas evolution under different conditions. This simulation provides theoretical support for battery design and operation, helps to improve battery materials and structures, and reduces the occurrence of gas evolution. Monitoring the concentration changes of battery gas evolution simulation data in combination with battery electrolyte gas composition data helps to evaluate the dynamic changes of the gas evolution process in real time. This monitoring provides data support for timely taking protective measures and optimizing operating parameters. By obtaining battery capacity performance decay data, the health status and service life of the battery can be fully evaluated, and this data provides a basis for subsequent performance analysis and management decisions. Analyzing gas concentration changes based on battery capacity performance decay data can reveal the relationship between battery performance degradation and gas evolution, providing key insights into battery aging mechanism research and helping to formulate effective maintenance strategies.

[0067] Optionally, step S33 is specifically:

[0068] Step S331: performing battery discharge simulation according to the energy storage device data to obtain battery discharge data;

[0069] Step S332: extracting the battery discharge voltage characteristics and the battery discharge current characteristics according to the battery discharge data, thereby obtaining the battery discharge voltage data and the battery discharge current data;

[0070] Step S333: Calculating the instantaneous internal resistance of the battery according to the battery discharge voltage data and the battery discharge current data, thereby obtaining the instantaneous internal resistance data of the battery;

[0071] Step S334: Obtain instantaneous internal resistance data of a standard battery;

[0072] Step S335: Compare the instantaneous internal resistance data of the battery with the instantaneous internal resistance data of the standard battery to obtain the battery internal resistance data.

[0073] The present invention can obtain the discharge performance data of the battery under different load conditions by simulating the battery discharge of the energy storage device data, which provides basic data for the subsequent analysis of the actual working state of the battery and can predict the performance of the battery in practical applications. The battery discharge voltage and current feature extraction is performed according to the battery discharge data, and detailed voltage and current change information can be obtained. These data are helpful to understand the behavior of the battery during the discharge process, which is convenient for optimizing the charge and discharge strategy and improving the battery efficiency. The instantaneous internal resistance of the battery is calculated by the battery discharge voltage and current data, and the internal resistance change of the battery can be evaluated in real time. This data is crucial for identifying the health of the battery. The increase in internal resistance usually indicates battery aging or failure. Obtaining the instantaneous internal resistance data of the standard battery can provide a benchmark for subsequent comparisons. This standard data helps engineers understand the internal resistance range under normal working conditions, thereby providing a reference for the performance evaluation of the battery. By comparing the instantaneous internal resistance data of the battery with the instantaneous internal resistance data of the standard battery, the performance deviation of the battery can be clearly identified, potential problems can be quickly discovered, and measures can be taken in time to extend the battery life and ensure the stable operation of the photovoltaic system. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0075] Figure 1 A schematic diagram of the steps of the control method of the solar photovoltaic device of the present invention;

[0076] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0077] Figure 3 Detailed step flow diagram of step S2 in the present invention;

[0078] 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

[0079] The following is a clear and complete description of the technical method of the present invention 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.

[0080] 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.

[0081] 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.

[0082] To achieve this, please refer to Figures 1 to 3 The present invention provides a control method for a solar photovoltaic device, the method comprising the following steps:

[0083] Step S1: Acquire the operation data of the solar photovoltaic equipment, and perform light intensity feature extraction and photovoltaic equipment operation voltage feature extraction according to the operation data of the solar photovoltaic equipment, so as to obtain light intensity data and photovoltaic equipment operation voltage data; perform maximum power point analysis according to the light intensity data and photovoltaic equipment operation voltage data, so as to obtain maximum power point data;

[0084] In this embodiment, in the process of obtaining the operating data of the solar photovoltaic device, the voltage and current data of the device under different lighting conditions are collected by sensors. The real-time light intensity value is recorded through the data collector, and the light intensity characteristics are extracted through the data analysis tool, and the mean and variance are calculated using statistical methods to evaluate the impact of light changes. At the same time, the voltage characteristics are extracted from the device operation data, and the peak value, mean value and instantaneous value of the voltage are calculated to generate the operating voltage data of the photovoltaic device. Next, the maximum power point analysis is performed using the extracted light intensity data and voltage data, and the maximum power point tracking (MPPT) algorithm is used to calculate the maximum power point of the photovoltaic device under the current lighting conditions. This process involves real-time processing of data to ensure that the maximum power point information can be quickly updated when the lighting conditions change.

[0085] Step S2: Acquire solar photovoltaic device component data, and perform inverter component feature extraction based on the solar photovoltaic device component data, thereby obtaining inverter component data; perform fault detection based on the inverter component data, thereby obtaining inverter fault data; perform electrical isolation based on the inverter fault data, thereby obtaining inverter isolation data;

[0086] In this embodiment, when obtaining the data of the solar photovoltaic equipment components, the intelligent monitoring system is used to monitor the operating status of the inverter components in real time, and the collected data includes operating temperature, current and voltage. Then, the feature extraction algorithm is used to analyze the collected inverter data to calculate the key performance indicators such as the power factor and harmonic distortion rate of the inverter. These indicators are used for subsequent fault detection. The fault detection algorithm is applied to determine whether the inverter is faulty by setting thresholds (such as temperature thresholds and power factor thresholds). Once a fault is detected, the electrical isolation mechanism is immediately triggered to ensure that the inverter is safely disconnected from the power grid to avoid greater system damage.

[0087] Step S3: extracting energy storage device features according to the solar photovoltaic equipment component data, thereby obtaining energy storage device data; performing battery aging analysis according to the energy storage device data, thereby obtaining battery aging data;

[0088] In this embodiment, in the energy storage device feature extraction stage, the voltage and current of the energy storage battery are monitored in real time, and the battery management system (BMS) is used for data collection. Based on the collected data, the characteristic parameters of the battery, including internal resistance and capacity decay rate, are extracted by analyzing the discharge curve and charging curve of the battery. When performing battery aging analysis, a battery aging model is used, historical operating data and usage conditions are input, and the degree of battery aging and estimated remaining service life are calculated through statistical analysis, thereby generating battery aging data to ensure that corresponding maintenance and management can be performed for batteries in different states.

[0089] Step S4: performing charging strategy analysis on the maximum power point data according to the battery aging data, thereby obtaining charging strategy data; performing battery operation mode switching on the battery aging data according to the inverter isolation data, thereby obtaining battery operation mode data;

[0090] In this embodiment, when analyzing the charging strategy based on the battery aging data, the current state of the battery is compared with the preset charging curve to determine the best charging strategy. By formulating a multi-stage charging strategy, the charging current and voltage are adjusted in stages to optimize the charging efficiency of the battery. At the same time, the battery operation mode is switched according to the inverter isolation data, and the real-time monitoring technology is used to determine the load demand of the battery, and switch to a suitable working mode (such as fast charging or floating charging mode) when necessary to improve the responsiveness and operating efficiency of the overall system.

[0091] Step S5: Perform battery protection control analysis according to the charging strategy data and the battery operation mode data to obtain battery protection control data, and upload the data to the solar photovoltaic equipment management cloud platform to execute the battery protection control task.

[0092] In this embodiment, during the battery protection control analysis process, the charging strategy data and the battery operation mode data are combined to set specific battery protection parameters such as overcharge voltage, discharge current and temperature limit through the logic control system. The monitoring system tracks these parameters in real time to ensure that the safety threshold is not exceeded under any circumstances. Once an abnormal situation is detected, the system immediately uploads relevant data to the management cloud platform and triggers the preset protection mechanism, such as limiting charging or stopping discharging, to ensure the safe operation of the battery and extend its service life.

[0093] Optionally, step S1 specifically includes:

[0094] Step S11: acquiring solar photovoltaic equipment operation data, and performing light intensity feature extraction and photovoltaic equipment operation voltage feature extraction according to the solar photovoltaic equipment operation data, thereby obtaining light intensity data and photovoltaic equipment operation voltage data;

[0095] In this embodiment, when obtaining the operating data of the solar photovoltaic device, an integrated sensor array is used. These sensors can monitor the light intensity and voltage of the device in real time. The light intensity sensor measures in watts per square meter (W / m 2 ), and the voltage sensor records the panel output voltage (unit: volt) in real time. The collected data is stored in the local database through the data acquisition module. Subsequently, the data processing algorithm is used to extract the features of the light intensity and voltage data, and calculate statistical parameters such as the average, maximum, minimum and standard deviation to evaluate the stability and change trend of the operating conditions. The final generated light intensity data and photovoltaic equipment operating voltage data will serve as the basis for subsequent analysis.

[0096] Step S12: Acquiring photovoltaic equipment operating current data;

[0097] In this embodiment, when obtaining the operating current data of the photovoltaic equipment, a high-precision current sensor is installed. The sensor is directly connected to the output end of the photovoltaic panel and can monitor the current change (unit: ampere) in real time. The sensor uses current transformer technology to ensure the accuracy and real-time performance of the measurement. The data is converted into a digital signal by an ADC (analog-to-digital converter) and stored in a data acquisition system. The collected current data is recorded in the system, sampled at regular intervals (such as 1 second), and the instantaneous value and average value of the current are calculated to provide accurate operating status information.

[0098] Step S13: Calculating the photovoltaic panel output power according to the photovoltaic device operating voltage data and the photovoltaic device operating current data, thereby obtaining the photovoltaic panel output power data;

[0099] In this embodiment, when calculating the output power of the photovoltaic panel based on the operating voltage data and operating current data of the photovoltaic device, the formula is determined as output power (unit: watt) equals voltage (volts) multiplied by current (amperes). The voltage and current data are extracted from the previous step, and the instantaneous voltage and current values ​​are used for real-time calculation during calculation to ensure the accuracy of the data. The calculation results are stored by the real-time monitoring system, and a power curve is generated to show the power output under different lighting conditions. Through this process, the photovoltaic panel output power data obtained will be used for subsequent analysis.

[0100] Step S14: Plotting a photovoltaic panel characteristic curve for the photovoltaic panel output power data according to the light intensity data, thereby obtaining photovoltaic panel characteristic curve data;

[0101] In this embodiment, when the photovoltaic panel characteristic curve is drawn for the photovoltaic panel output power data according to the light intensity data, the output power data under different light intensities are collected. These data are drawn into a characteristic curve with the light intensity as the horizontal coordinate and the output power as the vertical coordinate. Using a data visualization tool, each collection point is connected to form a curve to provide an intuitive characteristic curve display. The shape of the curve can reveal the performance characteristics of the photovoltaic panel under different lighting conditions. The generated photovoltaic panel characteristic curve data serves as the basis for subsequent maximum power point tracking analysis.

[0102] Step S15: Perform maximum power point tracking according to the photovoltaic panel characteristic curve data to obtain maximum power point data.

[0103] In this embodiment, when the maximum power point is tracked according to the characteristic curve data of the photovoltaic panel, a constant current method or a disturbance observation method is used to adjust the working state of the photovoltaic device in real time. The system continuously monitors the current photovoltaic output power and uses the previously obtained characteristic curve data to determine the theoretical maximum power point under the current lighting conditions. The system sets a threshold (for example, when the output power changes by more than a certain percentage) and automatically adjusts the output of the inverter to keep the device running near the maximum power point. This process is continuously iterated to optimize the energy conversion efficiency of the device in real time and ensure the accuracy and reliability of the maximum power point data.

[0104] Optionally, step S15 is specifically:

[0105] Step S151: extracting power characteristics according to the photovoltaic panel characteristic curve data, thereby obtaining power data;

[0106] In this embodiment, when extracting power features based on the characteristic curve data of the photovoltaic panel, effective power output data is extracted from the characteristic curve. The characteristic curve consists of power output under different light intensities, and the data is stored in a database. Using a data analysis tool, the corresponding power output value is calculated for each light intensity point. The extracted power features include the maximum power value, the power stability index, and the rate of change of the power output. This process involves long-term monitoring of the photovoltaic panel under multiple working conditions to ensure that the extracted data is accurate and representative. The power data finally obtained will be used for subsequent differential calculations to ensure that the basis for the analysis is reliable.

[0107] Step S152: performing differential calculation according to the power data to obtain power differential data;

[0108] In this embodiment, when performing differential calculations based on power data, the power data is first processed using a numerical differential method. In specific implementation, the change amount and time interval between each adjacent power output value are used for calculation. The calculation formula is: power differential data equals (P(t+Δt)-P(t)) / Δt, where P(t) is the power value at the current moment and Δt is the time interval. To ensure the accuracy of the calculation, a suitable time interval is selected, usually set to 1 second or less, to capture instantaneous power changes. After this process, the power differential data obtained will reflect the trend of power changes over time, providing key parameters for the subsequent derivation of power increment derivatives.

[0109] Step S153: deriving the power increment derivative according to the power differential data, so as to obtain the maximum power point data.

[0110] In this embodiment, when the power incremental derivative is derived based on the power differential data, the power change is analyzed by the derivative calculation method. The effective derivative value is extracted from the power differential data, and the calculation formula is: the incremental derivative is equal to dP / dt, where dP is the differential change of power and dt is the differential change of time. Then, through the threshold analysis of the power differential data, the critical value of the power change rate (for example, 0.1W / s) is set to determine the maximum power point. When the derivative value reaches the set threshold, it indicates that the system has approached or reached the maximum power point. Ultimately, the maximum power point data derived from the derivative will provide guidance for the operation optimization of the photovoltaic equipment to ensure that it operates in the best working state.

[0111] Optionally, step S2 specifically includes:

[0112] Step S21: acquiring solar photovoltaic equipment component data, and extracting inverter component features based on the solar photovoltaic equipment component data, thereby obtaining inverter component data;

[0113] In this embodiment, when obtaining the data of the solar photovoltaic equipment components, real-time operation data is collected from the inverter through the data acquisition system. This data includes output voltage (unit: volt), output current (unit: ampere), power factor and operating temperature (unit: Celsius). High-precision sensors are used for data acquisition to ensure the accuracy and reliability of the data. After the acquisition is completed, the data is cleaned using data processing software (such as MATLAB), outliers are excluded, and standardized. Then, the key performance indicators of the inverter components, including efficiency and energy loss rate, are extracted through feature extraction algorithms (such as principal component analysis). Finally, the inverter component data obtained lays the foundation for subsequent analysis.

[0114] Step S22: performing harmonic distortion analysis according to the inverter component data, thereby obtaining the inverter component harmonic distortion data;

[0115] In this embodiment, when performing harmonic distortion analysis, data is first collected from the output current waveform of the inverter, and the time domain signal is captured and stored using a digital oscilloscope. Subsequently, the time domain signal is converted into a frequency domain signal using a fast Fourier transform (FFT) to identify each harmonic component. When calculating the total harmonic distortion (THD), the formula THD = √(I2 2 +I3 2 +...+I n 2 ) / I1, where I1 is the fundamental current, and I2, I3, etc. are harmonic currents. Based on the calculation results, the harmonic distortion data is analyzed and compared with the set standard threshold (usually 5%) to determine whether the inverter is operating within the safe range. This analysis ensures the stability of the inverter under load conditions.

[0116] Step S23: performing temperature anomaly analysis according to the inverter component data, thereby obtaining inverter component temperature anomaly data;

[0117] In this embodiment, when performing temperature anomaly analysis, the real-time operating temperature of the inverter is monitored by a temperature sensor. The collected temperature data is filtered and compared with the safe operating temperature range of the inverter. The safe temperature range data generally comes from the technical manual of the equipment manufacturer and is usually set between -10°C and 85°C. If the monitored temperature exceeds the set upper limit (such as 85°C), it is marked as a temperature anomaly. Use a data recorder to record temperature changes in real time to ensure the integrity of the data. This analysis provides a basis for timely detection of potential overheating problems to prevent equipment damage.

[0118] Step S24: integrating fault data according to the harmonic distortion data of the inverter components and the abnormal temperature data of the inverter components, thereby obtaining inverter fault data;

[0119] In this embodiment, when fault data is integrated based on the harmonic distortion data and temperature anomaly data of the inverter components, the two sets of data are matched according to the timestamp using data fusion technology. Harmonic distortion and temperature anomaly events in each time period are classified and marked as "normal" or "abnormal". By analyzing the correlation between these two types of data, the fault mode in which harmonic distortion and temperature anomaly occur simultaneously is identified. A decision tree algorithm is used for fault judgment. If multiple anomalies are detected, they are recorded as inverter fault data. This process provides a reliable basis for subsequent fault handling and maintenance decisions.

[0120] Step S25: Perform electrical isolation according to the inverter fault data, thereby obtaining inverter isolation data.

[0121] In this embodiment, when electrical isolation is performed based on the inverter fault data, the severity of the fault is first evaluated. A control system is used to send a signal to start a relay or circuit breaker for electrical isolation. During this process, the status of the equipment needs to be monitored in real time to ensure the timeliness and effectiveness of electrical isolation. The specific operation is to disconnect the inverter from the power grid to ensure that the fault does not affect the operation of other equipment. During the isolation process, all operating data must be recorded for subsequent analysis and tracking. After the isolation is completed, an inverter isolation data report is generated to ensure that the equipment is maintained and repaired in a safe state.

[0122] Optionally, step S22 is specifically:

[0123] Step S221: collecting current waveform data according to inverter component data, thereby obtaining current waveform data;

[0124] In this embodiment, when collecting current waveform data, a high-precision current sensor is configured to obtain the current signal of the inverter component in real time. The sensor used should have a wide bandwidth and high sensitivity, and can accurately reflect the current waveform changes within the rated current range. The data acquisition system is set to continuously monitor at a sampling frequency of 10kHz to ensure the accuracy and stability of the collected data. The obtained current waveform data needs to be denoised, and external interference is eliminated through a filter, and stored as raw current waveform data for subsequent analysis.

[0125] Step S222: performing a discrete Fourier transform of the current signal according to the current waveform data, thereby obtaining current frequency domain data;

[0126] In this embodiment, when performing discrete Fourier transform on the current waveform data, the fast Fourier transform (FFT) algorithm is used to convert the time domain signal into a frequency domain signal. The required input parameters include the sampling frequency, the number of sampling points, and the window function type. The Hanning window is usually selected to reduce spectrum leakage. After the discrete Fourier transform, the current frequency domain data obtained contains multiple frequency components, which is convenient for analyzing the amplitude and phase information of each frequency component. During the data processing process, it is necessary to ensure the calculation accuracy of the transformation to avoid affecting the subsequent analysis results due to numerical errors.

[0127] Step S223: performing current signal separation according to the current frequency domain data, thereby obtaining current fundamental wave data and current harmonic data;

[0128] In this embodiment, when the current signal is separated, the fundamental component is first extracted from the current frequency domain data. The fundamental wave is usually the component with the lowest frequency (such as 50Hz or 60Hz), and other harmonic components (such as 3rd, 5th, 7th harmonics) are filtered out by setting the frequency threshold. The filter type used can be a low-pass filter, and the parameters are set so that the cut-off frequency is 1.5 times the fundamental frequency. After separation, clear current fundamental wave data and harmonic data will be obtained, which provide the basis for the subsequent total harmonic distortion rate calculation.

[0129] Step S224: Calculate the total harmonic distortion rate according to the current fundamental wave data and the current harmonic data, so as to obtain the current harmonic distortion data;

[0130] In this embodiment, when calculating the total harmonic distortion rate, the power of the current fundamental wave and harmonic wave data must first be calculated. 2 ) / V1 2 ) to obtain the harmonic distortion rate, where Vn represents the effective value of each harmonic and V1 is the effective value of the fundamental wave. To ensure the accuracy of the effective value calculation during the calculation process, precise mathematical tools and signal processing algorithms are required. The final result is the current harmonic distortion data, which reflects the current quality of the inverter components and can provide a basis for subsequent fault analysis.

[0131] Step S225: locating the harmonic distortion source of the inverter component data according to the current harmonic distortion data, thereby obtaining the inverter component harmonic distortion data.

[0132] In this embodiment, when locating the harmonic distortion source, a multidimensional data analysis method is used to analyze the fault source by combining the current harmonic distortion data with other operating parameters of the inverter component (such as temperature, load, etc.). By establishing a multivariate regression model, the harmonic distortion data is associated with other influencing factors to identify the main harmonic distortion sources. During the analysis process, a threshold standard is set. If the harmonic distortion rate exceeds the set value, it is marked as a fault source. Ultimately, the obtained harmonic distortion data of the inverter component will provide an important reference for system optimization and fault handling.

[0133] Optionally, step S23 is specifically:

[0134] Step S231: performing inverter power module temperature sensing extraction according to inverter component data, thereby obtaining inverter power module temperature sensing data;

[0135] In this embodiment, when performing temperature sensing extraction of the inverter power module, high-precision temperature sensors are installed at key positions of the inverter power module to ensure that the real-time temperature data of the module can be accurately obtained. The sensors used must have fast response and high sensitivity, and be suitable for working in the inverter operating environment. The acquisition system is set to sample once per second, record temperature data and perform denoising to ensure the accuracy and stability of the data. The final generated inverter power module temperature sensing data includes temperature value and sampling time, providing basic data for subsequent temperature fluctuation analysis.

[0136] Step S232: performing temperature fluctuation statistics on the temperature sensing data of the inverter power module, thereby obtaining frequent temperature fluctuation data of the power module;

[0137] In this embodiment, when performing temperature fluctuation statistics on the temperature sensor data of the inverter power module, the temperature change amplitude between each sampling point and the previous sampling point is first calculated to obtain temperature fluctuation information. The temperature change amplitude is stored in an array, and then the frequency of occurrence of each amplitude is counted. A temperature fluctuation amplitude threshold can be set, for example, set to ±2°C, to distinguish normal fluctuations from abnormal fluctuations. By calculating the number of times the fluctuation amplitude exceeds the threshold, the frequent temperature fluctuation data of the power module is obtained, reflecting the law and characteristics of temperature changes.

[0138] Step S233: Acquire inverter power module safety temperature range data;

[0139] In this embodiment, when obtaining the safe temperature range data of the inverter power module, refer to the inverter technical specifications provided by the manufacturer to determine its safe operating temperature range. Usually, the safe temperature range is between ±10°C and ±20°C of the ambient temperature. For example, when the ambient temperature is 25°C, the safe temperature range is set to 15°C to 35°C. In addition, it is also necessary to consider the load conditions of the inverter and set a more stringent safety temperature limit to avoid failures caused by overheating. The obtained safety temperature range data provides a benchmark for subsequent temperature anomaly analysis.

[0140] Step S234: performing abnormal high temperature comparison on the temperature sensing data of the inverter power module according to the safe temperature range data of the inverter power module, thereby obtaining abnormal high temperature data of the inverter power module;

[0141] In this embodiment, when the inverter power module temperature sensor data is compared with the inverter power module safety temperature range data for abnormal high temperature, the temperature sensor data is first compared with the safety temperature range one by one. The abnormal high temperature threshold is set to the upper limit of the safety temperature range plus a preset value, for example, it is set to the upper limit of the safety range plus 5°C. By traversing the temperature sensor data, the temperature exceeding the threshold is recorded as abnormal high temperature data. The abnormal high temperature data of the inverter power module finally obtained will help identify potential failure risks.

[0142] Step S235: performing abnormal temperature intersection operation according to the abnormal high temperature data of the inverter power module and the data of frequent temperature fluctuation of the power module, so as to obtain the abnormal temperature data of the inverter component.

[0143] In this embodiment, when performing abnormal temperature intersection operation, the abnormal high temperature data of the inverter power module and the data of frequent temperature fluctuations of the power module are subjected to intersection analysis. By setting the intersection conditions, such as the sampling points that meet the frequent temperature fluctuations and the abnormal high temperature at the same time, the data set that meets the conditions is extracted. The inverter component temperature abnormality data finally generated will include all temperature data recorded under the conditions of frequent temperature fluctuations and abnormal high temperature. These data can be used for further fault analysis and system optimization to help identify the cause of the fault of the inverter component.

[0144] Optionally, step S225 is specifically:

[0145] Perform harmonic component frequency statistics based on current harmonic distortion data to obtain current low-order harmonic data;

[0146] In this embodiment, when performing frequency statistics of harmonic components of current harmonic distortion data, digital signal processing technology is used to perform discrete Fourier transform (DFT) on the collected current waveform data to obtain its frequency domain representation. A high-precision FFT algorithm (fast Fourier transform) is selected to ensure computational efficiency and accuracy. The frequency domain data is analyzed to extract low-order harmonic components, which usually include the fundamental frequency and its first few harmonics (such as the 2nd and 3rd harmonics), and the amplitude and phase of each harmonic component are calculated. The final generated current low-order harmonic data contains the frequency, amplitude and corresponding distortion rate of each harmonic component for subsequent analysis.

[0147] Extracting load voltage characteristics according to inverter component data, thereby obtaining inverter component load voltage data;

[0148] In this embodiment, when extracting the load voltage characteristics according to the inverter component data, a voltage sensor is used to monitor the output voltage signal of the inverter in real time and record its waveform data. The sampling frequency is set to 10kHz to ensure that the voltage waveform characteristics under load changes can be captured. The collected voltage data is denoised to filter out high-frequency interference signals and retain stable load voltage characteristics. By analyzing the maximum, minimum and mean values ​​of the voltage waveform, the generated inverter component load voltage data provides a basis for subsequent waveform drawing.

[0149] The voltage waveform is drawn according to the load voltage data of the inverter component, thereby obtaining the voltage waveform data of the inverter component;

[0150] In this embodiment, when the voltage waveform is drawn according to the inverter component load voltage data, the processed voltage data is visualized using drawing software or programming tools (such as MATLAB, Python's Matplotlib library). Set the time axis as the x-axis and the voltage value as the y-axis to draw a complete voltage waveform curve. Use a smoothing algorithm to reduce the noise impact of the waveform graph to ensure that the waveform presents a true load voltage change. In addition, set appropriate legends and annotations to clearly show the voltage fluctuations and main features, and finally obtain the inverter component voltage waveform data.

[0151] Performing waveform distortion analysis based on the voltage waveform data of the inverter component to obtain nonlinear load data of the inverter component;

[0152] In this embodiment, when waveform distortion analysis is performed based on the voltage waveform data of the inverter component, the effective value, peak value, and root mean square value (RMS) of the voltage waveform are first calculated. The distortion factor (THD) indicator is used to evaluate the nonlinear characteristics of the waveform, and the difference between the calculated voltage waveform and the ideal sine wave is compared. If the THD value exceeds the set threshold (for example, 5%), it is identified as waveform distortion. Finally, the nonlinear load data of the inverter component is identified, reflecting the existing load problem.

[0153] Time domain analysis is performed based on the nonlinear load data of the inverter components and the current low-order harmonic data to obtain the harmonic distortion data of the inverter components.

[0154] In this embodiment, when time domain analysis is performed according to the nonlinear load data of the inverter component and the current low-order harmonic data, the data of the nonlinear load is compared and analyzed with the current low-order harmonic data. Through the cross-correlation analysis method, the correlation between the two is calculated to evaluate the influence of load change on harmonic distortion. Using time domain signal analysis tools (such as the signal processing toolbox in MATLAB), key time domain features such as rise time, fall time and duration are obtained, and combined with the characteristics of the nonlinear load, the inverter component harmonic distortion data finally generated will include the influence of various harmonic components on the load current, which is convenient for subsequent optimization and fault diagnosis.

[0155] Optionally, step S3 specifically includes:

[0156] Step S31: extracting energy storage device features according to solar photovoltaic equipment component data, thereby obtaining energy storage device data;

[0157] In this embodiment, when performing feature extraction of the energy storage device, it is necessary to obtain various operating data of the solar photovoltaic equipment, including voltage, current, temperature, and discharge status. Use a data acquisition system to monitor the performance parameters of the energy storage device in real time through sensors. The collected data needs to be preprocessed, including noise removal and outlier removal to ensure the accuracy of the data. Then, feature extraction techniques such as principal component analysis (PCA) or support vector machine (SVM) are used to extract key features, such as the charge and discharge efficiency, cycle life, and thermal characteristics of the battery, and finally form energy storage device data to provide a basis for subsequent analysis.

[0158] Step S32: performing battery gas evolution analysis according to the energy storage device data, thereby obtaining battery gas evolution data;

[0159] In this embodiment, when performing battery gas evolution analysis, it is necessary to extract gas release information related to battery discharge from the data of the energy storage device, with special attention paid to the type and concentration of the gas. A gas detection sensor (such as a hydrogen sensor) is used to monitor the gas evolution of the battery under different working conditions. During the analysis process, the operating voltage and discharge current of the battery are correlated with the gas evolution data to evaluate the probability of occurrence of gas evolution under specific conditions. By comparing the gas evolution data under different charging states, the threshold of gas evolution is determined, and battery gas evolution data is generated to evaluate the safety and performance of the battery.

[0160] Step S33: performing battery internal resistance analysis according to the energy storage device data, thereby obtaining battery internal resistance data;

[0161] In this embodiment, when performing battery internal resistance analysis, it is necessary to test the battery using an impedance analyzer by applying an alternating current signal of a certain frequency. The test measures the impedance value of the battery under different charging states and calculates the internal resistance data. The change in internal resistance is usually directly related to the health of the battery, so special attention should be paid to the change trend of the internal resistance of the battery, and a reasonable internal resistance threshold should be set, which is usually between a few milliohms and tens of milliohms. Statistical analysis is performed based on the measured internal resistance value to generate battery internal resistance data to provide support for subsequent aging analysis.

[0162] Step S34: performing a battery aging intersection operation according to the battery gas evolution data and the battery internal resistance data, thereby obtaining the battery aging data.

[0163] In this embodiment, when performing the intersection operation of battery aging, the battery gasification data is associated with the battery internal resistance data, and the data fusion technology is used to determine the intersection area of ​​the two in different time periods. An appropriate intersection threshold is set. For example, when the battery internal resistance exceeds the set standard (such as 10 milliohms) and the gasification amount exceeds the set limit, the battery is considered to be in an aged state. Using statistical analysis methods, the relationship between the degree of aging and gasification and internal resistance is calculated, and finally the battery aging data is formed to help evaluate the remaining service life and safety of the energy storage device, and provide a basis for subsequent decision-making.

[0164] Optionally, step S32 is specifically:

[0165] Step S321: extracting battery material features according to the energy storage device data, thereby obtaining battery material data;

[0166] In this embodiment, the battery material feature extraction is performed based on the energy storage device data, and the physical and chemical property data of the battery needs to be collected, mainly including the material composition, structure and manufacturing process information of the battery. Material analysis tools such as scanning electron microscope (SEM) and energy spectrum analysis (EDX) are used to observe and analyze the positive and negative electrode materials of the battery in detail. Combined with the chemical composition data, key material characteristic parameters, including conductivity, thermal stability and mechanical strength, are extracted through data processing software (such as Origin or MATLAB). These extracted data will form a battery material data set, providing a basis for subsequent electrolyte analysis.

[0167] Step S322: drawing a gas chromatogram of the battery electrolyte according to the battery material data, thereby obtaining gas chromatogram data of the battery electrolyte;

[0168] In this embodiment, the battery electrolyte gas chromatography is drawn according to the battery material data. First, the electrolyte sample is prepared and analyzed by gas chromatograph (GC). The analysis conditions, including carrier gas flow rate, column temperature, injection volume, etc., are set to ensure the stability of the experimental environment. In the experiment, the electrolyte sample is gradually injected, and the retention time of the gas components and the corresponding response signals are recorded. By comparing with standard substances, the concentration of each gas component is determined, and finally the gas chromatogram of the battery electrolyte is drawn. This graph will reflect the chemical composition of the electrolyte and provide data support for the subsequent component division.

[0169] Step S323: dividing the gas components according to the gas chromatography data of the battery electrolyte, thereby obtaining the gas composition data of the battery electrolyte;

[0170] In this embodiment, the gas components are divided according to the gas chromatography data of the battery electrolyte, and the chromatogram obtained needs to be analyzed using chromatographic analysis software. Set the threshold, and identify the main gas components through baseline correction and peak area integration. Compare the gas composition with the existing database to determine the gas composition and its concentration, including solvent composition and side reaction products. Use statistical analysis methods such as multivariate linear regression to further process the data to obtain the gas composition data of the battery electrolyte. This process needs to ensure the accuracy and repeatability of the data to provide reliable information for subsequent gas analysis simulation.

[0171] Step S324: performing battery gas evolution simulation according to the battery material data, thereby obtaining battery gas evolution simulation data;

[0172] In this embodiment, the battery gas evolution simulation is performed based on the battery material data, and the mathematical model of the battery is constructed using electrochemical simulation software (such as COMSOL Multiphysics), and the corresponding battery parameters are set, including electrode reaction kinetics, ion conductivity, and gas diffusion coefficient. The gas evolution of the battery under different working conditions is simulated by calculation, and the gas generation amount and its composition at different time points are recorded. The simulation results are analyzed and a gas evolution simulation data set is generated to help evaluate the impact of different materials and designs on the battery gas evolution behavior, and provide a basis for battery design optimization.

[0173] Step S325: monitoring the concentration change of the battery gas evolution simulation data according to the battery electrolyte gas composition data, thereby obtaining gas evolution concentration change data;

[0174] In this embodiment, the concentration change of the battery gas evolution simulation data is monitored based on the battery electrolyte gas composition data. First, a dynamic monitoring system is established to record the concentration change of the gas in the electrolyte in real time. Combined with the simulation data, the change trend of the gas composition is analyzed, and the time series analysis method is used to calculate the concentration change rate in different time periods. Set the concentration threshold, determine the abnormal fluctuation and record the data to identify potential safety hazards or performance degradation. This process can provide an important basis for subsequent fault diagnosis.

[0175] Step S326: Obtain battery capacity performance attenuation data;

[0176] In this embodiment, to obtain the battery capacity performance decay data, multiple discharge and charge cycle tests are required to record the capacity change after each cycle. Use standard discharge test equipment, set a fixed discharge current and voltage, and regularly measure the remaining capacity of the battery. Through long-term monitoring, draw the battery capacity decay curve, extract the decay rate and related parameters, and evaluate the battery's service life and reliability. This data will provide a basis for subsequent analysis.

[0177] Step S327: performing gas concentration change analysis according to the battery capacity performance decay data, thereby obtaining battery capacity performance decay gas concentration change data;

[0178] In this embodiment, the gas concentration change analysis is performed based on the battery capacity performance decay data, and the capacity decay data obtained above needs to be correlated with the gas composition change data. The multivariate regression analysis method is used to study the relationship between gas concentration change and capacity decay, and determine the impact of different gas components on battery performance. A standard value is set to determine whether there is a significant correlation. This analysis result can be used to improve battery design and extend service life.

[0179] Step S328: Perform concentration matching according to the gas evolution concentration change data and the battery capacity performance attenuation gas concentration change data, so as to obtain the battery gas evolution data.

[0180] In this embodiment, concentration matching is performed based on the gas evolution concentration change data and the battery capacity performance attenuation gas concentration change data. A concentration matching model needs to be constructed, and the similarity of the two sets of data is calculated using statistical methods. Methods such as the Pearson correlation coefficient are used to quantify the matching degree between the two, and a suitable matching threshold is set to determine the relationship between different data sets. Finally, the battery gas evolution data is formed to provide a basis for the comprehensive evaluation of battery performance. This data will help further study the failure mechanism of the battery and promote the improvement of battery technology.

[0181] Optionally, step S33 is specifically:

[0182] Step S331: performing battery discharge simulation according to the energy storage device data to obtain battery discharge data;

[0183] In this embodiment, the battery discharge simulation is performed according to the data of the energy storage device. First, it is necessary to connect the energy storage device to the battery testing equipment. At this time, the initial parameters of the discharge simulation are set, including the discharge current value (for example, 1A, 2A, etc.), the discharge time (for example, 3 hours), and the ambient temperature (should be maintained at 25°C ± 1°C). During the discharge process, a constant current discharge mode is adopted to record the voltage and current data of the battery in real time, and the data acquisition frequency is set to 1Hz. The data obtained by the high-precision voltage and current sensors will be stored in the data recording system to form a complete battery discharge data set. This data set contains the voltage, current and timestamp information during the discharge process, providing the necessary data basis for subsequent analysis.

[0184] Step S332: extracting the battery discharge voltage characteristics and the battery discharge current characteristics according to the battery discharge data, thereby obtaining the battery discharge voltage data and the battery discharge current data;

[0185] In this embodiment, feature extraction is performed on the obtained battery discharge data, and the battery discharge voltage data is analyzed to calculate its mean, maximum, minimum and change rate. This process uses digital signal processing tools, such as MATLAB or Python, to implement the sliding window method, with every 10 seconds as a window for analysis. At the same time, similar processing is performed on the battery discharge current data to extract the peak and mean of the instantaneous current. In the process of extracting features, the integrity of the data must be ensured, and any lost data must be compensated or eliminated. Finally, the processed battery discharge voltage data and battery discharge current data are stored as structured data for subsequent instantaneous internal resistance calculation.

[0186] Step S333: Calculating the instantaneous internal resistance of the battery according to the battery discharge voltage data and the battery discharge current data, thereby obtaining the instantaneous internal resistance data of the battery;

[0187] In this embodiment, the instantaneous internal resistance is calculated based on the extracted battery discharge voltage and current data. The formula R=\frac{V}{I} is used to calculate the internal resistance, where V is the battery discharge voltage and I is the battery discharge current. In this calculation process, the synchronization of the data must be ensured, and the voltage and current data at each time point are calculated point by point. The specific operation steps include selecting the corresponding points of the voltage and current data, and using the programming tool to perform batch calculations to output the battery instantaneous internal resistance data. This data set will show the real-time changes in the battery internal resistance under different discharge states, helping to evaluate the battery performance.

[0188] Step S334: Obtain instantaneous internal resistance data of a standard battery;

[0189] In this embodiment, the process of acquiring the instantaneous internal resistance data of the standard battery requires the selection of a standard battery with excellent performance to ensure that its technical specifications are consistent with the battery to be tested. Through the same discharge test process, the battery discharge simulation is performed on the standard battery, and the voltage and current data of the battery are recorded. Subsequently, the instantaneous internal resistance of the standard battery is calculated in the same way as above. To ensure that the test conditions are consistent, the test must be performed under the same environment (for example, the same temperature, the same discharge rate). The final standard battery instantaneous internal resistance data set will be used for subsequent comparative analysis to ensure the reliability of the benchmark data.

[0190] Step S335: Compare the instantaneous internal resistance data of the battery with the instantaneous internal resistance data of the standard battery to obtain the battery internal resistance data.

[0191] In this embodiment, a comparative analysis is performed based on the instantaneous internal resistance data of the standard battery and the instantaneous internal resistance data of the battery to be tested. First, the two sets of data are normalized to eliminate the impact of different magnitudes on the comparison results. Statistical methods are used to calculate the root mean square error (RMSE) and correlation coefficient to evaluate the difference between the internal resistance of the battery to be tested and the standard battery. During the analysis process, attention should be paid to the changes in the internal resistance of the battery under different discharge states, and a comprehensive analysis should be performed in combination with historical data to determine the health status of the battery. The final output of this step is the battery internal resistance data, which helps to evaluate the battery performance and aging degree, and provides a basis for subsequent maintenance decisions.

[0192] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0193] 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 control method for a solar photovoltaic device, characterized in that: The following steps are involved: Step S1: Acquire the operation data of the solar photovoltaic equipment, and perform light intensity feature extraction and photovoltaic equipment operation voltage feature extraction according to the operation data of the solar photovoltaic equipment, so as to obtain light intensity data and photovoltaic equipment operation voltage data; perform maximum power point analysis according to the light intensity data and photovoltaic equipment operation voltage data, so as to obtain maximum power point data; Step S2: Acquire solar photovoltaic device component data, and perform inverter component feature extraction based on the solar photovoltaic device component data, thereby obtaining inverter component data; perform fault detection based on the inverter component data, thereby obtaining inverter fault data; perform electrical isolation based on the inverter fault data, thereby obtaining inverter isolation data; Step S3: extracting energy storage device features according to the solar photovoltaic equipment component data, thereby obtaining energy storage device data; Perform battery aging analysis based on energy storage device data to obtain battery aging data; Step S4: performing charging strategy analysis on the maximum power point data according to the battery aging data, thereby obtaining charging strategy data; Switching the battery operation mode according to the battery aging data of the inverter isolation data, thereby obtaining the battery operation mode data; Step S5: Perform battery protection control analysis according to the charging strategy data and the battery operation mode data to obtain battery protection control data, and upload the data to the solar photovoltaic equipment management cloud platform to execute the battery protection control task.

2. The control method of solar photovoltaic equipment according to claim 4, characterized in that: Step S1 is specifically as follows: Step S11: acquiring solar photovoltaic equipment operation data, and performing light intensity feature extraction and photovoltaic equipment operation voltage feature extraction according to the solar photovoltaic equipment operation data, thereby obtaining light intensity data and photovoltaic equipment operation voltage data; Step S12: Acquiring photovoltaic equipment operating current data; Step S13: Calculating the photovoltaic panel output power according to the photovoltaic device operating voltage data and the photovoltaic device operating current data, thereby obtaining the photovoltaic panel output power data; Step S14: Plotting a photovoltaic panel characteristic curve for the photovoltaic panel output power data according to the light intensity data, thereby obtaining photovoltaic panel characteristic curve data; Step S15: Perform maximum power point tracking according to the photovoltaic panel characteristic curve data to obtain maximum power point data.

3. The control method of solar photovoltaic equipment according to claim 2, characterized in that: Step S15 is specifically as follows: Step S151: extracting power characteristics according to the photovoltaic panel characteristic curve data, thereby obtaining power data; Step S152: performing differential calculation according to the power data to obtain power differential data; Step S153: deriving the power increment derivative according to the power differential data, so as to obtain the maximum power point data.

4. The control method of solar photovoltaic equipment according to claim 1, characterized in that: Step S2 is specifically as follows: Step S21: acquiring solar photovoltaic equipment component data, and extracting inverter component features based on the solar photovoltaic equipment component data, thereby obtaining inverter component data; Step S22: performing harmonic distortion analysis according to the inverter component data, thereby obtaining the inverter component harmonic distortion data; Step S23: performing temperature anomaly analysis according to the inverter component data, thereby obtaining inverter component temperature anomaly data; Step S24: integrating fault data according to the harmonic distortion data of the inverter components and the abnormal temperature data of the inverter components, thereby obtaining inverter fault data; Step S25: Perform electrical isolation according to the inverter fault data, thereby obtaining inverter isolation data.

5. The control method of solar photovoltaic equipment according to claim 4, characterized in that: Step S22 is specifically as follows: Step S221: collecting current waveform data according to inverter component data, thereby obtaining current waveform data; Step S222: performing a discrete Fourier transform of the current signal according to the current waveform data, thereby obtaining current frequency domain data; Step S223: performing current signal separation according to the current frequency domain data, thereby obtaining current fundamental wave data and current harmonic data; Step S224: Calculate the total harmonic distortion rate according to the current fundamental wave data and the current harmonic data, so as to obtain the current harmonic distortion data; Step S225: locating the harmonic distortion source of the inverter component data according to the current harmonic distortion data, thereby obtaining the inverter component harmonic distortion data.

6. The control method of solar photovoltaic equipment according to claim 4, characterized in that: Step S23 is specifically as follows: Step S231: performing inverter power module temperature sensing extraction according to inverter component data, thereby obtaining inverter power module temperature sensing data; Step S232: performing temperature fluctuation statistics on the temperature sensing data of the inverter power module, thereby obtaining frequent temperature fluctuation data of the power module; Step S233: Acquire inverter power module safety temperature range data; Step S234: performing abnormal high temperature comparison on the temperature sensing data of the inverter power module according to the safe temperature range data of the inverter power module, thereby obtaining abnormal high temperature data of the inverter power module; Step S235: performing abnormal temperature intersection operation according to the abnormal high temperature data of the inverter power module and the data of frequent temperature fluctuation of the power module, so as to obtain the abnormal temperature data of the inverter component.

7. The control method of solar photovoltaic equipment according to claim 5, characterized in that: Step S225 is specifically as follows: Perform harmonic component frequency statistics based on current harmonic distortion data to obtain current low-order harmonic data; Extracting load voltage characteristics according to inverter component data, thereby obtaining inverter component load voltage data; The voltage waveform is drawn according to the load voltage data of the inverter component, thereby obtaining the voltage waveform data of the inverter component; Performing waveform distortion analysis based on the voltage waveform data of the inverter component to obtain nonlinear load data of the inverter component; Time domain analysis is performed based on the nonlinear load data of the inverter components and the current low-order harmonic data to obtain the harmonic distortion data of the inverter components.

8. The control method of solar photovoltaic equipment according to claim 1, characterized in that: Step S3 is specifically as follows: Step S31: extracting energy storage device features according to solar photovoltaic equipment component data, thereby obtaining energy storage device data; Step S32: performing battery gas evolution analysis according to the energy storage device data, thereby obtaining battery gas evolution data; Step S33: performing battery internal resistance analysis according to the energy storage device data, thereby obtaining battery internal resistance data; Step S34: performing a battery aging intersection operation according to the battery gas evolution data and the battery internal resistance data, thereby obtaining the battery aging data.

9. The control method of solar photovoltaic equipment according to claim 8, characterized in that: Step S32 is specifically as follows: Step S321: extracting battery material features according to the energy storage device data, thereby obtaining battery material data; Step S322: drawing a gas chromatogram of the battery electrolyte according to the battery material data, thereby obtaining gas chromatogram data of the battery electrolyte; Step S323: dividing the gas components according to the gas chromatography data of the battery electrolyte, thereby obtaining the gas composition data of the battery electrolyte; Step S324: performing battery gas evolution simulation according to the battery material data, thereby obtaining battery gas evolution simulation data; Step S325: monitoring the concentration change of the battery gas evolution simulation data according to the battery electrolyte gas composition data, thereby obtaining gas evolution concentration change data; Step S326: Obtain battery capacity performance attenuation data; Step S327: performing gas concentration change analysis according to the battery capacity performance decay data, thereby obtaining battery capacity performance decay gas concentration change data; Step S328: Perform concentration matching according to the gas evolution concentration change data and the battery capacity performance attenuation gas concentration change data, so as to obtain the battery gas evolution data.

10. The control method of solar photovoltaic equipment according to claim 8, characterized in that: Step S33 is specifically as follows: Step S331: performing battery discharge simulation according to the energy storage device data to obtain battery discharge data; Step S332: extracting the battery discharge voltage characteristics and the battery discharge current characteristics according to the battery discharge data, thereby obtaining the battery discharge voltage data and the battery discharge current data; Step S333: Calculating the instantaneous internal resistance of the battery according to the battery discharge voltage data and the battery discharge current data, thereby obtaining the instantaneous internal resistance data of the battery; Step S334: Obtaining instantaneous internal resistance data of a standard battery; Step S335: Compare the instantaneous internal resistance data of the battery with the instantaneous internal resistance data of the standard battery to obtain the battery internal resistance data.

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