Solar energy storage control system based on internet of things

Abstract

The application relates to the technical field of solar power storage, in particular to a solar power storage control system based on the Internet of Things, which comprises a photovoltaic array comprising a plurality of photovoltaic components and used for converting solar power into electric energy; a data acquisition module used for acquiring the surface temperature and the surface irradiance of a plurality of preset points of each photovoltaic component based on a preset period and acquiring the photovoltaic power storage parameters of each photovoltaic component in real time; a data analysis module used for determining whether the expected standard is met based on the power storage influence trend and the current power storage state, and if the expected standard is not met, determining a plurality of key photovoltaic components based on the photovoltaic power storage parameters of each photovoltaic component in a target time period to determine a local stability characteristic value; and a control adjustment module used for determining a control adjustment mode based on the comparison result of the local stability characteristic value and a preset stability characteristic value. The application can improve the power storage efficiency and quality and ensure the stability of the power storage process.

Description

Technical Field

[0001] This invention relates to the field of solar energy storage technology, and in particular to a solar energy storage control system based on the Internet of Things. Background Technology

[0002] Solar photovoltaic (PV) power generation, as a clean and renewable energy source, has been widely adopted globally in recent years. In distributed PV power generation systems, the solar energy storage control system, as a key component connecting the PV array and the energy storage battery, directly affects the energy utilization efficiency, operational reliability, and battery life of the entire system.

[0003] However, in actual operating environments, photovoltaic (PV) modules are exposed to the outdoor environment for extended periods, resulting in significant unevenness in surface temperature and irradiance distribution. Rapid cloud movement can cause drastic fluctuations in irradiance on the PV module surface within a short period. Shading from surrounding buildings, trees, or power lines, as well as uneven wind speed distribution, can lead to inconsistent heat dissipation conditions on the PV module surface, creating localized hotspots and negatively impacting the normal operation of solar energy storage systems in various ways. The open-circuit voltage of PV units in locally high-temperature areas will significantly decrease, while low-temperature areas will maintain a higher voltage. This voltage difference causes current mismatch between different areas within the same PV module. Traditional maximum power point tracking (MPPT) algorithms are prone to misjudgment under multi-peak curves, failing to locate the global maximum power point and resulting in power generation loss. This, in turn, leads to errors in the estimation of remaining capacity based on the Coulomb integral method or open-circuit voltage method, resulting in decreased energy storage efficiency, shortened battery cycle life, and even safety risks.

[0004] Chinese Patent Publication No. CN121192885A discloses an intelligent charging system and method based on adaptive adjustment of light intensity. The method includes: firstly, acquiring the current light intensity, incident light angle, and ambient temperature and sending them to a photovoltaic cell and a battery charging mode control module; the photovoltaic cell outputs corresponding voltage and current based on the above parameters and a temperature compensation mechanism; the battery charging mode control module converts the photovoltaic cell output into charging voltage and current for the battery, and dynamically determines a constant voltage or constant current charging mode by combining the light intensity, ambient temperature, incident light angle, and charging parameters, thereby achieving adaptive charging of the battery.

[0005] The existing technology has the following problems: it only considers the ambient temperature and does not take into account the uneven temperature distribution on the surface of the photovoltaic module, resulting in poor energy storage quality and insufficient stability of the energy storage process. Summary of the Invention

[0006] To address this issue, the present invention provides an Internet of Things-based solar energy storage control system to overcome the problems in the prior art that do not consider the uneven surface temperature distribution of photovoltaic modules, resulting in poor energy storage quality and insufficient stability of the energy storage process.

[0007] To achieve the above objectives, the present invention provides an Internet of Things-based solar energy storage control system, comprising: A photovoltaic array, which includes several photovoltaic modules, is used to convert solar energy into electrical energy; The data acquisition module is used to acquire the surface temperature and surface irradiance of several preset points of each photovoltaic module based on a preset period, and to acquire the photovoltaic energy storage parameters of each photovoltaic module in real time. The data analysis module is used to determine the energy storage influence trend based on the surface temperature and surface irradiance of each photovoltaic module at each preset point, and to determine the current energy storage state based on the photovoltaic energy storage parameters of each photovoltaic module. It also determines whether the expected standard is met based on the energy storage influence trend and the current energy storage state. If the expected standard is not met, it identifies several key photovoltaic modules based on the photovoltaic energy storage parameters of each photovoltaic module within the target time period, and determines the local stability characterization value based on the surface temperature and surface irradiance of each key photovoltaic module at each preset point. The control adjustment module is used to determine a control adjustment method based on the comparison result between the local stability characterization value and the preset stability characterization value, including a first adjustment method and a second adjustment method, wherein... Under the first adjustment method, a control adjustment coefficient is determined based on the photovoltaic energy storage parameters of each key photovoltaic module within the target time period in order to adjust the preset period. In the second adjustment method, the energy storage offset of each photovoltaic module is determined based on the surface temperature and surface irradiance of each photovoltaic module, so as to adjust the working state of each photovoltaic module.

[0008] Furthermore, the data analysis module includes: The impact analysis unit is used to determine the energy storage impact index of the photovoltaic module based on the surface temperature and surface irradiance of any of the photovoltaic modules at each of the preset points. The trend analysis unit is used to determine the energy storage impact trend based on the energy storage impact index corresponding to each photovoltaic module, wherein the energy storage impact trend includes a strong impact trend and a weak impact trend.

[0009] Furthermore, the data analysis module includes: The parameter analysis unit is used to determine the parameter fluctuation index corresponding to the photovoltaic module based on the photovoltaic energy storage parameters of any of the photovoltaic modules. The state analysis unit is used to determine the current energy storage state based on the parameter fluctuation index corresponding to each photovoltaic module, wherein the current energy storage state includes a stable energy storage state and a fluctuating energy storage state.

[0010] Furthermore, the data analysis module includes: The determination and analysis unit is used to determine whether the expected standard is met based on the energy storage influence trend and the current energy storage state. The key analysis unit is used to determine the parameter change characterization value corresponding to each photovoltaic module based on the photovoltaic energy storage parameters of each photovoltaic module within the target time period under the first judgment result, so as to identify a number of key photovoltaic modules, wherein the first judgment result is the energy storage influence trend and the current energy storage state does not meet the expected standard. The stability analysis unit is used to determine the local change index corresponding to each key photovoltaic module based on the surface temperature and surface irradiance of each key photovoltaic module at each preset point, so as to determine the local stability characterization value.

[0011] Further, the control adjustment module determines the control adjustment method as a first adjustment method based on the first comparison result, and determines the control adjustment method as a second adjustment method based on the second comparison result, wherein, The first comparison result is that the local stability characterization value is greater than the preset stability characterization value; The second comparison result is that the local stability characterization value is less than or equal to the preset stability characterization value.

[0012] Furthermore, the control adjustment module includes: The first adjustment unit is used to determine the control adjustment coefficient based on the comparison result of the photovoltaic energy storage parameters of each key photovoltaic module within the target time period and the preset energy storage parameters under the first adjustment method, so as to adjust the preset period.

[0013] Furthermore, the control adjustment module includes: The second adjustment unit is used to determine the temperature offset index corresponding to the photovoltaic module based on the comparison result of the surface temperature of any photovoltaic module and the preset surface temperature under the second adjustment method, and to determine the irradiance offset index corresponding to the photovoltaic module based on the comparison result of the surface irradiance of any photovoltaic module and the preset surface irradiance, and to determine the energy storage offset of the photovoltaic module based on the temperature offset index and the irradiance offset index corresponding to the photovoltaic module, so as to adjust the working state of each photovoltaic module.

[0014] Furthermore, the second adjustment unit adjusts the operating state of the photovoltaic module based on the comparison result of the energy storage offset of any of the photovoltaic modules with a preset offset, wherein the operating state includes a normal operating state and a stopped operating state.

[0015] Furthermore, the expected standard is that the energy storage influence trend is a weak influence trend and the current energy storage state is a stable energy storage state.

[0016] Furthermore, the key analysis unit determines several key photovoltaic modules based on the comparison results between the parameter change characterization values ​​corresponding to each photovoltaic module and the preset change characterization values.

[0017] Compared with existing technologies, the beneficial effects of this invention are as follows: by periodically collecting surface temperature and irradiance of photovoltaic modules at various locations, and combining this with photovoltaic energy storage parameters such as photovoltaic voltage, photovoltaic current, and battery voltage for multi-dimensional sensing, this invention can accurately capture subtle local changes on the surface of photovoltaic modules, enabling refined judgment of energy storage status. Based on temperature and irradiance data, it determines the energy storage impact trend, and based on photovoltaic energy storage parameters, it determines the current energy storage status. By comprehensively analyzing the energy storage impact trend and the current energy storage status, it accurately determines whether it meets the expected standards. Under the condition that it does not meet the expected standards, it further screens out key photovoltaic modules with abnormal parameter changes and calculates local stability characterization values, achieving accurate identification from global monitoring to local positioning, and improving the efficiency of anomaly location. The control strategy can be flexibly switched based on the comparison between the local stable characterization value and the preset stable characterization value, which can adapt to different degrees of local abnormal operating conditions. The first adjustment method dynamically corrects the data acquisition cycle by controlling the adjustment coefficient to avoid abnormal omissions caused by low-frequency sampling. The second adjustment method directly adjusts the working state of the photovoltaic module based on the energy storage offset, specifically compensates for the output deviation caused by uneven temperature and irradiance differences, optimizes the maximum power point tracking effect, further ensures the stability of the battery charging process, and improves the energy storage quality and efficiency.

[0018] Furthermore, the data analysis module of this invention calculates the energy storage impact index corresponding to any photovoltaic module based on its surface temperature and surface irradiance at various preset points. This enables a quantitative assessment of factors such as local temperature unevenness and local shading, accurately reflecting the degree of influence of local changes in the module on the energy storage process and avoiding misjudgments caused by relying solely on single-point temperature or irradiance. Determining the overall energy storage impact trend of the photovoltaic array based on the energy storage impact index of each photovoltaic module allows for a precise distinction between global environmental changes and local anomalies.

[0019] Furthermore, the data analysis module of this invention calculates the parameter fluctuation index corresponding to any photovoltaic module based on its photovoltaic energy storage parameters, thereby achieving a quantitative assessment of the electrical operating status. It promptly captures abnormal characteristics such as voltage fluctuations and current distortions, reflecting the stability of the module's output and the smoothness of the energy storage process. Through independent analysis of the parameter fluctuations of individual modules, it can accurately locate abnormal modules, improving the accuracy of fault diagnosis. Based on the parameter fluctuation index of each photovoltaic module, the current energy storage status of the entire photovoltaic array is determined, enabling real-time assessment of the stability of the energy storage process.

[0020] Furthermore, the data analysis module of this invention combines the energy storage influence trend with the current energy storage status to comprehensively determine whether the system meets the expected standards, thereby achieving a comprehensive assessment of the system's operating status. When the expected standards are not met, based on the energy storage parameters of each photovoltaic module within the target time period, the module calculates the parameter change characterization values ​​corresponding to each module, identifies several key photovoltaic modules, and achieves precise location of the anomaly source, avoiding excessive intervention. Based on the surface temperature and surface irradiance of each key photovoltaic module at each preset point, the module determines the corresponding local change index for each key photovoltaic module, and then determines the local stability characterization value, achieving a quantitative assessment of the degree of local anomalies in key photovoltaic modules, improving the accuracy of subsequent control, enhancing energy storage quality and efficiency, and ensuring the stability of the energy storage process.

[0021] Furthermore, the control and adjustment module of this invention uses local stable characterization values ​​and preset stable characterization values ​​as judgment benchmarks to accurately distinguish the severity of local anomalies, thereby improving the system's adaptability. By setting a first adjustment unit, under the first adjustment mode, it compares the photovoltaic energy storage parameters of key photovoltaic modules within the target time period with preset energy storage parameters to quantify and generate control adjustment coefficients. By dynamically correcting the preset data acquisition cycle through the control adjustment coefficients, it achieves dynamic matching between the monitoring cycle and the severity of anomalies. By setting a second adjustment unit, it calculates the temperature offset index and the irradiance offset index respectively, and combines the temperature offset index and the irradiance offset index to jointly calculate the energy storage offset, thereby achieving refined and differentiated control at the single photovoltaic module level. By comparing the energy storage offset with the preset offset a second time, it accurately divides the working conditions into two levels: normal working state and stopped working state, avoiding the impact of abnormal photovoltaic modules on the overall energy storage process, improving energy storage quality and efficiency, and ensuring the stability of the energy storage process. Attached Figure Description

[0022] Figure 1 This is a structural block diagram of the IoT-based solar energy storage control system according to an embodiment of the present invention; Figure 2 This is a structural block diagram of the data analysis module in an embodiment of the present invention; Figure 3 This is a structural block diagram of the control and adjustment module according to an embodiment of the present invention; Figure 4 This is a logic diagram for determining the control adjustment method in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0024] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0025] Please see Figure 1 The diagram shown is a structural block diagram of a solar energy storage control system based on the Internet of Things according to an embodiment of the present invention; the system according to the embodiment of the present invention includes: A photovoltaic array, which includes several photovoltaic modules, is used to convert solar energy into electrical energy; In this embodiment, the number and specific structure of photovoltaic modules are not limited. For example, any photovoltaic module may include several photovoltaic cells, each photovoltaic cell is connected in series and packaged into a photovoltaic module, and each photovoltaic module in the photovoltaic array is arranged in parallel.

[0026] The data acquisition module is connected to the photovoltaic array and is used to acquire the surface temperature and surface irradiance of several preset points of each photovoltaic module based on a preset period, and to acquire the photovoltaic energy storage parameters of each photovoltaic module in real time, wherein the photovoltaic energy storage parameters include, but are not limited to, photovoltaic voltage, photovoltaic current and battery voltage. In this embodiment, irradiance is the radiant flux received per unit area. Several preset points are distributed on the surface of each photovoltaic module to accommodate temperature and irradiance acquisition devices for data acquisition. These preset points are evenly distributed, and the data acquisition range corresponding to each preset point covers the entire photovoltaic array surface, eliminating blind spots in single-point acquisition. The specific structure of the temperature and irradiance acquisition devices is not limited; for example, infrared temperature sensors and PT100 resistance thermometers can be used to acquire surface temperature, and silicon-based irradiance sensors and thermopile devices can be used to acquire surface irradiance. The methods and devices for acquiring parameters such as photovoltaic voltage, photovoltaic current, and battery voltage are not limited; this is existing technology. In actual implementation, the application premise is that data acquisition is performed when the photovoltaic module is in normal working condition, and when it is stopped, it is considered to be outside the control range. Implementers can set preset cycles based on actual conditions. During implementation, the acquired surface temperature, surface irradiance, and various photovoltaic energy storage parameters are preprocessed, including outlier removal and normalization. Normalization can eliminate dimensional interference and facilitate subsequent data analysis. This is existing technology and will not be elaborated here.

[0027] The data analysis module, connected to the data acquisition module, is used to determine the energy storage influence trend based on the surface temperature and surface irradiance of each photovoltaic module at each preset point, and to determine the current energy storage state based on the photovoltaic energy storage parameters of each photovoltaic module. It also determines whether the expected standard is met based on the energy storage influence trend and the current energy storage state. If the expected standard is not met, it identifies several key photovoltaic modules based on the photovoltaic energy storage parameters of each photovoltaic module within the target time period, and determines the local stability characterization value based on the surface temperature and surface irradiance of each key photovoltaic module at each preset point. Please see Figure 2 The diagram shown is a structural block diagram of the data analysis module according to an embodiment of the present invention; specifically, the data analysis module includes: The impact analysis unit is used to determine the energy storage impact index of the photovoltaic module based on the surface temperature and surface irradiance of any of the photovoltaic modules at each of the preset points. In this embodiment, for any photovoltaic module, the energy storage impact index can comprehensively reflect the uniformity of the surface temperature and surface irradiance distribution of the photovoltaic module. The larger the energy storage impact index, the worse the uniformity of the surface temperature and surface irradiance distribution of the photovoltaic module at each preset point.

[0028] It is understandable that the coefficient of variation of surface temperature and the coefficient of variation of surface irradiance at each preset point are calculated separately. The coefficient of variation of surface temperature at each preset point is used as the temperature influence value to reflect the uniformity of surface temperature distribution of the photovoltaic module. The larger the temperature influence value, the worse the uniformity of surface temperature distribution of the photovoltaic module. Similarly, the coefficient of variation of surface irradiance at each preset point is used as the irradiance influence value to reflect the uniformity of surface irradiance distribution of the photovoltaic module. The larger the irradiance influence value, the worse the uniformity of surface irradiance distribution of the photovoltaic module. The energy storage influence index is positively correlated with both the temperature influence value and the irradiance influence value. For example, the sum or product of the temperature influence value and the irradiance influence value can be determined as the energy storage influence index.

[0029] A trend analysis unit, connected to the influence analysis unit, is used to determine the energy storage influence trend based on the energy storage influence index corresponding to each photovoltaic module, wherein the energy storage influence trend includes a strong influence trend and a weak influence trend.

[0030] In this embodiment, the energy storage impact index corresponding to each photovoltaic module is compared with a preset impact index. If the energy storage impact index corresponding to each photovoltaic module is less than the preset impact index, it indicates that the surface temperature and surface irradiance distribution uniformity of each photovoltaic module is relatively good and less affected by other factors. The energy storage impact trend is determined to be a weak impact trend. If the energy storage impact index corresponding to any photovoltaic module is greater than or equal to the preset impact index, then at least one photovoltaic module has a relatively poor surface temperature or surface irradiance distribution uniformity, which may be caused by other factors leading to changes in the local surface temperature or surface irradiance of the photovoltaic module. The energy storage impact trend is determined to be a strong impact trend. The smaller the preset impact index, the higher the requirement for the degree of local change in the surface temperature and surface irradiance of the photovoltaic module. Practitioners can set the preset impact index based on the average energy storage impact index calculated from the surface temperature and surface irradiance of photovoltaic modules marked as working normally in historical data, based on actual conditions or historical data.

[0031] Specifically, by calculating the energy storage impact index of any photovoltaic module based on its surface temperature and surface irradiance at various preset points, a quantitative assessment of factors such as local temperature unevenness and local shading is achieved. This accurately reflects the degree of impact of local changes in the module on the energy storage process, avoiding misjudgments caused by relying solely on single-point temperature or irradiance. Determining the overall energy storage impact trend of the photovoltaic array based on the energy storage impact index of each photovoltaic module allows for a precise distinction between global environmental changes and local anomalies.

[0032] Specifically, the data analysis module includes: The parameter analysis unit is used to determine the parameter fluctuation index corresponding to the photovoltaic module based on the photovoltaic energy storage parameters of any of the photovoltaic modules. In this embodiment, for any photovoltaic module, the parameter fluctuation index can comprehensively reflect the degree of deviation between the actual photovoltaic energy storage parameters of the photovoltaic module and the standard preset energy storage parameters. The larger the parameter fluctuation index, the smaller the degree of deviation between the actual photovoltaic energy storage parameters of the photovoltaic module and the standard preset energy storage parameters.

[0033] Understandably, the similarity between the photovoltaic energy storage parameters of the photovoltaic module and the preset energy storage parameters can be calculated as a parameter fluctuation index. There are no restrictions on the method for calculating the similarity, such as cosine similarity algorithm, Euclidean distance algorithm, etc.

[0034] A state analysis unit, connected to the parameter analysis unit, is used to determine the current energy storage state based on the parameter fluctuation index corresponding to each photovoltaic module, wherein the current energy storage state includes a stable energy storage state and a fluctuating energy storage state.

[0035] In this embodiment, the parameter fluctuation index corresponding to each photovoltaic module is compared with a preset fluctuation index. If the parameter fluctuation index corresponding to each photovoltaic module is greater than the preset fluctuation index, it indicates that the photovoltaic energy storage parameters of each photovoltaic module are highly similar to the preset energy storage parameters with small deviations. Therefore, the current energy storage state is determined as a stable energy storage state. If the parameter fluctuation index corresponding to any photovoltaic module is less than or equal to the preset fluctuation index, it indicates that at least one photovoltaic module has a large deviation from the preset energy storage parameters. Therefore, the current energy storage state is determined as a fluctuating energy storage state. Practitioners can set the preset fluctuation index according to actual conditions. The larger the preset fluctuation index, the higher the required matching degree between the photovoltaic energy storage parameters of the photovoltaic modules and the preset energy storage parameters. Practitioners can set the preset energy storage parameters based on actual conditions or the average energy storage parameters corresponding to the maximum power point of photovoltaic modules marked as operating normally in historical data.

[0036] Specifically, by calculating the parameter fluctuation index corresponding to any photovoltaic module based on its energy storage parameters, a quantitative assessment of the electrical operating status can be achieved. This allows for the timely detection of abnormal characteristics such as voltage fluctuations and current distortions, reflecting the stability of the module's output and the smoothness of the energy storage process. Independent analysis of the parameter fluctuations of individual modules enables precise location of abnormal modules, improving the accuracy of fault diagnosis. Based on the parameter fluctuation indices of each photovoltaic module, the current energy storage status of the entire photovoltaic array is determined, achieving real-time assessment of the stability of the energy storage process.

[0037] Specifically, the data analysis module includes: A determination and analysis unit, which is connected to the trend analysis unit and the state analysis unit respectively, is used to determine whether the expected standard is met based on the energy storage influence trend and the current energy storage state. Specifically, the expected standard is that the energy storage influence trend is a weak influence trend and the current energy storage state is a stable energy storage state.

[0038] A key analysis unit, connected to the judgment analysis unit, is used to determine the parameter change characterization value corresponding to each photovoltaic module based on the photovoltaic energy storage parameters of each photovoltaic module within the target time period under the first judgment result, so as to identify a number of key photovoltaic modules, wherein the first judgment result is the energy storage influence trend and the current energy storage state does not meet the expected standard. Specifically, the key analysis unit determines several key photovoltaic modules based on the comparison results between the parameter change characterization values ​​corresponding to each photovoltaic module and the preset change characterization values.

[0039] In this embodiment, for any photovoltaic module, the parameter change characterization value can reflect the degree of change of the photovoltaic energy storage parameters of the photovoltaic module within the target time period. The larger the parameter change characterization value, the greater the degree of change of the photovoltaic energy storage parameters of the photovoltaic module within the target time period.

[0040] Understandably, the mean coefficient of variation of the photovoltaic energy storage parameters of the photovoltaic module in the same dimension is calculated as the parameter change characterization value within the target time period. For any photovoltaic module, if the parameter change characterization value corresponding to the photovoltaic module is greater than the preset change characterization value, the photovoltaic module is identified as a critical photovoltaic module. The actual implementers can set the preset change characterization value based on the mean value of parameter change characterization value calculated from the photovoltaic energy storage parameters of photovoltaic modules that are marked as working normally in the same period of historical data, or based on the actual situation. The target time period can be set based on the actual situation.

[0041] A stability analysis unit, connected to the key analysis unit, is used to determine the local change index corresponding to each key photovoltaic module based on the surface temperature and surface irradiance of each key photovoltaic module at each preset point, so as to determine the local stability characterization value.

[0042] In this embodiment, for any key photovoltaic module, the ratio of the difference between the surface temperature and the preset surface temperature at each preset point is calculated as a local temperature change index. Similarly, the ratio of the difference between the surface irradiance and the preset surface irradiance at each preset point is calculated as a local irradiance change index. The average of the local temperature change index and the local irradiance change index is determined as the corresponding local change index. In practice, operators can set the preset surface temperature based on the average surface temperature corresponding to the maximum power point of normally functioning photovoltaic modules from actual conditions or historical data. Similarly, operators can set the preset surface irradiance based on the average surface irradiance corresponding to the maximum power point of normally functioning photovoltaic modules from actual conditions or historical data.

[0043] It is understandable that the local variation indices corresponding to each key photovoltaic module are arranged from smallest to largest, and the ratio of the smallest local variation index to the largest local variation index is determined as the local stability characterization value.

[0044] Specifically, by combining the trend of energy storage impact with the current energy storage status, a comprehensive assessment of whether the system meets the expected standards is achieved. When the expected standards are not met, based on the energy storage parameters of each photovoltaic module within the target time period, the parameter change characteristics of each module are calculated, and several key photovoltaic modules are selected to accurately locate the anomaly source and avoid excessive intervention. Based on the surface temperature and surface irradiance of each key photovoltaic module at each preset point, the local change index corresponding to each key photovoltaic module is determined, and then the local stability characteristic value is determined. This enables a quantitative assessment of the degree of local anomalies in key photovoltaic modules, improving the accuracy of subsequent control, enhancing energy storage quality and efficiency, and ensuring the stability of the energy storage process.

[0045] A control adjustment module, connected to both the data acquisition module and the data analysis module, is used to determine a control adjustment method based on a comparison between the local stability characterization value and a preset stability characterization value. This includes a first adjustment method and a second adjustment method. Under the first adjustment method, a control adjustment coefficient is determined based on the photovoltaic energy storage parameters of each key photovoltaic module within the target time period in order to adjust the preset period. In the second adjustment method, the energy storage offset of each photovoltaic module is determined based on the surface temperature and surface irradiance of each photovoltaic module, so as to adjust the working state of each photovoltaic module.

[0046] Please see Figure 3 , Figure 4 As shown, Figure 3 This is a structural block diagram of the control and adjustment module according to an embodiment of the present invention. Figure 4 This is a logic diagram for determining the control adjustment method in an embodiment of the present invention; specifically, the control adjustment module includes: A control determination unit is configured to determine the control adjustment mode as a first adjustment mode based on a first comparison result, and to determine the control adjustment mode as a second adjustment mode based on a second comparison result, wherein... The first comparison result is that the local stability characterization value is greater than the preset stability characterization value; The second comparison result is that the local stability characterization value is less than or equal to the preset stability characterization value.

[0047] In this embodiment, the implementer can set a preset stable characterization value based on the actual situation or the average local stable characterization value that has passed the qualification test in historical data.

[0048] Specifically, the control adjustment module includes: The first adjustment unit, which is connected to the control determination unit, is used to determine the control adjustment coefficient based on the comparison result of the photovoltaic energy storage parameters of each key photovoltaic module within the target time period and the preset energy storage parameters under the first adjustment mode, so as to adjust the preset period.

[0049] In this embodiment, if the local stability characterization value is greater than the preset stability characterization value, it is determined that the local change at the current moment is small and the interference to the energy storage process is within an acceptable range. There is no need to directly intervene in the working state of the photovoltaic module, but it is necessary to strengthen monitoring to track the abnormal development trend. The acquisition cycle can be shortened and the sensitivity of abnormal monitoring can be improved.

[0050] It is understandable that, for any key photovoltaic module, the average value of the parameter fluctuation index of the key photovoltaic module within the target time period is used as the control adjustment coefficient, and the product of the control adjustment coefficient and the preset period is determined as the adjusted preset period.

[0051] Specifically, the control adjustment module includes: The second adjustment unit, connected to the control determination unit, is used to determine the temperature offset index corresponding to the photovoltaic module based on the comparison result of the surface temperature of any photovoltaic module and the preset surface temperature under the second adjustment mode, and to determine the irradiance offset index corresponding to the photovoltaic module based on the comparison result of the surface irradiance of any photovoltaic module and the preset surface irradiance, and to determine the energy storage offset of the photovoltaic module based on the temperature offset index and the irradiance offset index, so as to adjust the working state of each photovoltaic module.

[0052] Specifically, the second adjustment unit adjusts the working state of the photovoltaic module based on the comparison result of the energy storage offset of any of the photovoltaic modules with a preset offset, wherein the working state includes a normal working state and a stopped working state.

[0053] In this embodiment, if the local stability characterization value is less than or equal to the preset stability characterization value, it is determined that at least one key photovoltaic module has exceeded the safety threshold in terms of local instability at the current moment, and the operating state of the module needs to be adjusted directly.

[0054] Understandably, for any photovoltaic (PV) module, the temperature difference is determined by the difference between the module's surface temperature and a preset surface temperature. The ratio of this temperature difference to the preset surface temperature is determined as the temperature offset index for that PV module. Similarly, the irradiance difference is determined by the difference between the module's surface irradiance and a preset surface irradiance. The ratio of this irradiance difference to the preset surface irradiance is determined as the irradiance offset index for that PV module. The energy storage offset for that PV module is the average of the temperature offset index and the irradiance offset index. If the energy storage offset for that PV module is greater than the preset offset, the module's operating state is adjusted to a stopped state. If the energy storage offset is less than or equal to the preset offset, the module's operating state is adjusted to a normal operating state. Implementers can set the preset offset based on actual conditions or the average energy storage offset calculated from PV modules that have passed qualification inspections using historical data.

[0055] Specifically, by using local stable characterization values ​​and preset stable characterization values ​​as judgment benchmarks, the severity of local anomalies is accurately distinguished, improving the system's adaptability. By setting up a first adjustment unit, under the first adjustment mode, the photovoltaic energy storage parameters of key photovoltaic modules within the target time period are compared with preset energy storage parameters to quantify and generate control adjustment coefficients. By dynamically correcting the preset data acquisition cycle through the control adjustment coefficients, dynamic matching between the monitoring cycle and the severity of anomalies is achieved. By setting up a second adjustment unit, the temperature offset index and irradiance offset index are calculated separately. The temperature offset index and irradiance offset index are combined to jointly calculate the energy storage offset, achieving refined and differentiated control at the single photovoltaic module level. By comparing the energy storage offset with the preset offset a second time, the two-level operating conditions of normal working state and stopped working state are accurately divided, avoiding the impact of abnormal photovoltaic modules on the overall energy storage process, improving energy storage quality and efficiency, and ensuring the stability of the energy storage process.

[0056] This invention employs periodic, multi-point data collection of photovoltaic (PV) module surface temperature and irradiance, combined with PV voltage, PV current, and battery voltage for multi-dimensional sensing. This allows for precise capture of subtle local changes on the PV module surface, enabling refined judgment of the energy storage status. Based on temperature and irradiance data, the trend of energy storage impact is determined; based on PV energy storage parameters, the current energy storage status is determined. By comprehensively analyzing the energy storage impact trend and the current energy storage status, it accurately determines whether the expected standards are met. If the expected standards are not met, key PV modules with abnormal parameter changes are further screened, and local stability characterization values ​​are calculated. This achieves precise identification from global monitoring to local location, improving anomaly location efficiency. The control strategy is flexibly switched based on the comparison results of local stability characterization values ​​and preset stability characterization values, adapting to different degrees of local anomaly conditions. The first adjustment method dynamically corrects the data acquisition cycle by controlling the adjustment coefficient, avoiding anomaly omissions caused by low-frequency sampling. The second adjustment method directly adjusts the PV module's operating status based on the energy storage offset, specifically compensating for output deviations caused by uneven temperature and irradiance differences, optimizing the maximum power point tracking effect, further ensuring stable battery charging, and improving energy storage quality and efficiency.

[0057] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A solar energy storage control system based on the Internet of Things, characterized in that, include: A photovoltaic array, which includes several photovoltaic modules, is used to convert solar energy into electrical energy; The data acquisition module is used to acquire the surface temperature and surface irradiance of several preset points of each photovoltaic module based on a preset period, and to acquire the photovoltaic energy storage parameters of each photovoltaic module in real time. The data analysis module is used to determine the energy storage influence trend based on the surface temperature and surface irradiance of each photovoltaic module at each preset point, and to determine the current energy storage state based on the photovoltaic energy storage parameters of each photovoltaic module. It also determines whether the expected standard is met based on the energy storage influence trend and the current energy storage state. If the expected standard is not met, it identifies several key photovoltaic modules based on the photovoltaic energy storage parameters of each photovoltaic module within the target time period, and determines the local stability characterization value based on the surface temperature and surface irradiance of each key photovoltaic module at each preset point. The control adjustment module is used to determine a control adjustment method based on the comparison result between the local stability characterization value and the preset stability characterization value, including a first adjustment method and a second adjustment method, wherein... Under the first adjustment method, a control adjustment coefficient is determined based on the photovoltaic energy storage parameters of each key photovoltaic module within the target time period in order to adjust the preset period. In the second adjustment method, the energy storage offset of each photovoltaic module is determined based on the surface temperature and surface irradiance of each photovoltaic module, so as to adjust the working state of each photovoltaic module.

2. The IoT-based solar energy storage control system according to claim 1, characterized in that, The data analysis module includes: The impact analysis unit is used to determine the energy storage impact index of the photovoltaic module based on the surface temperature and surface irradiance of any of the photovoltaic modules at each of the preset points. The trend analysis unit is used to determine the energy storage impact trend based on the energy storage impact index corresponding to each photovoltaic module, wherein the energy storage impact trend includes a strong impact trend and a weak impact trend.

3. The IoT-based solar energy storage control system according to claim 2, characterized in that, The data analysis module includes: The parameter analysis unit is used to determine the parameter fluctuation index corresponding to the photovoltaic module based on the photovoltaic energy storage parameters of any of the photovoltaic modules. The state analysis unit is used to determine the current energy storage state based on the parameter fluctuation index corresponding to each photovoltaic module, wherein the current energy storage state includes a stable energy storage state and a fluctuating energy storage state.

4. The IoT-based solar energy storage control system according to claim 3, characterized in that, The data analysis module includes: The determination and analysis unit is used to determine whether the expected standard is met based on the energy storage influence trend and the current energy storage state. The key analysis unit is used to determine the parameter change characterization value corresponding to each photovoltaic module based on the photovoltaic energy storage parameters of each photovoltaic module within the target time period under the first judgment result, so as to identify a number of key photovoltaic modules, wherein the first judgment result is the energy storage influence trend and the current energy storage state does not meet the expected standard. The stability analysis unit is used to determine the local change index corresponding to each key photovoltaic module based on the surface temperature and surface irradiance of each key photovoltaic module at each preset point, so as to determine the local stability characterization value.

5. The IoT-based solar energy storage control system according to claim 4, characterized in that, The control adjustment module determines the control adjustment method as a first adjustment method based on the first comparison result, and determines the control adjustment method as a second adjustment method based on the second comparison result, wherein... The first comparison result is that the local stability characterization value is greater than the preset stability characterization value; The second comparison result is that the local stability characterization value is less than or equal to the preset stability characterization value.

6. The IoT-based solar energy storage control system according to claim 5, characterized in that, The control adjustment module includes: The first adjustment unit is used to determine the control adjustment coefficient based on the comparison result of the photovoltaic energy storage parameters of each key photovoltaic module within the target time period and the preset energy storage parameters under the first adjustment method, so as to adjust the preset period.

7. The IoT-based solar energy storage control system according to claim 6, characterized in that, The control adjustment module includes: The second adjustment unit is used to determine the temperature offset index corresponding to the photovoltaic module based on the comparison result of the surface temperature of any photovoltaic module and the preset surface temperature under the second adjustment method, and to determine the irradiance offset index corresponding to the photovoltaic module based on the comparison result of the surface irradiance of any photovoltaic module and the preset surface irradiance, and to determine the energy storage offset of the photovoltaic module based on the temperature offset index and the irradiance offset index corresponding to the photovoltaic module, so as to adjust the working state of each photovoltaic module.

8. The IoT-based solar energy storage control system according to claim 7, characterized in that, The second adjustment unit adjusts the working state of the photovoltaic module based on the comparison result of the energy storage offset of any photovoltaic module with a preset offset, wherein the working state includes a normal working state and a stopped working state.

9. The IoT-based solar energy storage control system according to claim 8, characterized in that, The expected standard is that the energy storage influence trend is a weak influence trend and the current energy storage state is a stable energy storage state.

10. The IoT-based solar energy storage control system according to claim 9, characterized in that, The key analysis unit determines several key photovoltaic modules based on the comparison results of the parameter change characterization values ​​corresponding to each photovoltaic module and the preset change characterization values.

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