Photovoltaic module hot spot detection method based on thermal boundary condition and dynamic edge compensation

Abstract

The invention provides a photovoltaic module hot spot detection method based on a thermal boundary condition and dynamic edge compensation. The method comprises the following steps: performing standard shielding on a target photovoltaic module; acquiring surface temperature distribution data of the photovoltaic module by using infrared thermal imaging equipment; identifying an edge region and an internal region based on geometric features of the photovoltaic module; respectively setting thermal boundary conditions for the edge area and the internal area; establishing a multi-layer heat conduction model considering thermal boundary condition differences; correcting the temperature data of the edge area by using the dynamic edge compensation factor; performing spatial interpolation fusion on the corrected temperature data of the edge region and the temperature data of the internal region to obtain compensated temperature distribution data; and analyzing the compensated temperature distribution data through a hot spot judgment algorithm, and judging the existence and severity of the hot spots. According to the method, the thermal boundary conditions of the edge and the internal area are distinguished, the dynamic compensation factor is applied to correct the edge temperature data, and the gradient-threshold double-judgment method is combined to realize high-precision hot spot detection.

Description

Technical Field

[0001] This invention relates to the field of solar photovoltaic technology, specifically to a method for detecting hot spots in photovoltaic modules based on thermal boundary conditions and dynamic edge compensation, applicable to photovoltaic power plant operation and maintenance, fault diagnosis, and energy efficiency optimization scenarios. Background Technology

[0002] As a core component of solar power generation systems, photovoltaic (PV) modules directly impact the system's power generation efficiency and lifespan due to their performance and reliability. Hot spots are a common fault phenomenon in PV modules, manifesting as abnormally high localized temperatures that accelerate module aging and even pose safety hazards. Traditional hot spot detection primarily relies on infrared thermal imaging technology to identify hot spots by analyzing the temperature distribution on the module surface. However, PV modules are composed of multiple layers of materials, including glass, EVA, solar cells, and a backsheet, with significant differences in thermal conductivity between each layer (e.g., glass has a thermal conductivity of 1.0 W / (m²)). K), solar cell 148 W / (m The temperature distribution prediction is distorted due to the influence of convective heat transfer, especially at the edge of the module, where the temperature distortion is more severe and directly affects the accuracy of hot spot detection.

[0003] Therefore, the existing technology has the following drawbacks: Ignoring differences in thermal boundary conditions: Traditional models treat the edge and interior regions as having uniform thermal boundary conditions (such as adiabatic or convective), leading to biases in edge temperature predictions. Low edge hotspot recognition rate: Temperature distortion in the edge region makes it easy to miss mild hotspots. Experiments show that the accuracy of edge hotspot recognition by traditional methods is less than 50%. Lack of dynamic correction mechanism: Fixed threshold judgment is prone to misjudgment when the ambient temperature and convection conditions change. Summary of the Invention

[0004] The main objective of this invention is to provide a method for detecting hot spots in photovoltaic modules based on thermal boundary conditions and dynamic edge compensation. By distinguishing the thermal boundary conditions between the edge and the internal region, applying a dynamic compensation factor to correct the edge temperature data, and combining the gradient-threshold dual judgment method, high-precision hot spot detection is achieved.

[0005] The present invention achieves the above objectives through the following technical solutions: A method for detecting hot spots in photovoltaic modules based on thermal boundary conditions and dynamic edge compensation includes: Standardized shading treatment is applied to the target photovoltaic modules to simulate local shading conditions under actual working conditions. Infrared thermal imaging equipment is used to acquire surface temperature distribution data of photovoltaic modules to form an initial temperature field matrix; Identifying edge and interior regions based on the geometric features of photovoltaic modules; Different thermal boundary conditions are set for the edge region and the interior region respectively; among them, a dynamic convection boundary condition model is established for the edge region and an adiabatic boundary condition model is established for the interior region. A multilayer heat conduction model considering the differences in thermal boundary conditions is established. This model includes at least a glass layer, an EVA layer, a battery cell layer, and a backsheet layer. The temperature data in the edge region is corrected by applying a dynamic edge compensation factor; The corrected edge region temperature data and the interior region temperature data are spatially interpolated and fused to obtain the compensated temperature distribution data matrix. By analyzing the compensated temperature distribution data using a hotspot detection algorithm, the existence and severity of hotspots can be determined.

[0006] According to the present invention, a method for detecting hot spots in photovoltaic modules based on thermal boundary conditions and dynamic edge compensation is provided. When performing standardized shading treatment on the target photovoltaic module, an opaque material is used to uniformly shade the surface of the target photovoltaic module. The shape of the shading area is rectangular or a geometric shape that matches the edge contour of the module. The shading area is strictly controlled within 10% ± 1% of the total area of ​​the target photovoltaic module to ensure the stability and reproducibility of the internal heat conduction distribution of the module under shading conditions.

[0007] According to the present invention, a photovoltaic module hot spot detection method based on thermal boundary conditions and dynamic edge compensation is provided. When identifying the edge region and the inner region, based on the geometric characteristics of the photovoltaic module, an annular region extending 5cm inward from the edge of the module is defined as the edge region. The boundary of this region coincides with the physical edge of the module, and the width is constant at 5cm ± 0.5cm. The remaining central region is defined as the inner region. The edge region is directly exposed to the ambient airflow, so its thermal boundary condition is set as a convective boundary, and the convective heat transfer coefficient is dynamically adjusted according to the actual ambient wind speed. The inner region is surrounded by surrounding materials, so its thermal boundary condition is set as an adiabatic boundary, which means that it is assumed that no heat is conducted through the boundary.

[0008] According to the present invention, a method for detecting hot spots in photovoltaic modules based on thermal boundary conditions and dynamic edge compensation is provided. When establishing the dynamic convection boundary condition model, based on fluid dynamics principles and combined with environmental wind speed, wind direction, and module surface roughness parameters, a formula is used. h = h 0 ( v wind ) n Calculate the convective heat transfer coefficient h ,in, h 0 is the baseline convection coefficient. v wind For real-time wind speed, nThis is the wind speed correction index; When establishing an adiabatic boundary condition model, the heat flux density is set. q =0, simulating an adiabatic state with no heat exchange in the internal region, and using finite element analysis to verify the stability of the boundary layer temperature gradient approaching zero.

[0009] According to the present invention, a photovoltaic module hot spot detection method based on thermal boundary conditions and dynamic edge compensation is provided. Finite element analysis is used to verify the stability of the boundary layer temperature gradient approaching zero, including: Constructing a finite element model: Dividing the photovoltaic module into a mesh structure that includes edge regions and internal regions; Set adiabatic boundary conditions: apply zero heat flux density constraints at the boundary nodes of the internal region and fix the component backplane temperature to the ambient temperature; Solving for temperature field distribution: The steady-state temperature field is calculated using finite element analysis software, and the temperature gradient data of the boundary layer in the internal region is extracted; Stability verification criterion: If the maximum temperature gradient of the boundary layer... T max If the temperature is ≤0.1℃ / cm and the standard deviation of the solution results after 10 consecutive iterations is ≤0.05℃, then the adiabatic boundary condition model is deemed to meet the stability requirements.

[0010] According to the present invention, a photovoltaic module hot spot detection method based on thermal boundary conditions and dynamic edge compensation is provided, which establishes a multilayer heat conduction model considering differences in thermal boundary conditions, including: The photovoltaic module is divided into a four-layer structure stacked from top to bottom: a glass layer, an EVA layer, a cell layer, and a backsheet layer; material parameters are set, including at least the thermal conductivity of the glass layer. λ glass Thermal conductivity of EVA layer λ EVA Thermal conductivity of battery cells λ cell Thermal conductivity of the backing layer λ backsheet ; Apply dynamic convective boundary conditions to the edge region and calculate the convective heat transfer coefficient. h ; Apply adiabatic boundary conditions to the internal region, heat flux density q =0; Based on Fourier's law of heat conduction, a three-dimensional steady-state heat conduction equation is established, expressed as: ( λ T )=0 in, λ The thermal conductivity of each layer of material, T Temperature field distribution; The equations are discretized using the finite difference method or the finite element method, and the convergence condition for the iteration is that the temperature change is ≤0.01℃ / cycle; By comparing the measured temperature data from infrared thermal imaging with the model calculation results, if the maximum error is ≤1℃ and the root mean square error is ≤0.5℃, the model is deemed valid.

[0011] According to the present invention, a method for detecting hot spots in photovoltaic modules based on thermal boundary conditions and dynamic edge compensation is provided, which applies a dynamic edge compensation factor to correct the temperature data of the edge region, including: Raw temperature data of edge and interior regions are acquired from infrared thermal imaging equipment. T edge and T inner ; According to the formula K edge =1+ α ( T edge - T inner ) / T inner Calculate the edge compensation factor, where, α This is an empirical coefficient that is dynamically adjusted based on the type of photovoltaic module and environmental conditions. T edge The average temperature of the edge region. T inner The average temperature of the internal region; The temperature data in the edge region is corrected, and the corrected temperature is... T edge The calculation formula is: T edge ′= T edge K edge Corrected edge region temperature T edge ′ and internal region temperature T inner Compare; If temperature gradient T = T edge ′ T inner If the result meets the preset threshold range, the correction is deemed effective.

[0012] According to the present invention, a photovoltaic module hot spot detection method based on thermal boundary conditions and dynamic edge compensation is provided, which spatially interpolates and fuses corrected edge region temperature data with internal region temperature data, including: Based on the geometric coordinate system of photovoltaic modules, the corrected edge region temperature data is used. T edge Temperature data of the interior region T inner Mapped to the same two-dimensional grid plane; Bicubic spline interpolation is used to correct temperature data using edge regions. T edge The temperature values ​​of ′ and its neighboring points are used to generate a continuous temperature field in the edge region. T edge-interp ; Inverse distance weighted interpolation was used to analyze internal temperature data. T inner Based on the distance weighting factor , d i The distance from the interpolation point to the known point is used to generate a continuous temperature field in the internal region. T inner-interp ; At the boundary between the edge region and the interior region, a weighted average fusion strategy is adopted, which is represented as: T trans = β T edge-interp +(1 β ) T inner-interp Among them, the weighting coefficient β It decreases linearly with distance from the edge. β ∈[0,1]); Interpolation results of edge regions T edge-interp Interpolation results of internal regions T inner-interp and the results of the transition zone integration T trans Integrated into a complete two-dimensional temperature distribution data matrix T compensated The matrix dimensions are consistent with the surface dimensions of the photovoltaic module, and each element value corresponds to the compensated temperature value of the grid node. Calculate the compensated temperature matrix T compensatedThe root mean square error (RMSE) between the measured infrared thermal imaging data and the actual data is considered valid if the RMSE is less than or equal to 0.3℃.

[0013] The present invention provides a photovoltaic module hot spot detection method based on thermal boundary conditions and dynamic edge compensation, which analyzes the compensated temperature distribution data through a hot spot determination algorithm, including: Define global temperature gradient threshold T threshold =8℃ / cm, this threshold is set based on the thermal resistance of photovoltaic module materials and industry standards; Define local temperature anomaly threshold T anomaly = T avg +15℃, where, T avg The average surface temperature of the component; Traversing the temperature distribution matrix T compensated Calculate the temperature gradient between each grid node and its 8 neighboring nodes. T i,j ; If there exists a contiguous region where more than 50% of the nodes satisfy... T i,j ≥ T threshold and T i,j ≥ T anomaly If so, it is determined that there is a potential hot spot in the area; The severity of hot spots is graded as follows: Mild hot spot: The highest temperature in the hot spot area T max ∈[ T anomaly , T anomaly +5℃], and the area ratio is ≤5%; Moderate hot spot: The highest temperature in the hot spot area T max ∈( T anomaly +5℃, T anomaly [+15℃], and the area accounts for 5% to 15%; Severe hotspot: The highest temperature in the hotspot area. T max > T anomaly +15℃, or area percentage >15%.

[0014] According to the present invention, a method for detecting hot spots in photovoltaic modules based on thermal boundary conditions and dynamic edge compensation is provided. If the ambient temperature... T env If the temperature exceeds 40℃, the abnormal temperature threshold will be automatically applied. T anomaly Adjusted to T avg +10℃ to adapt to high-temperature operating conditions; If the photovoltaic module is partially shaded, then the gradient threshold will be applied. T threshold Reduce the temperature to 5℃ / cm to avoid misjudgment; Output the hotspot location coordinates, area, maximum temperature, and severity classification results.

[0015] Therefore, compared with the prior art, the photovoltaic module hot spot detection method based on thermal boundary conditions and dynamic edge compensation proposed in this invention has the following beneficial effects: 1. This invention distinguishes between edge (convective boundary) and interior (adiabatic boundary) regions in the thermal conduction analysis of photovoltaic modules, which can accurately simulate the actual heat conduction process and eliminate temperature prediction distortion caused by the simplification of boundary conditions; 2. This invention proposes a dynamic compensation algorithm, which corrects the temperature data of the edge region to eliminate the influence of thermal boundary differences on the detection results, thereby improving the accuracy of edge hot spot recognition by about 50%, especially significantly enhancing the detection effect of hot spots on component edges and corners.

[0016] 3. This invention achieves environmental adaptation through a dynamic correction mechanism, significantly improving the robustness of the method: High-temperature operating condition optimization: Automatically adjust the temperature anomaly detection threshold in high-temperature environments to prevent hot spot detection from failing due to increased ambient temperature; Optimization for shading conditions: For scenarios where components are partially shaded, dynamically adjust the gradient judgment threshold to prevent temperature gradient changes in the shaded area from being misjudged as hot spots. Wind speed variation adaptation: By dynamically adjusting the convective boundary conditions, it adapts to the convective heat transfer effect under different wind speed conditions, ensuring the stability of the test results.

[0017] 4. This invention accurately detects minor hot spots, triggering early warnings in the initial stages of a fault and preventing hot spots from expanding and causing module failure. The automated, high-precision detection method of this invention can replace manual visual inspection and traditional infrared detection, reducing labor costs and reliance on manual inspections. By promptly eliminating potential hot spot hazards, this invention slows down module aging, extends the lifespan of photovoltaic systems, and increases power generation revenue. The annual power degradation rate of modules caused by hot spots can reach 3%-5%, and it is expected that the application of this invention will reduce the degradation rate to below 1%.

[0018] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. Attached Figure Description

[0019] Figure 1 This is a flowchart of an embodiment of a photovoltaic module hot spot detection method based on thermal boundary conditions and dynamic edge compensation according to the present invention.

[0020] Figure 2 This is a schematic diagram illustrating the principle of setting thermal boundary conditions in an embodiment of a photovoltaic module hot spot detection method based on thermal boundary conditions and dynamic edge compensation according to the present invention.

[0021] Figure 3 This is a flowchart illustrating the verification of finite element analysis in an embodiment of the photovoltaic module hot spot detection method based on thermal boundary conditions and dynamic edge compensation according to the present invention.

[0022] Figure 4 This is a flowchart illustrating the establishment of a multilayer heat conduction model in an embodiment of a photovoltaic module hot spot detection method based on thermal boundary conditions and dynamic edge compensation according to the present invention.

[0023] Figure 5 This is a flowchart illustrating the correction of the application of the dynamic edge compensation factor in an embodiment of a photovoltaic module hot spot detection method based on thermal boundary conditions and dynamic edge compensation according to the present invention.

[0024] Figure 6 This is a flowchart of spatial interpolation fusion in an embodiment of a photovoltaic module hot spot detection method based on thermal boundary conditions and dynamic edge compensation according to the present invention.

[0025] Figure 7 This is an analysis flowchart of the hot spot determination algorithm in an embodiment of a photovoltaic module hot spot detection method based on thermal boundary conditions and dynamic edge compensation according to the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0027] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0028] An Example of a Photovoltaic Module Hot Spot Detection Method Based on Thermal Boundary Conditions and Dynamic Edge Compensation See Figures 1 to 7 This embodiment provides a method for detecting hot spots in photovoltaic modules based on thermal boundary conditions and dynamic edge compensation, including: Step S1: Perform standardized shading treatment on the target photovoltaic module to simulate local shading conditions under actual working conditions. Step S2: Use an infrared thermal imaging device to acquire surface temperature distribution data of the photovoltaic module and form an initial temperature field matrix; Step S3: Identify edge and interior regions based on the geometric features of the photovoltaic module; Step S4: Set different thermal boundary conditions for the edge region and the interior region respectively; wherein, a dynamic convection boundary condition model is established for the edge region and an adiabatic boundary condition model is established for the interior region. Step S5: Establish a multilayer heat conduction model that considers the differences in thermal boundary conditions. This model includes at least a glass layer, an EVA layer, a battery cell layer, and a backsheet layer. Step S6: Apply a dynamic edge compensation factor to correct the temperature data of the edge region; Step S7: Spatial interpolation is performed to fuse the corrected edge region temperature data with the internal region temperature data to obtain the compensated temperature distribution data matrix. Step S8: Analyze the compensated temperature distribution data using a hot spot detection algorithm to determine the existence and severity of hot spots.

[0029] In step S1 above, when performing standardized shading treatment on the target photovoltaic module, an opaque material is used to uniformly shade the surface of the target photovoltaic module. The shape of the shading area is rectangular or a geometric shape that matches the edge contour of the module. The shading area is strictly controlled within 10% ± 1% of the total area of ​​the target photovoltaic module to ensure the stability and reproducibility of the heat conduction distribution inside the module under shading conditions.

[0030] In step S3 above, when identifying the edge region and the inner region, based on the geometric characteristics of the photovoltaic module, the annular region extending 5cm inward from the edge of the module is defined as the edge region. The boundary of this region coincides with the physical edge of the module, and its width is constant at 5cm ± 0.5cm; the remaining central region is defined as the inner region. The edge region is directly exposed to the ambient airflow, so its thermal boundary condition is set as a convective boundary, and the convective heat transfer coefficient is dynamically adjusted according to the actual ambient wind speed. The inner region is surrounded by surrounding materials, so its thermal boundary condition is set as an adiabatic boundary, which means that it is assumed that no heat is conducted through the boundary.

[0031] In step S4 above, as Figure 2 As shown, when establishing the dynamic convection boundary condition model, based on the principles of fluid dynamics and combined with environmental wind speed, wind direction, and component surface roughness parameters, the formula is used. h = h 0 ( v wind ) n Calculate the convective heat transfer coefficient h ,in, h 0 is the baseline convection coefficient (range 2-10 W / (m²)). K)), v wind Real-time wind speed (m / s) n The wind speed correction index is 0.5-0.8. When establishing an adiabatic boundary condition model, the heat flux density is set. q =0, simulating an adiabatic state with no heat exchange in the internal region, and using finite element analysis to verify the stability of the boundary layer temperature gradient approaching zero.

[0032] As can be seen, the above models together form the basis for dynamic compensation of thermal boundary condition differences, ensuring the accuracy of temperature field prediction for edge and interior regions.

[0033] In this embodiment, as Figure 3 As shown, finite element analysis is used to verify the stability of the boundary layer temperature gradient approaching zero, including: Constructing a finite element model: Divide the photovoltaic module into a mesh structure containing edge and interior regions, with a mesh size ≤ 1mm × 1mm to ensure boundary layer resolution; Set adiabatic boundary conditions: Apply zero heat flux density constraints at the boundary nodes of the internal region ( q =0), and fix the component backplate temperature to the ambient temperature; Solving for temperature field distribution: The steady-state temperature field is calculated using finite element analysis software, and the temperature gradient data of the boundary layer (≥5cm from the edge) of the internal region is extracted; Stability verification criterion: If the maximum temperature gradient of the boundary layer... T max If the temperature is ≤0.1℃ / cm and the standard deviation of the solution results after 10 consecutive iterations is ≤0.05℃, then the adiabatic boundary condition model is deemed to meet the stability requirements. Dynamic correction mechanism: When the verification does not meet the stability requirements, the mesh density or adiabatic boundary parameters are automatically adjusted, and the calculation is iterated again until convergence.

[0034] In step S5 above, such as Figure 4 As shown, a multilayer heat conduction model considering differences in thermal boundary conditions is established, including: The photovoltaic module is divided into four layers stacked from top to bottom: glass layer, EVA layer, cell layer and backsheet layer, with thicknesses of 3mm, 2mm, 0.2mm and 2mm respectively. Set material parameters: thermal conductivity of the glass layer λ glass =1.0W / (m K); EVA layer thermal conductivity λ EVA =0.3W / (m K); Thermal conductivity of battery cells λ cell =148W / (m K); thermal conductivity of backing layer λ backsheet =0.2W / (m K); Apply dynamic convective boundary conditions to the edge region and calculate the convective heat transfer coefficient. h ; Apply adiabatic boundary conditions to the internal region, heat flux density q =0; Based on Fourier's law of heat conduction, a three-dimensional steady-state heat conduction equation is established. ( λ T )=0 in, λ The thermal conductivity of each layer of material, T Temperature field distribution; The equations are discretized using the finite difference method or the finite element method, with a mesh size ≤ 0.5 mm × 0.5 mm, and the iterative convergence condition is that the temperature change is ≤ 0.01 ℃ / cycle; By comparing the measured temperature data from infrared thermal imaging with the model calculation results, if the maximum error is ≤1℃ and the root mean square error is ≤0.5℃, the model is deemed valid.

[0035] In step S6 above, as Figure 5 As shown, a dynamic edge compensation factor is applied to correct the temperature data in the edge region, including: Raw temperature data of edge and interior regions are acquired from infrared thermal imaging equipment. T edge and T inner ; The raw data is filtered to eliminate noise interference and ensure the smoothness of the temperature data; According to the formula K edge =1+ α ( T edge - T inner ) / T inner Calculate the edge compensation factor, where, α This is an empirical coefficient, ranging from 0.1 to 0.3, and is dynamically adjusted according to the type of photovoltaic module and environmental conditions. T edge The average temperature of the edge region. T inner The average temperature of the internal region; The temperature data in the edge region is corrected, and the corrected temperature is... T edge The calculation formula is: T edge ′= T edge K edge Internal temperature data remain unchanged, or a uniform compensation factor is applied synchronously. K inner = K edge To maintain data consistency; Corrected edge region temperature T edge ′ and internal region temperature T inner Compare; If temperature gradient T = T edge ′ Tinner If the result meets the preset threshold range (≤5℃ / cm), the correction is deemed effective. If the verification fails, the empirical coefficients will be readjusted. α Or check the accuracy of temperature data acquisition.

[0036] In step S7 above, as Figure 6 As shown, spatial interpolation is performed to fuse the corrected edge region temperature data with the interior region temperature data, including: Based on the geometric coordinate system of photovoltaic modules, the corrected edge region temperature data is used. T edge Temperature data of the interior region T inner Mapped to the same two-dimensional grid plane; the grid size is set to 0.5mm × 0.5mm to ensure full coverage of the component surface and to match the resolution of the infrared thermal imaging device; Bicubic spline interpolation is used to correct temperature data using edge regions. T edge The temperature values ​​of ′ and its neighboring points are used to generate a continuous temperature field in the edge region. T edge-interp ; Inverse distance weighted interpolation was used to analyze internal temperature data. T inner Based on the distance weighting factor , d i The distance from the interpolation point to the known point is used to generate a continuous temperature field in the internal region. T inner-interp ; At the boundary between the edge region and the inner region (a transition zone with a width of 1 cm), a weighted average fusion strategy is adopted: T trans = β T edge-interp +(1 β ) T inner-interp Among them, the weighting coefficient β It decreases linearly with distance from the edge. β ∈[0,1]); Interpolation results of edge regions T edge-interp Interpolation results of internal regions T inner-interp and the results of the transition zone integration T transIntegrated into a complete two-dimensional temperature distribution data matrix T compensated The matrix dimensions are consistent with the surface dimensions of the photovoltaic module, and each element value corresponds to the compensated temperature value of the grid node. Calculate the compensated temperature matrix T compensated The root mean square error (RMSE) between the measured infrared thermal imaging data and the actual data is considered valid if the RMSE is ≤ 0.3℃. If the verification fails, adjust the interpolation method parameters or resize the mesh until the accuracy requirements are met.

[0037] In step S8 above, such as Figure 7 As shown, the compensated temperature distribution data is analyzed using a hotspot detection algorithm, including: Define global temperature gradient threshold T threshold =8℃ / cm, this threshold is set based on the thermal resistance of photovoltaic module materials and industry standards; Define local temperature anomaly threshold T anomaly = T avg +15℃, where, T avg The average surface temperature of the component; Traversing the temperature distribution matrix T compensated Calculate the temperature gradient between each grid node and its 8 neighboring nodes. T i,j ; If there exists a continuous region (area ≥ 1 cm²) where more than 50% of the nodes satisfy the condition... T i,j ≥ T threshold and T i,j ≥ T anomaly If so, it is determined that there is a potential hot spot in the area; The severity of hot spots is graded as follows: Mild hot spot: The highest temperature in the hot spot area T max ∈[ T anomaly , T anomaly +5℃], and the area ratio is ≤5%; Moderate hot spot: The highest temperature in the hot spot area T max∈( T anomaly +5℃, T anomaly [+15℃], and the area accounts for 5% to 15%; Severe hotspot: The highest temperature in the hotspot area. T max > T anomaly +15℃, or area percentage >15%.

[0038] If the ambient temperature T env If the temperature exceeds 40℃, the abnormal temperature threshold will be automatically applied. T anomaly Adjusted to T avg +10℃ to adapt to high-temperature operating conditions; If the component has partial occlusion (occlusion area ≥ 10%), then the gradient threshold will be adjusted. T threshold Reduce the temperature to 5℃ / cm to avoid misjudgment; Output the hotspot location coordinates, area, maximum temperature, and severity rating; The accuracy of the judgment is verified by comparing electroluminescence (EL) test images or power attenuation data. If the false judgment rate is ≤5%, the algorithm is considered effective.

[0039] In practical applications, as shown in Table 1, which contains the technical implementation and effect verification under different hot spot scenarios: Table 1: Test Data and Hot Spot Detection Results

[0040] In summary, through the verification of the above three embodiments, the present invention can be implemented in mild, moderate, and severe hot spot scenarios: Precise region division: distinguishing between edge and internal thermal boundary conditions; Dynamic temperature correction: eliminates thermal boundary differences through a compensation factor; High-precision hot spot identification: Combining temperature threshold and gradient dual standards improves detection accuracy by 35%-50%.

[0041] Therefore, this embodiment significantly improves the accuracy of edge hotspot recognition by setting different thermal boundary conditions for the edge and interior regions and applying an edge compensation factor to correct the temperature data of the edge region. Experimental results show that this method improves the accuracy of edge hotspot recognition by approximately 50%, especially for hotspot detection at component edges and corners.

[0042] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0043] The above embodiments are merely preferred embodiments of the present invention and should not be construed as limiting the scope of protection of the present invention. Any non-substantial changes and substitutions made by those skilled in the art based on the present invention shall fall within the scope of protection claimed by the present invention.

Claims

1. A method for detecting hot spot in a photovoltaic module based on thermal boundary conditions and dynamic edge compensation, characterized in that, The method comprises the following steps: standardized shading treatment is performed on the target photovoltaic module to simulate local shading conditions under actual working conditions; an infrared thermal imaging device is used to obtain photovoltaic module surface temperature distribution data to form an initial temperature field matrix; the edge region and the internal region are identified based on the geometric characteristics of the photovoltaic module; different thermal boundary conditions are set for the edge region and the internal region; wherein a dynamic convection boundary condition model is established for the edge region, and an adiabatic boundary condition model is established for the internal region; a multi-layer heat conduction model considering the difference in thermal boundary conditions is established, which at least includes a glass layer, an EVA layer, a cell layer and a backboard layer; a dynamic edge compensation factor is applied to correct the temperature data of the edge region; the corrected temperature data of the edge region and the temperature data of the internal region are spatially interpolated and fused to obtain a compensated temperature distribution data matrix; the compensated temperature distribution data is analyzed by a hot spot judgment algorithm to determine the existence and severity of the hot spot.

2. The method of claim 1, wherein: when the target photovoltaic module is subjected to standardized shading treatment, a uniform shading is performed on the surface of the target photovoltaic module using an opaque material, the shading area is in the shape of a rectangle or a geometric figure adapted to the edge profile of the module, and the shading area is strictly controlled within a predetermined range of the total area of the target photovoltaic module to ensure the stability and reproducibility of the internal heat conduction distribution of the module under shading conditions.

3. The method of claim 1, wherein: when the edge region and the internal region are identified, based on the geometric characteristics of the photovoltaic module, an annular region extending 5 cm inward from the edge of the module is defined as the edge region, and the boundary of the region coincides with the physical edge of the module; the remaining central region is defined as the internal region; wherein the edge region is directly exposed to the ambient airflow, and its thermal boundary condition is set as a convection boundary, and the convection heat transfer coefficient is dynamically adjusted according to the actual environmental wind speed; the internal region is wrapped by the surrounding material, and its thermal boundary condition is set as an adiabatic boundary, i.e. it is assumed that no heat is conducted through the boundary.

4. The method of claim 1, wherein: In establishing the dynamic convection boundary condition model, based on the principle of fluid dynamics, combining with the environmental wind speed, wind direction and component surface roughness parameter, the formula h = h 0 ( v wind ) n is used to calculate the convection heat transfer coefficient h , wherein, h 0 is the reference convection coefficient, v wind is the real-time wind speed, n is the wind speed correction index; In establishing the adiabatic boundary condition model, by setting the heat flux density q = 0, the adiabatic state of no heat exchange in the internal region is simulated, and the stability of the boundary layer temperature gradient approaching zero is verified by finite element analysis.

5. The method of claim 4, wherein, the stability of the temperature gradient of the boundary layer approaching zero is verified by finite element analysis, comprising: constructing a finite element model: dividing the photovoltaic module into a grid structure including the edge region and the internal region; setting the adiabatic boundary condition: applying a zero heat flow density constraint to the internal region boundary nodes, and fixing the backboard temperature of the module to the ambient temperature; solving the temperature field distribution: calculating the steady-state temperature field by the finite element analysis software, and extracting the temperature gradient data of the internal region boundary layer; Stability verification criterion: if the maximum temperature gradient of the boundary layer is T max ≤0.1℃ / cm, and the standard deviation of the solution results of 10 consecutive iterations is ≤0.05℃, it is determined that the adiabatic boundary condition model meets the stability requirements.

6. The method of claim 1, wherein, establishing a multi-layer heat conduction model considering the difference in thermal boundary conditions, comprising: The photovoltaic module is divided into four layers of glass layer, EVA layer, cell piece layer and back plate layer stacked from top to bottom; material parameters are set, at least including glass layer thermal conductivity Based on the Fourier heat conduction law, a three-dimensional steady-state heat conduction equation is established, expressed as: glass , EVA layer thermal conductivity Using finite difference method or finite element method to discretize the equation, and the iteration convergence condition is that the temperature change is less than or equal to 0.01℃ / time; EVA , cell piece layer thermal conductivity By comparing the measured temperature data of the infrared thermal imaging with the calculation results of the model, if the maximum error is less than or equal to 1℃ and the root mean square error is less than or equal to 0.5℃, the model validity is established. cell , back plate layer thermal conductivity applying a dynamic edge compensation factor to correct the temperature data of the edge region, comprising: backsheet ; Applying dynamic convection boundary conditions in the edge region, calculating the convection heat transfer coefficient h ; Applying an adiabatic boundary condition on the inner region, heat flux density q = 0; ​ ( ​ T )=0 wherein, ​ k is the thermal conductivity of the material of each layer, T T is the temperature field distribution; ​ ​ 7. The method according to any one of claims 1 to 6, characterized in that, ​ Acquiring raw temperature data of edge region and inner region from infrared thermal imaging device T edge and T inner ; According to the formula K edge = 1 + a ( T edge - T inner ) / T inner The edge compensation factor is calculated, where α is an empirical coefficient, dynamically adjusted according to the photovoltaic module type and environmental conditions; T edge is the average temperature of the edge region, T inner is the average temperature of the internal region; correcting the temperature data of the edge region, the corrected temperature T edge The calculation formula is: T edge ′= T edge K edge the modified edge region temperature T edge the internal region temperature T inner for comparison; If the temperature gradient T = T edge ′ T inner meets the preset threshold range, it is determined that the correction is effective.

8. The method of claim 7, wherein, The corrected edge region temperature data is spatially interpolated and fused with the internal region temperature data, including: The corrected edge region temperature data is mapped to the same two-dimensional grid plane based on a geometric coordinate system of the photovoltaic module T edge and the interior region temperature data T inner to the same two-dimensional grid plane. The edge region corrected temperature data is generated by using a bicubic spline interpolation method T edge and the temperature values of the neighborhood points thereof, to generate a continuous temperature field in the edge region T edge-interp ; Using inverse distance weighted interpolation method, with internal area temperature data T inner as a reference, combined with distance weight factor , d i as the distance from the interpolation point to the known point, generate continuous temperature field in internal area T inner-interp ; At the junction of the edge region and the internal region, a weighted average fusion strategy is adopted, expressed as: T trans = β T edge-interp +(1 β ) T inner-interp wherein the weight coefficient β linearly decreases with distance from the edge β ∈[0,1] interpolation results of the edge region T edge-interp interpolation results of the inner region T inner-interp and fusion results of the transition zone T trans integrated into a complete two-dimensional temperature distribution data matrix T compensated ; the matrix dimension is consistent with the surface size of the photovoltaic module, and each element value corresponds to the compensated temperature value of the grid node; Compensate the temperature matrix by calculation T compensated The root mean square error RMSE of the infrared thermal imaging measured data, if RMSE≤0.3℃, the fusion result is effective.

9. The method of claim 8, wherein, The compensated temperature distribution data is analyzed by a hot spot determination algorithm, including: Defining a global temperature gradient threshold T threshold = 8 °C / cm, which threshold is set based on the thermal resistance of the photovoltaic module materials and industry standards; Defining a local temperature anomaly threshold T anomaly = T avg + 15°C, wherein, T avg is the average temperature of the component surface; Traverse the temperature distribution matrix T compensated , calculate the temperature gradient of each grid node with its 8-neighbor nodes T i,j ; If more than 50% of the nodes in a contiguous region satisfy T i,j ≥ T threshold and T i,j ≥ T anomaly then the region is determined to have a potential hot spot; Wherein, the classification of hot spot severity includes: Mild hot spot: maximum temperature of hot spot area T max ∈[ T anomaly , T anomaly +5℃] and area ratio ≤5%; Moderate hot spot: maximum temperature of hot spot area T max ∈( T anomaly +5℃, T anomaly +15℃] and area ratio 5%~15%; Severe hot spot: maximum temperature in hot spot area T max > T anomaly +15°C, or area ratio > 15%.

10. The method of claim 9, characterized in that: If the ambient temperature T env > 40℃, the temperature anomaly threshold value T anomaly is automatically adjusted to T avg +10℃ to adapt to high temperature working conditions; If there is a local shading of the photovoltaic module, the gradient threshold T threshold is reduced to 5 °C / cm to avoid false positives; The hot spot position coordinates, area, maximum temperature and severity classification results are output.

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