Temperature detection method, system and equipment for photovoltaic module

Abstract

The invention relates to the technical field of photovoltaic module temperature monitoring, and discloses a temperature detection method for a photovoltaic module. The method comprises the following steps: acquiring temperature data of different positions; a temperature gradient network is constructed through the temperature difference and the physical distance between the adjacent temperature nodes; setting a local temperature difference threshold value; the actual temperature of the photovoltaic module is calculated by combining the temperature gradient network data, the environment temperature increment and the current and voltage data; compared with the prior art that component temperature distribution is difficult to reflect accurately only through single-point or contact type temperature monitoring, and real-time and accurate temperature anomaly monitoring cannot be realized especially under the complex conditions of large illumination intensity fluctuation or local overheating and the like, the method has the advantages that the temperature gradient network is constructed and combined with multi-dimensional data analysis, so that the real-time and accurate temperature anomaly monitoring can be realized; real-time monitoring of local temperature difference of the module is realized, so that temperature detection blind spots are avoided, and the accuracy and response speed of temperature detection of the photovoltaic module are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic module temperature monitoring, and in particular relates to a temperature detection method, system and device for photovoltaic modules. Background Art

[0002] At present, the temperature monitoring technology of photovoltaic modules mainly relies on single-point contact sensors or infrared imaging technology, but these methods still have shortcomings in terms of accuracy and real-time performance. For example, contact sensors can only obtain temperature data at the installation location and cannot cover the temperature distribution on the surface of the module; and infrared imaging is greatly affected by external light, and it is difficult to provide accurate temperature measurement under strong light or complex lighting conditions. The existing technology is difficult to fully meet the needs of high-precision and real-time monitoring of photovoltaic modules when the ambient temperature changes suddenly, the light intensity fluctuates greatly, or overheating occurs in local areas. Therefore, there is an urgent need for a method that can still achieve real-time and accurate detection of the surface temperature distribution of photovoltaic modules under complex environmental conditions such as light fluctuations or local heat concentration, so as to improve the overall performance monitoring accuracy of photovoltaic modules, extend the life of the modules and ensure their working efficiency. Summary of the invention

[0003] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to propose a temperature detection method for photovoltaic modules, aiming to solve the technical problem that it is difficult to accurately reflect the temperature distribution of the modules through only single-point or contact temperature monitoring in the prior art, especially under complex conditions such as large fluctuations in light intensity or local overheating, and it is impossible to achieve real-time and accurate temperature anomaly monitoring.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solution: The present invention provides a temperature detection method for a photovoltaic module.

[0005] The temperature detection method for photovoltaic modules comprises:

[0006] Step S10: n thermocouple sensors are arranged at key positions on the surface of the photovoltaic module, the key positions including the four corners, the middle of the edge and the center position, and the thermocouple sensors are used to collect the temperature value T of the corresponding position k k ;

[0007] Step S20: setting each thermocouple sensor as a temperature node;

[0008] The data format of the temperature node is a data dictionary or a key-value pair, including a node number field, a temperature value field, and a physical location coordinate field;

[0009] The edges between the temperature nodes are connected by an adjacency matrix, and the temperature difference and distance between each pair of adjacent nodes are recorded;

[0010] Step S30: Calculate the physical distance d between two temperature nodes i and j using the Euclidean distance formula ij , when the physical distance between two nodes is d ij If the distance is less than the set distance threshold, the pair of nodes are defined as adjacent nodes and their temperature difference ΔT is calculated. ij =|T i -t j |, where T i is the temperature value of node i, t j is the temperature value of node j, and this temperature difference is recorded as the connection weight in the adjacency matrix, thereby constructing a temperature gradient network, which is used to represent the local temperature difference on the surface of the photovoltaic module;

[0011] Step S40: Set the local temperature difference threshold. When the temperature difference between adjacent nodes is ΔT ij When the local temperature difference threshold is exceeded, it is determined that a local temperature anomaly exists in the corresponding area;

[0012] Step S50: When the local temperature difference on the surface of the photovoltaic module is within a preset range, the current ambient temperature and light intensity I light , using the preset relationship matrix M env→ΔT Calculate the temperature increment ΔT of the PV module caused by the environment env , where the temperature increment ΔT env According to the formula ΔT env =M env→ΔT ×I light Calculated;

[0013] Step S60: Calculate the actual temperature T of the photovoltaic module by combining the temperature difference data, temperature value data, current I and voltage data U of the photovoltaic module in the temperature gradient network and the temperature increment calculated in step S50. real , the formula used is:

[0014]

[0015] Among them, T average Represents the weighted average temperature of each temperature node in the photovoltaic module temperature collection network, ΔT grad It represents the key temperature difference data in the temperature gradient network. α is the gradient temperature difference coefficient, which is used to adjust the weight of the influence of the temperature gradient on the actual temperature. β is the electrical parameter adjustment coefficient, which is used to make corrections based on the influence of the current and voltage changes on the temperature. are the rates of change of voltage and current;

[0016] Step S70: Setting a temperature threshold. When the calculated actual temperature T real When the temperature threshold is exceeded, the control cuts off the grid connection of the PV modules whose actual temperature exceeds the temperature threshold.

[0017] Preferably, in step S60, the key temperature difference data ΔT in the temperature gradient network grad The calculation steps are as follows: by analyzing the temperature differences of all adjacent nodes in the temperature acquisition network, the maximum temperature difference or the temperature difference in the direction with the most concentrated temperature changes is selected as the representative value, which is specifically expressed as: ΔT grad =max(ΔT nm ) or ΔT grad =∑ΔT nm , where ΔT nm Represents the set of temperature differences between adjacent nodes.

[0018] Preferably, in step S30, the data structure of the temperature gradient network adopts a graph structure.

[0019] Preferably, in step S10, the thermocouple sensor is installed on the surface of the photovoltaic module and fixed by a bracket.

[0020] Preferably, in step S20, the temperature node data is transmitted to the data processing background via wireless transmission.

[0021] Preferably, in step S50, the relationship matrix M env→ΔT It is a preset matrix that is adjusted based on the characteristic parameters of the PV module materials.

[0022] Preferably, in step S70, the temperature threshold is adjusted according to different photovoltaic module models.

[0023] The present invention also provides a temperature detection system for a photovoltaic module, comprising:

[0024] The temperature sensor arrangement module is used to arrange n thermocouple sensors at key positions on the surface of the photovoltaic module, wherein the key positions include the four corners, the middle of the edge and the center position, and the thermocouple sensors are used to collect the temperature value T of the corresponding position k. k ;

[0025] A node setting module, used for setting each thermocouple sensor as a temperature node;

[0026] The data format of the temperature node is a data dictionary or a key-value pair, including a node number field, a temperature value field, and a physical location coordinate field;

[0027] The edges between the temperature nodes are connected by an adjacency matrix, and the temperature difference and distance between each pair of adjacent nodes are recorded;

[0028] Distance calculation module, used to calculate the physical distance d between two temperature nodes i and j using the Euclidean distance formula ij , when the physical distance between two nodes is d ijIf the distance is less than the set distance threshold, the pair of nodes are defined as adjacent nodes and their temperature difference ΔT is calculated. ij =|T i -t j |, where T i is the temperature value of node i, t j is the temperature value of node j, and this temperature difference is recorded as the connection weight in the adjacency matrix, thereby constructing a temperature gradient network, which is used to represent the local temperature difference on the surface of the photovoltaic module;

[0029] The temperature anomaly detection module is used to set the local temperature difference threshold. ij When the local temperature difference threshold is exceeded, it is determined that a local temperature anomaly exists in the corresponding area;

[0030] The environmental compensation module is used to compensate for the local temperature difference on the surface of the photovoltaic module within the preset range according to the current ambient temperature and light intensity I light , using the preset relationship matrix M env→ΔT Calculate the temperature increment ΔT of the PV module caused by the environment env , where the temperature increment ΔT env According to the formula ΔT env =M env→ΔT ×I light Calculated;

[0031] The actual temperature calculation module is used to calculate the actual temperature T of the photovoltaic module by combining the temperature difference data, temperature value data, current I and voltage data U of the photovoltaic module in the temperature gradient network and the temperature increment calculated in step S50. real , the formula used is:

[0032]

[0033] Among them, T average Represents the weighted average temperature of each temperature node in the photovoltaic module temperature collection network, ΔT grad It represents the key temperature difference data in the temperature gradient network. α is the gradient temperature difference coefficient, which is used to adjust the weight of the influence of the temperature gradient on the actual temperature. β is the electrical parameter adjustment coefficient, which is used to make corrections based on the influence of the current and voltage changes on the temperature. are the rates of change of voltage and current;

[0034] The cooling system control module is used to set the temperature threshold. real When the temperature threshold is exceeded, the control cuts off the grid connection of the PV modules whose actual temperature exceeds the temperature threshold.

[0035] The present invention also provides a temperature detection device for photovoltaic components, comprising a memory, a processor, and a temperature detection program for photovoltaic components stored in the memory and executable on the processor. When the temperature detection program for photovoltaic components is executed by the processor, the temperature detection method for photovoltaic components is implemented.

[0036] The present invention also provides a computer program product, comprising a temperature detection program for a photovoltaic module, wherein the temperature detection program for a photovoltaic module implements the temperature detection method for a photovoltaic module when the temperature detection program for a photovoltaic module is executed by a processor.

[0037] The beneficial effect of the present invention is that compared with the prior art, which only uses single-point or contact temperature monitoring to accurately reflect the temperature distribution of components, especially under complex conditions such as large fluctuations in light intensity or local overheating, and cannot achieve real-time and accurate temperature anomaly monitoring, the present application realizes real-time monitoring of component temperature and local temperature difference by constructing a temperature gradient network and combining it with multi-dimensional data analysis, thereby avoiding temperature detection blind spots and improving the accuracy and response speed of photovoltaic component temperature detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 The figure is a flow chart of a first embodiment of a temperature detection method for a photovoltaic module according to the present invention.

[0040] Figure 2 The present invention is a schematic diagram of a device for a temperature detection method of a photovoltaic module. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] Embodiment 1: Figure 1 , which is a flow chart of a first embodiment of a temperature detection method for a photovoltaic module according to the present invention, provides a first embodiment of a temperature detection method for a photovoltaic module according to the present invention.

[0043] In a first embodiment, the temperature detection method for a photovoltaic module includes:

[0044] Step S10: n thermocouple sensors are arranged at key positions on the surface of the photovoltaic module, the key positions including the four corners, the middle of the edge and the center position, and the thermocouple sensors are used to collect the temperature value T of the corresponding position k k ;

[0045] It should be noted that the selection of the key positions is based on the typical characteristics of the temperature distribution of photovoltaic modules. The four corners, the middle of the edge and the center are usually the areas where the temperature of the photovoltaic module changes significantly during operation. The sensors arranged at these positions can effectively capture the temperature changes of the module and ensure the comprehensiveness of temperature detection.

[0046] It is understandable that by arranging n thermocouple sensors, it is possible to collect multi-point temperature on the surface of photovoltaic modules and provide data support for subsequent temperature difference calculation and temperature anomaly detection. This multi-point arrangement can not only reflect the temperature level of the module, but also identify temperature anomalies in local areas, avoiding the limitations of single-point measurement.

[0047] It should be understood that in order to ensure the accuracy of measurement and the stability of the sensor position, the thermocouple sensor can be fixed to the surface of the photovoltaic module by a bracket or adhesive to prevent the displacement of the sensor position caused by external environmental factors such as wind, vibration, etc. This can ensure the accuracy of the temperature collection position and the reliability of the data, and improve the accuracy and stability of temperature monitoring.

[0048] Step S20: setting each thermocouple sensor as a temperature node;

[0049] The data format of the temperature node is a data dictionary or a key-value pair, including a node number field, a temperature value field, and a physical location coordinate field;

[0050] The edges between the temperature nodes are connected by an adjacency matrix, and the temperature difference and distance between each pair of adjacent nodes are recorded;

[0051] It should be noted that the data format of the temperature node is designed as a data dictionary or key-value pair to facilitate subsequent data processing and temperature gradient calculation. In the implementation process, the data of each temperature node can be dynamically updated, so that the temperature changes of each node can be reflected in real time, meeting the needs of real-time temperature monitoring of photovoltaic modules.

[0052] It can be understood that the temperature difference and physical distance between adjacent temperature nodes are recorded through the connection method of the adjacency matrix, forming a structured temperature gradient network. This network can represent the local temperature difference and temperature change direction on the surface of the photovoltaic module, and provide basic information for temperature anomaly monitoring and thermal distribution analysis. The data in the adjacency matrix will be updated regularly to ensure that the temperature gradient network can accurately reflect the real-time temperature change trend. In practical applications, the adjacency matrix can also be dynamically adjusted according to the physical characteristics and environmental conditions of the module to adapt to the temperature change characteristics of the photovoltaic module, thereby improving the accuracy and effectiveness of temperature monitoring.

[0053] Step S30: Calculate the physical distance d between two temperature nodes i and j using the Euclidean distance formula ij , when the physical distance between two nodes is d ij If the distance is less than the set distance threshold, the pair of nodes are defined as adjacent nodes and their temperature difference ΔT is calculated. ij =|T i -T j |, where T i is the temperature value of node i, T j is the temperature value of node j, and this temperature difference is recorded as the connection weight in the adjacency matrix, thereby constructing a temperature gradient network, which is used to represent the local temperature difference on the surface of the photovoltaic module;

[0054] It should be noted that the distance threshold is set based on the actual size and temperature distribution characteristics of the photovoltaic module to ensure that the temperature difference between adjacent nodes can accurately reflect the thermal gradient characteristics of the module surface. By selecting an appropriate distance threshold, a temperature gradient network can be effectively constructed to achieve spatial correlation of temperature data.

[0055] It can be understood that the Euclidean distance formula can provide accurate distance calculation between nodes, ensuring that the adjacent node connections in the temperature gradient network have physical meaning and reflect the real spatial distribution of the component surface temperature.

[0056] It should be understood that the adjacency matrix not only records the temperature difference, but also uses the temperature difference as the connection weight, providing reliable basic data for subsequent temperature anomaly detection and heat flow analysis. The construction of the temperature gradient network enables the system to quickly identify temperature anomaly areas.

[0057] Step S40: Setting a local temperature difference threshold. When the temperature difference between adjacent nodes is ΔT ij When the local temperature difference threshold is exceeded, it is determined that a local temperature anomaly exists in the corresponding area;

[0058] It should be noted that the setting of the local temperature difference threshold is determined based on the temperature tolerance of the photovoltaic module and the temperature variation range of the working environment, ensuring that it will not be misjudged as abnormal within the normal temperature fluctuation range, but when the temperature difference exceeds the set threshold, the local abnormal area can be identified in time. This design can effectively balance the risks of false alarms and missed alarms. By analyzing the direction of temperature change, the direction of temperature gradient concentration can be detected, and whether there is a concentrated heat source or temperature abnormality trend on the surface of the photovoltaic module can be quickly identified, thereby preventing the module from being in a high-temperature working state for a long time.

[0059] The determination of local temperature anomalies and the detection of temperature anomaly trends in this step can effectively improve the accuracy and reliability of the photovoltaic module temperature detection system, thereby timely monitoring and warning the working status of the components and ensuring the safe and efficient operation of the photovoltaic modules.

[0060] Step S50: When the local temperature difference on the surface of the photovoltaic module is within a preset range, the current ambient temperature and light intensity I light , using the preset relationship matrix M env→ΔT Calculate the temperature increment ΔT of the PV module caused by the environment env , where the temperature increment ΔT env According to the formula ΔT env =M env→ΔT ×I light Calculated;

[0061] It should be noted that the relationship matrix M env→ΔT It is pre-set based on the material properties of photovoltaic modules and the influence of ambient temperature on module temperature. The matrix can effectively reflect the influence of ambient temperature and light intensity on module temperature change, thereby providing accurate temperature increment compensation under different environmental conditions.

[0062] It should be understood that the temperature increment calculated by this formula can reflect the impact of dynamic changes in environmental conditions on component temperature in real time, providing more accurate data support for temperature anomaly monitoring. This method can adapt to a variety of complex climatic conditions and ensure that stable temperature detection results can be obtained even when the environment changes significantly.

[0063] Step S60: Calculate the actual temperature T of the photovoltaic module by combining the temperature difference data, temperature value data, current I and voltage data U of the photovoltaic module in the temperature gradient network and the temperature increment calculated in step S50. real , the formula used is:

[0064]

[0065] Among them, T average Represents the weighted average temperature of each temperature node in the photovoltaic module temperature collection network, ΔTgrad It represents the key temperature difference data in the temperature gradient network. α is the gradient temperature difference coefficient, which is used to adjust the weight of the influence of the temperature gradient on the actual temperature. β is the electrical parameter adjustment coefficient, which is used to make corrections based on the influence of the current and voltage changes on the temperature. are the rates of change of voltage and current;

[0066] It should be understood that the introduction of adjustment coefficients α and β enables the formula to adaptively reflect temperature changes under different environments and working conditions. For example, under high light intensity conditions, the α value can be appropriately increased to more sensitively respond to the influence of temperature gradients, while β is used to correct the influence of current and voltage changes on temperature.

[0067] It should be understood that by combining the comprehensive calculation of temperature gradient, environmental compensation and electrical parameter correction, the actual operating temperature of the photovoltaic module can be estimated more accurately. This fusion of multi-dimensional data improves the response speed and monitoring accuracy of the system, allowing temperature anomalies to be detected at an early stage.

[0068] For example, the working environment conditions of photovoltaic modules are as follows:

[0069]

[0070] Substitute the formula to calculate the actual temperature T real :

[0071]

[0072] Step S70: Setting a temperature threshold. When the calculated actual temperature T real When the temperature threshold is exceeded, the control cuts off the grid connection of the PV modules whose actual temperature exceeds the temperature threshold.

[0073] It should be noted that the temperature threshold is set to ensure the safety and stability of the system. By presetting a temperature upper limit, namely the temperature threshold, the system can take corresponding protective measures when the actual temperature of the device exceeds this safety range. This temperature threshold is usually determined based on the operating temperature range of the device and the temperature resistance of the material to avoid device damage or performance degradation due to overheating.

[0074] In addition, the temperature detection system for photovoltaic modules provided by the present invention adopts a temperature detection method for photovoltaic modules in the above embodiment, which can solve a technical problem of temperature detection for photovoltaic modules. Compared with the prior art, the beneficial effects of the temperature detection system for photovoltaic modules provided by the present invention are the same as the beneficial effects of the temperature detection method for photovoltaic modules provided by the above embodiment, and other technical features of the temperature detection system for photovoltaic modules are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0075] The present invention provides a temperature detection device for photovoltaic modules, please refer to Figure 2, a temperature detection device for photovoltaic modules includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a temperature detection method for photovoltaic modules in the above-mentioned embodiment 1. A temperature detection device for photovoltaic modules in the embodiment of the present invention may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. A temperature detection device for photovoltaic modules is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention. A temperature detection device for photovoltaic components may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of a temperature detection device for photovoltaic components are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow a temperature detection device for photovoltaic components to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a temperature detection device for photovoltaic components with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.

[0076] The present invention also provides a computer program product, including a computer program, which implements the steps of the temperature detection method for photovoltaic modules as described above when the computer program is executed by a processor. The computer program product provided by the present invention can solve a technical problem of temperature detection for photovoltaic modules. Compared with the prior art, the beneficial effects of the computer program product provided by the present invention are the same as the beneficial effects of the temperature detection method for photovoltaic modules provided in the above embodiment, which will not be repeated here.

[0077] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through a communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present invention are executed.

[0078] It should be understood that the various parts disclosed in the present invention can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0079] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A temperature detection method for a photovoltaic module, characterized in that: Methods include: Step S10: n thermocouple sensors are arranged at key positions on the surface of the photovoltaic module, the key positions including the four corners, the middle of the edge and the center position, and the thermocouple sensors are used to collect the temperature value T of the corresponding position k k ; Step S20: setting each thermocouple sensor as a temperature node; The data format of the temperature node is a data dictionary or a key-value pair, including a node number field, a temperature value field, and a physical location coordinate field; The edges between the temperature nodes are connected by an adjacency matrix, and the temperature difference and distance between each pair of adjacent nodes are recorded; Step S30: Calculate the physical distance d between two temperature nodes i and j using the Euclidean distance formula ij , when the physical distance between two nodes is d ij If the distance is less than the set distance threshold, the pair of nodes are defined as adjacent nodes and their temperature difference ΔT is calculated. ij =|T i -t j |, where T i is the temperature value of node i, T j is the temperature value of node j, and this temperature difference is recorded as the connection weight in the adjacency matrix, thereby constructing a temperature gradient network, which is used to represent the local temperature difference on the surface of the photovoltaic module; Step S40: Set the local temperature difference threshold. When the temperature difference between adjacent nodes is ΔT ij When the local temperature difference threshold is exceeded, it is determined that a local temperature anomaly exists in the corresponding area; Step S50: When the local temperature difference on the surface of the photovoltaic module is within a preset range, the current ambient temperature and light intensity I light , using the preset relationship matrix M env-ΔT Calculate the temperature increment ΔT of the PV module caused by the environment env , where the temperature increment ΔT env According to the formula ΔT env =M env→ΔT ×I light Calculated; Step S60: Calculate the actual temperature T of the photovoltaic module by combining the temperature difference data, temperature value data, current I and voltage data U of the photovoltaic module in the temperature gradient network and the temperature increment calculated in step S50. real , the formula used is: Among them, T average Represents the weighted average temperature of each temperature node in the photovoltaic module temperature collection network, ΔT grad It represents the key temperature difference data in the temperature gradient network. α is the gradient temperature difference coefficient, which is used to adjust the weight of the influence of the temperature gradient on the actual temperature. β is the electrical parameter adjustment coefficient, which is used to make corrections based on the influence of the current and voltage changes on the temperature. are the rates of change of voltage and current; Step S70: Setting a temperature threshold. When the calculated actual temperature T real When the temperature threshold is exceeded, the control cuts off the grid connection of the PV modules whose actual temperature exceeds the temperature threshold.

2. A temperature detection method for a photovoltaic module according to claim 1, characterized in that: In step S60, the key temperature difference data ΔT in the temperature gradient network grad The calculation steps are as follows: by analyzing the temperature differences of all adjacent nodes in the temperature acquisition network, the maximum temperature difference or the temperature difference in the direction with the most concentrated temperature changes is selected as the representative value, which is specifically expressed as: ΔT grad =max(ΔT nm ) or ΔT grad =∑ΔT nm , where ΔT nm Represents the set of temperature differences between adjacent nodes.

3. A temperature detection method for a photovoltaic module according to claim 1, characterized in that: In step S30, the data structure of the temperature gradient network adopts a graph structure.

4. A temperature detection method for a photovoltaic module according to claim 1, characterized in that: In step S10, a thermocouple sensor is installed on the surface of the photovoltaic module and fixed by a bracket.

5. A temperature detection method for a photovoltaic module according to claim 1, characterized in that: In step S20, the temperature node data is transmitted to the data processing background via wireless transmission.

6. A temperature detection method for a photovoltaic module according to claim 1, characterized in that: In step S50, the relationship matrix M env→ΔT It is a preset matrix that is adjusted based on the characteristic parameters of the PV module materials.

7. A temperature detection method for a photovoltaic module according to claim 1, characterized in that: In step S70, the temperature threshold is adjusted according to different photovoltaic module models.

8. A temperature detection system for photovoltaic modules, characterized in that: The temperature detection system for photovoltaic modules comprises: The temperature sensor arrangement module is used to arrange n thermocouple sensors at key positions on the surface of the photovoltaic module, wherein the key positions include the four corners, the middle of the edge and the center position, and the thermocouple sensors are used to collect the temperature value T of the corresponding position k. k ; A node setting module, used for setting each thermocouple sensor as a temperature node; The data format of the temperature node is a data dictionary or a key-value pair, including a node number field, a temperature value field, and a physical location coordinate field; The edges between the temperature nodes are connected by an adjacency matrix, and the temperature difference and distance between each pair of adjacent nodes are recorded; Distance calculation module, used to calculate the physical distance d between two temperature nodes i and j using the Euclidean distance formula ij , when the physical distance between two nodes is d ij If the distance is less than the set distance threshold, the pair of nodes are defined as adjacent nodes and their temperature difference ΔT is calculated. ij =|T i -T j |, where T i is the temperature value of node i, T j is the temperature value of node j, and this temperature difference is recorded as the connection weight in the adjacency matrix, thereby constructing a temperature gradient network, which is used to represent the local temperature difference on the surface of the photovoltaic module; The temperature anomaly detection module sets the local temperature difference threshold. When the temperature difference between adjacent nodes is ΔT ij When the local temperature difference threshold is exceeded, it is determined that a local temperature anomaly exists in the corresponding area; The environmental compensation module is used to compensate for the local temperature difference on the surface of the photovoltaic module within the preset range according to the current ambient temperature and light intensity I light , using the preset relationship matrix M env→ΔT Calculate the temperature increment ΔT of the PV module caused by the environment env , where the temperature increment ΔT env According to the formula ΔT env =M env→ΔT ×I light Calculated; The actual temperature calculation module is used to calculate the actual temperature T of the photovoltaic module by combining the temperature difference data, temperature value data, current I and voltage data U of the photovoltaic module in the temperature gradient network and the temperature increment calculated in step S50. real , the formula used is: Among them, T average Represents the weighted average temperature of each temperature node in the photovoltaic module temperature collection network, ΔT grad It represents the key temperature difference data in the temperature gradient network. α is the gradient temperature difference coefficient, which is used to adjust the weight of the influence of the temperature gradient on the actual temperature. β is the electrical parameter adjustment coefficient, which is used to make corrections based on the influence of the current and voltage changes on the temperature. are the rates of change of voltage and current; The system control module is used to set the temperature threshold. real When the temperature threshold is exceeded, the control cuts off the grid connection of the PV modules whose actual temperature exceeds the temperature threshold.

9. A temperature detection device for a photovoltaic module, characterized in that: The temperature detection device for photovoltaic modules comprises: A memory, a processor, and a temperature detection program for photovoltaic components stored in the memory and executable on the processor, wherein the temperature detection program for photovoltaic components, when executed by the processor, implements the temperature detection method for photovoltaic components described in any one of Claims 1 to 7.

10. A computer program product, characterized in that The computer program product comprises a temperature detection program for a photovoltaic module, and when the temperature detection program for a photovoltaic module is executed by a processor, the temperature detection method for a photovoltaic module described in any one of claims 1 to 7 is implemented.

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