Intelligent heat dissipation control method and control system of photovoltaic inverter

Abstract

The invention discloses an intelligent heat dissipation control method and system of a photovoltaic inverter, and relates to the technical field of heat dissipation control of photovoltaic inverters, and the method comprises the following steps: collecting the temperature of parts in the photovoltaic inverter and the temperature of the environment in the photovoltaic inverter through a temperature sensor, collecting an internal thermal imaging image, and carrying out the first screening processing; establishing a thermal imaging temperature correction model; performing temperature correction processing on the thermal imaging image in the photovoltaic inverter to obtain corrected thermal imaging temperature data; dynamically controlling a cooling fan of the photovoltaic inverter; the method is used for solving the problems that when heat dissipation control is carried out by measuring the internal temperature of the photovoltaic inverter in an existing photovoltaic inverter heat dissipation control technology, error correction cannot be carried out on a thermal imaging image through the thermal imaging image and a temperature sensor, accurate internal overall temperature information of the photovoltaic inverter cannot be obtained, and then heat dissipation control cannot be carried out.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic inverter heat dissipation control, and specifically to an intelligent heat dissipation control method and control system for a photovoltaic inverter. Background Art

[0002] Photovoltaic inverter heat dissipation control technology refers to managing and regulating the heat generation, conduction, and dissipation processes inside a photovoltaic inverter through various methods and devices to ensure that the photovoltaic inverter operates stably within an appropriate temperature range, thereby guaranteeing the working efficiency, reliability, and service life of the photovoltaic inverter.

[0003] Existing photovoltaic inverter heat dissipation control technologies control the heat dissipation device according to the temperature changes inside the photovoltaic inverter. When measuring the temperature inside the photovoltaic inverter, temperature sensors installed inside the photovoltaic inverter are often used for measurement. However, the temperature sensors can only be installed at a limited number of key positions, such as on key components like power semiconductor devices, transformers, and inductors. The temperature conditions of many other components and regions inside the inverter cannot be directly known. For example, in the patent application with the publication number CN117529070A, a heat dissipation control method and system for the stable operation of a photovoltaic inverter are disclosed. This solution measures the temperature at several points through temperature sensors and then conducts heat dissipation control. However, even if the temperatures of the key components are normal, overheating points may still appear in other unmonitored regions. For example, in a certain corner of the inverter, local temperature may rise due to factors such as poor ventilation or electromagnetic interference, resulting in overheating points. Since these overheating points are not monitored by temperature sensors, problems may only be discovered when the heat conducts to the key components equipped with temperature sensors or when the failure has already caused a decline in the performance of the inverter, which leads to a lag in fault warning and increases the risk of equipment damage. Although the overall temperature information inside the photovoltaic inverter can be obtained through a thermal imaging device, the thermal imaging device is easily interfered by the ambient temperature, and there are certain measurement errors in the thermal imaging device itself. When measuring the temperature inside the inverter, different temperature distributions are measured under different ambient temperatures and component temperatures, resulting in deviations in heat dissipation control. Therefore, when the existing photovoltaic inverter heat dissipation control technology conducts heat dissipation control by measuring the temperature inside the photovoltaic inverter, it cannot obtain accurate overall temperature information inside the photovoltaic inverter through thermal imaging images and use temperature sensors to correct the errors of the thermal imaging images, and then conduct heat dissipation control. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems in the prior art to a certain extent. By using a temperature sensor to collect the component temperature and the internal ambient temperature inside the photovoltaic inverter, and at the same time collecting the internal thermal imaging image and performing the first screening process, and then establishing a thermal imaging temperature correction model; performing temperature correction processing on the thermal imaging image inside the photovoltaic inverter to obtain corrected thermal imaging temperature data; and then dynamically controlling the cooling fan of the photovoltaic inverter; to solve the problem that in the existing heat dissipation control technology of photovoltaic inverters, when performing heat dissipation control by measuring the internal temperature of the photovoltaic inverter, the accurate overall internal temperature information of the photovoltaic inverter cannot be obtained through the thermal imaging image and using the temperature sensor to correct the error of the thermal imaging image, and then perform heat dissipation control.

[0005] To achieve the above object, in a first aspect, the present application provides an intelligent heat dissipation control method for a photovoltaic inverter, including the following steps: Use a temperature sensor to collect the component temperature and the internal ambient temperature inside the photovoltaic inverter, and at the same time collect the thermal imaging image inside the photovoltaic inverter and perform the first screening process to obtain sensor temperature data and thermal imaging temperature data; Establish a thermal imaging temperature correction model based on the sensor temperature data and the thermal imaging temperature data; Perform temperature correction processing on the thermal imaging image inside the photovoltaic inverter based on the thermal imaging temperature correction model and the data of the internal temperature sensor of the photovoltaic inverter to obtain corrected thermal imaging temperature data; Dynamically control the cooling fan of the photovoltaic inverter based on the corrected thermal imaging temperature data and the data of the internal temperature sensor of the photovoltaic inverter.

[0006] Further, using a temperature sensor to collect the component temperature and the internal ambient temperature inside the photovoltaic inverter, and at the same time collecting the thermal imaging image inside the photovoltaic inverter includes the following sub-steps: Use the temperature sensor inside the photovoltaic inverter to continuously collect the component temperature of the corresponding components inside the photovoltaic inverter at a first time interval, and record the collection time and the collection position, marked as the first component temperature data; at the same time, continuously collect the internal ambient temperature inside the photovoltaic inverter at a first time interval, and record the collection time, marked as the first internal temperature data, and the first time interval is t1; Use a thermal imager to keep the relative position of the thermal imager and the photovoltaic inverter in space unchanged, and continuously collect the overall thermal imaging image inside the photovoltaic inverter at a first time interval, marked as the first thermal imaging data; Mark the position inside the photovoltaic inverter where the temperature can be collected by both the temperature sensor and the thermal imaging image as the first reference position.

[0007] Further, the first screening process is performed to obtain the sensor temperature data and the thermal imaging temperature data, including the following sub-steps: Based on the first thermal imaging data and the first component temperature data, the temperatures corresponding to the first thermal imaging data and the temperatures corresponding to the first component temperature data are respectively extracted at all acquisition times and all first reference positions, and are sequentially marked as the second thermal imaging data and the second component temperature data; Data cleaning processing is performed on the second thermal imaging data, the second component temperature data, and the first internal temperature data respectively. After completion, thermal imaging temperature data, third component temperature data, and second internal temperature data are obtained; The data cleaning process includes: sorting the corresponding data according to the corresponding first reference position and acquisition time, denoted as the time data sequence, calculating the average value and standard deviation of the time data sequence, denoted as E0 and E1 respectively. For any data in the time data sequence, denoted as Ei, where i represents the serial number in the time data sequence, if Ei satisfies |Ei - E0| > 3 * E1, then mark Ei as abnormal data. If Ei does not satisfy |Ei - E0| > 3 * E1, then mark Ei as normal data, and repeat marking all data in the time data sequence; The third component temperature data and the second internal temperature data are combined and stored according to the acquisition time, and are marked as sensor temperature data.

[0008] Further, establishing a thermal imaging temperature correction model based on the sensor temperature data and the thermal imaging temperature data includes the following sub-steps: The thermal imaging temperature data and the sensor temperature data are combined according to the acquisition time and the first reference position collected, and are denoted as the first sample data; For the first sample data, the first sample data at the same acquisition time and the same first reference position are combined into a sample vector, denoted as M = [m1, m2, m3], where m1 represents the temperature of the first reference position measured by the temperature sensor, m2 represents the temperature of the corresponding first reference position displayed by the thermal imaging image, and m3 represents the ambient temperature inside the photovoltaic inverter; if there is abnormal data in m1, m2, and m3 in the sample vector M, it is removed; after completion, the second sample data is obtained; The second sample data is normalized respectively according to the component types of the sample vectors, and the magnitudes of all components of all sample vectors in the second sample data are scaled to [0, 1]. After completion, the third sample data is obtained; The third sample data is divided into a training data set and a test data set according to a ratio of 9:1.

[0009] Further, establishing a thermal imaging temperature correction model based on the sensor temperature data and the thermal imaging temperature data further includes the following sub-steps: Construct a first temperature correction model based on a multi-layer perceptron. The first temperature correction model includes an input layer, a hidden layer, and an output layer. Set the number of neurons in the input layer as a1, the number of layers in the hidden layer as a2, the number of neurons in each hidden layer as a3, and the number of neurons in the output layer as a4; Use the training data set to train the first temperature correction model, and after completion, obtain the thermal imaging temperature correction model; Use the test data set to test the thermal imaging temperature correction model, and calculate the average error AE of the thermal imaging temperature correction model; The average error calculation formula is as follows: , where Yj is the temperature value of the first reference position measured by the temperature sensor, Xj is the temperature value of the first reference position output by the thermal imaging temperature correction model, and n is the number of data input into the thermal imaging temperature correction model.

[0010] Further, performing temperature correction processing on the thermal imaging image inside the photovoltaic inverter based on the thermal imaging temperature correction model and the data of the internal temperature sensor of the photovoltaic inverter to obtain the corrected thermal imaging temperature data includes the following sub-steps: Collect the overall thermal imaging image inside the photovoltaic inverter at a first time interval, and simultaneously collect the component temperature and the internal ambient temperature obtained by the internal temperature sensor of the photovoltaic inverter; Extract the temperature values corresponding to all pixel points in the collected thermal imaging image, denoted as the original thermal imaging temperature information. Normalize the original thermal imaging temperature information with the corresponding internal ambient temperature of the photovoltaic inverter, and input it into the thermal imaging temperature correction model to obtain the first corrected temperature information.

[0011] Further, performing temperature correction processing on the thermal imaging image inside the photovoltaic inverter based on the thermal imaging temperature correction model and the data of the internal temperature sensor of the photovoltaic inverter to obtain the corrected thermal imaging temperature data further includes the following sub-steps: Based on the first corrected temperature information and the component temperature obtained by the temperature sensor at the same moment, calculate the first error between the first corrected temperature information and the temperature obtained by the temperature sensor at all first reference positions through the first error calculation formula. The first error calculation formula is as follows: , where AF represents the first error, U represents the temperature value of the first reference position measured by the temperature sensor, and V is the temperature value of the corresponding first reference position in the first corrected temperature information; And average all the first errors to obtain the second error AG; Then calculate the temperature correction error through the correction error calculation formula. The correction error calculation formula is as follows: , where AH is the temperature correction error, q1 and q2 are weight coefficients, q1 + q2 = 1, and the value ranges of q1 and q2 are [0, 1]; Based on the temperature correction error, the error correction is performed on the first corrected temperature information by using the temperature correction formula, and the temperature correction formula is as follows: , where TY is the temperature value in the first corrected temperature information, and TX is the temperature value after error correction; after completion, the second corrected temperature information is obtained and marked as the corrected thermal imaging temperature data.

[0012] Furthermore, the dynamic control of the cooling fan of the photovoltaic inverter based on the corrected thermal imaging temperature data and the data of the internal temperature sensor of the photovoltaic inverter includes the following sub-steps: Merge the corrected thermal imaging temperature data and the data of the internal temperature sensor of the photovoltaic inverter at the same moment, and replace the temperature value corresponding to the first reference position in the corrected thermal imaging temperature data with the temperature value collected by the corresponding temperature sensor. After completion, the internal temperature distribution data is obtained; Set the safe temperature range, denoted as [B1, C1]. According to the safe temperature range, set the optimal temperature range, denoted as [B2, C2], where B1 < B2 and C2 > C1.

[0013] Furthermore, the dynamic control of the cooling fan of the photovoltaic inverter based on the corrected thermal imaging temperature data and the data of the internal temperature sensor of the photovoltaic inverter further includes the following sub-steps: Extract the maximum value of the internal temperature distribution data, denoted as the internal highest temperature TM. Obtain the internal highest temperature TM at the first time interval, denoted as the internal highest temperature sequence. Perform linear fitting on the internal highest temperature sequence to obtain the internal highest temperature sequence of the future first time length, denoted as the predicted highest temperature sequence. Denote any temperature value in the predicted highest temperature sequence as Tk, where k represents the serial number in the predicted highest temperature sequence, and the first time length is t2; If the internal highest temperature TM at the current moment satisfies TM ∈ [B2, C2] and any Tk ∈ [B2, C2], then do not adjust the rotation speed of the cooling fan; If the internal highest temperature TM at the current moment satisfies TM ∈ [B2, C2] and there exists Tk > C2, then increase the rotation speed of the cooling fan; If the internal highest temperature TM at the current moment satisfies TM ∈ [B2, C2] and there exists Tk < B2, then decrease the rotation speed of the cooling fan; If the internal highest temperature TM at the current moment satisfies TM ∈ (C2, C1] and any Tk < C1, then increase the rotation speed of the cooling fan; If the internal maximum temperature TM at the current moment satisfies TM ∈ (C2, C1] and any Tk > C1 or TM > C1, then increase the rotational speed of the cooling fan to the maximum rotational speed and issue a warning signal; If the internal maximum temperature TM at the current moment satisfies TM ∈ [B1, B2) and any Tk > B1, then reduce the rotational speed of the cooling fan; If the internal maximum temperature TM at the current moment satisfies TM ∈ [B1, B2) and any Tk < C1 or TM < B1, then reduce the rotational speed of the cooling fan to the minimum rotational speed and issue a warning signal.

[0014] In a second aspect, the present application provides an intelligent heat dissipation control system for a photovoltaic inverter, including a temperature acquisition module, a model establishment module, a temperature correction module, and a heat dissipation control module; The temperature acquisition module includes a data acquisition unit and a data screening unit. The data acquisition unit uses temperature sensors to acquire the component temperature and the internal ambient temperature inside the photovoltaic inverter, and simultaneously acquires the thermal imaging image inside the photovoltaic inverter. The data screening unit is used to perform a first screening process to obtain sensor temperature data and thermal imaging temperature data; The model establishment module establishes a thermal imaging temperature correction model based on the sensor temperature data and the thermal imaging temperature data; The temperature correction module performs temperature correction processing on the thermal imaging image inside the photovoltaic inverter based on the thermal imaging temperature correction model and the data of the temperature sensors inside the photovoltaic inverter to obtain corrected thermal imaging temperature data; The heat dissipation control module dynamically controls the cooling fan of the photovoltaic inverter based on the corrected thermal imaging temperature data and the data of the temperature sensors inside the photovoltaic inverter.

[0015] The beneficial effects of the present invention: By using temperature sensors to acquire the component temperature and the internal ambient temperature inside the photovoltaic inverter, and simultaneously acquiring the thermal imaging image inside the photovoltaic inverter, and performing a first screening process to obtain sensor temperature data and thermal imaging temperature data; establishing a thermal imaging temperature correction model based on the sensor temperature data and the thermal imaging temperature data; performing temperature correction processing on the thermal imaging image inside the photovoltaic inverter based on the thermal imaging temperature correction model and the data of the temperature sensors inside the photovoltaic inverter to obtain corrected thermal imaging temperature data; dynamically controlling the cooling fan of the photovoltaic inverter based on the corrected thermal imaging temperature data and the data of the temperature sensors inside the photovoltaic inverter; it is possible to obtain accurate overall temperature information inside the photovoltaic inverter through the thermal imaging image and use temperature sensors to correct the error of the thermal imaging image, and then perform heat dissipation control to ensure that there are no local overheating points inside the photovoltaic inverter; The present invention corrects errors by establishing a thermal imaging temperature correction model and combining the internal ambient temperature. The advantage is that the internal ambient temperature will change the deviation between the temperature measured by the thermal imaging image and the actual temperature. By taking the internal ambient temperature as the input, the model can learn these complex relationships, thereby more accurately correcting the temperature measurement error of the thermal imager, improving the accuracy of the final temperature data, and enhancing the versatility and adaptability of the model under different ambient temperature conditions. By performing two error corrections on the thermal imaging image to obtain corrected thermal imaging temperature data, the advantage is that the first time reduces the error caused by environmental interference in the thermal imaging image, making the temperature data closer to the true value, and the second time calibrates the temperature data again, further eliminating possible deviations, resulting in higher accuracy of the final temperature data and providing a more reliable basis for subsequent heat dissipation control. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a schematic block diagram of the system of the present invention; Figure 2 is a flowchart of the steps of the method of the present invention; Figure 3 is a flowchart of the temperature correction process of the present invention; Figure 4 is a flowchart of the dynamic control of the cooling fan of the present invention; Figure 5 is a schematic structural diagram of the electronic device of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0018] Embodiment 1. Please refer to Figure 1 As shown, the present application provides an intelligent heat dissipation control system for a photovoltaic inverter, including a temperature acquisition module, a model establishment module, a temperature correction module, and a heat dissipation control module; The temperature acquisition module includes a data acquisition unit and a data screening unit. The data acquisition unit uses temperature sensors to acquire the component temperature and the internal ambient temperature inside the photovoltaic inverter, and simultaneously acquires the thermal imaging image inside the photovoltaic inverter. The data screening unit is used for the first screening process to obtain sensor temperature data and thermal imaging temperature data; The data acquisition unit is configured with a data acquisition strategy, which includes: using the temperature sensors inside the photovoltaic inverter to continuously collect the component temperatures of the corresponding components inside the photovoltaic inverter at a first time interval, recording the acquisition time and the acquisition location, and marking them as the first component temperature data; at the same time, continuously collecting the ambient temperature inside the photovoltaic inverter at the first time interval, and recording the acquisition time, marking it as the first internal temperature data. The first time interval is t1. In this embodiment, the first time interval t1 is 3 seconds; that is, it is collected once every 3 seconds. Using a thermal imager, keeping the relative position of the thermal imager and the photovoltaic inverter in space unchanged, continuously collecting the thermal imaging image of the whole inside the photovoltaic inverter at a first time interval, and marking it as the first thermal imaging data; Mark the positions inside the photovoltaic inverter where the temperature can be collected by both the temperature sensor and the thermal imaging image as the first reference positions; for example, some temperature sensors inside the photovoltaic inverter are located on the surface of the circuit board, and the temperature of the measured position can also be collected by the thermal imager, while some temperature sensors are located inside the components and cannot be collected by the imager; The data screening unit is configured with a data screening strategy, which includes: based on the first thermal imaging data and the first component temperature data, respectively extracting the temperatures corresponding to the first thermal imaging data and the temperatures corresponding to the first component temperature data at all acquisition times and all first reference positions, and marking them as the second thermal imaging data and the second component temperature data in sequence; that is, only the temperature sensor data, thermal imaging image, and internal ambient temperature at the same acquisition time at the first reference position are required; Perform data cleaning processing on the second thermal imaging data, the second component temperature data, and the first internal temperature data respectively. After completion, obtain the thermal imaging temperature data, the third component temperature data, and the second internal temperature data; The data cleaning processing includes: sorting the corresponding data according to the corresponding first reference position and acquisition time, denoted as the time data sequence, calculating the average value and standard deviation of the time data sequence, denoted as E0 and E1 in sequence. For any data in the time data sequence, denoted as Ei, where i represents the serial number in the time data sequence. If Ei satisfies |Ei - E0| > 3 * E1, then mark Ei as abnormal data. If Ei does not satisfy |Ei - E0| > 3 * E1, then mark Ei as normal data, and repeat marking all the data in the time data sequence; for example, in the time data sequence of the temperature sensor at a certain first position reference position, E2 = 20.1 °C, while E0 = 35.0 °C and E1 = 2.2 °C in this time data sequence, then |20.1 - 35.0| > 3 * 2.2, so mark E2 as abnormal data The third component temperature data and the second internal temperature data are merged and stored according to the acquisition time, and marked as sensor temperature data; In the specific implementation process, the ambient temperature inside the photovoltaic inverter will affect the measurement results of the thermal imaging image. For example, in a high-temperature environment, the thermal noise will increase, which will interfere with the accurate capture of the thermal radiation of the target object by the detector, resulting in problems such as increased noise and reduced resolution in the thermal imaging image, and further affecting the accuracy of the temperature measured by the thermal imager. Therefore, collecting the ambient temperature inside for subsequent error correction can more accurately correct the error of the thermal imaging image and improve the accuracy of the final temperature data.

[0019] The model establishment module establishes a thermal imaging temperature correction model based on the sensor temperature data and the thermal imaging temperature data; The model establishment module is configured with a model establishment strategy, and the model establishment strategy includes: merging the thermal imaging temperature data and the sensor temperature data according to the acquisition time and the first reference position of the acquisition, and recording it as the first sample data; For the first sample data, the first sample data at the same acquisition time and the same first reference position are combined into a sample vector, denoted as M = [m1, m2, m3], where m1 represents the temperature of the first reference position measured by the temperature sensor, m2 represents the temperature of the first reference position corresponding to m1 displayed in the thermal imaging image, and m3 represents the ambient temperature inside the photovoltaic inverter; if there are abnormal data in m1, m2, and m3 in the sample vector M, they are removed; after completion, the second sample data is obtained; abnormal data will affect the results of subsequent model training, so the sample vector containing abnormal data needs to be removed; The second sample data is normalized respectively according to the component types of the sample vector, that is, m1, m2, and m3 of the sample vector are normalized respectively; the magnitudes of all components of all sample vectors in the second sample data are scaled to [0, 1], and after completion, the third sample data is obtained; The third sample data is divided into a training data set and a test data set according to a ratio of 9:1; Based on a multi-layer perceptron, a first temperature correction model is constructed. The first temperature correction model includes an input layer, a hidden layer, and an output layer. The number of neurons in the input layer is set to a1, the number of hidden layers is a2, the number of neurons in each hidden layer is a3, and the number of neurons in the output layer is a4; in this embodiment, a1 = 2, which is used to input the temperature of the first reference position displayed in the thermal imaging image and the ambient temperature inside the photovoltaic inverter, that is, m2 and m3 of the sample vector; a2 = 2; a3 = 24; a4 = 1, which is used to output the temperature after m2 error correction, taking the temperature of the first reference position measured by the temperature sensor as a reference, that is, m1; The first temperature correction model is trained using a training data set, and after completion, a thermal imaging temperature correction model is obtained; The thermal imaging temperature correction model is tested using a test data set, and the average error AE of the thermal imaging temperature correction model is calculated; the formula for calculating the average error is as follows: , where Yj is the temperature value of the first reference position measured by the temperature sensor, Xj is the temperature value of the first reference position output by the thermal imaging temperature correction model, and n is the number of data input into the thermal imaging temperature correction model; if the average error AE of the thermal imaging temperature correction model input is too large, for example, AE > 0.2, the training data set can be used for secondary training until the average error of the thermal imaging temperature correction model reaches a reasonable range; In the specific implementation process, the number of hidden layers a2 and the number of neurons in each hidden layer a3 can be increased or decreased according to the actual application scenario. Generally, it can be tried starting from 1 - 3 layers, starting with a smaller number of neurons and gradually increasing the number of neurons; however, it should not be too large, as too many hidden layers and a large number of neurons will significantly increase the computational complexity and the number of parameters of the model, resulting in high consumption of computing resources and low computing efficiency. Nor should it be too small, as it will not be able to fully learn the complex non-linear relationship between the input data and the output data; in the thermal imaging error correction model, it may not be able to accurately correct the error of the thermal imaging image, resulting in a large deviation between the corrected temperature data and the actual temperature, and unable to provide a reliable basis for heat dissipation control.

[0020] The temperature correction module performs temperature correction processing on the thermal imaging image inside the photovoltaic inverter based on the thermal imaging temperature correction model and the data of the internal temperature sensor of the photovoltaic inverter to obtain corrected thermal imaging temperature data; The temperature correction module is configured with a temperature correction strategy, and the temperature correction strategy includes: collecting the overall thermal imaging image inside the photovoltaic inverter at a first time interval, and simultaneously collecting the component temperature and the internal ambient temperature obtained by the internal temperature sensor of the photovoltaic inverter; Please refer to Figure 3 As shown, extract the temperature values corresponding to all pixel points in the collected thermal imaging image, denoted as the original thermal imaging temperature information, perform normalization processing on the original thermal imaging temperature information and the corresponding internal ambient temperature of the photovoltaic inverter, and input it into the thermal imaging temperature correction model to obtain the first corrected temperature information; that is, the ambient temperature and the temperature corresponding to the pixel point at the same moment are input into the thermal imaging temperature correction model to obtain the temperature after error correction corresponding to the pixel point; for the first time, based on the thermal imaging error correction model, the ambient temperature and the thermal imaging image data are used to obtain the corrected temperature for the first time, which preliminarily reduces the error caused by environmental temperature interference, etc. of the thermal imager and makes the temperature data closer to the true value; Based on the first corrected temperature information and the component temperature obtained by the temperature sensor at the same moment, calculate the first error between the first corrected temperature information at all first reference positions and the temperature obtained by the temperature sensor through the first error calculation formula. The first error calculation formula is as follows: , where AF represents the first error, U represents the temperature magnitude of the first reference position measured by the temperature sensor, and V is the temperature magnitude of the corresponding first reference position in the first corrected temperature information; for example, if the temperature measured by the temperature sensor at a certain first reference position is 35.2 °C, and it is 34.8 °C in the first corrected temperature information, then the first error at this first reference position is 35.2 - 34.8 = 0.4; and average all the first errors to obtain the second error AG, that is, average the first errors at all first reference positions; Then calculate the temperature correction error through the correction error calculation formula. The correction error calculation formula is as follows: , where AH is the temperature correction error, q1 and q2 are weight coefficients, q1 + q2 = 1, and the value ranges of q1 and q2 are [0, 1]. In this embodiment, q1 = q2 = 0.5, and q1 and q2 can be set according to the actual application scenario; for example, if AG = -0.5 and AE = -0.4, then AH = 0.5 * (-0.5) + 0.5 * (-0.4) = -0.45; Based on the temperature correction error, use the temperature correction formula to correct the error of the first corrected temperature information. The temperature correction formula is as follows: , where TY is the temperature value in the first corrected temperature information, and TX is the temperature value after error correction; after completion, obtain the second corrected temperature information, marked as the corrected thermal imaging temperature data; for example, if AH = -0.45, then add -0.45 to all the temperature values in the first corrected temperature information; the second time, calculate the difference between the internal temperature sensor measurement value and the first corrected temperature, combine the model average error for weighted average to obtain the average correction error, and then correct the first corrected temperature information to obtain the second corrected temperature information; this process calibrates the temperature data again, further eliminates the possible deviation, makes the final temperature data more accurate, can more accurately reflect the actual temperature situation inside the photovoltaic inverter, and provides a more reliable basis for subsequent heat dissipation control; In the specific implementation process, the first correction mainly focuses on the relationship between the measurement error of the thermal imager and the ambient temperature, and adjusts the error from a macroscopic level; when making the second correction, the difference between the measured value of the temperature sensor and the first corrected temperature is considered; through this two - correction method, various complex factors such as the characteristics of the thermal imager itself, environmental factors, and differences between different measurement devices that affect temperature measurement are comprehensively considered, enabling a more comprehensive understanding of the error sources in the internal temperature measurement of the inverter, thereby achieving more accurate error correction and ensuring that the corrected temperature data can truly reflect the complex thermal state inside the inverter.

[0021] The heat dissipation control module dynamically controls the cooling fan of the photovoltaic inverter based on the corrected thermal imaging temperature data and the data of the internal temperature sensor of the photovoltaic inverter; The heat dissipation control module is configured with a heat dissipation control strategy, which includes: merging the corrected thermal imaging temperature data and the data of the internal temperature sensor of the photovoltaic inverter at the same moment, and replacing the temperature value corresponding to the first reference position in the corrected thermal imaging temperature data with the temperature value collected by the corresponding temperature sensor. After completion, the internal temperature distribution data is obtained; because the temperature measured by the temperature sensor is more accurate, directly replacing a part can improve the accuracy of subsequent heat dissipation control; Set a safety temperature range, denoted as [B1, C1], and according to the safety temperature range, set an optimal temperature range, denoted as [B2, C2], where B1 < B2 and C2 > C1; in this embodiment, the safety temperature range is [-20°C, 90°C], and the optimal temperature range is [0°C, 50°C]. The optimal temperature range refers to the optimal operating temperature range of the photovoltaic inverter; extract the maximum value of the internal temperature distribution data, denoted as the internal maximum temperature TM, obtain the internal maximum temperature TM at a first time interval, denoted as the internal maximum temperature sequence, perform a linear fit on the internal maximum temperature sequence, that is, establish a relationship between the maximum temperature TM and time; obtain the internal maximum temperature sequence for the future first time length, denoted as the predicted maximum temperature sequence, denote any temperature value in the predicted maximum temperature sequence as Tk, where k represents the serial number in the predicted maximum temperature sequence, the first time length is t2, in this embodiment, the first time length t2 is 1 minute, that is, obtain the internal maximum temperature for the next 1 minute, where the first time interval is 3 seconds, that is, obtain 20 internal maximum temperatures within the next 1 minute; Please refer to Figure 4 As shown, if the internal maximum temperature TM at the current moment satisfies TM ∈ [B2, C2] and any Tk ∈ [B2, C2], then the rotation speed of the cooling fan is not adjusted; that is, the internal maximum temperature at the current moment and the internal maximum temperatures for a period of time in the future are all within the optimal temperature range; If the internal maximum temperature TM at the current moment satisfies TM ∈ [B2, C2] and there exists Tk > C2, then increase the rotational speed of the cooling fan; that is, the internal maximum temperature at the current moment is within the optimal temperature range, but the internal maximum temperature in a future period of time is greater than the optimal temperature range; If the internal maximum temperature TM at the current moment satisfies TM ∈ [B2, C2] and there exists Tk < B2, then decrease the rotational speed of the cooling fan; that is, the internal maximum temperature at the current moment is within the optimal temperature range, but the internal maximum temperature in a future period of time is less than the optimal temperature range; If the internal maximum temperature TM at the current moment satisfies TM ∈ (C2, C1] and for any Tk < C1, then increase the rotational speed of the cooling fan; that is, the internal maximum temperature at the current moment is greater than the optimal temperature range, but within the safe temperature range, and the internal maximum temperature in a future period of time is within the safe temperature range; If the internal maximum temperature TM at the current moment satisfies TM ∈ (C2, C1] and for any Tk > C1 or TM > C1, then increase the rotational speed of the cooling fan to the maximum rotational speed and issue a warning signal; that is, the internal maximum temperature at the current moment is greater than the optimal temperature range, but within the safe temperature range, and the internal maximum temperature in a future period of time is greater than the safe temperature range, or the internal maximum temperature at the current moment is greater than the safe temperature range If the internal maximum temperature TM at the current moment satisfies TM ∈ [B1, B2) and for any Tk > B1, then decrease the rotational speed of the cooling fan; that is, the internal maximum temperature at the current moment is less than the optimal temperature range, but within the safe temperature range, and the internal maximum temperature in a future period of time is within the safe temperature range; If the internal maximum temperature TM at the current moment satisfies TM ∈ [B1, B2) and for any Tk < C1 or TM < B1, then decrease the rotational speed of the cooling fan to the minimum rotational speed and issue a warning signal; that is, the internal maximum temperature at the current moment is less than the optimal temperature range, but within the safe temperature range, and the internal maximum temperature in a future period of time is less than the safe temperature range, or the internal maximum temperature at the current moment is less than the safe temperature range; In the specific implementation process, linear fitting is used to predict the future change trend of the maximum temperature, and the rotational speed of the cooling fan is adjusted in advance according to the prediction result and the current temperature; this forward-looking control strategy can effectively avoid the occurrence of over-high temperature conditions, respond to temperature changes in a timely manner, and ensure the stable operation of the photovoltaic inverter.

[0022] Embodiment 2, please refer to Figure 2 As shown, the present application provides an intelligent heat dissipation control method for a photovoltaic inverter, including the following steps: Step S1: Use a temperature sensor to collect the component temperature and the internal ambient temperature inside the PV inverter. At the same time, collect the thermal imaging image inside the PV inverter and perform a first screening process to obtain sensor temperature data and thermal imaging temperature data. Step S1 includes the following sub-steps: Step S101: Use the temperature sensors inside the PV inverter to continuously collect the component temperatures of the corresponding components inside the PV inverter at a first time interval, and record the collection time and the collection location, which is marked as the first component temperature data. Step S102: At the same time, continuously collect the internal ambient temperature inside the PV inverter at the first time interval, and record the collection time, which is marked as the first internal temperature data. The first time interval is t1. Step S103: Use a thermal imager to keep the relative position of the thermal imager and the PV inverter in space unchanged, and continuously collect the overall thermal imaging image inside the PV inverter at the first time interval, which is marked as the first thermal imaging data. Step S104: Mark the positions inside the PV inverter that can collect temperature through both the temperature sensor and the thermal imaging image as the first reference positions. Step S105: Based on the first thermal imaging data and the first component temperature data, extract the temperatures corresponding to the first thermal imaging data and the temperatures corresponding to the first component temperature data at all collection times and all first reference positions, and mark them as the second thermal imaging data and the second component temperature data in sequence. Step S106: Perform data cleaning processing on the second thermal imaging data, the second component temperature data, and the first internal temperature data respectively. After completion, obtain the thermal imaging temperature data, the third component temperature data, and the second internal temperature data. Step S107: Data cleaning processing. Step S107 includes the following sub-steps: Step S1071: Sort the corresponding data according to the corresponding first reference position and collection time, denoted as the time data sequence. Calculate the average value and standard deviation of the time data sequence, and denote them as E0 and E1 in sequence. Step S1072: For any data in the time data sequence, denoted as Ei, where i represents the serial number in the time data sequence. If Ei satisfies |Ei - E0| > 3 * E1, then mark Ei as abnormal data. Step S1073: If Ei does not satisfy |Ei - E0| > 3 * E1, then mark Ei as normal data, and repeat the marking for all data in the time data sequence. Step S108: Combine and store the third component temperature data and the second internal temperature data according to the collection time, and mark them as the sensor temperature data.

[0023] Step S2, establish a thermal imaging temperature correction model based on the sensor temperature data and the thermal imaging temperature data; Step S2 includes the following sub-steps: Step S201, merge the thermal imaging temperature data and the sensor temperature data according to the acquisition time and the first reference position of the acquisition, and record it as the first sample data; Step S202, for the first sample data, combine the first sample data at the same acquisition time and the same first reference position into a sample vector, denoted as M = [m1, m2, m3], where m1 represents the temperature of the first reference position measured by the temperature sensor, m2 represents the temperature of the first reference position corresponding to m1 displayed by the thermal imaging image, and m3 represents the ambient temperature inside the photovoltaic inverter; Step S203, if there are abnormal data in m1, m2, and m3 in the sample vector M, remove them; after completion, obtain the second sample data; Step S204, perform normalization processing on the second sample data according to the component types of the sample vectors, and scale the magnitudes of all components of all sample vectors in the second sample data to [0, 1]. After completion, obtain the third sample data; Step S205, divide the third sample data into a training data set and a test data set according to a ratio of 9:1; Step S206, construct a first temperature correction model based on a multi-layer perceptron. The first temperature correction model includes an input layer, a hidden layer, and an output layer. Set the number of neurons in the input layer as a1, the number of layers in the hidden layer as a2, the number of neurons in each hidden layer as a3, and the number of neurons in the output layer as a4; Step S207, use the training data set to train the first temperature correction model. After completion, obtain the thermal imaging temperature correction model; Step S208, use the test data set to test the thermal imaging temperature correction model, and calculate the average error AE of the thermal imaging temperature correction model; the average error calculation formula is as follows: , where Yj is the temperature magnitude of the first reference position measured by the temperature sensor, Xj is the temperature magnitude of the first reference position output by the thermal imaging temperature correction model, and n is the number of data input into the thermal imaging temperature correction model.

[0024] Step S3, perform temperature correction processing on the thermal imaging image inside the photovoltaic inverter based on the thermal imaging temperature correction model and the data of the temperature sensor inside the photovoltaic inverter to obtain corrected thermal imaging temperature data; Step S3 includes the following sub-steps: Step S301, collect the overall thermal imaging image inside the photovoltaic inverter at a first time interval, and simultaneously collect the component temperature and the internal ambient temperature obtained by the temperature sensor inside the photovoltaic inverter; Step S302: Extract the temperature magnitudes corresponding to all pixel points in the collected thermal imaging image, denoted as the original thermal imaging temperature information. Normalize the original thermal imaging temperature information with the corresponding ambient temperature inside the photovoltaic inverter and input it into the thermal imaging temperature correction model to obtain the first corrected temperature information; Step S303: Based on the first corrected temperature information at the same moment and the component temperatures obtained by the temperature sensor, calculate the first error between the first corrected temperature information at all first reference positions and the temperatures obtained by the temperature sensor through the first error calculation formula. The first error calculation formula is as follows: , where AF represents the first error, U represents the temperature magnitude of the first reference position measured by the temperature sensor, and V is the temperature of the corresponding first reference position in the first corrected temperature information; and average all the first errors to obtain the second error AG; Step S304: Then calculate the temperature correction error through the correction error calculation formula. The correction error calculation formula is as follows: , where AH is the temperature correction error, q1 and q2 are weight coefficients, q1 + q2 = 1, and the value ranges of q1 and q2 are [0, 1]; Step S305: Based on the temperature correction error, use the temperature correction formula to correct the error of the first corrected temperature information. The temperature correction formula is as follows: , where TY is the temperature value in the first corrected temperature information, and TX is the temperature value after error correction; after completion, obtain the second corrected temperature information, marked as the corrected thermal imaging temperature data.

[0025] Step S4: Dynamically control the cooling fan of the photovoltaic inverter based on the corrected thermal imaging temperature data and the data of the temperature sensor inside the photovoltaic inverter; Step S4 includes the following sub-steps: Step S401: Merge the corrected thermal imaging temperature data at the same moment with the data of the temperature sensor inside the photovoltaic inverter, and replace the temperature value corresponding to the first reference position in the corrected thermal imaging temperature data with the temperature value collected by the corresponding temperature sensor. After completion, obtain the internal temperature distribution data; Step S402: Set the safe temperature range, denoted as [B1, C1]. According to the safe temperature range, set the optimal temperature range, denoted as [B2, C2], where B1 < B2 and C2 > C1; Step S403: Extract the maximum value of the internal temperature distribution data, denoted as the internal maximum temperature TM. Obtain the internal maximum temperature TM at the first time interval, denoted as the internal maximum temperature sequence. Perform linear fitting on the internal maximum temperature sequence to obtain the internal maximum temperature sequence for the future first time length, denoted as the predicted maximum temperature sequence. Denote any temperature value in the predicted maximum temperature sequence as Tk, where k represents the sequence number in the predicted maximum temperature sequence, and the first time length is t2. Step S404: If the internal maximum temperature TM at the current moment satisfies TM ∈ [B2, C2] and any Tk ∈ [B2, C2], then do not adjust the rotational speed of the cooling fan. Step S405: If the internal maximum temperature TM at the current moment satisfies TM ∈ [B2, C2] and there exists Tk > C2, then increase the rotational speed of the cooling fan. Step S406: If the internal maximum temperature TM at the current moment satisfies TM ∈ [B2, C2] and there exists Tk < B2, then decrease the rotational speed of the cooling fan. Step S407: If the internal maximum temperature TM at the current moment satisfies TM ∈ (C2, C1] and any Tk < C1, then increase the rotational speed of the cooling fan. Step S408: If the internal maximum temperature TM at the current moment satisfies TM ∈ (C2, C1] and any Tk > C1 or TM > C1, then increase the rotational speed of the cooling fan to the maximum rotational speed and issue a warning signal. Step S409: If the internal maximum temperature TM at the current moment satisfies TM ∈ [B1, B2) and any Tk > B1, then decrease the rotational speed of the cooling fan. Step S410: If the internal maximum temperature TM at the current moment satisfies TM ∈ [B1, B2) and any Tk < C1 or TM < B1, then decrease the rotational speed of the cooling fan to the minimum rotational speed and issue a warning signal.

[0026] Example 3, please refer to Figure 5 as shown in Figure 5The structure diagram of an electronic device is exemplified. The electronic device may include: a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete mutual communication through the communication bus. The memory stores computer-readable instructions. The processor can call the instructions in the memory. When the computer-readable instructions are executed by the processor, the steps in an intelligent heat dissipation control method of a photovoltaic inverter are run to achieve the following functions: using a temperature sensor to collect the component temperature and the internal ambient temperature inside the photovoltaic inverter, and at the same time collecting the thermal imaging image inside the photovoltaic inverter, and performing a first screening process to obtain sensor temperature data and thermal imaging temperature data; establishing a thermal imaging temperature correction model based on the sensor temperature data and the thermal imaging temperature data; performing temperature correction processing on the thermal imaging image inside the photovoltaic inverter based on the thermal imaging temperature correction model and the data of the temperature sensor inside the photovoltaic inverter to obtain corrected thermal imaging temperature data; dynamically controlling the cooling fan of the photovoltaic inverter based on the corrected thermal imaging temperature data and the data of the temperature sensor inside the photovoltaic inverter.

[0027] In addition, when the logical instructions in the above-mentioned memory are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0028] Embodiment 4. The present application also provides a computer-readable storage medium. The present application provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the intelligent heat dissipation control method of a photovoltaic inverter as described above are run to achieve the following functions: using a temperature sensor to collect the component temperature and the internal ambient temperature inside the photovoltaic inverter, and at the same time collecting the thermal imaging image inside the photovoltaic inverter, and performing a first screening process to obtain sensor temperature data and thermal imaging temperature data; establishing a thermal imaging temperature correction model based on the sensor temperature data and the thermal imaging temperature data; performing temperature correction processing on the thermal imaging image inside the photovoltaic inverter based on the thermal imaging temperature correction model and the data of the temperature sensor inside the photovoltaic inverter to obtain corrected thermal imaging temperature data; and dynamically controlling the cooling fan of the photovoltaic inverter based on the corrected thermal imaging temperature data and the data of the temperature sensor inside the photovoltaic inverter.

[0029] Through the description of the above embodiments, the embodiments of the present invention can be provided as a method, a system or a computer program product. Based on such an understanding, the above technical solution, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0030] In the embodiments provided by the present application, it should be understood that the disclosed system or method can be implemented in other ways. The above-described embodiments are merely illustrative. For example, the division of modules or units is only a logical function division, and there can be other division methods in actual implementation. Also, for example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some communication interfaces. The indirect coupling or communication connection of systems, modules and units can be in an electrical, mechanical or other form.

[0031] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present application.

Claims

1. An intelligent heat dissipation control method for a photovoltaic inverter, characterized in that: The steps include: Using a temperature sensor to collect component temperatures and internal ambient temperature of the photovoltaic inverter, and simultaneously collecting a thermal imaging image of the photovoltaic inverter, and performing a first screening process to obtain sensor temperature data and thermal imaging temperature data; Establish a thermal imaging temperature correction model based on sensor temperature data and thermal imaging temperature data; Based on the thermal imaging temperature correction model and the data of the temperature sensor inside the photovoltaic inverter, the thermal imaging image inside the photovoltaic inverter is subjected to temperature correction processing to obtain corrected thermal imaging temperature data; The cooling fan of the photovoltaic inverter is dynamically controlled based on the corrected thermal imaging temperature data and the data of the temperature sensor inside the photovoltaic inverter.

2. The intelligent heat dissipation control method for a photovoltaic inverter according to claim 1, characterized in that: Using a temperature sensor to collect the temperature of components inside the photovoltaic inverter and the internal ambient temperature, and collecting a thermal imaging image inside the photovoltaic inverter includes the following sub-steps: Using the temperature sensor inside the photovoltaic inverter, the component temperature of the corresponding component inside the photovoltaic inverter is continuously collected at a first time interval, and the collection time and the collection position are recorded, which are marked as first component temperature data; at the same time, the ambient temperature inside the photovoltaic inverter is continuously collected at a first time interval, and the collection time is recorded, which is marked as first internal temperature data, and the first time interval is t1; Using a thermal imager, keeping the relative positions of the thermal imager and the photovoltaic inverter in space unchanged, continuously collecting thermal imaging images of the entire interior of the photovoltaic inverter at a first time interval, and marking them as first thermal imaging data; A position inside the photovoltaic inverter where the temperature can be collected by the temperature sensor and obtained by the thermal imaging image is marked as a first reference position.

3. The intelligent heat dissipation control method for a photovoltaic inverter according to claim 2, characterized in that: Performing the first screening process to obtain the sensor temperature data and the thermal imaging temperature data includes the following sub-steps: Based on the first thermal imaging data and the first component temperature data, respectively extract the temperature corresponding to the first thermal imaging data and the temperature corresponding to the first component temperature data at all acquisition times and all first reference positions, and mark them in sequence as second thermal imaging data and second component temperature data, respectively; The second thermal imaging data, the second component temperature data and the first internal temperature data are respectively cleaned, and after the cleaning process is completed, the thermal imaging temperature data, the third component temperature data and the second internal temperature data are obtained; The data cleaning process includes: sorting the corresponding data according to the corresponding first reference position and the acquisition time, recording as a time data sequence, calculating the mean value and standard deviation of the time data sequence, recording them as E0 and E1 respectively in order, and for any data in the time data sequence, recording it as Ei, where i represents the sequence number in the time data sequence, if Ei satisfies |Ei-E0|>3*E1, then marking Ei as abnormal data, if Ei does not satisfy |Ei-E0|>3*E1, then marking Ei as normal data, and repeating the marking of all data in the time data sequence; The third component temperature data and the second internal temperature data are combined and stored according to the collection time, and marked as sensor temperature data.

4. The intelligent heat dissipation control method for a photovoltaic inverter according to claim 3, characterized in that: Establishing a thermal imaging temperature correction model based on sensor temperature data and thermal imaging temperature data includes the following sub-steps: The thermal imaging temperature data and the sensor temperature data are combined according to the acquisition time and the first reference position of the acquisition, and recorded as the first sample data; For the first sample data, the first sample data at the same acquisition time and the same first reference position are combined into a sample vector, denoted as M=[m1, m2, m3], where m1 represents the temperature of the first reference position measured by the temperature sensor, m2 represents the temperature of the first reference position corresponding to m1 displayed in the thermal imaging image, and m3 represents the ambient temperature inside the photovoltaic inverter; If there are abnormal data in m1, m2 and m3 in the sample vector M, remove them; after completion, obtain the second sample data; Normalizing the second sample data according to the component types of the sample vectors, scaling the sizes of all components of all sample vectors in the second sample data to [0, 1], and obtaining the third sample data after completion; The third sample data is divided into a training data set and a test data set in a ratio of 9:

1.

5. The intelligent heat dissipation control method for a photovoltaic inverter according to claim 4, characterized in that: Establishing a thermal imaging temperature correction model based on the sensor temperature data and the thermal imaging temperature data also includes the following sub-steps: A first temperature correction model is constructed based on a multi-layer perceptron. The first temperature correction model includes an input layer, a hidden layer, and an output layer. The number of neurons in the input layer is set to a1, the number of hidden layers is set to a2, the number of neurons in each hidden layer is set to a3, and the number of neurons in the output layer is set to a4; The first temperature correction model is trained using the training data set, and a thermal imaging temperature correction model is obtained after the training is completed; The thermal imaging temperature correction model is tested using the test data set, and the average error AE of the thermal imaging temperature correction model is calculated; the average error calculation formula is as follows: , where Yj is the temperature of the first reference position measured by the temperature sensor, Xj is the temperature of the first reference position output by the thermal imaging temperature correction model, and n is the number of data input to the thermal imaging temperature correction model.

6. The intelligent heat dissipation control method for a photovoltaic inverter according to claim 5, characterized in that: Based on the thermal imaging temperature correction model and the data of the temperature sensor inside the photovoltaic inverter, the thermal imaging image inside the photovoltaic inverter is subjected to temperature correction processing to obtain the corrected thermal imaging temperature data, including the following sub-steps: Collecting a thermal imaging image of the entire interior of the photovoltaic inverter at a first time interval, and simultaneously collecting component temperatures obtained by an internal temperature sensor of the photovoltaic inverter and an internal ambient temperature; The temperature corresponding to all pixels in the collected thermal imaging image is extracted and recorded as the original thermal imaging temperature information. The original thermal imaging temperature information is normalized with the corresponding ambient temperature inside the photovoltaic inverter and input into the thermal imaging temperature correction model to obtain the first corrected temperature information.

7. The intelligent heat dissipation control method for a photovoltaic inverter according to claim 6, characterized in that: Based on the thermal imaging temperature correction model and the data of the temperature sensor inside the photovoltaic inverter, the temperature correction processing is performed on the thermal imaging image inside the photovoltaic inverter to obtain the corrected thermal imaging temperature data, which also includes the following sub-steps: Based on the first corrected temperature information at the same time and the component temperature obtained by the temperature sensor, the first error between the first corrected temperature information at all first reference positions and the temperature obtained by the temperature sensor is calculated by a first error calculation formula. The first error calculation formula is as follows: , where AF represents the first error, U represents the temperature of the first reference position measured by the temperature sensor, and V is the temperature of the first reference position corresponding to the first corrected temperature information; and all the first errors are averaged to obtain the second error AG; Then calculate the temperature correction error through the correction error calculation formula. The correction error calculation formula is as follows: , where AH is the temperature correction error, q1 and q2 are weight coefficients, q1+q2=1, and the value range of q1 and q2 is [0, 1]; Based on the temperature correction error, the temperature correction formula is used to correct the error of the first correction temperature information. The temperature correction formula is as follows: , where TY is the temperature value in the first corrected temperature information, and TX is the temperature value after error correction; After completion, the second corrected temperature information is obtained and marked as corrected thermal imaging temperature data.

8. The intelligent heat dissipation control method for a photovoltaic inverter according to claim 7, characterized in that: Dynamically controlling the cooling fan of the photovoltaic inverter based on the corrected thermal imaging temperature data and the data of the temperature sensor inside the photovoltaic inverter includes the following sub-steps: The corrected thermal imaging temperature data at the same time is combined with the data of the temperature sensor inside the photovoltaic inverter, and the temperature value corresponding to the first reference position in the corrected thermal imaging temperature data is replaced with the temperature value collected by the corresponding temperature sensor, and the internal temperature distribution data is obtained after completion; Set a safety temperature range, denoted as [B1, C1], and based on the safety temperature range, set an optimal temperature range, denoted as [B2, C2], where B1 < B2 and C2 > C1.

9. The intelligent heat dissipation control method for a photovoltaic inverter according to claim 8, characterized in that: The dynamic control of the cooling fan of the photovoltaic inverter based on the corrected thermal imaging temperature data and the data of the internal temperature sensor of the photovoltaic inverter further includes the following sub-steps: Extract the maximum value of the internal temperature distribution data, denoted as the internal maximum temperature TM, obtain the internal maximum temperature TM at the first time interval, denoted as the internal maximum temperature sequence, perform linear fitting on the internal maximum temperature sequence to obtain the internal maximum temperature sequence of the future first time length, denoted as the predicted maximum temperature sequence, and denote any temperature value in the predicted maximum temperature sequence as Tk, where k represents the serial number in the predicted maximum temperature sequence, and the first time length is t2; If the internal maximum temperature TM at the current moment satisfies TM ∈ [B2, C2] and any Tk ∈ [B2, C2], then do not adjust the rotation speed of the cooling fan; If the internal maximum temperature TM at the current moment satisfies TM ∈ [B2, C2] and there exists Tk > C2, then increase the rotation speed of the cooling fan; If the internal maximum temperature TM at the current moment satisfies TM ∈ [B2, C2] and there exists Tk < B2, then decrease the rotation speed of the cooling fan; If the internal maximum temperature TM at the current moment satisfies TM ∈ (C2, C1] and any Tk < C1, then increase the rotation speed of the cooling fan; If the internal maximum temperature TM at the current moment satisfies TM ∈ (C2, C1] and any Tk > C1 or TM > C1, then increase the rotation speed of the cooling fan to the maximum rotation speed and issue a warning signal; If the internal maximum temperature TM at the current moment satisfies TM ∈ [B1, B2) and any Tk > B1, then decrease the rotation speed of the cooling fan; If the internal maximum temperature TM at the current moment satisfies TM ∈ [B1, B2) and any Tk < C1 or TM < B1, then decrease the rotation speed of the cooling fan to the minimum rotation speed and issue a warning signal.

10. An intelligent heat dissipation control system for a photovoltaic inverter, used to implement an intelligent heat dissipation control method for a photovoltaic inverter according to any one of claims 1 to 9, characterized in that: It includes a temperature acquisition module, a model establishment module, a temperature correction module, and a heat dissipation control module; The temperature acquisition module includes a data acquisition unit and a data screening unit. The data acquisition unit uses a temperature sensor to acquire the component temperature inside the photovoltaic inverter and the internal ambient temperature, and simultaneously acquires the thermal imaging image inside the photovoltaic inverter. The data screening unit is used for the first screening process to obtain the sensor temperature data and the thermal imaging temperature data; The model establishment module establishes a thermal imaging temperature correction model based on the sensor temperature data and the thermal imaging temperature data; The temperature correction module performs temperature correction processing on the thermal imaging image inside the photovoltaic inverter based on the thermal imaging temperature correction model and the data of the internal temperature sensor of the photovoltaic inverter to obtain the corrected thermal imaging temperature data; The heat dissipation control module performs dynamic control on the cooling fan of the photovoltaic inverter based on the corrected thermal imaging temperature data and the data of the internal temperature sensor of the photovoltaic inverter.

Images