Early warning method for heating defects of large power transformers

Abstract

The present invention provides an early warning method for heating defects of large power transformers. The method comprises: obtaining real-time power conditions and historical power conditions in a monitoring area, predicting and obtaining a number of target large power transformers based on the real-time power conditions and historical power conditions; obtaining first temperature measurement data of multiple external points of each target large power transformer to form a first temperature measurement data set, predicting and calculating each first temperature measurement data according to a preset inversion temperature field distribution model, and obtaining second temperature measurement data to form a second temperature measurement data set; when any second temperature measurement data in the second temperature measurement data set is higher than a preset value, it is determined that the large power transformer has a heating defect and outputs an early warning. The solution of the present invention realizes remote and efficient monitoring of heating defects of large power transformers.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment monitoring, and in particular to an early warning method for heating defects of a large power transformer. Background Art

[0002] It is very necessary to monitor and warn large power transformers for heating defects, otherwise it is easy for large power transformers to fail due to abnormal heating, resulting in serious power failures. The existing monitoring and early warning methods for heating defects rely too much on on-site detection, and it is obvious that the detection efficiency and real-time performance are difficult to guarantee, which cannot meet actual needs. Summary of the invention

[0003] In order to at least solve the technical problems existing in the above-mentioned background technology, the present invention provides an early warning method, system, electronic equipment and computer storage medium for heating defects of large power transformers.

[0004] The present invention provides an early warning method for heating defects of a large power transformer, the method comprising the following steps:

[0005] Acquire real-time power conditions and historical power conditions in the monitoring area, and predict a number of target large power transformers based on the real-time power conditions and historical power conditions;

[0006] Acquire first temperature measurement data of multiple external points of each of the target large-scale power transformers to form a first temperature measurement data set, perform prediction calculation on each of the first temperature measurement data according to a preset inversion temperature field distribution model, and obtain second temperature measurement data to form a second temperature measurement data set;

[0007] When any of the second temperature measurement data in the second temperature measurement data set is higher than the corresponding preset value, it is determined that the large power transformer has a heating defect and an early warning is output.

[0008] In some embodiments, the obtaining of real-time power conditions and historical power conditions in the monitoring area includes:

[0009] Acquire the real-time power operating conditions in the monitoring area, wherein the real-time power operating conditions include the real-time load levels of each large power transformer in the monitoring area;

[0010] Calculating the difference between each of the real-time load levels and the rated load of the corresponding large power transformer, and calculating the variance of each of the differences;

[0011] The acquisition time span of the historical data is determined according to the variance, and the historical power operating conditions are acquired according to the acquisition time span.

[0012] In some embodiments, the predicting of a number of target large power transformers based on the real-time power condition and the historical power condition includes:

[0013] Obtaining a topological diagram of electrical connection relationships of each of the large power transformers in the monitoring area;

[0014] The real-time power operating conditions, the historical power operating conditions and the electrical connection relationship topology diagram are input into a trained prediction model to obtain a number of predicted target large power transformers.

[0015] In some embodiments, the first temperature measurement data of a plurality of external points of each of the target large power transformers are obtained to form a first temperature measurement data set, including:

[0016] Analyzing whether the target large power transformer is equipped with a temperature sensor, wherein the temperature sensor is installed at different points on the housing of the target large power transformer;

[0017] If yes, obtaining the first temperature measurement data of each temperature sensor;

[0018] If not, controlling the mobile inspection device to obtain the first temperature measurement data of different points on the casing of the target large power transformer by a non-contact temperature measurement method;

[0019] The first temperature measurement data obtained constitute the first temperature measurement data set.

[0020] In some embodiments, the predictive calculation of each of the first temperature measurement data according to the preset inversion temperature field distribution model to obtain the second temperature measurement data includes:

[0021] The inversion temperature field distribution model adapted to the target large power transformer is obtained by screening according to the real-time power operating conditions;

[0022] The first temperature measurement data is matched and calculated with the inverted temperature field distribution model to obtain the second temperature measurement data.

[0023] In some embodiments, the inversion temperature field distribution model is determined by:

[0024] Measuring third temperature measurement data of a plurality of internal points of a specific type of large power transformer under different power working conditions, and fourth temperature measurement data of a plurality of external points;

[0025] Adjusting the power operating condition of the power transformer to obtain fifth temperature measurement data corresponding to each of the third temperature measurement data and sixth temperature measurement data corresponding to each of the fourth temperature measurement data;

[0026] Calculate a first difference between the third temperature measurement data and the fifth temperature measurement data, and a second difference between the fourth temperature measurement data and the sixth temperature measurement data, and determine a plurality of first characteristic evolution graphs and a second characteristic evolution graph according to the first difference, the second difference and the corresponding time series characteristics;

[0027] A similarity calculation is performed on each of the first feature evolution graphs and the second feature evolution graphs to obtain the first feature evolution graphs and the second feature evolution graphs of cluster pairing relationships and corresponding temperature conversion functions, and the inversion temperature field distribution model is constructed using each of the pairing relationships and the temperature conversion functions.

[0028] The present invention also provides an early warning system for heating defects of large power transformers, comprising an acquisition module, a processing module, and a storage module; the processing module is connected to the acquisition module and the storage module;

[0029] The acquisition module is used to acquire the real-time power conditions and historical power conditions in the monitoring area, and to acquire the first temperature measurement data of multiple external points of each of the target large power transformers, and transmit the data to the processing module;

[0030] The storage module is used to store executable computer program code;

[0031] The processing module is used to execute the method as described in any of the preceding items by calling the executable computer program code in the storage module, and output an early warning when it is determined that a large power transformer has a heating defect.

[0032] The present invention also provides an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the method as described in any of the preceding items.

[0033] The present invention also provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method described in any of the above items is executed.

[0034] The present invention also provides a computer program product, comprising a computer program stored on a non-transitory computer-readable medium, wherein the computer program implements any of the methods described above when executed by a processor.

[0035] The beneficial effect of the present invention is that the solution of the present invention realizes remote and efficient monitoring of heating defects of large power transformers. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0037] Figure 1 It is a flow chart of a method for early warning of heating defects of a large power transformer disclosed in an embodiment of the present invention;

[0038] Figure 2 The invention discloses a structural diagram of an early warning system for heating defects of a large power transformer. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all 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.

[0040] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, and unless the context clearly indicates other meanings, "multiple" generally includes at least two.

[0041] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0042] It should be understood that although the terms first, second, third, etc. may be used to describe ... in the embodiments of the present application, these ... should not be limited to these terms. These terms are only used to distinguish .... For example, without departing from the scope of the embodiments of the present application, the first ... may also be referred to as the second ..., and similarly, the second ... may also be referred to as the first ....

[0043] As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0044] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a product or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such a product or system. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the product or system including the elements.

[0045] See also Figure 1 As shown, an embodiment of the present invention discloses an early warning method for heating defects of a large power transformer, the method comprising the following steps:

[0046] Acquire real-time power conditions and historical power conditions in the monitoring area, and predict a number of target large power transformers based on the real-time power conditions and historical power conditions;

[0047] Acquire first temperature measurement data of multiple external points of each of the target large-scale power transformers to form a first temperature measurement data set, perform prediction calculation on each of the first temperature measurement data according to a preset inversion temperature field distribution model, and obtain second temperature measurement data to form a second temperature measurement data set;

[0048] When any of the second temperature measurement data in the second temperature measurement data set is higher than the corresponding preset value, it is determined that the large power transformer has a heating defect and an early warning is output.

[0049] In the scheme of the present invention, the present invention first predicts a number of target large power transformers based on the real-time power conditions and historical power conditions in the monitoring area. These target large power transformers are devices that may have abnormal heating; then these target large power transformers are remotely inspected one by one, that is, the first temperature measurement data of multiple external points are obtained to form a first temperature measurement data set, and then the first temperature measurement data are predicted and calculated according to the preset inversion temperature field spatiotemporal distribution model to obtain the corresponding second temperature measurement data. When any second temperature measurement data is higher than the preset value, it can be determined that the large power transformer has a heating defect, and an early warning signal is output in time. Therefore, the scheme of the present invention realizes remote and efficient monitoring of heating defects of large power transformers.

[0050] The preset value may be a preset value data set, which includes a plurality of preset values, and each preset value corresponds to a respective internal point of a large power transformer of a specific type.

[0051] In some embodiments, the obtaining of real-time power conditions and historical power conditions in the monitoring area includes:

[0052] Acquire the real-time power operating conditions in the monitoring area, wherein the real-time power operating conditions include the real-time load levels of each large power transformer in the monitoring area;

[0053] Calculating the difference between each of the real-time load levels and the rated load of the corresponding large power transformer, and calculating the variance of each of the differences;

[0054] The acquisition time span of the historical data is determined according to the variance, and the historical power operating conditions are acquired according to the acquisition time span.

[0055] In this embodiment, abnormal heating of large power transformers is mostly caused by load overload, so the present invention obtains the real-time load level of large power transformers in the monitoring area, calculates the difference between it and the rated load, and then calculates the variance of all the differences. The variance characterizes the overall load fluctuation degree of all large power transformers in the monitoring area. The greater the overall load fluctuation degree, the greater the influence of other power transformers on the large power transformers in the monitoring area, and vice versa.

[0056] In this regard, the present invention determines the acquisition time span of historical data according to the variance, and acquires the historical power conditions in the corresponding period according to the acquisition time span. That is, on the basis of considering the real-time power conditions, the historical power conditions are further used to predict the probability of overload and abnormal heating of each large power transformer, so as to determine the target large power transformer that needs to be remotely inspected.

[0057] The acquisition time span and the variance satisfy a positive correlation function, which may be, for example, ΔT=t*N*e 1+E , where ΔT is the time span (starting from the current moment), t is the reference time span, E is the variance, and N is the number of large power transformers in the monitoring area.

[0058] It should be noted that the large power transformers in the monitoring area can be in parallel or serial relationship with each other, so the fluctuation of power load will affect other large power transformers in the monitoring area, especially large power transformers with serial relationship. Moreover, the larger the variance, the greater the proportion of the above serial relationship in the monitoring area, and the greater the probability of large power transformers affecting each other.

[0059] In some embodiments, the predicting of a number of target large power transformers based on the real-time power condition and the historical power condition includes:

[0060] Obtaining a topological diagram of electrical connection relationships of each of the large power transformers in the monitoring area;

[0061] The real-time power operating conditions, the historical power operating conditions and the electrical connection relationship topology diagram are input into a trained prediction model to obtain a number of predicted target large power transformers.

[0062] In this embodiment, the electrical connection relationship topology of each large power transformer in the monitoring area can be determined in advance, and the electrical connection relationship topology refers to the electrical connection relationship diagram between multiple large power transformers (the transformer ranges may be different). Then, the electrical connection relationship topology together with the real-time power conditions and historical power conditions of each large power transformer obtained above are input into a pre-trained prediction model, and the prediction model is used for comprehensive processing and analysis to obtain a number of target large power transformers, which are objects with a higher probability of heating defects.

[0063] The prediction model in the present invention is the ARIMA prediction model. The full name of the ARIMA model is the Autoregressive Integrated Moving Average Model, which is a time series (Time-series Approach) prediction method. Among them, ARIMA (p, d, q) is called the differential autoregressive moving average model, AR is autoregressive, p is the autoregressive term; MA is the moving average, q is the number of moving average terms, and d is the number of differences made when the time series becomes stable. The ARIMA model can convert non-stationary time series into a stationary time series, which is suitable for the prediction of load levels in the present invention. The construction and training methods of the ARIMA prediction model will not be repeated.

[0064] In some embodiments, the first temperature measurement data of a plurality of external points of each of the target large power transformers are obtained to form a first temperature measurement data set, including:

[0065] Analyzing whether the target large power transformer is equipped with a temperature sensor, wherein the temperature sensor is installed at different points on the housing of the target large power transformer;

[0066] If yes, obtaining the first temperature measurement data of each temperature sensor;

[0067] If not, controlling the mobile inspection device to obtain the first temperature measurement data of different points on the casing of the target large power transformer by a non-contact temperature measurement method;

[0068] The first temperature measurement data obtained constitute the first temperature measurement data set.

[0069] In this embodiment, some large power transformers are equipped with multiple temperature sensors (such as platinum resistance sensors) on the outer shell, and these temperature sensors will transmit the measured first temperature measurement data to the remote monitoring platform in real time. However, some older large power transformers are not equipped with temperature sensors. In this case, the present invention controls the mobile inspection equipment to go to the area where such large power transformers are located, and obtains the first temperature measurement data of different points on the outer shell through non-contact temperature measurement.

[0070] Among them, the mobile inspection equipment can be the inspection drones and unmanned vehicles commonly used in the power system, or it can send inspection orders to the terminal equipment of the inspectors and dispatch nearby inspectors to conduct on-site temperature measurement. In addition, non-contact temperature measurement refers to infrared temperature measurement, ultrasonic temperature measurement, laser temperature measurement, etc., which are not specifically limited.

[0071] In some embodiments, the predictive calculation of each of the first temperature measurement data according to the preset inversion temperature field distribution model to obtain the second temperature measurement data includes:

[0072] The inversion temperature field distribution model adapted to the target large power transformer is obtained by screening according to the real-time power operating conditions;

[0073] The first temperature measurement data is matched and calculated with the inverted temperature field distribution model to obtain the second temperature measurement data.

[0074] In this embodiment, the present invention pre-configures an inversion temperature field distribution model suitable for different power operating conditions for each type of large-scale power transformer. The inversion temperature field distribution model characterizes the pairing relationship between the internal temperature measurement points and the external temperature measurement points of the type of large-scale power transformer and the corresponding temperature conversion function, wherein the pairing relationship refers to the temperature measured by the external temperature measurement point being mainly affected by the external diffusion of the temperature at the specific internal temperature measurement point, that is, the temperature of the external temperature measurement point can characterize the temperature of the corresponding internal temperature measurement point (but the temperature is slightly lower); the temperature conversion function refers to the conversion function of the internal and external temperature measurement points with the above-mentioned pairing relationship, and the temperature of the internal temperature measurement point can be reversely deduced from the temperature of the external temperature measurement point using the conversion function.

[0075] In some embodiments, the inversion temperature field distribution model is determined by:

[0076] Measuring third temperature measurement data of a plurality of internal points of a specific type of large power transformer under different power working conditions, and fourth temperature measurement data of a plurality of external points;

[0077] Adjusting the power operating condition of the power transformer to obtain fifth temperature measurement data corresponding to each of the third temperature measurement data and sixth temperature measurement data corresponding to each of the fourth temperature measurement data;

[0078] Calculate a first difference between the third temperature measurement data and the fifth temperature measurement data, and a second difference between the fourth temperature measurement data and the sixth temperature measurement data, and determine a plurality of first characteristic evolution graphs and a second characteristic evolution graph according to the first difference, the second difference and the corresponding time series characteristics;

[0079] A similarity calculation is performed on each of the first feature evolution graphs and the second feature evolution graphs to obtain the first feature evolution graphs and the second feature evolution graphs of cluster pairing relationships and corresponding temperature conversion functions, and the inversion temperature field distribution model is constructed using each of the pairing relationships and the temperature conversion functions.

[0080] In this embodiment, the large power transformer is controlled to be in a specific power condition, at which time the temperature of each internal temperature measurement point and external temperature measurement point of the large power transformer is measured respectively, and then the specific power condition is fine-tuned in multiple steps (for example, the power load is increased), the temperature of the new internal and external temperature measurement points is detected, and the difference between the two sets of temperature data before and after the fine-tuning is calculated. The first difference and the second difference obtained above are calculated for time series characteristics, so as to obtain the first characteristic evolution diagram of each internal temperature measurement point and the second characteristic evolution diagram of each external temperature measurement point, and the similarity between the first characteristic evolution diagram and the second characteristic evolution diagram is calculated to obtain a pairing relationship; and the deviation value between the paired first characteristic evolution diagram and the second characteristic evolution diagram reflects the temperature difference between the paired internal temperature measurement point and the external temperature measurement point, and the temperature conversion function can be fitted based on multiple deviation values.

[0081] See also Figure 2 The embodiment of the present invention also discloses an early warning system for heating defects of a large power transformer, including an acquisition module, a processing module, and a storage module; the processing module is connected to the acquisition module and the storage module;

[0082] The acquisition module is used to acquire the real-time power conditions and historical power conditions in the monitoring area, and to acquire the first temperature measurement data of multiple external points of each of the target large power transformers, and transmit the data to the processing module;

[0083] The storage module is used to store executable computer program code;

[0084] The processing module is used to execute the method as described in any of the preceding items by calling the executable computer program code in the storage module, and output an early warning when it is determined that a large power transformer has a heating defect.

[0085] The present invention also provides an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the method described in the above embodiment.

[0086] The present invention further provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method described in the above embodiment is executed.

[0087] The present invention also provides a computer program product, including a computer program stored on a non-transitory computer-readable medium, wherein when the computer program is executed by a processor, the method described in the above embodiment is implemented.

[0088] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0089] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product can be stored in a storage medium such as ROM / RAM, a disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, an information push server, or a network device, etc.) to execute the methods described in the various implementation methods of the present application or certain parts of the implementation methods.

[0090] The device implementation described above is merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present implementation scheme. Those of ordinary skill in the art may understand and implement it without creative work.

[0091] The present application can be used in many general or special computing system environments or configurations, such as personal computers, information push server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices.

[0092] The present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0093] It should be understood that although this specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each implementation mode may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

[0094] The series of detailed descriptions listed above are only specific descriptions of feasible implementation methods of the present invention. They are not intended to limit the scope of protection of the present invention. Any equivalent implementation methods or changes that do not deviate from the technical spirit of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for early warning of heating defects of large power transformers, characterized in that: The method comprises the following steps: Acquire real-time power conditions and historical power conditions in the monitoring area, and predict a number of target large power transformers based on the real-time power conditions and historical power conditions; Acquire first temperature measurement data of multiple external points of each of the target large-scale power transformers to form a first temperature measurement data set, perform prediction calculation on each of the first temperature measurement data according to a preset inversion temperature field distribution model, and obtain second temperature measurement data to form a second temperature measurement data set; When any of the second temperature measurement data in the second temperature measurement data set is higher than the corresponding preset value, it is determined that the large power transformer has a heating defect and an early warning is output; The step of performing prediction calculation on each of the first temperature measurement data according to a preset inversion temperature field distribution model to obtain second temperature measurement data includes: The inversion temperature field distribution model adapted to the target large power transformer is obtained by screening according to the real-time power operating conditions; Matching and calculating the first temperature measurement data with the inverted temperature field distribution model to obtain the second temperature measurement data; The inversion temperature field distribution model is determined by the following method: Measuring third temperature measurement data of several internal points of a large power transformer under different power working conditions, and fourth temperature measurement data of several external points; Adjusting the power operating condition of the power transformer to obtain fifth temperature measurement data corresponding to each of the third temperature measurement data and sixth temperature measurement data corresponding to each of the fourth temperature measurement data; Calculate a first difference between the third temperature measurement data and the fifth temperature measurement data, and a second difference between the fourth temperature measurement data and the sixth temperature measurement data, and determine a plurality of first characteristic evolution graphs and a second characteristic evolution graph according to the first difference, the second difference and the corresponding time series characteristics; A similarity calculation is performed on each of the first feature evolution graphs and the second feature evolution graphs to obtain the first feature evolution graphs and the second feature evolution graphs of cluster pairing relationships, as well as a corresponding temperature conversion function, and the inversion temperature field distribution model is constructed using each of the pairing relationships and the temperature conversion function.

2. The early warning method for heating defects of a large power transformer according to claim 1 is characterized in that: The obtaining of real-time power conditions and historical power conditions in the monitoring area includes: Acquire the real-time power operating conditions in the monitoring area, wherein the real-time power operating conditions include the real-time load levels of each large power transformer in the monitoring area; Calculating the difference between each of the real-time load levels and the rated load of the corresponding large power transformer, and calculating the variance of each of the differences; The acquisition time span of the historical data is determined according to the variance, and the historical power operating conditions are acquired according to the acquisition time span.

3. The early warning method for heating defects of a large power transformer according to claim 1 or 2, characterized in that: The method of predicting a number of target large power transformers based on the real-time power condition and the historical power condition includes: Obtaining a topological diagram of electrical connection relationships of the large power transformers in the monitoring area; The real-time power operating conditions, the historical power operating conditions and the electrical connection relationship topology diagram are input into a trained prediction model to obtain a number of predicted target large power transformers.

4. The early warning method for heating defects of a large power transformer according to claim 1 is characterized in that: The first temperature measurement data obtained from a plurality of external points of each target large power transformer constitutes a first temperature measurement data set, including: Analyzing whether the target large power transformer is equipped with a temperature sensor, wherein the temperature sensor is installed at different points on the housing of the target large power transformer; If yes, obtaining the first temperature measurement data of each temperature sensor; If not, controlling the mobile inspection device to obtain the first temperature measurement data of different points on the casing of the target large power transformer by a non-contact temperature measurement method; The first temperature measurement data obtained constitute the first temperature measurement data set.

5. An early warning system for heating defects of large power transformers, comprising an acquisition module, a processing module, and a storage module; the processing module is connected to the acquisition module and the storage module; The acquisition module is used to acquire the real-time power conditions and historical power conditions in the monitoring area, and to acquire the first temperature measurement data of multiple external points of each of the target large power transformers, and transmit the data to the processing module; The storage module is used to store executable computer program code; Features: The processing module is used to execute the method according to any one of claims 1 to 4 by calling the executable computer program code in the storage module, and output an early warning when it is determined that a large power transformer has a heating defect.

6. An electronic device comprising: A memory storing executable program code; A processor coupled to the memory; characterized in that: the processor calls the executable program code stored in the memory to execute the method according to any one of claims 1-4.

7. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is executed.

8. A computer program product comprising a computer program stored on a non-transitory computer readable medium, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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