A transformer status monitoring method, system, device and medium

Abstract

The present application discloses a transformer status monitoring method, system, device and medium, comprising the following steps: obtaining a measured temperature change curve of a target transformer; obtaining a heat dissipation performance coefficient of the target transformer according to the measured temperature change curve; obtaining an abnormal noise characteristic value of the target transformer; obtaining an abnormal vibration characteristic value of the target transformer; inputting the heat dissipation performance coefficient, the abnormal noise characteristic value and the abnormal vibration characteristic value into a preset fault warning model to obtain a fault warning value; judging whether to send a warning message according to the fault warning value. The present application has the advantages of being able to accurately monitor and warn potential faults of the transformer and reduce the probability of transformer faults.

Description

Technical Field

[0001] The present application relates to the technical field of transformer fault prediction, and in particular to a transformer status monitoring method, system, device and medium. Background Art

[0002] Dry-type transformers are widely used in local lighting, high-rise buildings, airports, docks, CNC machinery and equipment and other places. Simply put, dry-type transformers refer to transformers whose cores and windings are not immersed in insulating oil. At present, if a dry-type transformer fails during operation, an alarm message can be generated to notify management personnel to conduct timely maintenance. However, due to the large number of reasons for dry-type transformer failures and the large number of fault types, it is difficult to accurately monitor and warn. Therefore, it is difficult to troubleshoot dry-type transformers before a failure occurs, which affects the normal and continuous operation of dry-type transformers. Summary of the invention

[0003] The main purpose of this application is to provide a transformer status monitoring method, system, equipment and medium, aiming to solve the technical problem that it is difficult to accurately monitor and warn the existing dry-type transformers, which affects the continuous operation.

[0004] To achieve the above object, the present application provides a transformer status monitoring method, comprising the following steps:

[0005] Obtain a measured temperature variation curve graph of a target transformer;

[0006] According to the measured temperature change curve, the heat dissipation performance coefficient of the target transformer is obtained;

[0007] Obtain abnormal noise characteristic values ​​of the target transformer;

[0008] Obtaining abnormal vibration characteristic values ​​of the target transformer;

[0009] Inputting the heat dissipation performance coefficient, the abnormal noise characteristic value and the abnormal vibration characteristic value into a preset fault warning model to obtain a fault warning value;

[0010] Determine whether to send a warning message based on the fault warning value.

[0011] Optionally, obtaining a measured temperature variation curve graph of the target transformer includes:

[0012] Acquire multiple infrared images of the target transformer in a preset unit time;

[0013] Acquire a first temperature variation curve diagram of the target transformer according to the plurality of infrared images;

[0014] Obtaining measured temperature data of the target transformer based on a thermometer in unit time to obtain a second temperature change curve diagram of the target transformer;

[0015] Determine whether the first temperature change curve graph and the second temperature change curve graph meet the similarity requirement;

[0016] If not, return to acquiring a plurality of infrared images of the target transformer in a preset unit time; if so, average the first temperature change curve graph and the second temperature change curve graph to obtain a measured temperature change curve graph.

[0017] Optionally, after returning to acquiring a plurality of infrared images of the target transformer in a preset unit time, the method further includes:

[0018] Get the current iteration number;

[0019] If the current number of iterations reaches the preset target number, the first temperature change curve graph or the second temperature change curve graph corresponding to a higher average temperature value is output as the measured temperature change curve graph.

[0020] Optionally, according to the measured temperature change curve, the heat dissipation performance coefficient of the target transformer is obtained, including:

[0021] Obtain the peak value Q in the measured temperature change curve;

[0022] Obtain the cooling time t0 corresponding to the peak value Q in the measured temperature change curve graph dropping to the target temperature value; wherein the target temperature value is the temperature value when the target transformer is operating normally;

[0023] According to the peak value Q and cooling time t0, the heat dissipation performance coefficient M is obtained. The expression of M is:

[0024] M=Q / t0.

[0025] Optionally, obtaining abnormal noise characteristic values ​​of the target transformer includes:

[0026] Obtain the noise decibel value of the target transformer per unit time to obtain a noise change curve;

[0027] Constructing a first standard line in the noise variation curve diagram; wherein the first standard line is a horizontal line corresponding to the decibel value of the noise generated when the target transformer is operating normally;

[0028] Marking the area above the first standard line in the noise variation curve as the first abnormal area;

[0029] The area S1 of the first abnormal region is obtained, and the area S1 is output as an abnormal noise characteristic value.

[0030] Optionally, obtaining an abnormal vibration characteristic value of a target transformer includes:

[0031] Acquire multiple detection images of the target transformer in a specific assembly structure in a unit time; wherein the specific assembly structure includes a base, a groove is provided on the top of the base, a plurality of support springs are provided in the groove, the target transformer is provided on the plurality of support springs, and the target transformer can slide up and down along the inner wall of the groove;

[0032] Performing image processing on a plurality of detection images to obtain black and white images;

[0033] Obtain the spacing value between the top surface contour line of the target transformer and the bottom surface contour line of the base in each black and white image;

[0034] According to multiple spacing values, a vibration variation curve diagram is obtained;

[0035] Constructing a second standard line in the vibration change curve diagram; wherein the second standard line is a horizontal line corresponding to the standard spacing value obtained when the target transformer is operating normally;

[0036] Marking the area above the second standard line in the vibration change curve as a second abnormal area;

[0037] The area S2 of the second abnormal region is obtained, and the area S2 is output as the abnormal vibration characteristic value.

[0038] Optionally, the expression of the fault warning model is:

[0039] E=(K1*S1+K2*S2) / (K3*M);

[0040] Wherein, E is the fault warning value, K1 is the first adjustment coefficient, K2 is the second adjustment coefficient, and K3 is the third adjustment coefficient.

[0041] To achieve the above objectives, the present application also provides a transformer status monitoring system, comprising:

[0042] A curve graph acquisition module is used to obtain a measured temperature change curve graph of a target transformer;

[0043] A first acquisition module is used to acquire the heat dissipation performance coefficient of the target transformer according to the measured temperature change curve diagram;

[0044] A second acquisition module is used to obtain the abnormal noise characteristic value of the target transformer;

[0045] A third acquisition module is used to obtain the abnormal vibration characteristic value of the target transformer;

[0046] A data processing module, used for inputting the heat dissipation performance coefficient, the abnormal noise characteristic value and the abnormal vibration characteristic value into a preset fault warning model to obtain a fault warning value;

[0047] The information sending module is used to determine whether to send warning information according to the fault warning value.

[0048] To achieve the above objectives, the present application also provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above method.

[0049] To achieve the above objectives, the present application also provides a computer-readable storage medium, on which a computer program is stored, and a processor executes the computer program to implement the above method.

[0050] The beneficial effects that this application can achieve are as follows:

[0051] The present application jointly characterizes the state change of the target transformer from the three characteristics of the heat dissipation performance, abnormal noise and abnormal vibration of the target transformer. This is because when the target transformer is about to fail, it is easy to be obviously reflected in the above three characteristics. By drawing the measured temperature change curve of the target transformer, the heat dissipation performance coefficient can be quantified and calculated, and the abnormal noise characteristic value and the abnormal vibration characteristic value can be quantified and calculated at the same time. The heat dissipation performance coefficient, the abnormal noise characteristic value and the abnormal vibration characteristic value are input into the preset fault warning model, and the fault warning value can be accurately calculated. According to the size of the fault warning value, it can be used as a criterion for judging whether to send a warning information, so as to realize accurate fault monitoring and warning, and promptly notify the staff to troubleshoot potential faults, thereby reducing the probability of transformer failure. Therefore, the present application not only comprehensively considers the impact of the three characteristic changes of heat dissipation performance, abnormal noise and abnormal vibration on the target transformer, but also quantifies the degree of change of the three characteristics, so as to effectively and accurately perform fault warning evaluation on the transformer. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the prior art description. In all drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0053] Figure 1 A schematic diagram of a flow chart of a transformer status monitoring method in an embodiment of the present application;

[0054] Figure 2 A schematic diagram of a temperature variation curve obtained per unit time according to an embodiment of the present application;

[0055] Figure 3 A schematic diagram of a noise variation curve obtained per unit time according to an embodiment of the present application;

[0056] Figure 4 A schematic diagram of a black and white image obtained for an embodiment of the present application;

[0057] Figure 5 This is a schematic diagram of a vibration variation curve obtained per unit time according to an embodiment of the present application.

[0058] Reference numerals:

[0059] 110 - target transformer, 120 - base, 121 - groove, 130 - support spring.

[0060] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0062] It should be noted that all directional indications in the embodiments of the present application (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the components in a certain specific posture. If the specific posture changes, the directional indication will also change accordingly.

[0063] In this application, unless otherwise clearly specified and limited, the terms "connection", "fixation", etc. should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0064] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the meaning of "and / or" appearing in the full text includes three parallel schemes. Taking "A and / or B" as an example, it includes scheme A, or scheme B, or a scheme that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0065] Example 1

[0066] Reference Figure 1-Figure 5 , this embodiment provides a transformer state monitoring method, comprising the following steps:

[0067] Obtaining a measured temperature variation curve graph of the target transformer 110;

[0068] According to the measured temperature change curve, the heat dissipation performance coefficient of the target transformer 110 is obtained;

[0069] Acquire an abnormal noise characteristic value of the target transformer 110;

[0070] Acquire an abnormal vibration characteristic value of the target transformer 110;

[0071] Inputting the heat dissipation performance coefficient, the abnormal noise characteristic value and the abnormal vibration characteristic value into a preset fault warning model to obtain a fault warning value;

[0072] Determine whether to send a warning message based on the fault warning value.

[0073] In this embodiment, the state change of the target transformer 110 (generally referring to a dry-type transformer) is jointly characterized from the three characteristics of heat dissipation performance, abnormal noise and abnormal vibration. This is because when the target transformer 110 is about to fail, it is easy to be clearly reflected in the above three characteristics. By drawing a measured temperature change curve of the target transformer 110, the heat dissipation performance coefficient can be quantified and calculated, and the abnormal noise characteristic value and the abnormal vibration characteristic value can be quantified and calculated at the same time. The heat dissipation performance coefficient, the abnormal noise characteristic value and the abnormal vibration characteristic value are input into a preset fault warning model, and the fault warning value can be accurately calculated. According to the size of the fault warning value, it can be used as a criterion for judging whether to send a warning information, so that accurate fault monitoring and warning can be achieved, and the staff can be notified in time to troubleshoot potential faults, thereby reducing the probability of transformer failure. Therefore, this embodiment not only comprehensively considers the impact of the three characteristic changes of heat dissipation performance, abnormal noise and abnormal vibration on the target transformer 110, but also quantifies the degree of change of the three characteristics, so that the transformer can be effectively and accurately evaluated for fault warning.

[0074] It should be noted that a warning threshold can be set here (set according to actual conditions). When the fault warning value exceeds the fault warning value, a warning message can be sent to the management personnel. The fault warning model is a model trained based on historical data (i.e., the degree of correlation between characteristic data corresponding to heat dissipation performance, abnormal noise and abnormal vibration and the occurrence of faults, etc.). The model can accurately output the fault warning value. The higher the fault warning value, the greater the tendency of failure.

[0075] As an optional implementation, obtaining a measured temperature change curve diagram of the target transformer 110 includes:

[0076] Acquire a plurality of infrared images of the target transformer 110 within a preset unit time;

[0077] Acquire a first temperature variation curve diagram of the target transformer 110 according to the plurality of infrared images;

[0078] Acquire measured temperature data of the target transformer 110 based on a thermometer in unit time to obtain a second temperature change curve diagram of the target transformer 110;

[0079] Determine whether the first temperature change curve graph and the second temperature change curve graph meet the similarity requirement;

[0080] If not, the process returns to acquiring a plurality of infrared images of the target transformer 110 in a preset unit time. If yes, the first temperature change curve graph and the second temperature change curve graph are averaged to obtain a measured temperature change curve graph.

[0081] In this embodiment, by acquiring multiple infrared images of the target transformer 110 in a unit time, non-contact measurement can be achieved based on the multiple infrared images using the principle of thermal imaging, thereby drawing a corresponding first temperature change curve diagram. At the same time, considering the influence of errors, the measured temperature data of the target transformer 110 is collected in combination with a thermometer, and then a second temperature change curve diagram is drawn based on the measured temperature data. If the first temperature change curve diagram and the second temperature change curve diagram meet the similarity requirements, it can be shown that both test methods are within the error range, and then the first temperature change curve diagram and the second temperature change curve diagram are averaged to draw the final measured temperature change curve diagram, further improving the accuracy; if the similarity requirements are not met, it means that one of the measurement methods has a large error, resulting in a large difference between the curve diagram and the actual situation, so the measurements are performed again and the corresponding temperature change curve diagrams are drawn, thereby improving the accuracy and reliability of the temperature measurement data, so as to ensure that a truly reliable measured temperature change curve diagram is drawn, and reliable data is provided for the subsequent calculation of the fault warning value.

[0082] It should be noted that during the averaging process, the temperature values ​​corresponding to the first temperature change curve and the second temperature change curve at the same time are averaged and calculated, and finally a plurality of average temperature values ​​corresponding to different times are obtained, and then a measured temperature change curve can be drawn based on the plurality of average temperature values; here the similarity can be set according to the actual requirements, and since both measurement methods have errors, the probability that the first temperature change curve and the second temperature change curve just meet the similarity requirements is extremely low, so it can be ignored. The unit time of the above formula can be set according to actual needs, such as 0.5 hours, 1 hour or 1.5 hours.

[0083] As an optional implementation, after returning to acquiring a plurality of infrared images of the target transformer 110 in a preset unit time, the method further includes:

[0084] Get the current iteration number;

[0085] If the current number of iterations reaches the preset target number, the first temperature change curve graph or the second temperature change curve graph corresponding to a higher average temperature value is output as the measured temperature change curve graph.

[0086] In this embodiment, if after repeated measurements, the first temperature change curve and the second temperature change curve drawn still do not meet the similarity requirement, it means that the measuring instrument corresponding to one of the measurement methods is faulty. Therefore, taking this special situation into consideration, a target number of times is set. If the current number of iterations reaches the target number of times, the iteration will no longer continue, and the first temperature change curve or the second temperature change curve with a higher corresponding temperature average value will be directly used as the measured temperature change curve, so as to ensure that data feedback can be performed normally in the end. The output here is a curve with a higher temperature average value, considering that the data measured by a faulty measuring instrument is generally lower than the actual data.

[0087] As an optional implementation, obtaining the heat dissipation performance coefficient of the target transformer 110 according to the measured temperature change curve diagram includes:

[0088] Obtain the peak value Q in the measured temperature change curve;

[0089] Obtain the cooling time t0 corresponding to the peak value Q in the measured temperature change curve graph dropping to the target temperature value; wherein the target temperature value is the temperature value of the target transformer 110 when it is operating normally;

[0090] According to the peak value Q and cooling time t0, the heat dissipation performance coefficient M is obtained. The expression of M is:

[0091] M=Q / t0.

[0092] In this embodiment, since the target transformer 110 is generally equipped with a heat dissipation system, during normal operation, the temperature of the key components of the target transformer 110 can be maintained within a certain range under the action of the heat dissipation system. When the target transformer 110 is overloaded and the temperature of the key components is high, the heat dissipation system can automatically increase the heat dissipation degree to cool the key components to the target temperature value. In this process, the heat dissipation capacity of the heat dissipation system is an important indicator that directly reflects the heat dissipation performance coefficient of the target transformer 110. If the heat dissipation performance coefficient becomes smaller, it means that the heat dissipation capacity is reduced, and the risk of failure is greater. Therefore, the peak value Q in the temperature change curve diagram and the corresponding cooling time t0 can be selected as reference indicators. The peak value Q represents the highest overload temperature collected per unit time, which is more positive. Substituting it into the formula M=Q / t0 can quantitatively calculate the heat dissipation performance coefficient. In the formula, under the premise that the peak value Q remains unchanged, the larger t0 is, the lower the cooling efficiency is, that is, the lower the heat dissipation performance coefficient is, that is, the cooling time t0 is inversely proportional to the heat dissipation performance coefficient. Therefore, based on the above formula, the heat dissipation performance coefficient can be effectively characterized, which is of reference and guidance.

[0093] As an optional implementation manner, obtaining the abnormal noise characteristic value of the target transformer 110 includes:

[0094] Obtain the noise decibel value of the target transformer 110 per unit time to obtain a noise variation curve graph;

[0095] Constructing a first standard line in the noise variation curve diagram; wherein the first standard line is a horizontal line corresponding to the decibel value of the noise generated when the target transformer 110 is operating normally;

[0096] Marking the area above the first standard line in the noise variation curve as the first abnormal area;

[0097] The area S1 of the first abnormal region is obtained, and the area S1 is output as an abnormal noise characteristic value.

[0098] In this embodiment, when calculating the abnormal noise characteristic value, the noise decibel value of the target transformer 110 can be collected at regular intervals within a unit time, and then a noise change curve can be obtained by curve fitting. At the same time, combined with the first standard line, the area above the first standard line can be intuitively identified and marked as the first abnormal area. The first abnormal area is the abnormal noise distribution of the target transformer 110 that exceeds the normal noise. The abnormal noise characteristic value is characterized by calculating the area S1 of the first abnormal area, which makes the calculation more efficient and quick, reducing the data processing pressure.

[0099] As an optional implementation manner, obtaining the abnormal vibration characteristic value of the target transformer 110 includes:

[0100] Acquire multiple detection images of the target transformer 110 in a specific assembly structure per unit time; wherein the specific assembly structure includes a base 120, a groove 121 is provided on the top of the base 120, a plurality of support springs 130 are provided in the groove 121, the target transformer 110 is provided on the plurality of support springs 130, and the target transformer 110 can slide up and down along the inner wall of the groove 121;

[0101] Performing image processing on a plurality of detection images to obtain black and white images;

[0102] Obtaining a spacing value between a top surface contour line of the target transformer 110 and a bottom surface contour line of the base 120 in each black-and-white image;

[0103] According to multiple spacing values, a vibration variation curve diagram is obtained;

[0104] Constructing a second standard line in the vibration change curve diagram; wherein the second standard line is a horizontal line corresponding to the standard spacing value obtained when the target transformer 110 is operating normally;

[0105] Marking the area above the second standard line in the vibration change curve as a second abnormal area;

[0106] The area S2 of the second abnormal region is obtained, and the area S2 is output as the abnormal vibration characteristic value.

[0107] In this embodiment, when the target transformer 110 vibrates, if a traditional fixed assembly structure is used, its amplitude is small and it is difficult to capture its amplitude change by machine vision measurement. Therefore, in combination with a specific assembly structure, the present embodiment uses a base 120 with a groove 121 and a support spring 130 to assemble the target transformer 110. When the target transformer 110 vibrates, its amplitude can be amplified under the action of the support spring 130. Therefore, a non-contact machine vision recognition technology can be used to obtain multiple detection images of the target transformer 110 under the specific assembly structure, and a black-and-white image is obtained after image processing (including grayscale processing and binarization processing, etc.) for subsequent contour recognition. By calculating the spacing value (i.e., the spacing value between the top surface contour line of the target transformer 110 and the bottom surface contour line of the base 120 in each black-and-white image) Figure 4 The vibration change curve can be drawn according to multiple spacing values, and the part exceeding the standard spacing value is above the second standard line, forming a second abnormal area. Similarly, the abnormal vibration characteristic value is characterized by calculating the area S2 of the second abnormal area, thereby improving data processing efficiency.

[0108] As an optional implementation, the expression of the fault warning model is:

[0109] E=(K1*S1+K2*S2) / (K3*M);

[0110] Wherein, E is the fault warning value, K1 is the first adjustment coefficient, K2 is the second adjustment coefficient, and K3 is the third adjustment coefficient.

[0111] In this embodiment, based on the above formula, after substituting each parameter, the fault warning value can be accurately calculated, wherein the larger the area S1 and the area S2, the greater the potential fault risk, and therefore it is proportional to the fault warning value E, and the smaller the heat dissipation performance coefficient M, the greater the potential fault risk, and therefore it is inversely proportional to the fault warning value E. Therefore, based on the above formula, the size of the fault warning value can be accurately characterized, and based on the structure of the above formula, when one of the parameters is abnormal and causes it to be too large or too small, even if other parameters are normal, it may cause the fault warning value E to exceed the threshold, so that early warning monitoring can be performed for a certain abnormal indicator alone, or multiple parameters have low-level abnormalities, which may eventually cause the fault warning value E to exceed the threshold, so that early warning monitoring when multiple parameters are abnormal can also provide accurate and effective reference data for the operation and maintenance management of the transformer.

[0112] It should be noted that since area S1, area S2 and heat dissipation performance coefficient M represent parameters of different properties respectively, they are transformed by the first adjustment coefficient K1, the second adjustment coefficient K2 and the third adjustment coefficient K3 respectively, so that each parameter can be calculated by superposition, and at the same time, a weight coefficient can be introduced into each adjustment coefficient to represent the influence of each parameter on the fault warning value respectively, so as to calculate the fault warning value more reasonably and accurately. In addition, both area S1 and area S2 can be 0, indicating that there is no abnormal noise and abnormal vibration. At this time, E=0. Generally speaking, when there is an obvious fault in heat dissipation performance, it will cause the target transformer 110 to overload and overheat, and synergistically cause abnormal noise and abnormal vibration of the corresponding degree. Therefore, the three parameters are related. If the calculated area S1 and area S2 are both 0, and the heat dissipation performance coefficient M is very small at this time, it means that there may be an error in the calculation of the heat dissipation performance coefficient, so the final E=0 is also a reliable calculation result.

[0113] Example 2

[0114] Based on the same inventive concept as the above-mentioned embodiment, this embodiment further provides a transformer state monitoring system, including:

[0115] A curve graph acquisition module, used to acquire a measured temperature change curve graph of the target transformer 110;

[0116] A first acquisition module, used to acquire the heat dissipation performance coefficient of the target transformer 110 according to the measured temperature change curve diagram;

[0117] A second acquisition module, used to acquire an abnormal noise characteristic value of the target transformer 110;

[0118] A third acquisition module is used to acquire an abnormal vibration characteristic value of the target transformer 110;

[0119] A data processing module, used for inputting the heat dissipation performance coefficient, the abnormal noise characteristic value and the abnormal vibration characteristic value into a preset fault warning model to obtain a fault warning value;

[0120] The information sending module is used to determine whether to send warning information according to the fault warning value.

[0121] The relevant explanations and examples of each module in the device of this embodiment can refer to the method of the aforementioned embodiment, and will not be repeated here.

[0122] Example 3

[0123] Based on the same inventive concept as the above-mentioned embodiment, this embodiment provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-mentioned method.

[0124] Example 4

[0125] Based on the same inventive concept as the aforementioned embodiment, this embodiment provides a computer-readable storage medium, on which a computer program is stored, and a processor executes the computer program to implement the aforementioned method.

[0126] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A transformer status monitoring method, characterized in that: The following steps are involved: Obtain a measured temperature variation curve graph of a target transformer; According to the measured temperature change curve, the heat dissipation performance coefficient of the target transformer is obtained; including: obtaining the peak value Q in the measured temperature change curve; obtaining the cooling time t0 corresponding to the peak value Q in the measured temperature change curve dropping to the target temperature value; wherein the target temperature value is the temperature value of the target transformer when it is operating normally; according to the peak value Q and the cooling time t0, the heat dissipation performance coefficient M is obtained, and the expression of M is: M=Q / t0; Obtaining an abnormal noise characteristic value of the target transformer; Obtaining an abnormal vibration characteristic value of the target transformer; The heat dissipation performance coefficient, the abnormal noise characteristic value and the abnormal vibration characteristic value are input into a preset fault warning model to obtain a fault warning value; wherein the expression of the fault warning model is: E=(K1*S1+K2*S2) / (K3*M); Wherein, E is the fault warning value, K1 is the first adjustment coefficient, K2 is the second adjustment coefficient, K3 is the third adjustment coefficient, S1 is the abnormal noise characteristic value, and S2 is the abnormal vibration characteristic value; According to the fault warning value, it is determined whether to send a warning message.

2. A transformer status monitoring method according to claim 1, characterized in that: The step of obtaining a temperature variation curve of a target transformer includes: Acquire a plurality of infrared images of the target transformer in a preset unit time; Acquire a first temperature change curve diagram of the target transformer according to the plurality of infrared images; Acquiring measured temperature data of the target transformer based on a thermometer within the unit time to obtain a second temperature change curve diagram of the target transformer; Determining whether the first temperature change curve graph and the second temperature change curve graph meet a similarity requirement; If not, return to the step of acquiring a plurality of infrared images of the target transformer in a preset unit time; if so, average the first temperature change curve graph and the second temperature change curve graph to obtain a measured temperature change curve graph.

3. A transformer status monitoring method according to claim 2, characterized in that: After returning to acquiring a plurality of infrared images of the target transformer in a preset unit time, the method further includes: Get the current iteration number; If the current number of iterations reaches a preset target number, the first temperature change curve graph or the second temperature change curve graph corresponding to a higher average temperature value is output as a measured temperature change curve graph.

4. A transformer status monitoring method according to claim 2 or 3, characterized in that: The obtaining of the abnormal noise characteristic value of the target transformer includes: Obtaining the noise decibel value of the target transformer in the unit time to obtain a noise change curve graph; Constructing a first standard line in the noise change curve diagram; wherein the first standard line is a horizontal line corresponding to the decibel value of the noise generated when the target transformer is operating normally; Marking an area above the first standard line in the noise variation curve as a first abnormal area; The area S1 of the first abnormal region is obtained, and the area S1 is output as an abnormal noise characteristic value.

5. A transformer status monitoring method according to claim 4, characterized in that: The obtaining of the abnormal vibration characteristic value of the target transformer includes: Acquire a plurality of detection images of the target transformer in a specific assembly structure in the unit time; wherein the specific assembly structure comprises a base, a groove is provided on the top of the base, a plurality of support springs are provided in the groove, the target transformer is provided on the plurality of support springs, and the target transformer can slide up and down along the inner side wall of the groove; Performing image processing on the plurality of detection images to obtain black and white images; Obtaining a spacing value between a top surface contour line of the target transformer and a bottom surface contour line of the base in each of the black-and-white images; According to the plurality of spacing values, obtaining a vibration variation curve diagram; Constructing a second standard line in the vibration change curve diagram; wherein the second standard line is a horizontal line corresponding to a standard spacing value obtained when the target transformer is operating normally; Marking the area above the second standard line in the vibration change curve as a second abnormal area; The area S2 of the second abnormal region is acquired, and the area S2 is output as an abnormal vibration characteristic value.

6. A transformer status monitoring system, characterized in that: include: A curve graph acquisition module is used to obtain a measured temperature change curve graph of a target transformer; The first acquisition module is used to acquire the heat dissipation performance coefficient of the target transformer according to the measured temperature change curve graph; including: acquiring the peak value Q in the measured temperature change curve graph; acquiring the cooling time t0 corresponding to the peak value Q in the measured temperature change curve graph dropping to the target temperature value; wherein the target temperature value is the temperature value of the target transformer when it is operating normally; acquiring the heat dissipation performance coefficient M according to the peak value Q and the cooling time t0, wherein the expression of M is: M=Q / t0; A second acquisition module is used to obtain the abnormal noise characteristic value of the target transformer; A third acquisition module is used to obtain the abnormal vibration characteristic value of the target transformer; A data processing module is used to input the heat dissipation performance coefficient, the abnormal noise characteristic value and the abnormal vibration characteristic value into a preset fault warning model to obtain a fault warning value; wherein the expression of the fault warning model is: E=(K1*S1+K2*S2) / (K3*M); Wherein, E is the fault warning value, K1 is the first adjustment coefficient, K2 is the second adjustment coefficient, K3 is the third adjustment coefficient, S1 is the abnormal noise characteristic value, and S2 is the abnormal vibration characteristic value; The information sending module is used to determine whether to send warning information according to the fault warning value.

7. A computer device, characterized in that: The computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a transformer state monitoring method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the processor executes the computer program to implement a transformer state monitoring method according to any one of claims 1 to 5.

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