A method and system for calculating near-surface porosity defect parameters of a GIS epoxy insulation pull rod

Abstract

The application discloses a kind of GIS epoxy insulation pull rod near-surface pore defect parameter calculation method and system, comprising: obtaining the fitting relationship of the first optimal detection time and the first defect depth of pore defect in the first target area in the target insulation pull rod under the first thermal excitation;Obtain the second optimal detection time of the near-surface pore defect of the insulation pull rod to be measured under the same first thermal excitation and the first actual defect size;According to the second optimal detection time and the first actual defect size, the actual defect depth of the near-surface pore defect of the insulation pull rod to be measured is obtained by combining the fitting relationship.The calculation method and system can improve the accuracy of detection, reduce the possibility of misjudgment and omission.Through the relationship model between pore defect size and defect depth, the health status of the insulation pull rod can be more accurately evaluated, so as to provide a scientific basis for maintenance and replacement decision.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of GIS epoxy insulating pull rod near-surface pore defect parameter calculation, and particularly relates to a GIS epoxy insulating pull rod near-surface pore defect parameter calculation method and system. BACKGROUND

[0002] The insulating pull rod is one of the key components in the gas insulated switch (GIS). In the production, transportation, assembly and operation process, defects such as near-surface air gap will inevitably occur, which will seriously affect the reliability of the insulating pull rod. The existing non-destructive testing technologies, such as ultrasonic wave and X-ray, have the disadvantages of poor anti-interference ability, complex equipment and high detection cost. In the production and operation, when an accident occurs, the traditional method is difficult to determine the cause of the accident, and there is also a lack of effective prevention means. In addition, in the production process, the withstand voltage and partial discharge method can only qualitatively judge the electrical characteristics of the insulating pull rod, and cannot establish the internal relationship between the internal defects and the electrical performance, which may lead to the use of the insulating component with defects, and further seriously affect the safety and stability of the electrical equipment. The present application aims to use a new type of infrared wave, which has good penetration ability for non-polar, non-metal and dry materials, to study the insulating pull rod defect detection method based on infrared imaging technology, so as to realize the rapid detection of the internal defects of the GIS insulating pull rod, and to improve and optimize the production and operation conditions of the insulating pull rod.

[0003] The thermal conductivity of epoxy resin is relatively weak, so it takes a long time for the heat applied on the surface of the epoxy resin to be conducted to the inside of the material. When collecting data, the infrared thermal wave non-destructive testing technology also uses a very high acquisition frequency (frame frequency / frame rate) to obtain higher detection accuracy when detecting defects. Therefore, the long heat conduction process required by the sample made of epoxy resin will make the infrared thermal imager work for a longer time at the same acquisition frequency in order to completely collect the temperature changes in the surface cooling process of the epoxy resin. At present, the infrared thermal wave non-destructive testing technology still relies on technical personnel to manually lock the time period when the defect appears from a large image library (several thousand thermal images are often generated in one detection) and to find those abnormal areas in the thermal image. The longer acquisition time required by the epoxy resin will greatly increase the number of thermal images collected in a single detection, greatly increasing the detection and analysis time of technical personnel for a single sample, greatly affecting the detection efficiency. Therefore, by estimating the best detection time of the defect within a certain range, technical personnel can focus on the thermal images collected near the best detection time, which not only greatly improves the detection efficiency and shortens the detection time of a single sample, but also improves the detection accuracy. SUMMARY

[0004] This section is intended to summarize some aspects of the embodiments of the present application and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of the specification in order to avoid obscuring the purpose of this section, the abstract and the title of the specification, and such simplifications or omissions are not to be construed as limiting the scope of the present application.

[0005] In view of the above existing problems, the present application is proposed.

[0006] Therefore, the present application provides a GIS epoxy insulating pull rod near-surface pore defect parameter calculation method and system, which can solve the problems mentioned in the background art.

[0007] To solve the above technical problems, the present application provides the following technical solutions:

[0008] In a first aspect, the present application provides a GIS epoxy insulating pull rod near-surface pore defect parameter calculation method, comprising:

[0009] Obtaining a fitting relationship between a first optimal detection time and a first defect depth of a pore defect in a first target region in a target insulating pull rod under a first thermal excitation;

[0010] Obtaining a second optimal detection time of a near-surface pore defect of a to-be-tested insulating pull rod and a first actual defect size under the same first thermal excitation;

[0011] According to the second optimal detection time and the first actual defect size, and combining the fitting relationship, obtaining an actual defect depth of the near-surface pore defect of the to-be-tested insulating pull rod.

[0012] As a preferred scheme of the GIS epoxy insulating pull rod near-surface pore defect parameter calculation method, wherein the pore defect in the first target region in the target insulating pull rod comprises:

[0013] A target insulating pull rod near-surface pore defect library for calibration is constructed;

[0014] The target insulating pull rod near-surface pore defect library comprises a first defect size and a first defect depth;

[0015] The first defect depth in the target insulating pull rod near-surface pore defect library covers all target defect depth values.

[0016] As a preferred scheme of the GIS epoxy insulating pull rod near-surface pore defect parameter calculation method, wherein the fitting relationship between the first optimal detection time and the first defect depth comprises:

[0017] A first thermal excitation is applied to the first target region in the target insulating pull rod;

[0018] collecting a temperature change process of the pore defect in the first target area over time under the first thermal excitation;

[0019] establishing a first optimal detection time and a first defect depth of each pore defect based on the temperature change process over time.

[0020] As a preferred scheme of the GIS epoxy insulating pull rod near-surface pore defect parameter calculation method, the fitting relationship of the first optimal detection time and the first defect depth further comprises:

[0021] The first optimal detection time and the first defect depth are related and fitted using the first fitting model.

[0022] As a preferred scheme of the GIS epoxy insulating pull rod near-surface pore defect parameter calculation method, the obtaining of the second optimal detection time and the first actual defect size of the near-surface pore defect of the to-be-tested insulating pull rod under the same first thermal excitation comprises:

[0023] The first thermal excitation is applied to the first target area in the to-be-tested insulating pull rod, and the second optimal detection time is obtained.

[0024] According to the second optimal detection time, the actual defect size of the near-surface pore defect of the to-be-tested insulating pull rod is obtained in combination with the thermal image.

[0025] As a preferred scheme of the GIS epoxy insulating pull rod near-surface pore defect parameter calculation method, the obtaining of the actual defect depth of the near-surface pore defect of the to-be-tested insulating pull rod according to the second optimal detection time and the first actual defect size in combination with the fitting relationship comprises:

[0026] Based on the fitting relationship after the first optimal detection time and the first defect depth are related and fitted using the first fitting model, the second defect depth is inversely solved using the second optimal detection time, and the second defect depth is the actual defect depth.

[0027] As a preferred scheme of the GIS epoxy insulating pull rod near-surface pore defect parameter calculation method, the near-surface pore defect parameter comprises: the actual defect size and the actual defect depth.

[0028] In a second aspect, the present application provides a GIS epoxy insulating pull rod near-surface pore defect parameter calculation system, characterized in that it comprises:

[0029] The fitting relationship acquisition module is configured to acquire a fitting relationship of a first optimal detection time and a first defect depth of a pore defect in a first target area in a target insulating pull rod under a first thermal excitation.

[0030] an actual size obtaining module configured to obtain a second optimal detection time of the near-surface pore defect of the to-be-tested insulating pull rod under the same first thermal excitation and a first actual defect size;

[0031] an actual defect calculating module configured to obtain an actual defect depth of the near-surface pore defect of the to-be-tested insulating pull rod according to the second optimal detection time and the first actual defect size in combination with the fitting relationship.

[0032] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method as described above when executing the computer program.

[0033] In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the steps of the method as described above.

[0034] Compared with the prior art, the present application has the following beneficial effects: the present application provides a method and system for calculating near-surface pore defect parameters of a GIS epoxy insulating pull rod, obtains a fitting relationship between a first optimal detection time and a first defect depth of a pore defect in a first target region of a target insulating pull rod under a first thermal excitation, obtains a second optimal detection time of a near-surface pore defect of a to-be-tested insulating pull rod under the same first thermal excitation and a first actual defect size, and obtains an actual defect depth of the near-surface pore defect of the to-be-tested insulating pull rod according to the second optimal detection time and the first actual defect size in combination with the fitting relationship. The method and system can improve the accuracy of detection and reduce the possibility of misjudgment and missed judgment. By establishing a relationship model between the size of the pore defect and the defect depth, the health status of the insulating pull rod can be more accurately evaluated, thereby providing a scientific basis for maintenance and replacement decisions. In addition, the technical solution has the advantages of simple operation and low cost, because it does not require complex equipment and high detection costs. Through the method and system provided by the present application, the service life of the GIS equipment can be effectively prolonged, the maintenance cost can be reduced, and the operation reliability of the power system can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor. Among them:

[0036] Figure 1A method flow chart of a GIS epoxy insulating pull rod near-surface pore defect parameter calculation method and system provided by an embodiment of the present application is provided.

[0037] Figure 2 A detailed flowchart of a GIS epoxy insulating pull rod near-surface pore defect parameter calculation method and system provided by an embodiment of the present application is provided.

[0038] Figure 3 A best detection time-defect depth fitting image diagram of a GIS epoxy insulating pull rod near-surface pore defect parameter calculation method and system provided by an embodiment of the present application is provided.

[0039] Figure 4 An internal structure diagram of a computer device of a GIS epoxy insulating pull rod near-surface pore defect parameter calculation method and system provided by an embodiment of the present application is provided. DETAILED DESCRIPTION

[0040] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within the scope of protection of the present application.

[0041] Embodiment 1

[0042] Reference Figures 1-4 For the first embodiment of the present application, the embodiment provides a GIS epoxy insulating pull rod near-surface pore defect parameter calculation method and system, which comprises:

[0043] Before detailing the embodiments of the present application, for the sake of clarity, some related concepts are first explained.

[0044] GIS epoxy insulating pull rod: This is a component used in gas insulated switchgear (GIS). The pull rod is made of epoxy resin, which has good insulation performance and mechanical strength. It plays a role in fixing and supporting in GIS, while ensuring the insulation isolation between electrical parts.

[0045] Thermal excitation: generally refers to the process of an object being affected by external environment or internal conditions due to temperature change, resulting in its deformation or stress. In power equipment, thermal excitation can be caused by the joule heat generated when current passes through the conductor, which can cause the temperature of the material to rise and may cause the thermal expansion of the material. In extreme cases, if the temperature is not properly controlled, it may have a negative impact on the structural integrity of the equipment. In GIS equipment, thermal excitation may involve the change of stress of epoxy insulation pull rod due to temperature change, which may affect its electrical and mechanical properties.

[0046] In the prior related art, there are some technical problems, such as the difficulty in accurately detecting and evaluating the pore defects in the epoxy insulation pull rod. These defects may affect the insulation performance of the equipment, and even cause failure.

[0047] The present application provides a method for calculating the near-surface pore defect parameters of the GIS epoxy insulation pull rod, which can effectively solve the above-mentioned problems. Next, how to realize the method will be described in detail in combination with multiple embodiments.

[0048] Figure 1 A method flow chart of a method for calculating the near-surface pore defect parameters of the GIS epoxy insulation pull rod and a system is shown, which includes:

[0049] S101, obtaining a fitting relationship between the first optimal detection time and the first defect depth of the pore defect in the first target area in the target insulation pull rod under the first thermal excitation;

[0050] In the embodiments of the present application, the near-surface pore defect parameters include the actual defect size and the actual defect depth.

[0051] It should be noted that the actual defect size in the present application only considers the defect diameter, but if other technical personnel use the same type of technical means to calculate other related parameters, such as defect radius, defect curvature, etc., they should be within the scope of protection of the present application.

[0052] In the embodiments of the present application, the pore defects in the first target area in the target insulation pull rod include:

[0053] A target insulation pull rod near-surface pore defect library is constructed for calibration;

[0054] The target insulation pull rod near-surface pore defect library includes the first defect size and the first defect depth;

[0055] The first defect depth in the target insulation pull rod near-surface pore defect library covers all target defect depth values.

[0056] In an optional embodiment, the constructing the target insulation rod near-surface porosity defect library for calibration specifically comprises constructing a series of target insulation rod near-surface porosity defects d1h1, d1h2, …, d1h n wherein the defect sizes d can be different from each other, but the defect depths h must completely cover all possible defect depth values. The insulation rod calibration sample containing the above near-surface porosity defects can be set with different porosity defects in multiple insulation rod calibration samples, and the defects contained in each sample are labeled;

[0057] In an optional embodiment, the target insulation rod is selected from the target insulation rod near-surface porosity defect library for calibration. The near-surface porosity defect library can be in the form of a table, for example, using rows and columns to represent defect diameters or defect depths. The near-surface porosity defect library can also be in the form of a matrix, for example, using rows and columns to represent defect diameters or defect depths. The near-surface porosity defect library can also be in the form of a set, for example, containing parameters for the combination of defect diameters or defect depths of each defect, etc.

[0058] In this application, the first target area includes the area that needs to be considered for the target insulation rod. This area can be the entire target insulation rod area, or the upper surface of the target insulation rod, or the lower surface, or a fixed position area, etc. Thus, the selection of the first target area can be adjusted flexibly according to actual application requirements. For example, in some cases, only the specific part of the insulation rod, such as the connection point or the stress concentration area, needs to be concerned to ensure the reliability of these key parts. In other cases, the entire insulation rod may need to be detected to obtain comprehensive defect parameter information.

[0059] In the embodiments of this application, the fitting relationship between the first optimal detection time and the first defect depth comprises:

[0060] applying a first thermal excitation to the first target area of the target insulation rod;

[0061] collecting the temperature change process of the porosity defects in the first target area over time under the first thermal excitation;

[0062] establishing the first optimal detection time and the first defect depth of each porosity defect based on the temperature change process over time.

[0063] In the embodiments of this application, the fitting relationship between the first optimal detection time and the first defect depth further comprises:

[0064] using the first fitting model to fit the relationship between the first optimal detection time and the first defect depth.

[0065] In an optional embodiment, the first thermal excitation includes applying a transient heat flux to the surface of the insulating tie rod calibration sample, and using an infrared imaging device to acquire the surface temperature change over time of the near-surface pore defects and the surface of defect-free areas of the insulating tie rod after external thermal excitation, and calculating the first optimal detection time t for each pore defect based on this. m To obtain a sequence of the first optimal detection time for porosity defects. Each of the components Both correspond to a first defect depth h of pores. x ;

[0066] In an optional embodiment, the relationship fitting process can be based on the first optimal detection time series of near-surface porosity defects on the insulating tie rod. With the first defect depth sequence The correspondence is represented by a second-order polynomial model t. max =p1h 2 +p2h+p3 Fitting the first optimal detection time t for near-surface porosity defects in the insulating tie rod m The numerical relationship t between the first defect depth h and the first defect depth h m = f(h), where p1, p2, and p3 are three constant terms in the fitting model. The first optimal detection time t for near-surface porosity defects on the insulating tie rod under the same detection environment was obtained. m The one-to-one correspondence with the first defect depth h completed the calibration of near-surface porosity defects in the insulating tie rod;

[0067] In an alternative embodiment, the fitting relationship can also be fitted using other methods or fitting models, such as linear models, exponential models, or other mathematical models to fit the relationship between the first optimal detection time and the first defect depth. By selecting a suitable fitting model, the accuracy and efficiency of detection can be improved. For example, a linear model may be suitable for situations where there is a linear relationship between defect depth and detection time, while an exponential model may be more suitable for describing situations where there is an exponential relationship between defect depth and detection time. In practical applications, the most suitable fitting model can be selected based on experimental data and experience to ensure the reliability of the detection results.

[0068] It should be noted that the second-order polynomial model used in this application can better fit the experimental data, especially when there is a nonlinear relationship between defect depth and detection time. The second-order polynomial model can more accurately describe this nonlinear relationship by including the quadratic term, thereby improving the accuracy of detection. In addition, the three constant terms of the model can be optimized by experimental data to adapt to different detection conditions and insulating rod material properties, thereby enhancing the universality and adaptability of the model. In practical applications, by adjusting the parameters of the fitting model, the detection results can be further refined to ensure accurate evaluation of the near-surface pore defects of the insulating rod.

[0069] S102, obtaining the second optimal detection time of the near-surface pore defect of the insulating rod under test under the same first thermal excitation and the first actual defect size;

[0070] In the embodiments of the present application, obtaining the second optimal detection time of the near-surface pore defect of the insulating rod under test under the same first thermal excitation and the first actual defect size comprises:

[0071] applying a first thermal excitation to the first target area in the insulating rod under test, and obtaining the second optimal detection time;

[0072] According to the second optimal detection time, in combination with the thermal image, the actual defect size of the near-surface pore defect of the insulating rod under test is obtained.

[0073] In an optional embodiment, the same first thermal excitation as in the calibration test of the near-surface pore defect of the target insulating rod is used, and a thermal image is collected. From the thermal image, an abnormal area where the near-surface pore defect of the insulating rod exists is preliminarily determined, and a reference point is selected in the abnormal area, and temperature change data with time corresponding to the abnormal area where the near-surface pore defect of the insulating rod exists is derived and temperature change data with time of a normal area

[0074] Further, based on the above data, the second optimal detection time t of the near-surface pore defect of the insulating rod is calculated m (because the underlying meaning of the first optimal detection time and the second optimal detection time in this application is similar, therefore for the convenience of understanding, the same variable is used), the temperature difference vector of the air gap defect surface is calculated

[0075]

[0076] wherein, is the temperature difference vector of the air gap defect surface, is the temperature vector of the defect area, is the temperature vector of the defect-free area, ΔT1, ΔT2, …, ΔTN Temperature difference vector of the concave surface The experiment consists of N components, where N is the total number of data collections during the experiment, and T is the total number of data collections. d1 ,T d2 ,...,T dN With T s1 ,T s2 ,…,T sN They are respectively and The N components. Search for the maximum temperature difference value ΔT from the temperature difference vector of the air gap defect surface. max And record the maximum temperature difference value ΔT. max The time t when the defect appears is taken as the unique optimal detection time corresponding to the depth of the defect.

[0077] In this embodiment, the first actual defect size is the diameter or radius of the defect or other parameter variables related to the defect circle. Since the defect is assumed to be circular in this embodiment, the consideration process is based on the circle.

[0078] In an optional embodiment, the calculated optimal detection time t for near-surface porosity defects on the insulating tie rod is used. m During the lock-in test at time t m The thermal images acquired before and after the acquisition were used to select the one with the clearest defect outline and the highest contrast as the optimal thermal image for detecting near-surface porosity defects in the insulating tie rod. The number of pixels p occupied by the abnormal region containing near-surface porosity defects in the insulating tie rod was calculated from the optimal thermal image. d Then, based on the ratio of the total number of pixels in one direction of the image to the actual size of the image in the same direction, r = p i / L, calculated to obtain the actual defect size d of the near-surface porosity defect of the insulating tie rod. * =(p d / p i )·L=p d / r;

[0079] It should be noted that step S102 can accurately determine the depth of near-surface porosity defects in the insulating tie rod, thereby assessing its impact on overall insulation performance. By comparing the corresponding times in the second optimal detection time series with those in the first optimal detection time series, the accuracy of the calibration test can be verified, and a more in-depth analysis of the health condition of the insulating tie rod can be conducted. Furthermore, by analyzing the relationship between the temperature difference vector ΔT and time, the thermal conductivity characteristics of the defective area can be further understood, providing a scientific basis for the maintenance and replacement of the insulating tie rod. In practical applications, this method can effectively assist engineers in fault diagnosis, improving the safety and reliability of GIS equipment.

[0080] S103. Based on the second optimal detection time and the first actual defect size, and combined with the fitting relationship, the actual defect depth of the near-surface porosity defect of the insulating rod to be tested is obtained.

[0081] In this embodiment of the application, the actual defect depth of the near-surface porosity defect of the insulating tie rod to be tested is obtained by combining the second optimal detection time and the first actual defect size with the fitting relationship, including:

[0082] Based on the fitting relationship between the first optimal detection time and the first defect depth using the first fitting model, the second defect depth is obtained by inversely solving the second optimal detection time. The second defect depth is the actual defect depth.

[0083] In an optional embodiment, the optimal detection time t for near-surface porosity defects of the insulating tie rod is calculated from the calibration process of near-surface porosity defects of the insulating tie rod. m Numerical relationship t between the defect depth h and the defect depth h m =f(h), and the optimal detection time for near-surface porosity defects with this parameter unknown. The actual defect depth z of near-surface porosity defects with unknown parameters can be calculated. * ;

[0084] Furthermore, a method for calculating the depth and size of arbitrary pore defects near the surface of an insulating tie rod can be formed by two parts: the calibration process of the insulating tie rod calibration sample and the calculation process of defect parameters for the insulating tie rod containing unknown pore defects. Specifically, before starting the infrared thermography test of the insulating tie rod in a new environment, the insulating tie rod calibration sample is first used for calibration to obtain the relationship between defect depth and optimal detection time; then, the infrared thermography test of the sample to be tested is formally started, and the optimal detection time is obtained by conducting the experiment, collecting data, and analyzing the data. Calculate the defect size d * Calculate the defect depth z * The parameters for near-surface porosity defects in the insulating tie rod are calculated sequentially. For example... Figure 2As shown, calibration sample preparation 401: First, some calibration samples with known parameters are needed. Near-surface porosity defects with known parameters 402: In the calibration samples, it is known that there are porosity defects near the surface, and the specific parameters of these defects, such as location, size, shape, and depth, etc. are known. These defect samples with known parameters are used in subsequent calibration processes to establish a reliable model or algorithm to identify and quantify unknown defects. Insulation rod calibration sample 403: Select insulation rods with known near-surface porosity defects as calibration samples. Thermal excitation 404: Thermal excitation is performed on these calibration samples to stimulate temperature changes at the defects. Temperature change over time data 405: Record and collect the temperature change over time data of the defect area after thermal excitation. Calculate the optimal detection time 406: According to the collected data, determine the optimal detection time point that can most clearly show the defect characteristics. Fit the numerical relationship between the optimal detection time and the defect depth 407: Fit the numerical relationship between the optimal detection time and the defect depth through a mathematical model. Complete the calibration process 408: After completing the above steps, a method for predicting the depth of unknown defects is obtained. Infrared wave test of insulation rod with unknown defects 409: Next, use this method to test insulation rods with unknown defects. Collect defect surface temperature 410: After thermal excitation, measure and record the temperature change of the defect surface. Calculate the defect optimal detection time 411: According to the collected temperature data, find the time point that can best show the defect characteristics. Lock the optimal detection thermal image 412: Take a thermal image at this optimal detection time for subsequent analysis. Calculate the defect optimal detection time again 413: To ensure accuracy, confirm the optimal detection time again. Calculate the number of pixels occupied by the defect 414: From the locked thermal image, count how many pixels the defect occupies. Substitute the calibrated fitting relationship to calculate the defect depth 415: Substitute the pixel value obtained into the previously established fitting relationship to calculate the depth of the defect. Calculate the defect size 416: Combine other information such as image scale to further calculate the actual size of the defect. Form a set of parameter calculation methods for insulation rod near-surface porosity defects 417: Finally, a complete set of parameter calculation methods is formed, which can be used to evaluate the degree of near-surface porosity defects of insulation rods.

[0085] In summary, the present application provides a GIS epoxy insulating pull rod near-surface pore defect parameter calculation method and system, obtains the fitting relationship of the first optimal detection time and the first defect depth of the pore defect in the first target area in the target insulating pull rod under the first thermal excitation, obtains the second optimal detection time and the first actual defect size of the near-surface pore defect of the insulating pull rod to be measured under the same first thermal excitation, and obtains the actual defect depth of the near-surface pore defect of the insulating pull rod to be measured according to the second optimal detection time and the first actual defect size in combination with the fitting relationship. The calculation method and system can improve the accuracy of detection and reduce the possibility of misjudgment and omission. By establishing the relationship model between the pore defect size and the defect depth, the health status of the insulating pull rod can be more accurately evaluated, thereby providing a scientific basis for maintenance and replacement decisions. In addition, the technical solution also has the advantages of simple operation and low cost, because it does not require complex equipment and high detection costs. Through the method and system provided by the present application, the service life of the GIS equipment can be effectively prolonged, the maintenance cost can be reduced, and the operation reliability of the power system can be improved.

[0086] The present application also provides a GIS epoxy insulating pull rod near-surface pore defect parameter calculation system, which comprises:

[0087] The fitting relationship acquisition module is configured to obtain the fitting relationship of the first optimal detection time and the first defect depth of the pore defect in the first target area in the target insulating pull rod under the first thermal excitation.

[0088] The actual size acquisition module is configured to obtain the second optimal detection time and the first actual defect size of the near-surface pore defect of the insulating pull rod to be measured under the same first thermal excitation.

[0089] The actual defect calculation module is configured to obtain the actual defect depth of the near-surface pore defect of the insulating pull rod to be measured according to the second optimal detection time and the first actual defect size in combination with the fitting relationship.

[0090] The above-mentioned various unit modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to call and execute the operations of the above-mentioned various modules by the processor.

[0091] The present application also provides a computer device, which can be a terminal, and the internal structure diagram thereof can be as shown in Figure 4The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected by a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved by WIFI, operator network, NFC (near field communication) or other technologies. The computer program is executed by the processor to implement a GIS epoxy insulating pull rod near-surface pore defect parameter calculation method. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0092] The embodiment also provides a computer readable storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement the following steps:

[0093] Obtaining a fitting relationship between the first optimal detection time and the first defect depth of the pore defect in the first target area in the target insulating pull rod under the first thermal excitation;

[0094] Obtaining a second optimal detection time of the near-surface pore defect of the to-be-tested insulating pull rod under the same first thermal excitation and a first actual defect size;

[0095] According to the second optimal detection time and the first actual defect size, and in combination with the fitting relationship, an actual defect depth of the near-surface pore defect of the to-be-tested insulating pull rod is obtained.

[0096] Embodiment 2

[0097] Reference Figures 2-3 For an embodiment of the present application, a GIS epoxy insulating pull rod near-surface pore defect parameter calculation method and system are provided. In order to verify the beneficial effects of the present application, scientific demonstration is carried out through experiments.

[0098] First, a calibration test of the insulating pull rod near-surface pore defect detection is performed. A series of near-surface pore defects are set on the near-surface of the GIS epoxy insulating pull rod as the insulating pull rod calibration sample for the near-surface pore defect calibration test. The five defect sizes are randomly distributed, but the defect depths are regularly distributed in gradients. Each depth corresponds to a unique optimal detection time.

[0099] Table 1: Depth information of defects contained in the calibration sample

[0100] Defect depth (mm) [h1 = 1] [h2 = 1.3] h3 = 1.5 h4 = 1.7 [h5 = 2] Defect name [d1h1] [d1h2] [d1h3] [d1h4] [d1h5]

[0101] The insulation pull rod calibration sample containing the above defects is subjected to thermal excitation, the thermal excitation is pulse excitation, the maximum heat flux density is Q=10 6 W / m 2 , the pulse duration is t=0.1s, and the infrared thermal imager is used to collect the temperature change data of the surface of the insulation pull rod, the collection frequency is f=10Hz, and the collected area contains the temperature change of the defect area and the temperature change of the non-defect area.

[0102] According to the calculation method in the summary, the optimal detection time of each defect surface is calculated. The optimal detection time table can be calculated as follows:

[0103] Table 2: Optimal detection time table

[0104] Defect [d1h1] [d1h2] [d1h3] [d1h4] [d1h5] [CAT max ]] 0.6s 0.8s 1s 1.2s 1.5s

[0105] Therefore, the optimal detection time vector is:

[0106]

[0107] The optimal detection time vector is fitted with the defect depth , and the fitting model uses the above-mentioned second-order polynomial fitting model, and the fitting result is obtained:

[0108] Table 3: Fitting result

[0109]

[0110]

[0111] In the window Figure 3 , the scatter plot of the optimal detection time t m and the defect depth h and the fitting curve plot are drawn. It can be seen that the fitting curve is an open upward quadratic function, and it better reflects the numerical relationship between the optimal detection time t m and the defect depth h:

[0112] For the fitting function t m of the optimal detection time t m and the defect depth z, t 2 =0.2200h +0.2539h–0.1187, thus the calibration process of the insulation pull rod calibration sample in the same detection environment has been completed.

[0113] The following section describes the testing of an insulating tie rod containing an unknown near-surface porosity defect d1h6. The same thermal excitation as in the calibration test was applied to the insulator surface, and temperature data over time was collected. Based on this data, the optimal testing time t for the near-surface porosity defect on the insulating tie rod was calculated. m Calculations yield: ΔT m =4.8574℃, Next, thermal images acquired between 0.4s and 1.0s were selected, and the thermal image acquired at 0.6s was chosen as the optimal detection thermal image. From this, the number of pixels p of the abnormal region containing near-surface porosity defects in the insulating tie rod was calculated. d =11, then based on the ratio of the total pixels in one direction of the image to the actual size of the image in the same direction, r = p i / L, calculated to obtain the actual defect size d of the near-surface porosity defect of the insulating tie rod. * =5.5mm.

[0114] Since the optimal detection time for this defect has been calculated Therefore Substitute the optimal detection time t m Fitting function t with defect depth h m =0.2200h 2 From +0.2539h–0.1187, we can obtain:

[0115] 0.7 = 0.2200h 2 +0.2539h–0.1187

[0116] Solving for h, we get: * = 1.4366 mm. This completes the solution for all parameters of the unknown near-surface porosity defect.

[0117] Example 3

[0118] Based on the analysis in the previous embodiment, the optimal detection time t m The fitting function with respect to the defect depth h is:

[0119] t m =0.2200h 2 +0.2539h–0.1187

[0120] Under the same test conditions, when it is necessary to detect new unknown near-surface porosity defects, there is no need to recalibrate; the fitting results of the previous embodiment can be used directly.

[0121] An unknown near-surface porosity defect, d1h7, was detected. The optimal detection time was determined based on experimental data. Next, the thermal images collected during the lock-in time of 1.0s-1.6s are selected, and the thermal image collected at 1.5s is selected as the best detection thermal image, in which the number of pixels p of the abnormal area of the insulating pull rod near-surface pore defect is calculated d = 9, and the ratio r = p i / L, and the actual defect size d of the insulating pull rod near-surface pore defect is converted * = 4.5mm.

[0122] Since the best detection time of the defect has been calculated Therefore, t is substituted into the best detection time t m and the fitting function t m = 0.2200h 2 + 0.2539h-0.1187, we have:

[0123] 1.3 = 0.2200h 2 + 0.2539h-0.1187

[0124] The solution is: h * = 2.0252mm. Thus, the complete parameter solving of the unknown near-surface pore defect is completed.

[0125] It should be noted that the above examples are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application, which should be covered in the scope of the claims of the present application.

[0126] Those skilled in the art should understand that the embodiments of the present application can provide methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages, such as object-oriented programming languages Java and interpreted scripting languages JavaScript, etc.

[0127] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0128] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0129] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0130] While the preferred embodiments of the application have been described, additional variations and modifications can be employed by those skilled in the art. Therefore, the appended claims intend to cover all such modifications and variations as fall within the true spirit and scope of the application.

[0131] It is apparent that a person skilled in the art can make various changes and modifications to the application without departing from the spirit and scope thereof. Thus, if these modifications and changes fall within the scope of the claims and their equivalents, it is intended to include them in the application.

Claims

1. A method for calculating the near-surface porosity defect parameter of a GIS epoxy insulating pull rod, characterized in that, The method comprises the following steps: obtaining a fitting relationship between a first optimal detection time and a first defect depth of a pore defect in a first target region of a target insulating pull rod under a first thermal excitation; the pore defect in the first target region of the target insulating pull rod comprises: constructing a target insulating pull rod near-surface pore defect library for calibration; the target insulating pull rod near-surface pore defect library comprises a first defect size and a first defect depth; the first defect depth in the target insulating pull rod near-surface pore defect library covers all target defect depth values; the fitting relationship between the first optimal detection time and the first defect depth comprises: applying a first thermal excitation to a first target region of a target insulating pull rod; collecting a temperature change process of a pore defect in the first target region under the first thermal excitation; establishing a first optimal detection time and a first defect depth of each pore defect based on the temperature change process; obtaining a second optimal detection time and a first actual defect size of a near-surface pore defect of a to-be-tested insulating pull rod under the same first thermal excitation; obtaining an actual defect depth of the near-surface pore defect of the to-be-tested insulating pull rod according to the second optimal detection time and the first actual defect size in combination with the fitting relationship.

2. The method for calculating near-surface porosity defect parameters of GIS epoxy insulated tie rods as described in claim 1, characterized in that, the fitting relationship between the first optimal detection time and the first defect depth further comprises: using a first fitting model to perform relationship fitting on the first optimal detection time and the first defect depth.

3. The method for calculating near-surface porosity defect parameters of GIS epoxy insulated tie rods as described in claim 2, characterized in that, the obtaining of the second optimal detection time and the first actual defect size of the near-surface pore defect of the to-be-tested insulating pull rod under the same first thermal excitation comprises: applying the first thermal excitation to a first target region of a to-be-tested insulating pull rod and obtaining a second optimal detection time; obtaining an actual defect size of a near-surface pore defect of the to-be-tested insulating pull rod according to the second optimal detection time in combination with a thermal image.

4. The method for calculating near-surface porosity defect parameters of GIS epoxy insulated tie rods as described in claim 3, characterized in that, the obtaining of the actual defect depth of the near-surface pore defect of the to-be-tested insulating pull rod according to the second optimal detection time and the first actual defect size in combination with the fitting relationship comprises: based on the fitting relationship obtained by using the first fitting model to perform relationship fitting on the first optimal detection time and the first defect depth, using the second optimal detection time to inversely solve a second defect depth, and the second defect depth is the actual defect depth.

5. The method for calculating near-surface porosity defect parameters of GIS epoxy insulated tie rods as described in claim 4, characterized in that, the near-surface pore defect parameters comprise an actual defect size and an actual defect depth.

6. A system for calculating parameters of a near-surface porosity defect of a GIS epoxy insulation pull rod, applying the method for calculating parameters of a near-surface porosity defect of a GIS epoxy insulation pull rod according to any one of claims 1-5, characterized in that, The method comprises the following steps: a fitting relationship obtaining module is configured to obtain a fitting relationship between a first optimal detection time and a first defect depth of a pore defect in a first target region of a target insulating pull rod under a first thermal excitation; the pore defect in the first target region of the target insulating pull rod comprises: constructing a target insulating pull rod near-surface pore defect library for calibration; the target insulating pull rod near-surface pore defect library comprises a first defect size and a first defect depth; the first defect depth in the target insulating pull rod near-surface pore defect library covers all target defect depth values; the fitting relationship between the first optimal detection time and the first defect depth comprises: applying a first thermal excitation to a first target region of a target insulating pull rod; collecting a temperature change process of a pore defect in the first target region under the first thermal excitation; establishing a first optimal detection time and a first defect depth of each pore defect based on the temperature change over time; an actual size acquisition module, configured to acquire a second optimal detection time and a first actual defect size of the near-surface pore defect of the insulating pull rod under the same first thermal excitation; an actual defect calculation module, configured to acquire an actual defect depth of the near-surface pore defect of the insulating pull rod according to the second optimal detection time and the first actual defect size in combination with the fitting relationship. 7.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-6 when the computer program is executed by the processor. The processor executes the computer program to implement the steps of the method in any one of claims 1 to 5.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method in any one of claims 1 to 5.

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