Method and system for calculating near-surface pore defect parameters of GIS (Gas Insulated Switchgear) epoxy insulating pull rod

Abstract

The invention discloses a method and a system for calculating near-surface pore defect parameters of a GIS (Gas Insulated Switchgear) epoxy insulating pull rod. The method comprises the following steps: acquiring a fitting relationship between first optimal detection time and first defect depth of a pore defect in a first target area in a target insulating pull rod under first thermal excitation; acquiring a second optimal detection time and a first actual defect size of a near-surface pore defect of the to-be-detected insulating pull rod under the same first thermal excitation; and according to the second optimal detection time and the first actual defect size, combining the fitting relationship to obtain the actual defect depth of the near-surface pore defect of the to-be-detected insulating pull rod. According to the calculation method and system, the detection accuracy can be improved, and the possibility of misjudgment and missed judgment is reduced. By establishing the relation model between the pore defect size and the defect depth, the health condition of the insulating pull rod can be evaluated more accurately, so that a scientific basis is provided for maintenance and replacement decisions.

Description

Technical Field

[0001] The invention relates to the technical field of calculating the near-surface pore defect parameters of a GIS epoxy insulation tie rod, and in particular to a method and system for calculating the near-surface pore defect parameters of a GIS epoxy insulation tie rod. Background Art

[0002] The insulating rod is one of the key components in the gas insulated switch (GIS). During the production, transportation, assembly and operation, defects such as air gaps near the surface will inevitably appear, which will seriously affect the reliability of the insulating rod. Existing non-destructive testing technologies, such as ultrasound and X-ray, have shortcomings such as poor anti-interference ability, complex equipment and high testing costs. During production and operation, when an accident occurs, it is difficult to clarify the cause of the accident by traditional methods, and there is also a lack of effective prevention measures. In addition, during the production process, the withstand voltage and partial discharge methods can only make qualitative judgments on the electrical characteristics of the insulating rod, and it is impossible to establish the intrinsic relationship between its internal defects and electrical performance, which may cause defective insulating components to be put into operation, thereby seriously affecting the safety and stability of electrical equipment. The present invention aims to use a new type of infrared wave, which has good penetration ability for non-polar, non-metallic and dry materials, to study an insulating rod defect detection method based on infrared imaging technology, so as to achieve rapid detection of internal defects of GIS insulating rods, thereby improving and optimizing the production and operation conditions of insulating rods.

[0003] The thermal conductivity of epoxy resin is relatively weak, so it takes a long time for the heat applied to its surface by external thermal stimulation to be transferred to the inside of the material. Infrared thermal wave nondestructive testing technology uses a very high acquisition frequency (frame rate / frame rate) when collecting data in order to obtain higher detection accuracy when detecting defects. Therefore, the longer heat conduction process required for samples made of epoxy resin materials will make the infrared thermal imager work for a longer time at the same acquisition frequency in order to fully collect the temperature changes during the cooling process of the epoxy resin surface. At present, infrared thermal wave nondestructive testing technology still relies on technicians to manually lock the time period when defects appear from a huge image library (one test often produces thousands of thermal images) and find those areas where abnormalities appear in the thermal images. The longer acquisition time required for epoxy resin will lead to a significant increase in the number of thermal images collected in a single test, which greatly increases the detection and analysis time of technicians for a single sample, greatly affecting the detection efficiency. Therefore, by estimating the optimal detection time of defects within a range, technicians can focus their main attention on the thermal images collected near the optimal detection time, which can not only greatly improve the detection efficiency, shorten the detection time of a single sample, but also improve the detection accuracy. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

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

[0006] Therefore, the present invention provides a method and system for calculating near-surface pore defect parameters of GIS epoxy insulation rods, which can solve the problems mentioned in the background technology.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a method for calculating near-surface pore defect parameters of a GIS epoxy insulation rod, comprising:

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

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

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

[0012] As a preferred solution of the method for calculating the near-surface pore defect parameters of the GIS epoxy insulating rod of the present invention, the pore defects in the first target area of ​​the target insulating rod include:

[0013] Construct a near-surface porosity defect library of target insulating rod for calibration;

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

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

[0016] As a preferred solution of the method for calculating the near-surface pore defect parameters of the GIS epoxy insulation pull rod described in the present invention, the fitting relationship between the first optimal detection time and the first defect depth includes:

[0017] Applying a first thermal excitation to a first target region in a target insulating pull rod;

[0018] Collecting the temperature variation of the pore defect in the first target area under the first thermal excitation over time;

[0019] A first optimal detection time and a first defect depth of each pore defect based on the temperature variation process over time are established.

[0020] As a preferred solution of the method for calculating the near-surface pore defect parameters of the GIS epoxy insulation pull rod described in the present invention, the fitting relationship between the first optimal detection time and the first defect depth also includes:

[0021] A first fitting model is used to fit the relationship between the first optimal detection time and the first defect depth.

[0022] As a preferred solution of the method for calculating the near-surface pore defect parameters of the GIS epoxy insulating pull rod described in the present invention, the method of obtaining the second optimal detection time and the first actual defect size of the near-surface pore defect of the insulating pull rod to be tested under the same first thermal excitation includes:

[0023] Applying a first thermal excitation to a first target area in the insulating pull rod to be tested, and obtaining a second optimal detection time;

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

[0025] As a preferred solution of the method for calculating the near-surface pore defect parameters of the GIS epoxy insulating rod described in the present invention, wherein: the actual defect depth of the near-surface pore defect of the insulating rod to be tested is obtained according to the second optimal detection time and the first actual defect size, combined with the fitting relationship, including:

[0026] 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 inversely solved using the second optimal detection time, and the second defect depth is the actual defect depth.

[0027] As a preferred solution of the method for calculating the near-surface pore defect parameters of the GIS epoxy insulation pull rod described in the present invention, the near-surface pore defect parameters include: actual defect size and actual defect depth.

[0028] In a second aspect, the present invention provides a calculation system for near-surface pore defect parameters of GIS epoxy insulation rods, characterized in that it includes:

[0029] A fitting relationship acquisition module, used to acquire a fitting relationship between a first optimal detection time and a first defect depth of a porosity defect in a first target area of ​​a target insulating pull rod under a first thermal excitation;

[0030] An actual size acquisition module, used to acquire a second optimal detection time and a first actual defect size of a near-surface pore defect of the insulating pull rod to be tested under the same first thermal excitation;

[0031] The actual defect calculation module is used to obtain the actual defect depth of the near-surface pore defect of the insulating pull rod to be tested 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 invention 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 above-mentioned method when executing the computer program.

[0033] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the method described above when executed by a processor.

[0034] Compared with the prior art, the present invention has the following beneficial effects: the present invention proposes a method and system for calculating the near-surface pore defect parameters of GIS epoxy insulating rods, obtains the first optimal detection time and the fitting relationship of the first defect depth of the pore defect in the first target area of ​​the target insulating 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 rod to be tested under the same first thermal excitation; according to the second optimal detection time and the first actual defect size, combined with the fitting relationship, obtains the actual defect depth of the near-surface pore defect of the insulating rod to be tested. The calculation 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 pore defect size and the defect depth, the health of the insulating 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 invention, the service life of GIS equipment can be effectively extended, maintenance costs can be reduced, and the operation reliability of the power system can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0036] Figure 1A method flow chart of a method and system for calculating near-surface pore defect parameters of GIS epoxy insulation tie rods provided by one embodiment of the present invention;

[0037] Figure 2 A detailed flow chart of a method and system for calculating near-surface pore defect parameters of a GIS epoxy insulation tie rod provided by an embodiment of the present invention;

[0038] Figure 3 A method for calculating near-surface pore defect parameters of a GIS epoxy insulation tie rod and a schematic diagram of an optimal detection time-defect depth fitting image of the system provided by an embodiment of the present invention;

[0039] Figure 4 An internal structure diagram of a computer device of a method and system for calculating near-surface porosity defect parameters of a GIS epoxy insulation tie rod provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0041] Example 1

[0042] Reference Figure 1-Figure 4 , which is the first embodiment of the present invention, provides a method and system for calculating near-surface pore defect parameters of GIS epoxy insulation tie rods, including:

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

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

[0045] Thermal excitation: Generally refers to the process by which an object is deformed or stressed due to changes in temperature caused by the external environment or internal conditions. In power equipment, thermal excitation can come from the Joule heat generated when current passes through the conductor, which causes the material temperature to rise and may cause 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 stress changes in epoxy insulation rods caused by temperature changes, which may affect their electrical and mechanical properties.

[0046] In the existing related technologies, there are some technical difficulties, such as the difficulty in accurately detecting and evaluating the porosity defects in epoxy insulation rods. These defects may affect the insulation performance of the equipment and even cause failures.

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

[0048] Figure 1 A method and a flow chart of a system for calculating near-surface pore defect parameters of GIS epoxy insulation tie rods are shown, including:

[0049] S101, obtaining a fitting relationship between a first optimal detection time and a first defect depth of a porosity defect in a first target area of ​​a target insulating rod under a first thermal excitation;

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

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

[0052] In the embodiment of the present application, the porosity defects in the first target region of the target insulating pull rod include:

[0053] Construct a near-surface porosity defect library of target insulating rod for calibration;

[0054] The target insulating rod near-surface pore defect library includes a first defect size and a first defect depth;

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

[0056] In an optional embodiment, constructing a near-surface pore defect library of a target insulating pull rod for calibration specifically includes constructing a series of near-surface pore defects d1h1, d1h2, ..., d1h n , where the defect size d can be different, but the defect depth h must completely cover all possible defect depth values. To prepare an insulating rod calibration sample containing the above-mentioned near-surface pore defects, different pore defects can be set in multiple insulating rod calibration samples, and the defects contained in each sample are marked;

[0057] In an optional embodiment, the target insulating rod is selected from a near-surface pore defect library of the target insulating rod used for calibration. The near-surface pore defect library may be in a table form, for example, using rows and columns to represent defect diameters or defect depths; the near-surface pore defect library may also be in a matrix form, for example, rows and columns of the matrix each represent defect diameters or defect depths; the near-surface pore defect library may also be in a set form, for example, the parameters contained in the set are a combination of defect diameters or defect depths of each defect, and so on;

[0058] In the present application, the first target area includes the area that needs to be considered for the target insulating rod, which can be the entire target insulating rod area, or the upper surface of the target insulating rod, or the lower surface, or the fixed position area, etc., so that the selection of the first target area can be flexibly adjusted according to the actual application requirements. For example, in some cases, it may be necessary to pay attention to only specific parts of the insulating rod, such as near the connection point or the stress concentration area, to ensure the reliability of these key parts. In other cases, it may be necessary to test the entire insulating rod to obtain comprehensive defect parameter information.

[0059] In the embodiment of the present application, the fitting relationship between the first optimal detection time and the first defect depth includes:

[0060] Applying a first thermal excitation to a first target region in a target insulating pull rod;

[0061] Collecting the temperature variation of the pore defect in the first target area under the first thermal excitation over time;

[0062] A first optimal detection time and a first defect depth of each pore defect based on a temperature variation process over time are established.

[0063] In the embodiment of the present application, the fitting relationship between the first optimal detection time and the first defect depth also includes:

[0064] A first fitting model is used 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 rod calibration sample, and using an infrared imaging device to collect the surface temperature change of each near-surface pore defect of the insulating rod and the surface of the defect-free area after the external thermal excitation, and based on this, the first optimal detection time t of each pore defect is calculated. m , obtain a sequence of the first optimal detection time of pore defects Each of the components Each corresponds to a first defect depth h of the pore x ;

[0066] In an optional embodiment, the relationship fitting process can be based on the first best detection time sequence of the near-surface pore defect of the insulating pull rod. Sequence with the first defect depth The corresponding relationship is based on the second-order polynomial model t max =p1h 2 +p2h+p3 fitting the first optimal detection time t of pore defects near the surface of insulating rod m The numerical relationship between the first defect depth h and t m =f(h), where p1, p2, and p3 are three constants in the fitting model. The first optimal detection time t of the pore defect near the surface of the insulating rod under the same detection environment is obtained. m The one-to-one correspondence with the first defect depth h completes the calibration of the near-surface pore defect of the insulating rod;

[0067] In an optional embodiment, the fitting relationship can also be fitted using other methods or fitting models, for example, using a linear model, an exponential model 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 the detection can be improved. For example, a linear model may be suitable for situations where there is a linear relationship between the defect depth and the detection time, while an exponential model may be more suitable for describing situations where there is an exponential relationship between the defect depth and the 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 fit the experimental data well, especially when there is a nonlinear relationship between the defect depth and the detection time. The second-order polynomial model can more accurately describe this nonlinear relationship by including quadratic terms, thereby improving the accuracy of detection. In addition, the three constant terms of the model can be optimized through 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 near-surface pore defects of insulating rods.

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

[0070] In the embodiment of the present application, obtaining the second optimal detection time and the first actual defect size of the near-surface pore defect of the insulating pull rod to be tested under the same first thermal excitation includes:

[0071] Applying a first thermal excitation to a first target area in the insulating pull rod to be tested, and obtaining a second optimal detection time;

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

[0073] In an optional embodiment, the same first thermal excitation as that in the calibration test of the near-surface pore defect of the target insulating pull rod is used, and a thermal image is collected. The abnormal area where the near-surface pore defect of the insulating pull rod exists is preliminarily determined from the thermal image, and a reference point is selected in this abnormal area to derive the temperature variation data of the abnormal area where the near-surface pore defect of the insulating pull rod exists. and the temperature variation data of the normal area over time

[0074] Furthermore, 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 first optimal detection time and the second optimal detection time have similar underlying meanings in this application, the same variable is used for ease of understanding), calculate the temperature difference vector of the air gap defect surface

[0075]

[0076] in, is the temperature difference vector on 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 sink surface N is the total number of times data is collected in the experiment, T d1 ,T d2 ,...,T dN With T s1 ,T s2 ,…,T sN They are and 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 at which the defect occurs is taken as the only optimal detection time corresponding to the depth of the defect.

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

[0078] In an optional embodiment, the calculated optimal detection time t of the near-surface pore defect of the insulating rod is used. m , during the locking test at time t m From the thermal images collected before and after, select the one with the clearest defect outline and the highest contrast as the best detection thermal image for the pore defect near the surface of the insulating rod. Calculate the number of pixels p occupied by the abnormal area where the pore defect near the surface of the insulating rod exists from the best detection thermal image d , and then according to the ratio of the total pixels in one direction of the picture to the actual size of the picture in the same direction r = p i / L, the actual defect size d of the pore defect near the surface of the insulating rod is converted * =(p d / p i )·L=p d / r;

[0079] It should be noted that step S102 can accurately determine the depth of the near-surface pore defect of the insulating rod, and then evaluate its impact on the overall insulation performance. By comparing the second best detection time with the corresponding time in the first best detection time sequence, the accuracy of the calibration test can be verified, and a more in-depth analysis of the health of the insulating rod can be performed. In addition, 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 rod. In practical applications, this method can effectively assist engineers in fault diagnosis and improve the safety and reliability of GIS equipment.

[0080] S103, obtaining the actual defect depth of the near-surface pore defect of the insulating rod to be tested according to the second optimal detection time and the first actual defect size in combination with the fitting relationship.

[0081] In the embodiment of the present application, according to the second optimal detection time and the first actual defect size, combined with the fitting relationship, obtaining the actual defect depth of the near-surface pore defect of the insulating pull rod to be tested includes:

[0082] Based on the fitting relationship after fitting the first optimal detection time and the first defect depth 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.

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

[0084] Furthermore, the calibration process of the insulating rod calibration sample and the defect parameter calculation process of the insulating rod containing unknown pore defects can form a calculation method for the depth and size of any pore defect near the surface of the insulating rod, that is: before starting the infrared thermal wave detection test of the insulating rod in a new environment, first use the insulating rod calibration sample for calibration to obtain the relationship between the defect depth and the optimal detection time; then officially start the infrared thermal wave detection process of the sample to be tested, and obtain the optimal detection time according to the test, data collection, and data analysis. Calculate defect size d * , calculate the defect depth z * The parameter calculation of the near-surface pore defect in the insulating rod is completed in the following order. Figure 2As shown, calibration sample preparation 401: First, some calibration samples with known parameters need to be prepared. Near-surface pore defects with known parameters 402: In the calibration sample, it is known that there are pore defects near the surface, and the specific parameters of these defects such as location, size, shape and depth are known. These defect samples with known parameters are used in the subsequent calibration process to establish a reliable model or algorithm to identify and quantify unknown defects. Insulating rod calibration sample 403: Insulating rods with known near-surface pore defects are selected as calibration samples. Thermal excitation 404: These calibration samples are thermally excited to stimulate temperature changes at the defects. Temperature change data over time 405: Record and collect data on the temperature change over time of the defect area after thermal excitation. Calculate the best detection time 406: Based on the collected data, determine the best detection time point that can most clearly display the defect characteristics. Fit the numerical relationship between the best detection time and the defect depth 407: Fit the numerical relationship between the best detection time and the defect depth through a mathematical model. Complete the calibration process 408: After completing the above steps, a method that can be used to predict the depth of unknown defects is obtained. Infrared wave test of insulating pull rods with unknown defects to be tested 409: Next, this method is used to test insulating pull rods with unknown defects. Collect defect surface temperature 410: After thermal excitation, measure and record the temperature change of the defect surface. Calculate the best defect detection time 411: According to the collected temperature data, find the time point that can best show the defect characteristics. Lock the best detection thermal image 412: Take a thermal image within this best detection time for subsequent analysis. Calculate the best defect detection time again 413: To ensure accuracy, confirm the best detection time again. Calculate the number of pixels occupied by the defect 414: From the locked thermal image, count how many pixels the defect part occupies. Substitute the calibrated fitting relationship to calculate the defect depth 415: Substitute the obtained pixel number value 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 deduce the actual size of the defect. Forming a set of parameter calculation methods for near-surface porosity defects of insulating tie rods 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 insulating tie rods.

[0085] In summary, the present invention proposes a method and system for calculating the near-surface pore defect parameters of GIS epoxy insulating rods, obtaining the first optimal detection time and the fitting relationship of the first defect depth of the pore defect in the first target area of ​​the target insulating rod under the first thermal excitation; obtaining the second optimal detection time and the first actual defect size of the near-surface pore defect of the insulating rod to be tested under the same first thermal excitation; according to the second optimal detection time and the first actual defect size, combined with the fitting relationship, obtaining the actual defect depth of the near-surface pore defect of the insulating rod to be tested. The calculation 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 pore defect size and the defect depth, the health status of the insulating 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 invention, the service life of GIS equipment can be effectively extended, maintenance costs can be reduced, and the operational reliability of the power system can be improved.

[0086] This embodiment also provides a calculation system for near-surface pore defect parameters of GIS epoxy insulation tie rods, including:

[0087] A fitting relationship acquisition module, used to acquire a fitting relationship between a first optimal detection time and a first defect depth of a porosity defect in a first target area of ​​a target insulating pull rod under a first thermal excitation;

[0088] An actual size acquisition module, used to acquire a second optimal detection time and a first actual defect size of a near-surface pore defect of the insulating pull rod to be tested under the same first thermal excitation;

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

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

[0091] This embodiment also provides a computer device, which may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 4As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, 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 operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for calculating the near-surface pore defect parameters of a GIS epoxy insulation rod is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0092] This embodiment further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

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

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

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

[0096] Example 2

[0097] Reference Figure 2-Figure 3 , which is an embodiment of the present invention, provides a method and system for calculating the near-surface pore defect parameters of a GIS epoxy insulation rod. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0098] First, a calibration test for the detection of near-surface pore defects of insulating rods was carried out. A series of near-surface pore defects were set on the near-surface of GIS epoxy insulating rods as insulating rod calibration samples for the near-surface pore defect calibration test. The sizes of the five defects set were randomly distributed, but the defect depths were distributed in a regular gradient, and each depth would correspond to a unique optimal detection time.

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

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

[0101] Thermal excitation is performed on the calibration sample of the insulating pull rod containing the above defects. The thermal excitation is a pulse type excitation, and its maximum heat flux density is Q=10 6 W / m 2 The pulse duration is t = 0.1s, and an infrared thermal imager is used to collect temperature change data on the surface of the insulating pull rod. The acquisition frequency is f = 10Hz. The acquired area includes the temperature changes in the defective area and the temperature changes in the non-defective area.

[0102] The optimal detection time for each defect surface is calculated according to the calculation method in the invention content. The following optimal detection time table can be obtained by calculation:

[0103] Table 2: Optimal testing schedule

[0104] defect <![CDATA[d1h1]]> <![CDATA[d1h2]]> <![CDATA[d1h3]]> <![CDATA[d1h4]]> <![CDATA[d1h5]]> <![CDATA[t max ]]> 0.6s 0.8s 1s 1.2s 1.5s

[0105] So the optimal detection time vector for:

[0106]

[0107] For the optimal detection time vector Depth of defect Perform fitting, and the fitting model adopts the above-mentioned second-order polynomial fitting model, and the fitting result can be obtained:

[0108] Table 3: Fitting results

[0109]

[0110]

[0111] exist Figure 3 Draw the best detection time t in the window m The scatter plot of the true value of the defect depth h and the fitting curve show that the fitting curve is a quadratic function with an opening upward, and it reflects the optimal detection time t well. m Numerical relationship with defect depth h:

[0112] For the optimal detection time t m Fitting function t with defect depth z m =0.2200h 2 +0.2539h–0.1187. So far, the calibration process of the insulating pull rod calibration samples under the same test environment has been completed.

[0113] Next, we will test an insulating rod with an unknown near-surface pore defect d1h6. Apply the same thermal excitation as in the calibration test to the surface of the insulator and collect temperature variation data over time. Based on the above data, we can calculate the optimal detection time t for the near-surface pore defect of the insulating rod. m . Calculation yields: ΔT m =4.8574℃, Next, the thermal images collected during the period of 0.4s-1.0s were locked, and the thermal image collected at 0.6s was selected as the best detection thermal image, from which the number of pixels p occupied by the abnormal area where the pore defect existed near the surface of the insulating rod was calculated. d =11, and then according to the ratio of the total pixels in one direction of the picture to the actual size of the picture in the same direction r=p i / L, the actual defect size d of the pore defect near the surface of the insulating rod is converted * =5.5mm.

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

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

[0116] The solution is: h * =1.4366mm. So far, all the parameters of the unknown near-surface pore defect have been solved.

[0117] Example 3

[0118] According to the analysis of the previous embodiment, the optimal detection time t m The fitting function with the defect depth h is:

[0119] t m =0.2200h 2 +0.2539h–0.1187

[0120] Under the same test environment, when a new unknown near-surface pore defect needs to be detected, there is no need to recalibrate, and the fitting result of the previous embodiment can be directly used.

[0121] An unknown near-surface porosity defect d1h7 is detected. The optimal detection time is calculated from the test data. Next, the thermal images collected during the period of 1.0s-1.6s were locked, and the thermal image collected at 1.5s was selected as the best detection thermal image, from which the number of pixels p occupied by the abnormal area where the pore defect existed near the surface of the insulating rod was calculated. d =9, and then according to the ratio of the total pixels in one direction of the picture to the actual size of the picture in the same direction r=p i / L, the actual defect size d of the pore defect near the surface of the insulating rod is converted * =4.5mm.

[0122] Since the optimal detection time for the defect has been calculated So will Substitute the optimal detection time t m Fitting function t with defect depth h m =0.2200h 2 +0.2539h–0.1187, we can get:

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

[0124] The solution is: h * =2.0252mm. So far, all the parameters of the unknown near-surface pore defect have been solved.

[0125] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention 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 invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0126] Those skilled in the art will appreciate that the embodiments of the present application can provide methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0127] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0128] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0129] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0130] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0131] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for calculating the near-surface pore defect parameters of GIS epoxy insulation rods, characterized in that: include: Obtaining a fitting relationship between a first optimal detection time of a porosity defect in a first target area of ​​a target insulating pull rod under a first thermal excitation and a first defect depth; Obtaining a second optimal detection time and a first actual defect size of a near-surface porosity defect of the insulating pull rod to be tested under the same first thermal excitation; According to the second optimal detection time and the first actual defect size, combined with the fitting relationship, the actual defect depth of the near-surface pore defect of the insulating pull rod to be tested is obtained.

2. The method for calculating the near-surface pore defect parameters of GIS epoxy insulation pull rod according to claim 1 is characterized in that: The porosity defects in the first target region of the target insulating pull rod include: Construct a near-surface porosity defect library of target insulating rod for calibration; The target insulating pull rod near-surface pore defect library includes a first defect size and a first defect depth; The first defect depth in the target insulating rod near-surface pore defect library covers all target defect depth values.

3. The method for calculating the near-surface pore defect parameters of GIS epoxy insulation rod according to claim 2 is characterized in that: The fitting relationship between the first optimal detection time and the first defect depth includes: Applying a first thermal excitation to a first target region in a target insulating pull rod; Collecting the temperature variation of the pore defect in the first target area under the first thermal excitation over time; A first optimal detection time and a first defect depth of each pore defect based on the temperature variation process over time are established.

4. The method for calculating the near-surface pore defect parameters of GIS epoxy insulation rod according to claim 3 is characterized in that: The fitting relationship between the first optimal detection time and the first defect depth also includes: A first fitting model is used to fit the relationship between the first optimal detection time and the first defect depth.

5. The method for calculating the near-surface pore defect parameters of GIS epoxy insulation rod according to claim 4 is characterized in that: The method of obtaining the second optimal detection time and the first actual defect size of the near-surface pore defect of the insulating pull rod to be tested under the same first thermal excitation comprises: Applying a first thermal excitation to a first target area in the insulating pull rod to be tested, and obtaining a second optimal detection time; According to the second optimal detection time and in combination with the thermal image, the actual defect size of the pore defect near the surface of the insulating pull rod to be tested is obtained.

6. The method for calculating the near-surface pore defect parameters of GIS epoxy insulation rod according to claim 5, characterized in that: The step of obtaining the actual defect depth of the near-surface pore defect of the insulating rod to be tested 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 after fitting the first optimal detection time and the first defect depth 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.

7. The method for calculating the near-surface pore defect parameters of GIS epoxy insulation rod according to claim 6 is characterized in that: The near-surface pore defect parameters include: actual defect size and actual defect depth.

8. A calculation system for the near-surface pore defect parameters of GIS epoxy insulation rods, characterized in that: include: A fitting relationship acquisition module, used to acquire a fitting relationship between a first optimal detection time and a first defect depth of a porosity defect in a first target area of ​​a target insulating pull rod under a first thermal excitation; An actual size acquisition module, used to acquire a second optimal detection time and a first actual defect size of a near-surface pore defect of the insulating pull rod to be tested under the same first thermal excitation; The actual defect calculation module is used to obtain the actual defect depth of the near-surface pore defect of the insulating pull rod to be tested according to the second optimal detection time and the first actual defect size in combination with the fitting relationship.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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

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