Construction method and test method of dry-type reactor aging resistance test model

Abstract

The present application relates to a method, device, computer equipment, and storage medium for constructing and testing an aging resistance model for a dry-type reactor. The method comprises: constructing a digital twin model of the dry-type reactor by acquiring aging state parameters of the dry-type reactor, constructing an aging resistance test model for the dry-type reactor using a Gaussian cloud model, a hierarchical analysis method, and an entropy weight method, which can support accurate aging resistance testing of the dry-type reactor, obtaining actual aging state parameters of the dry-type reactor to be tested, inputting the actual aging state parameters into the aging resistance test model for the dry-type reactor, constructing a comprehensive evaluation matrix, a comprehensive evaluation vector, and a comprehensive evaluation score, obtaining an aging resistance test result corresponding to the dry-type reactor to be tested, establishing a relationship between the actual aging state parameters of the dry-type reactor to be tested and its aging resistance, and accurately reflecting the aging resistance of the dry-type reactor.

Description

Technical Field

[0001] The present application relates to the technical field of online monitoring and fault diagnosis, and in particular to a method, device, computer equipment and storage medium for constructing a test model for the aging resistance of a dry-type reactor, as well as a method, device, computer equipment and storage medium for testing the aging resistance of a dry-type reactor. Background Art

[0002] With the expansion of power grid construction and the promotion of the digital economy, power grid digitization and intelligence have become the development trend of the power industry. Digital twins can realize the simulation of multiple physical quantities, multiple scales and multiple probabilities, and establish a mirror image of digital virtual and physical entities, realizing multi-level real-time dynamic perception and ultra-real-time virtual deduction, thereby improving the state perception level of dry-type reactors.

[0003] In traditional technology, the aging resistance test of dry-type reactors is mainly carried out by maintenance personnel through regular maintenance and overhaul and routine power outage tests according to national standards. Since the dry-type reactor encapsulation is solidified and cannot be disassembled, when maintenance personnel test the aging resistance of dry-type reactors, they largely need to rely on subjective experience to make judgments. Therefore, this test method relying on subjective experience has the problem of inaccurate test results for the aging resistance of dry-type reactors. Summary of the Invention

[0004] Based on this, it is necessary to address the problem of inaccurate test results of the aging resistance of dry-type reactors mentioned above, and provide a method, device, computer equipment and storage medium for constructing a test model for the aging resistance of dry-type reactors that supports accurate testing, as well as a method, device, computer equipment and storage medium for testing the aging resistance of dry-type reactors that can accurately test the aging resistance of dry-type reactors.

[0005] A method for constructing a dry-type reactor aging resistance test model, the method comprising:

[0006] Obtain the aging status parameters of the dry-type reactor, build a digital twin model of the dry-type reactor, and establish the aging status indicators and aging status levels of the digital twin model of the dry-type reactor;

[0007] According to the aging state indicator and the aging state level, a Gaussian cloud model of the aging state indicator under the aging state level is obtained;

[0008] The subjective and objective comprehensive weights of aging status indicators were obtained through the analytic hierarchy process and entropy weight method.

[0009] Based on the digital twin model of dry-type reactor, aging status indicators, aging status level, Gaussian cloud model and subjective and objective comprehensive weights, a aging resistance test model for dry-type reactor is constructed.

[0010] In one embodiment, the above-mentioned method for constructing a dry-type reactor aging resistance test model further includes:

[0011] Obtain aging status parameters of dry-type reactors, including online monitoring data and experimental simulation data;

[0012] Based on online monitoring data and experimental simulation data, the dry-type reactor is refined in mathematical modeling and a digital twin model of the dry-type reactor is constructed.

[0013] In one embodiment, the online monitoring data includes partial discharge signal, dielectric loss factor and hydrophobicity, and the experimental simulation data includes activation energy, contact angle and surface morphology;

[0014] The aging status indicators and aging status levels for establishing the digital twin model of dry-type reactors include:

[0015] Based on online monitoring data and experimental simulation data, the aging status indicators of the dry-type reactor digital twin model are established;

[0016] According to the different impacts of aging status indicators on the aging status of dry-type reactors, the aging status level of the dry-type reactor digital twin model is established.

[0017] In one embodiment, the above-mentioned method for constructing a dry-type reactor aging resistance test model further includes:

[0018] The subjective weights of aging status indicators were obtained through the analytic hierarchy process;

[0019] The objective weights of aging status indicators are obtained through the entropy weight method;

[0020] According to the subjective weight and objective weight, the subjective and objective comprehensive weight of the aging status index is obtained.

[0021] A device for constructing a dry-type reactor aging resistance test model, the device comprising:

[0022] The status indicator and level acquisition module is used to obtain the aging status parameters of the dry-type reactor, build a digital twin model of the dry-type reactor, and establish the aging status indicators and aging status levels of the digital twin model of the dry-type reactor;

[0023] A cloud model acquisition module is used to obtain a Gaussian cloud model of the aging state indicator under the aging state level according to the aging state indicator and the aging state level;

[0024] Comprehensive weight acquisition module, used to obtain the subjective and objective comprehensive weights of aging status indicators through hierarchical analysis method and entropy weight method;

[0025] The test model acquisition module is used to construct an aging resistance test model for dry-type reactors based on the dry-type reactor digital twin model, aging status indicators, aging status levels, Gaussian cloud model, and subjective and objective comprehensive weights.

[0026] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0027] The aging status parameters of the dry-type reactor are obtained, a digital twin model of the dry-type reactor is constructed, and the aging status indicators and aging status levels of the digital twin model of the dry-type reactor are established. According to the aging status indicators and aging status levels, the Gaussian cloud model of the aging status indicators under the aging status levels is obtained. The subjective and objective comprehensive weights of the aging status indicators are obtained through the hierarchical analysis method and the entropy weight method. According to the digital twin model of the dry-type reactor, the aging status indicators, the aging status level, the Gaussian cloud model and the subjective and objective comprehensive weights, a aging resistance test model of the dry-type reactor is constructed.

[0028] The above-mentioned method, device, computer equipment and storage medium for constructing a dry-type reactor aging resistance test model obtains the aging state parameters of the dry-type reactor to construct a dry-type reactor digital twin model, establishes the aging state index and aging state level of the dry-type reactor digital twin model, obtains the Gaussian cloud model of the aging state index under the aging state level based on the aging state index and the aging state level, obtains the subjective and objective comprehensive weight of the aging state index through the hierarchical analysis method and the entropy weight method, and constructs the dry-type reactor aging resistance test model based on the dry-type reactor digital twin model, aging state index, aging state level, Gaussian cloud model and subjective and objective comprehensive weight. The above-mentioned scheme obtains the aging state parameters of the dry-type reactor to construct a dry-type reactor digital twin model, and uses the Gaussian cloud model, the hierarchical analysis method and the entropy weight method to obtain the dry-type reactor aging resistance test model, which can support accurate dry-type reactor aging resistance testing.

[0029] A method for testing the aging resistance of a dry-type reactor, the method comprising:

[0030] Obtain the actual aging status parameters of the dry-type reactor to be tested;

[0031] The actual aging state parameters are input into the dry-type reactor aging resistance test model to obtain the aging resistance test result corresponding to the dry-type reactor to be tested, wherein the dry-type reactor aging resistance test model is obtained using the above-mentioned dry-type reactor aging resistance test model construction method.

[0032] In one embodiment, the above-mentioned dry-type reactor aging resistance testing method further includes:

[0033] Input the actual aging state parameters into the dry-type reactor aging resistance test model to obtain the Gaussian cloud model corresponding to the dry-type reactor to be tested;

[0034] Based on the aging status indicators and the Gaussian cloud model corresponding to the dry-type reactor to be tested, a comprehensive evaluation matrix of the aging status indicators is constructed;

[0035] According to the comprehensive evaluation matrix and the subjective and objective comprehensive weights, a comprehensive evaluation vector is obtained;

[0036] According to the comprehensive evaluation vector, the weighted average method is used to obtain the comprehensive evaluation score of the aging resistance of the dry-type reactor to be tested;

[0037] According to the comprehensive evaluation score, the corresponding aging resistance test result of the dry-type reactor to be tested is obtained.

[0038] In one embodiment, the above-mentioned dry-type reactor aging resistance testing method further includes:

[0039] Obtaining a correlation between the aging state indicator and the Gaussian cloud model according to the aging state indicator and the Gaussian cloud model corresponding to the dry-type reactor to be tested;

[0040] According to the numerical value of the correlation, a comprehensive evaluation matrix of aging status indicators is constructed.

[0041] A device for testing the aging resistance of a dry-type reactor, comprising:

[0042] Parameter acquisition module, used to obtain the actual aging status parameters of the dry-type reactor to be tested;

[0043] The test result acquisition module is used to input the actual aging state parameters into the dry-type reactor aging resistance test model to obtain the aging resistance test result corresponding to the dry-type reactor to be tested, wherein the dry-type reactor aging resistance test model is obtained using the dry-type reactor aging resistance test model construction method.

[0044] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it further implements the following steps:

[0045] The actual aging state parameters are input into the aging resistance test model of the dry-type reactor to obtain the Gaussian cloud model corresponding to the dry-type reactor to be tested. According to the aging state index and the Gaussian cloud model corresponding to the dry-type reactor to be tested, the correlation between the aging state index and the Gaussian cloud model is obtained. According to the value of the correlation, a comprehensive evaluation matrix of the aging state index is constructed. According to the comprehensive evaluation matrix and the subjective and objective comprehensive weights, a comprehensive evaluation vector is obtained. According to the comprehensive evaluation vector, the weighted average method is used to obtain the comprehensive evaluation score of the aging resistance corresponding to the dry-type reactor to be tested. According to the comprehensive evaluation score, the aging resistance test result of the dry-type reactor to be tested is obtained.

[0046] The above-mentioned dry-type inductor aging resistance test method, device, computer equipment and storage medium obtain the actual aging state parameters of the dry-type inductor to be tested, input the actual aging state parameters into the dry-type inductor aging resistance test model, and obtain the aging resistance test results corresponding to the dry-type inductor to be tested by constructing a comprehensive evaluation matrix, a comprehensive evaluation vector and a comprehensive evaluation score. The relationship between the actual aging state parameters of the dry-type inductor to be tested and the aging resistance is established, which can accurately reflect the aging resistance of the dry-type inductor. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 2. A diagram showing an application environment of a method for testing the aging resistance of a dry-type reactor in one embodiment;

[0048] Figure 2 Schematic diagram of a flow chart of a method for constructing a dry-type reactor aging resistance test model in one embodiment;

[0049] Figure 3 A schematic flow chart of a method for constructing a dry-type reactor aging resistance test model in another embodiment;

[0050] Figure 4 This is a Y-direction screenshot of the epoxy resin surface of the dry-type reactor insulation material;

[0051] Figure 5 is the grayscale histogram of the epoxy resin image of the dry-type reactor insulation material;

[0052] Figure 6 This is a three-dimensional model diagram of the epoxy resin surface of the dry-type reactor insulation material;

[0053] Figure 7 This is the surface diagram of epoxy resin, the insulation material of dry-type reactor;

[0054] Figure 8 This is the scale meaning diagram of the dry-type reactor aging status indicator judgment matrix;

[0055] Figure 9A structural block diagram of a device for constructing a dry-type reactor aging resistance test model in one embodiment;

[0056] Figure 10 Schematic diagram of a flow chart of a method for testing the aging resistance of a dry-type reactor in one embodiment;

[0057] Figure 11 A schematic flow chart of a method for testing the aging resistance of a dry-type reactor in another embodiment;

[0058] Figure 12 1 is a structural block diagram of a device for testing the aging resistance of a dry-type reactor in one embodiment;

[0059] Figure 13 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0061] The dry-type reactor aging resistance test method provided in this application can be applied to Figure 1 In the application environment shown, terminal 102 communicates with dry-type reactor 104 via a network. Terminal 102 obtains aging state parameters of dry-type reactor 104, constructs a digital twin model of the dry-type reactor, establishes an aging state indicator and aging state level for the digital twin model of the dry-type reactor, obtains a Gaussian cloud model of the aging state indicator at each aging state level based on the aging state indicator and aging state level, obtains the subjective and objective comprehensive weights of the aging state indicator using the analytic hierarchy process and entropy weight method, constructs an aging resistance test model for the dry-type reactor based on the digital twin model of the dry-type reactor, the aging state indicator, the aging state level, the Gaussian cloud model, and the subjective and objective comprehensive weights, obtains the actual aging state parameters of the dry-type reactor 104 to be tested, inputs the actual aging state parameters into the aging resistance test model, and obtains an aging resistance test result corresponding to the dry-type reactor 104 to be tested. Terminal 102 may be, but is not limited to, various personal computers, laptops, smartphones, tablet computers, and portable wearable devices.

[0062] In one embodiment, Figure 2 As shown in the figure, a method for constructing a dry-type reactor aging resistance test model is provided. Figure 1 The following steps are used as an example to illustrate the terminal in the figure:

[0063] Step 201: Acquire aging status parameters of the dry-type reactor, construct a digital twin model of the dry-type reactor, and establish an aging status indicator and aging status level of the digital twin model of the dry-type reactor.

[0064] Reactors are electrical devices used to block current changes in AC circuits. Dry-type reactors are reactors whose windings and core (if any) are not immersed in a liquid insulating medium. Dry-type reactors operate in natural environments for long periods of time, and their insulation material ages gradually under single and multiple stress conditions. Insulation aging primarily manifests as electrical, thermal, mechanical, and environmental aging. Testing the aging state of dry-type reactors is essentially testing their insulation lifespan. Digital twins leverage physical models, sensor updates, and operational history data to integrate multidisciplinary, multi-physics, multi-scale, and multi-probability simulation processes. These processes are mapped in a virtual space, reflecting the entire lifecycle of the corresponding physical equipment. A digital twin is a concept that transcends reality and can be viewed as a digital mapping system for one or more critical, interdependent equipment systems. Based on the aging parameters of dry-type reactors, aging indicators and aging levels are selected for the dry-type reactor digital twin model. These indicators and aging levels are used to digitally reflect the aging state of dry-type reactors, helping to ensure accurate aging test results.

[0065] Specifically, the aging status parameters of the dry-type reactor are obtained, a digital twin model of the dry-type reactor is constructed, and the aging status indicators and aging status levels of the digital twin model of the dry-type reactor are established.

[0066] Step 202: Obtain a Gaussian cloud model of the aging state indicator under the aging state level according to the aging state indicator and the aging state level.

[0067] Among them, the cloud model realizes the uncertain conversion between qualitative concepts and their quantitative representations. The Gaussian cloud model uses the universality of Gaussian distribution and Gaussian membership function to express the digital characteristics of a qualitative concept with three independent parameters, reflecting the randomness and fuzziness of the concept. In probability distribution, Gaussian distribution is one of the important distributions in probability theory, usually represented by two digital characteristics: mean and variance. Gaussian membership function is the most commonly used membership function in fuzzy theory, usually expressed by To express it, the Gaussian cloud model is a brand new model developed on the basis of the two. It uses the three digital characteristics of the cloud model, expectation Ex, entropy En and super entropy He, replaces the original fixed variance with a random distribution, and uses this random distribution in combination with the membership function to generate random certainty, which is a way to implement the cloud.

[0068] Specifically, according to the aging state indicator and the aging state level, a Gaussian cloud model of the aging state indicator under the aging state level is obtained.

[0069] Step 203: Obtain the subjective and objective comprehensive weights of the aging status indicators through the analytic hierarchy process and the entropy weight method.

[0070] The Analytic Hierarchy Process (AHP) is an effective way to transform semi-qualitative and semi-quantitative problems into quantitative ones. It organizes various factors into hierarchies, providing a comparable quantitative basis for analyzing and predicting the development of things. The entropy weighting method is an objective weighting method that leverages the characteristic of entropy in expressing information. Specifically, the greater the difference between the evaluation objects, the more information it contains and the lower its entropy. In the AHP, although pairwise comparison data can be converted using objective absolute data, they are generally determined subjectively by domain experts. Therefore, the AHP is generally a subjective weighting method. The entropy weighting method, on the other hand, is an objective weighting method that uses existing objective data to determine the weights of each evaluation indicator (the lower the entropy of the evaluation indicator, the greater the weight). If these two methods are to be combined to solve a practical problem, the problem should have the following characteristics: some or all evaluation indicators are suitable for abstraction using a hierarchical model; and some evaluation indicators meet the calculation requirements for objective weighting (objective data from multiple samples). The two methods can be combined in two ways: construct a hierarchical model, use the entropy weight method to obtain the weights of some evaluation indicators with objective data, and convert them into pairwise comparison data in the AHP judgment matrix, and finally calculate the AHP total ranking weight; or more directly, use the entropy weight method to obtain the weights of some evaluation indicators with appropriate objective data; construct a hierarchical model for other evaluation indicators, use the hierarchical analysis method to obtain their weights, and finally merge the weights of all evaluation indicators into one.

[0071] Specifically, the subjective and objective comprehensive weights of aging status indicators were obtained through the analytic hierarchy process and entropy weight method.

[0072] Step 204 : constructing an aging resistance test model for the dry-type reactor based on the dry-type reactor digital twin model, aging status index, aging status level, Gaussian cloud model, and subjective and objective comprehensive weights.

[0073] Specifically, based on the dry-type reactor digital twin model, aging status indicators, aging status level, Gaussian cloud model and subjective and objective comprehensive weights, a dry-type reactor aging resistance test model is constructed to support accurate dry-type reactor aging resistance test.

[0074] In the above-mentioned method for constructing a dry-type reactor aging resistance test model, the aging state parameters of the dry-type reactor are obtained to construct a dry-type reactor digital twin model, and the aging state index and aging state level of the dry-type reactor digital twin model are established. According to the aging state index and aging state level, a Gaussian cloud model of the aging state index under the aging state level is obtained. The subjective and objective comprehensive weights of the aging state index are obtained through the analytic hierarchy process and the entropy weight method. According to the dry-type reactor digital twin model, the aging state index, the aging state level, the Gaussian cloud model and the subjective and objective comprehensive weights, the aging resistance test model of the dry-type reactor is constructed. The above-mentioned scheme, by obtaining the aging state parameters of the dry-type reactor and constructing a dry-type reactor digital twin model, and using the Gaussian cloud model, the analytic hierarchy process and the entropy weight method to obtain the aging resistance test model of the dry-type reactor, can support accurate aging resistance testing of dry-type reactors.

[0075] In one embodiment, Figure 3 As shown, a method for constructing a dry-type reactor aging resistance test model is provided, the method comprising:

[0076] Step 301: Obtain aging status parameters of the dry-type reactor, perform refined mathematical modeling on the dry-type reactor based on online monitoring data and experimental simulation data, and construct a digital twin model of the dry-type reactor.

[0077] In this embodiment, the aging state parameters of the dry-type reactor are stored in an aging parameter sample database, and the aging state parameters of the dry-type reactor are obtained from the sample database. The aging state parameters include online monitoring data and experimental simulation data. Based on the online monitoring data and experimental simulation data, the dry-type reactor is refined mathematically modeled using modeling software to construct a digital twin model of the dry-type reactor. Most of the insulation monitoring methods for dry-type reactors use pulse voltage methods and power outage methods to test the reactance and resistance values. However, dry-type reactors in high-load power grids do not have the conditions for power-off detection, and power outage maintenance often results in a waste of manpower and financial resources. Therefore, establishing a digital twin model of a dry-type reactor to test the insulation aging state of the dry-type reactor helps maintenance personnel accurately test the insulation aging state of the dry-type reactor, effectively ensure electricity safety, and avoid sudden power outages.

[0078] The solution of the above embodiment obtains the aging status parameters of the dry-type inductor, performs refined mathematical modeling of the dry-type inductor based on online monitoring data and experimental simulation data, and constructs a digital twin model of the dry-type inductor, which can provide the prerequisite for constructing a dry-type inductor aging resistance test model that can support accurate dry-type inductor aging resistance test.

[0079] Step 302: Establish an aging status indicator of the dry-type reactor digital twin model based on the online monitoring data and experimental simulation data, and establish an aging status level of the dry-type reactor digital twin model based on the different effects of the aging status indicator on the aging status of the dry-type reactor.

[0080] In this embodiment, the online monitoring data includes partial discharge signals, dielectric loss factor, and hydrophobicity. The experimental simulation data includes activation energy, contact angle, and surface morphology. The aging status indicators and aging status levels for establishing the dry-type reactor digital twin model include:

[0081] Based on online monitoring data and experimental simulation data, the partial discharge signal, dielectric loss factor, hydrophobicity, activation energy, contact angle and surface morphology are used as aging status indicators of the digital twin model of the dry-type reactor; according to the different effects of the aging status indicators on the aging status of the dry-type reactor, the aging status level of the digital twin model of the dry-type reactor is established, specifically including: using the partial discharge signal to measure the aging status level of the dry-type reactor, using the dielectric loss factor to measure the aging status level of the dry-type reactor, using the hydrophobicity to measure the aging status level of the dry-type reactor, using the activation energy to measure the aging status level of the dry-type reactor, using the contact angle to measure the aging status level of the dry-type reactor, and using the surface morphology to measure the aging status level of the dry-type reactor.

[0082] Among them, using partial discharge signals to measure the aging status level of dry-type reactors includes: detecting the partial discharge signal of the dry-type reactor through a TEV sensor adsorbed on the grounding wire, focusing on scanning the ends of each layer of the dry-type reactor's envelope, the lower part of the top of each layer of the envelope, and the middle of the innermost layer of the envelope, and taking the average value of the discharge signal data measured 10 times as the final test result.

[0083] When the partial discharge signal (dB) of the dry-type reactor is within the range of (0, 30), the aging status is normal; when the partial discharge signal (dB) of the dry-type reactor equipment is within the range of (30, 100), the aging status is cautionary; when the partial discharge signal (dB) of the dry-type reactor equipment is greater than 100, the aging status is severe.

[0084] Among them, the use of dielectric loss factor to determine the aging status of dry-type reactors includes: dielectric loss factor tanδ test according to DL47.3-92 on-site insulation test implementation guide, the test uses a high-voltage dielectric loss tester to measure the dielectric loss factor, because the shell of the dry-type reactor is directly grounded, the AC bridge reverse connection method is used for on-site measurement, in order to avoid the error caused by the winding induction excitation loss to the measurement, the test needs to be short-circuited in each phase of the measuring winding, and the non-measuring winding phases are short-circuited and grounded. When the measuring temperature does not match the temperature of the product factory test, it should first be measured according to the formula A=1.3 K / 10 The values ​​converted to the same temperature are compared. Where K represents the temperature difference and A represents the conversion coefficient. When the measured temperature is above 20℃, tanδ 20 =tanδ t / A; When the measured temperature is below 20℃, tanδ 20 =Atanδ t , where tanδ 20 Indicates the dielectric loss value when corrected to 20℃, tanδ t Indicates the dielectric loss value at the measurement temperature t.

[0085] When the tanδ of the tested winding is not greater than 1.5% at 20℃, the aging state is normal; when the tanδ of the tested winding is not greater than 3% at 20℃, the aging state is cautionary; when the tanδ of the tested winding is not greater than 5% at 20℃, the aging state is serious.

[0086] Among them, the use of hydrophobicity to measure the aging status of dry-type reactors includes: using an electric charged water spray device to spray water on the key test areas on the outside of the equipment, observing the shape of the water droplets and comparing them with the grades in the regulations. A total of 10 water spray data are read, and the amount of water sprayed each time is (0.7~1) ml; the spray water flow divergence angle is 50°~70°, and the kettle is filled with deionized water. The test area of ​​the test piece should be between 50 and 100 cm2. The nozzle of the water spray device is 25 cm away from the test piece, and water is sprayed once per second for a total of 25 times. After the water is sprayed, water should flow down the surface. The spray direction should be as perpendicular to the test piece surface as possible. The HC value of the hydrophobicity classification is read within 30 seconds after the water spray ends.

[0087] When the water spray level of the dry-type reactor equipment is HC3 or above, the insulation material is hydrophobic and the aging state is normal; when the water spray level of the dry-type reactor equipment is within the range of (HC4, HC5), the insulation material belongs to the intermediate transition level. At this time, water droplets and water films exist at the same time, and the aging state is caution; when the water spray level of the dry-type reactor equipment is within the range of (HC6, HC7), the insulation material is hydrophilic and the aging state is serious.

[0088] Determining the aging status of dry-type reactors using activation energy involves conducting dielectric spectrum tests at two different temperatures and calculating the activation energy of the tested insulation material after the test. The low-frequency inflection point of the frequency-domain dielectric spectrum obtained at the two temperatures is used to determine the temperature shift of the dielectric spectrum. The shift is then calculated using the following formula:

[0089]

[0090] Where T is the thermodynamic temperature; f0 is the frequency corresponding to the frequency domain dielectric spectrum of dielectric loss at temperature T0 before translation, f is the frequency corresponding to the dielectric spectrum at temperature T after translation, E0 is the activation energy of the epoxy resin sample, and k is the Boltzmann constant, k = 1.38×10-23J / K.

[0091] When the activation energy (eV) of the dry-type reactor equipment is greater than 0.761, the aging status is normal; when the activation energy (eV) of the dry-type reactor equipment is within the range of (0.761, 0.510), the aging status is cautionary; when the activation energy (eV) of the dry-type reactor equipment is lower than 0.510, the aging status is severe.

[0092] Among them, using the contact angle to determine the aging status level of the dry-type inductor includes: measuring the contact angle of the sample using the "five-point fitting method" through a contact angle meter, performing contact angle tests on multiple samples, measuring each sample five times, and taking the average value after the five measurements as the final contact angle measurement result.

[0093] When the static contact angle of the sample belongs to the interval of (69.72°, 67.93°), the aging state is normal; when the static contact angle of the sample belongs to the interval of (67.93°, 65.37°), the aging state is cautionary; when the static contact angle of the sample belongs to the interval of (65.37°, 63.92°), the aging state is severe.

[0094] Among them, the use of surface morphology to measure the aging status of dry-type reactors includes: the SEM image of the epoxy resin surface at the dry-type reactor encapsulation layer is a grayscale image, and its grayscale value depends on the distance between the particles and the light source. The higher the grayscale value, the closer to the light source, and vice versa. To establish a three-dimensional model of its surface roughness, the x and y axes are established along the edge of the image, and the z axis is established with the grayscale value. To ensure that the model is relatively smooth, interpolation is added and modeling is performed using MATLAB. The results are shown as follows: Figure 4 As shown. Sample discrete points on the x and y axes, the number is M and N respectively, and let the average gray value of the absolute value of the offset be μ,

[0095]

[0096] The roughness of epoxy resin can be calculated by the three-dimensional arithmetic mean deviation S a Characterized by

[0097]

[0098] Figure 6 for Figure 5 The gray value in the Y direction is relatively uniform. Figure 6 For further analysis, MATLAB is used to convert the image into a matrix, where the values ​​represent the grayscale values. Assuming that the pixels of the image are m*n, that is, the matrix is ​​an m*n matrix, the grayscale median is calculated along the X direction, and an XZ relationship curve is established to show the surface roughness in a more intuitive way, as shown in the following example: Figure 7 The horizontal axis represents the pixel in the X-axis, and the vertical axis represents the median grayscale value at that pixel. By comparing horizontally, the depth of epoxy damage at different aging levels can be expressed. The horizontal line represents the average interface height. When the three-dimensional arithmetic mean deviation of the specimen falls within the interval (0.06, 0.09), the aging state is normal; when the three-dimensional arithmetic mean deviation of the specimen falls within the interval (0.09, 0.15), the aging state is cautionary; and when the three-dimensional arithmetic mean deviation of the specimen falls within the interval (0.15, 0.25), the aging state is severe.

[0099] The scheme of the above embodiment establishes aging status indicators of the digital twin model of the dry-type reactor based on online monitoring data and experimental simulation data, that is, the aging status indicators include local discharge signal, dielectric loss factor, hydrophobicity, activation energy, contact angle and surface morphology, and establishes aging status levels of the digital twin model of the dry-type reactor according to the different effects of the aging status indicators on the aging status of the dry-type reactor, that is, the aging status levels are divided into normal, warning and severe. The established aging status indicators and aging status levels can support accurate aging resistance testing of dry-type reactors, and can provide prerequisites for constructing a dry-type reactor aging resistance testing model that can support accurate aging resistance testing of dry-type reactors.

[0100] Step 303: Obtain the subjective weight of the aging status indicator through the hierarchical analysis method, obtain the objective weight of the aging status indicator through the entropy weight method, and obtain the subjective and objective comprehensive weight of the aging status indicator based on the subjective weight and the objective weight.

[0101] In this embodiment, the subjective weight of the aging status indicator is obtained by the hierarchical analysis method, which includes: using a 1 to 9 scale method to obtain the judgment matrix A of the aging status indicator. The meaning of the judgment matrix scale is shown in Figure 8 As shown, the aging status indicators are compared with each other to obtain their relative importance,

[0102]

[0103] Among them, a ij It represents the reliability of the i-th indicator relative to the j-th indicator. The eigenvectors of the judgment matrix A of the aging status indicator can be obtained by the following formula and then normalized.

[0104] where i=1,2,...,6

[0105] where i=1,2,…,6

[0106] in, Represents the eigenvectors found by row, W i Represents the normalized value of the i-th weight.

[0107] The entropy weight method is used to obtain the objective weight of the aging status indicator. The entropy weight method is an objective weighting method based on information entropy theory. The larger the entropy value of an indicator, the smaller the degree of information change of the indicator, the smaller the impact of the evaluation, and thus the smaller its weight. For the aging evaluation indicators of dry-type reactors in this solution, the information entropy of the jth evaluation indicator is:

[0108]

[0109] Where u j Indicates the proportion of the jth indicator in the six indicators. j represents the normalized value of the jth indicator. The entropy weight of the jth indicator can be obtained by the following formula:

[0110]

[0111] According to the subjective weight and objective weight, the subjective and objective comprehensive weight of the aging status index is obtained, which includes: for the original data, forming a two-dimensional relationship matrix Q = (q ij ) 1×6 After dimensionless processing of Q, we get the matrix S=(s ij ) 1×6 , it is known that the subjective and objective weights of the jth indicator in this scheme are ψ j and The comprehensive weight of the combination optimization is W j , the evaluation value of the i-th evaluation object is:

[0112]

[0113] The total deviation F(w) of the evaluation value obtained based on subjective and objective weights should be as small as possible, so a nonlinear programming model is constructed as shown in the following formula:

[0114]

[0115] By solving the above nonlinear model, the comprehensive weight value of the evaluation index after combined optimization can be obtained.

[0116] The scheme of the above embodiment obtains the subjective weight of the aging status index through the hierarchical analysis method, obtains the objective weight of the aging status index through the entropy weight method, and obtains the subjective and objective comprehensive weight of the aging status index based on the subjective weight and the objective weight, which can provide the prerequisite for constructing a dry-type inductor aging resistance test model that can support accurate dry-type inductor aging resistance test.

[0117] In this embodiment, by obtaining the aging status parameters of the dry-type inductor, the dry-type inductor is refined mathematically modeled according to the online monitoring data and experimental simulation data, and a digital twin model of the dry-type inductor is constructed. According to the online monitoring data and experimental simulation data, the aging status index of the digital twin model of the dry-type inductor is established. According to the different influences of the aging status index on the aging status of the dry-type inductor, the aging status level of the digital twin model of the dry-type inductor is established. The subjective weight of the aging status index is obtained through the hierarchical analysis method, and the objective weight of the aging status index is obtained through the entropy weight method. According to the subjective weight and the objective weight, the subjective and objective comprehensive weight of the aging status index is obtained, which can provide the prerequisite for constructing a dry-type inductor aging resistance test model that can support accurate dry-type inductor aging resistance test.

[0118] In one embodiment, Figure 9 As shown, a dry-type reactor aging resistance test model construction device is provided. The device 900 includes: a state index and level acquisition module, a cloud model acquisition module and a comprehensive weight acquisition module, wherein:

[0119] The state indicator and level acquisition module 901 is used to obtain the aging state parameters of the dry-type reactor, build a digital twin model of the dry-type reactor, and establish the aging state indicator and aging state level of the digital twin model of the dry-type reactor;

[0120] A cloud model acquisition module 902 is configured to obtain a Gaussian cloud model of the aging state indicator under the aging state level according to the aging state indicator and the aging state level;

[0121] Comprehensive weight acquisition module 903, used to obtain the subjective and objective comprehensive weights of the aging status indicators through the hierarchical analysis method and the entropy weight method;

[0122] The test model acquisition module 904 is used to construct an aging resistance test model for the dry-type reactor based on the dry-type reactor digital twin model, aging status index, aging status level, Gaussian cloud model and subjective and objective comprehensive weights.

[0123] In one embodiment, the status indicator and level acquisition module 901 is also used to obtain the aging status parameters of the dry-type reactor, perform refined mathematical modeling of the dry-type reactor based on online monitoring data and experimental simulation data, and construct a digital twin model of the dry-type reactor.

[0124] In another embodiment, the status indicator and level acquisition module 901 is also used to establish the aging status indicator of the dry-type inductor digital twin model based on online monitoring data and experimental simulation data, and to establish the aging status level of the dry-type inductor digital twin model according to the different impacts of the aging status indicator on the aging status of the dry-type inductor.

[0125] In one embodiment, the comprehensive weight acquisition module 903 is also used to obtain the subjective weight of the aging status indicator through the hierarchical analysis method, obtain the objective weight of the aging status indicator through the entropy weight method, and obtain the subjective and objective comprehensive weight of the aging status indicator based on the subjective weight and the objective weight.

[0126] In one embodiment, the dry-type reactor aging resistance test model construction device 900 is further used to perform an aging resistance test on the dry-type reactor according to actual aging state parameters of the dry-type reactor.

[0127] Regarding the specific limitations of the device for constructing a test model for the aging resistance of dry-type reactors, please refer to the limitations of the method for constructing a test model for the aging resistance of dry-type reactors mentioned above, which will not be repeated here. The various modules in the above-mentioned device for constructing a test model for the aging resistance of dry-type reactors can be implemented in whole or in part through software, hardware, and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0128] In one embodiment, Figure 10 As shown in the figure, a method for testing the aging resistance of dry-type reactors is provided. Figure 1 The following steps are used as an example to illustrate the terminal in the figure:

[0129] Step 1001: Acquire actual aging state parameters of the dry-type reactor to be tested.

[0130] Among them, the actual aging state parameters of the dry-type inductor to be tested correspond to the aging state parameters of the dry-type inductor in the sample database used to construct the dry-type inductor aging resistance test model, and are the aging state parameters of the dry-type inductor to be tested collected on-site in actual production applications.

[0131] Specifically, the actual aging state parameters of the dry-type reactor to be tested are obtained.

[0132] Step 1002: Input the actual aging state parameters into the dry-type reactor aging resistance test model to obtain the aging resistance test result corresponding to the dry-type reactor to be tested.

[0133] Among them, the aging resistance test model of the dry-type inductor is obtained using the above-mentioned dry-type inductor aging resistance test model construction method. The aging resistance test results corresponding to the dry-type inductor to be tested correspond to the normal state, warning state and severe state in the aging state level. The test results are divided into normal, warning and severe. The relationship between the actual aging state parameters and aging resistance of the dry-type inductor to be tested is established. The test results can accurately and intuitively reflect the aging resistance of the dry-type inductor.

[0134] Specifically, the actual aging state parameters are input into the dry-type reactor aging resistance test model to obtain the aging resistance test result corresponding to the dry-type reactor to be tested.

[0135] In the above-mentioned dry-type inductor aging resistance test method, the actual aging state parameters of the dry-type inductor to be tested are obtained, and the actual aging state parameters are input into the dry-type inductor aging resistance test model to obtain the aging resistance test results corresponding to the dry-type inductor to be tested. The relationship between the actual aging state parameters of the dry-type inductor to be tested and the aging resistance is established, which can accurately reflect the aging resistance of the dry-type inductor.

[0136] In one embodiment, Figure 11 As shown, a method for testing the aging resistance of a dry-type reactor is provided, the method comprising:

[0137] Step 1101: Input the actual aging state parameters into the dry-type reactor aging resistance test model to obtain a Gaussian cloud model corresponding to the dry-type reactor to be tested.

[0138] In this embodiment, the actual aging state parameters are input into the dry-type reactor aging resistance test model to obtain the Gaussian cloud model corresponding to the dry-type reactor to be tested. The optimal cloud entropy calculation method is used to solve the Gaussian cloud model, and the digital feature expectation E of the aging state index under the Gaussian cloud model is obtained. x 、Super entropy H e and entropy E n The aging state level limit is regarded as a double constraint space [C min , C max ], then the expectation E of the Gaussian cloud model x The calculation formula is:

[0139]

[0140] Among them, C max Indicates the optimal state value of the evaluation parameter, C minIndicates the worst state value of the evaluation parameter. e Generally, a constant is taken, which can be adjusted based on the actual experience and uncertainty of the aging status indicators. n Adopting the “3E n The cloud entropy calculation method based on the rule is as follows:

[0141]

[0142] The solution of the above embodiment inputs the actual aging state parameters into the dry-type inductor aging resistance test model, obtains the Gaussian cloud model corresponding to the dry-type inductor to be tested, and calculates the expectation, super entropy and entropy of the Gaussian cloud model, providing preparation conditions for obtaining accurate aging resistance test results corresponding to the dry-type inductor to be tested.

[0143] Step 1102 : Obtain the correlation between the aging status indicator and the Gaussian cloud model corresponding to the dry-type reactor to be tested, and construct a comprehensive evaluation matrix of the aging status indicator based on the correlation value.

[0144] In this embodiment, the aging status indicator x is subject to Ex as the expectation, E n ′ is the Gaussian distribution of variance, that is, x~N(E x ,E n ′), and satisfy E n 'Subject to En as expectation, that is, E n ′~N(E n ,H e 2 ), H e 2 If the variance is a Gaussian distribution, then the correlation between the aging status index x and the Gaussian cloud model is:

[0145] According to the value of the correlation degree, a comprehensive evaluation matrix P of the aging status indicators is constructed. The comprehensive evaluation matrix is ​​a 6*3 or 3*6 matrix, in which 6 rows or 6 columns correspond to 6 aging status indicators, and 3 columns or 3 rows correspond to 3 aging status levels. The elements of the comprehensive evaluation matrix are the corresponding correlation degree values.

[0146] The scheme of the above embodiment obtains the correlation between the aging status indicator and the Gaussian cloud model corresponding to the dry-type inductor to be tested, and constructs a comprehensive evaluation matrix P of the aging status indicator based on the numerical value of the correlation, thereby providing preparation conditions for obtaining accurate aging resistance test results corresponding to the dry-type inductor to be tested.

[0147] Step 1103: Obtain a comprehensive evaluation vector based on the comprehensive evaluation matrix and the subjective and objective comprehensive weights.

[0148] In this embodiment, the comprehensive evaluation result vector B is obtained by multiplying the comprehensive evaluation matrix P by the subjective and objective comprehensive weight W, that is, B=WP=[b1, b2, b3].

[0149] The solution of the above embodiment obtains a comprehensive evaluation vector based on the comprehensive evaluation matrix and the subjective and objective comprehensive weights, which provides preparation conditions for obtaining accurate aging resistance test results corresponding to the dry-type reactor to be tested.

[0150] Step 1104 : Obtain a comprehensive evaluation score of the aging resistance of the dry-type reactor to be tested according to the comprehensive evaluation vector using a weighted average method.

[0151] In this embodiment, based on the comprehensive evaluation vector B, the weighted average method is used to obtain the comprehensive evaluation score r of the aging resistance corresponding to the dry-type reactor to be tested. The calculation formula is:

[0152]

[0153] Where, f i is the score value of state i, and the comprehensive assessment scores corresponding to the normal state, caution state and severe state of the aging state level are 1, 2 and 3 respectively.

[0154] The solution of the above embodiment uses a weighted average method based on a comprehensive evaluation vector to obtain a comprehensive evaluation score of the aging resistance capability of the dry-type reactor to be tested, thereby providing preparation conditions for obtaining accurate aging resistance test results of the dry-type reactor to be tested.

[0155] Step 1105: Obtain an aging resistance test result corresponding to the dry-type reactor to be tested according to the comprehensive evaluation score.

[0156] In this embodiment, the aging resistance test results corresponding to the dry-type inductor to be tested are obtained according to the comprehensive evaluation scores of 1, 2 and 3, wherein the aging resistance test results corresponding to the dry-type inductor to be tested are divided into normal, caution and severe, corresponding to the comprehensive evaluation scores of 1, 2 and 3 respectively.

[0157] The solution of the above embodiment obtains the aging resistance test result corresponding to the dry-type reactor to be tested according to the comprehensive evaluation score, and can obtain accurate aging resistance test result corresponding to the dry-type reactor to be tested.

[0158] In this embodiment, by inputting the actual aging state parameters into the aging resistance test model of the dry-type inductor, a Gaussian cloud model corresponding to the dry-type inductor to be tested is obtained. According to the aging state index and the Gaussian cloud model corresponding to the dry-type inductor to be tested, the correlation between the aging state index and the Gaussian cloud model is obtained. According to the numerical value of the correlation, a comprehensive evaluation matrix of the aging state index is constructed. According to the comprehensive evaluation matrix and the subjective and objective comprehensive weights, a comprehensive evaluation vector is obtained. According to the comprehensive evaluation vector, the weighted average method is used to obtain the comprehensive evaluation score of the aging resistance corresponding to the dry-type inductor to be tested. According to the comprehensive evaluation score, the aging resistance test result corresponding to the dry-type inductor to be tested is obtained. The relationship between the actual aging state parameters and the aging resistance of the dry-type inductor to be tested is established, which can accurately reflect the aging resistance of the dry-type inductor.

[0159] It should be understood that although Figure 2-3 and Figure 10-11 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 2-3 and Figure 10-11 At least part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but can be performed at different times. The order of execution of these steps or stages is not necessarily one by one, but can be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

[0160] In one embodiment, Figure 12 As shown, a dry-type reactor aging resistance test device is provided. The device 1200 includes: a parameter acquisition module and a test result acquisition module, wherein:

[0161] The parameter acquisition module 1201 is used to obtain the actual aging state parameters of the dry-type reactor to be tested.

[0162] The test result acquisition module 1202 is used to input the actual aging state parameters into the dry-type inductor aging resistance test model to obtain the aging resistance test result corresponding to the dry-type inductor to be tested, wherein the dry-type inductor aging resistance test model is obtained using the above-mentioned dry-type inductor aging resistance test model construction method.

[0163] In one embodiment, the test result acquisition module 1202 is also used to input the actual aging state parameters into the dry-type inductor aging resistance test model, obtain the Gaussian cloud model corresponding to the dry-type inductor to be tested, obtain the correlation between the aging state index and the Gaussian cloud model according to the aging state index and the Gaussian cloud model corresponding to the dry-type inductor to be tested, construct a comprehensive evaluation matrix of the aging state index according to the value of the correlation, obtain a comprehensive evaluation vector according to the comprehensive evaluation matrix and the subjective and objective comprehensive weights, and obtain a comprehensive evaluation score of the aging resistance corresponding to the dry-type inductor to be tested using the weighted average method according to the comprehensive evaluation vector, and obtain the aging resistance test result corresponding to the dry-type inductor to be tested according to the comprehensive evaluation score.

[0164] In one embodiment, the above-mentioned dry-type reactor aging resistance testing device 1200 is further used to perform aging resistance testing on the dry-type reactor to be tested.

[0165] For the specific definition of the dry-type reactor aging resistance test device, please refer to the definition of the dry-type reactor aging resistance test method above, which will not be repeated here. The various modules in the above-mentioned dry-type reactor aging resistance test device can be implemented in whole or in part through software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0166] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 13 As shown. The computer device includes a processor, a memory and a network interface connected via 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, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store aging status parameter data of the dry-type inductor. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for testing the aging resistance of a dry-type inductor is implemented.

[0167] Those skilled in the art will understand that Figure 13 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0168] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0169] Obtain the aging status parameters of the dry-type reactor, build a digital twin model of the dry-type reactor, and establish the aging status indicators and aging status levels of the digital twin model of the dry-type reactor;

[0170] According to the aging state indicator and the aging state level, a Gaussian cloud model of the aging state indicator under the aging state level is obtained;

[0171] The subjective and objective comprehensive weights of aging status indicators were obtained through the analytic hierarchy process and entropy weight method.

[0172] Based on the digital twin model of dry-type reactor, aging status indicators, aging status level, Gaussian cloud model and subjective and objective comprehensive weights, a aging resistance test model for dry-type reactor is constructed.

[0173] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0174] Obtain aging status parameters of dry-type reactors, including online monitoring data and experimental simulation data;

[0175] Based on online monitoring data and experimental simulation data, the dry-type reactor is refined in mathematical modeling and a digital twin model of the dry-type reactor is constructed.

[0176] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0177] Based on online monitoring data and experimental simulation data, the aging status indicators of the dry-type reactor digital twin model are established;

[0178] According to the different impacts of aging status indicators on the aging status of dry-type reactors, the aging status level of the dry-type reactor digital twin model is established.

[0179] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0180] The subjective weights of aging status indicators were obtained through the analytic hierarchy process;

[0181] The objective weights of aging status indicators are obtained through the entropy weight method;

[0182] According to the subjective weight and objective weight, the subjective and objective comprehensive weight of the aging status index is obtained.

[0183] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0184] Obtain the actual aging status parameters of the dry-type reactor to be tested;

[0185] The actual aging state parameters are input into the dry-type reactor aging resistance test model to obtain the aging resistance test result corresponding to the dry-type reactor to be tested, wherein the dry-type reactor aging resistance test model is obtained using the above-mentioned dry-type reactor aging resistance test model construction method.

[0186] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0187] Input the actual aging state parameters into the dry-type reactor aging resistance test model to obtain the Gaussian cloud model corresponding to the dry-type reactor to be tested;

[0188] Based on the aging status indicators and the Gaussian cloud model corresponding to the dry-type reactor to be tested, a comprehensive evaluation matrix of the aging status indicators is constructed;

[0189] According to the comprehensive evaluation matrix and the subjective and objective comprehensive weights, a comprehensive evaluation vector is obtained;

[0190] According to the comprehensive evaluation vector, the weighted average method is used to obtain the comprehensive evaluation score of the aging resistance of the dry-type reactor to be tested;

[0191] According to the comprehensive evaluation score, the corresponding aging resistance test result of the dry-type reactor to be tested is obtained.

[0192] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0193] According to the aging state index and the Gaussian cloud model corresponding to the dry-type reactor to be tested, the correlation degree between the aging state index and the Gaussian cloud model is obtained;

[0194] According to the numerical value of the correlation, a comprehensive evaluation matrix of aging status indicators is constructed.

[0195] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0196] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0197] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for constructing a dry-type reactor aging resistance test model, characterized in that: The method comprises: Obtaining aging status parameters of the dry-type reactor, constructing a digital twin model of the dry-type reactor, and establishing an aging status indicator and an aging status level of the digital twin model of the dry-type reactor; Obtaining a Gaussian cloud model of the aging state indicator at the aging state level according to the aging state indicator and the aging state level; Obtaining the subjective and objective comprehensive weights of the aging status indicators through the analytic hierarchy process and the entropy weight method; Constructing an aging resistance test model for a dry-type reactor according to the dry-type reactor digital twin model, the aging status index, the aging status level, the Gaussian cloud model, and the subjective and objective comprehensive weights; The step of obtaining the aging state parameters of the dry-type reactor and constructing the digital twin model of the dry-type reactor includes: Acquiring aging state parameters of the dry-type reactor, wherein the aging state parameters include online monitoring data and experimental simulation data; Based on the online monitoring data and the experimental simulation data, a refined mathematical model is performed on the dry-type reactor to construct a digital twin model of the dry-type reactor; The online monitoring data includes partial discharge signal, dielectric loss factor and hydrophobicity, and the experimental simulation data includes activation energy, contact angle and surface morphology. The aging status index and aging status level for establishing the digital twin model of the dry-type reactor include: Establishing an aging status indicator of a dry-type reactor digital twin model based on the online monitoring data and the experimental simulation data; According to the different influences of the aging status indicators on the aging status of the dry-type reactor, an aging status level of the dry-type reactor digital twin model is established.

2. The method for constructing a dry-type reactor aging resistance test model according to claim 1, wherein: The subjective and objective comprehensive weights of the aging status indicators are obtained by the hierarchical analysis method and the entropy weight method, including: Obtaining the subjective weight of the aging status indicator through the hierarchical analysis method; Obtaining the objective weight of the aging status indicator by using the entropy weight method; According to the subjective weight and the objective weight, a subjective and objective comprehensive weight of the aging status indicator is obtained.

3. A method for testing the aging resistance of dry-type reactors, characterized in that: The method comprises: Obtain the actual aging status parameters of the dry-type reactor to be tested; The actual aging state parameters are input into the dry-type reactor aging resistance test model to obtain the aging resistance test result corresponding to the dry-type reactor to be tested. The dry-type reactor aging resistance test model is obtained using the dry-type reactor aging resistance test model construction method according to any one of claims 1-2.

4. The method for testing the aging resistance of dry-type reactor according to claim 3, characterized in that: The step of inputting the actual aging state parameter into the dry-type reactor aging resistance test model to obtain the aging resistance test result corresponding to the dry-type reactor to be tested includes: Inputting the actual aging state parameters into the dry-type reactor aging resistance test model to obtain a Gaussian cloud model corresponding to the dry-type reactor to be tested; Constructing a comprehensive evaluation matrix of the aging status indicator according to the aging status indicator and the Gaussian cloud model corresponding to the dry-type reactor to be tested; Obtaining a comprehensive evaluation vector according to the comprehensive evaluation matrix and the subjective and objective comprehensive weights; Obtaining, according to the comprehensive evaluation vector, a comprehensive evaluation score of the aging resistance corresponding to the dry-type reactor to be tested by using a weighted average method; According to the comprehensive evaluation score, an aging resistance test result corresponding to the dry-type reactor to be tested is obtained.

5. The method for testing the aging resistance of dry-type reactor according to claim 4, characterized in that: The step of constructing a comprehensive evaluation matrix of the aging status indicator according to the aging status indicator and the Gaussian cloud model corresponding to the dry-type reactor to be tested includes: Obtaining a correlation between the aging state indicator and the Gaussian cloud model according to the aging state indicator and the Gaussian cloud model corresponding to the dry-type reactor to be tested; According to the numerical value of the correlation degree, a comprehensive evaluation matrix of the aging status index is constructed.

6. A dry-type reactor aging resistance test model construction device, characterized in that: The device comprises: A status indicator and level acquisition module is used to obtain the aging status parameters of the dry-type reactor, build a digital twin model of the dry-type reactor, and establish the aging status indicator and aging status level of the digital twin model of the dry-type reactor; a cloud model acquisition module, configured to obtain, according to the aging state indicator and the aging state level, a Gaussian cloud model of the aging state indicator at the aging state level; A comprehensive weight acquisition module is used to obtain the subjective and objective comprehensive weights of the aging status indicators through the hierarchical analysis method and the entropy weight method; A test model acquisition module is used to construct an aging resistance test model for the dry-type reactor based on the dry-type reactor digital twin model, the aging status index, the aging status level, the Gaussian cloud model, and the subjective and objective comprehensive weight; The state indicator and level acquisition module is further used to obtain aging state parameters of the dry-type reactor, wherein the aging state parameters include online monitoring data and experimental simulation data; based on the online monitoring data and the experimental simulation data, the dry-type reactor is refined mathematical modeled to construct a digital twin model of the dry-type reactor; The online monitoring data includes partial discharge signals, dielectric loss factor and hydrophobicity, and the experimental simulation data includes activation energy, contact angle and surface morphology; the status index and level acquisition module is also used to establish the aging status index of the digital twin model of the dry-type reactor based on the online monitoring data and the experimental simulation data; and establish the aging status level of the digital twin model of the dry-type reactor based on the different effects of the aging status index on the aging status of the dry-type reactor.

7. A dry-type reactor aging resistance test device, characterized in that: The device comprises: Parameter acquisition module, used to obtain the actual aging status parameters of the dry-type reactor to be tested; A test result acquisition module is used to input the actual aging state parameters into the dry-type inductor aging resistance test model to obtain the aging resistance test result corresponding to the dry-type inductor to be tested. The dry-type inductor aging resistance test model is obtained using the dry-type inductor aging resistance test model construction method according to any one of claims 1-2.

8. 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 for constructing a dry-type reactor aging resistance test model according to any one of claims 1 to 2 or the method for testing the dry-type reactor aging resistance according to claims 3 to 5 are implemented.

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