A method and system for evaluating the lifetime of an insulating coating of an amorphous alloy soft magnetic material

Abstract

This invention provides a method and system for assessing the lifespan of insulating coatings on amorphous alloy soft magnetic materials. The method includes: classifying amorphous alloy soft magnetic tape materials from decommissioned transformers according to their models to obtain several sample sets of tape materials and transformers of the same model; acquiring coating parameters for the sample sets of tape materials and transformer statistics for the sample sets of transformers of the same model; selecting resistance-related parameters based on the calculated transformer thermal lifetime loss and coating parameters, and establishing a multivariate regression model; obtaining a fitted coating resistivity coefficient based on the multivariate regression model and the resistance-related parameters of the tape to be assessed; calculating the fitted failure probability and the actual failure probability of the fitted coating resistivity coefficient and the corresponding actual coating resistivity coefficient under a preset failure mode; and combining the preset critical value to obtain the lifespan assessment result. This invention provides a simple and effective criterion for coating lifespan termination, improving the reliability and accuracy of lifespan assessment.

Description

Technical Field

[0001] This invention relates to the field of electrical material life assessment technology, and in particular to a method and system for assessing the life of insulating coatings of amorphous alloy soft magnetic materials based on statistical data. Background Technology

[0002] Amorphous alloy soft magnetic materials possess excellent properties such as high and low coercivity, high permeability, low saturation magnetic induction, and high resistivity, making them widely used in the electronics, power, and new energy industries. To reduce eddy current losses in amorphous alloy strips, they are typically manufactured as thin amorphous alloy strips. Amorphous alloy strips are primarily used in distribution transformers to reduce no-load losses due to the low magnetic loss characteristic of amorphous alloys. The surface of the amorphous alloy strip is coated with an insulating layer, the main components of which are high-temperature resistant inorganic materials (SiO2, MgO, etc.). The performance of the insulating layer on the strip surface is a crucial performance indicator, directly determining the service performance of the transformer core. However, the coating is subject to corrosion from heat, mechanical stress, and chemical substances within the transformer, leading to performance degradation. For example, during long-term service, coating peeling and micropore formation on the strip surface will increase no-load losses in the core, causing abnormal noise and localized overheating in the transformer equipment. Therefore, it is necessary to conduct a coating life assessment to determine the reasonable service life of the coating.

[0003] Existing methods for assessing coating life primarily rely on accelerated aging tests to determine the performance degradation patterns of coatings on grain-oriented silicon steel surfaces. While accelerated aging tests can rapidly simulate the coating performance degradation process, they lack large-sample analysis and statistically significant analysis of coating performance degradation. They cannot fully reflect the impact of factors such as transformer operating load and ambient temperature on the performance degradation of amorphous alloy strip coatings in real-world transformer operating environments. Furthermore, they fail to provide mathematical formulas for assessing the lifespan of amorphous alloy strip coatings, cannot directly assess remaining lifespan through measured amorphous alloy strip performance parameters, and lack direct parameter indicators and reliability indicators for determining the end of coating lifespan, making it impossible to directly determine whether the coating retains remaining lifespan. Summary of the Invention

[0004] The purpose of this invention is to provide a life assessment method for insulating coatings of amorphous alloy soft magnetic materials. Based on statistical methods, it considers factors such as transformer hot spot temperature, coating resistivity, coating adhesion, and the number of micropores on the coating surface. It employs a multiple linear regression model combined with failure modes to calculate the reliability of the insulating coating, overcoming the application defects of existing coating life assessment methods. Based on the analysis of the influence of thermal, mechanical stress, and chemical corrosion on coating performance degradation in transformers, it provides simple and effective criteria for directly determining the end of the life of amorphous alloy strip coatings and corresponding reliability indicators, thereby improving the reliability and accuracy of life assessment for insulating coatings of amorphous alloy soft magnetic materials, and thus providing a reliable guarantee for the safe operation of transformers.

[0005] To achieve the above objectives, it is necessary to provide a method and system for assessing the lifetime of insulating coatings on amorphous alloy soft magnetic materials, addressing the aforementioned technical problems.

[0006] In a first aspect, embodiments of the present invention provide a method for evaluating the lifetime of an insulating coating on an amorphous alloy soft magnetic material, the method comprising the following steps:

[0007] Amorphous alloy soft magnetic tape materials were obtained from the returned transformers, and the amorphous alloy soft magnetic tape materials were classified according to their models to obtain several sample sets of tape materials of the same model and corresponding sample sets of transformers with the same model of tape materials.

[0008] The coating parameters of the same type of strip sample set are obtained, and statistical analysis is performed on the corresponding transformer sample set of the same type of strip to obtain transformer statistical data. The coating parameters include coating resistivity, coating adhesion, average crack length of coating cracks, and number of micropores on the coating surface. The transformer statistical data includes transformer operating years, operating load, ambient temperature, and winding temperature.

[0009] Based on the transformer statistics, the transformer hot spot temperature is obtained, and based on the transformer hot spot temperature and the transformer statistics, the transformer thermal life loss is obtained.

[0010] Based on the coating parameters and the transformer thermal life loss, resistance-related parameters are selected, and a multiple regression model is established based on the resistance-related parameters and the coating resistivity.

[0011] Obtain the resistance-related parameters of the strip to be evaluated and the corresponding true coating resistance coefficient, and obtain the fitted coating resistance coefficient based on the multivariate regression model and the resistance-related parameters of the strip to be evaluated.

[0012] Based on the actual coating resistivity and the fitted coating resistivity, the actual failure probability and the fitted failure probability corresponding to the preset failure mode are obtained, and the life assessment result is obtained based on the actual failure probability, the fitted failure probability and the preset critical value.

[0013] Furthermore, the step of obtaining the transformer hot spot temperature based on the transformer statistical data includes:

[0014] Based on the operating load, the ambient temperature, and the winding temperature, the hot spot temperature of the transformer is obtained through finite element analysis or the difference equation method.

[0015] Furthermore, the step of obtaining the transformer thermal life loss based on the transformer hot spot temperature and the transformer statistical data includes:

[0016] Obtain the rated load of the transformer, and based on the operating load, obtain the average load of the transformer;

[0017] The transformer's thermal life loss is obtained by using the transformer's rated load, the transformer's service life, the transformer's average load, and the transformer's hot spot temperature; the transformer's thermal life loss is expressed as:

[0018]

[0019] Among them, F A Indicates the transformer's thermal life loss; P a P0, T, and θ represent the transformer's average load, rated load, service life, and hot spot temperature, respectively.

[0020] Furthermore, the step of selecting resistance-related parameters based on the coating parameters and the transformer thermal lifetime loss includes:

[0021] Calculate the correlation coefficients between the coating adhesion, the average crack length of the coating, the number of micropores on the coating surface, and the transformer thermal life loss and the coating resistivity, respectively.

[0022] Based on the correlation coefficients, the resistance-related parameters are obtained through correlation testing methods.

[0023] Furthermore, the step of obtaining the resistance-related parameters based on each correlation coefficient using a correlation test method includes:

[0024] Based on each correlation coefficient, the corresponding one-sided t-test characteristic values ​​are obtained; the one-sided t-test characteristic values ​​are expressed as:

[0025]

[0026] Where t, ρ, and n represent the eigenvalues ​​of the one-sided t-test, the correlation coefficient, and the number of samples in the sample set of strip transformers of the same model, respectively;

[0027] Based on the characteristic values ​​of each one-sided t-test, the corresponding confidence probabilities are obtained, and it is determined whether the confidence probabilities are less than the preset significance level. If so, the corresponding parameters are determined as the resistance-related parameters.

[0028] Further, the step of obtaining the actual failure probability and the fitted failure probability corresponding to the preset failure mode based on the actual coating resistivity and the fitted coating resistivity includes:

[0029] The actual coating resistivity and the fitted coating resistivity are respectively input into the coating failure probability function under the preset failure mode to obtain the corresponding actual failure probability and fitted failure probability; the preset failure mode includes step failure mode and progressive failure mode, and the corresponding coating failure probability functions are expressed as follows:

[0030]

[0031]

[0032] Where P1(R) and P2(R) represent the failure probabilities of the insulating coating under step failure mode and progressive failure mode, respectively; R represents the actual coating resistivity or the fitted coating resistivity; R 1,d and R 2,d These represent the minimum allowable coating resistivity under step failure mode and progressive failure mode, respectively; R 2,w This represents the warning value of the resistance coefficient under progressive failure mode.

[0033] Further, the step of obtaining the lifetime assessment result based on the actual failure probability, the fitted failure probability, and the preset critical value includes:

[0034] If both the actual failure probability and the fitted failure probability are less than the preset threshold, then the life assessment result is determined to be the end of the coating life; otherwise, it is determined whether both the actual failure probability and the fitted failure probability are greater than the preset threshold.

[0035] If both the actual failure probability and the fitted failure probability are greater than the preset critical value, the lifetime assessment result is determined to be that the coating has remaining lifetime; otherwise, the lifetime assessment result is determined to be that the coating lifetime has reached a warning state.

[0036] Secondly, embodiments of the present invention provide a life assessment system for insulating coatings of amorphous alloy soft magnetic materials, the system comprising:

[0037] The sample set acquisition module is used to acquire amorphous alloy soft magnetic tape materials in decommissioned transformers, and classify the amorphous alloy soft magnetic tape materials according to their models to obtain several sample sets of the same model of tape materials and corresponding sample sets of transformers with the same model of tape materials.

[0038] The basic data acquisition module is used to acquire the coating parameters of the same type of strip sample set, and to perform statistical analysis on the corresponding same type of strip transformer sample set to obtain transformer statistical data; the coating parameters include coating resistivity, coating adhesion, average crack length of coating cracks and number of micropores on the coating surface; the transformer statistical data includes transformer operating years, operating load, ambient temperature and winding temperature;

[0039] The life loss calculation module is used to obtain the transformer hot spot temperature based on the transformer statistical data, and to obtain the transformer thermal life loss based on the transformer hot spot temperature and the transformer statistical data.

[0040] The regression model construction module is used to select resistance-related parameters based on the coating parameters and the transformer thermal life loss, and to establish a multiple regression model based on the resistance-related parameters and the coating resistivity.

[0041] The evaluation data acquisition module is used to acquire the resistance-related parameters of the strip to be evaluated and the corresponding true coating resistance coefficient, and to obtain the fitted coating resistance coefficient based on the multivariate regression model and the resistance-related parameters of the strip to be evaluated.

[0042] The life assessment module is used to obtain the actual failure probability and the fitted failure probability corresponding to the preset failure mode based on the actual coating resistivity and the fitted coating resistivity, and to obtain the life assessment result based on the actual failure probability, the fitted failure probability and the preset critical value.

[0043] Thirdly, embodiments of the present invention also provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0044] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method.

[0045] The present application provides a method and system for assessing the lifespan of insulating coatings for amorphous alloy soft magnetic materials. This method involves classifying amorphous alloy soft magnetic tape materials from decommissioned transformers into several sample sets of the same type of tape and corresponding sample sets of transformers with the same type of tape. Then, it obtains coating parameters for the sample sets of the same type of tape, including coating resistivity, coating adhesion, average crack length, and number of micropores on the coating surface, and transformer statistical data for the corresponding sample sets of transformers with the same type of tape, including transformer operating years, operating load, ambient temperature, and winding temperature. Finally, it obtains transformer thermal analysis data based on the transformer statistical data. This technical solution involves calculating the transformer's thermal life loss based on a specific temperature. Then, based on the correlation analysis between coating parameters and transformer thermal life loss, resistance-related parameters and coating resistivity are selected. A multiple regression model is established to obtain the resistance-related parameters and corresponding true coating resistivity of the strip to be evaluated. A fitted coating resistivity is obtained based on the multiple regression model and the resistance-related parameters of the strip to be evaluated. Finally, based on the true and fitted coating resistivity, the true and fitted failure probabilities corresponding to preset failure modes are obtained. The life assessment result is then obtained based on the true failure probability, the fitted failure probability, and the preset critical value. Compared with existing technologies, this life assessment method for amorphous alloy soft magnetic material insulating coatings can, based on the analysis of the influence of thermal, mechanical stress, and chemical corrosion on coating performance degradation in transformers, simply and effectively provide valid criteria and corresponding reliability indicators for directly judging the termination of the life of amorphous alloy strip coatings. This improves the reliability and accuracy of life assessment for amorphous alloy soft magnetic material insulating coatings, thereby providing a reliable guarantee for the safe operation of transformers. Attached Figure Description

[0046] Figure 1 This is a schematic diagram illustrating the application scenario of the life assessment method for the insulating coating of amorphous alloy soft magnetic materials in this invention.

[0047] Figure 2 This is a flowchart illustrating the life assessment method for the insulating coating of amorphous alloy soft magnetic material in an embodiment of the present invention.

[0048] Figure 3 This is a schematic diagram of the life assessment system for the insulating coating of amorphous alloy soft magnetic material in an embodiment of the present invention;

[0049] Figure 4 This is an internal structural diagram of the computer device in an embodiment of the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and beneficial effects of this application clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Obviously, the embodiments described below are only part of the embodiments of the present invention and are used to illustrate the present invention, but are not intended to limit the scope of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0051] The present invention provides a method for assessing the lifetime of insulating coatings on amorphous alloy soft magnetic materials, which can be applied to... Figure 1 The terminal and server shown are described. The terminal can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The server can be a standalone server or a server cluster consisting of multiple servers. Based on several sample sets of the same type of amorphous alloy soft magnetic magnetic tape obtained from the acquired decommissioned transformers and corresponding sample sets of transformers with the same type of tape, the server, according to the statistical data-based lifetime assessment method for amorphous alloy soft magnetic material insulation coatings provided by this invention, performs performance degradation analysis on the tape to be evaluated based on thermal, mechanical stress, and chemical corrosion in the transformer. The obtained lifetime assessment results of the amorphous alloy soft magnetic material insulation coating are used for subsequent research on the server or sent to the terminal for users to view and analyze. The following embodiments will provide a detailed description of the lifetime assessment method for amorphous alloy soft magnetic material insulation coatings of this invention.

[0052] In one embodiment, such as Figure 2 As shown, a method for evaluating the lifetime of an insulating coating on an amorphous alloy soft magnetic material is provided, comprising the following steps:

[0053] S11. Obtain amorphous alloy soft magnetic tape material from the returned transformer, and classify the amorphous alloy soft magnetic tape material according to the model to obtain several sample sets of the same model tape material and corresponding sample sets of transformers with the same model tape material.

[0054] S12. Obtain the coating parameters of the same type of strip sample set, and perform statistical analysis on the corresponding same type of strip transformer sample set to obtain transformer statistical data; wherein, the coating parameters include coating resistivity, coating adhesion, average crack length of coating, and number of micropores on the coating surface, and the corresponding acquisition methods are as follows: 1) Coating resistivity can be obtained by sampling 50 points on the coating surface and taking the average value; 2) Coating adhesion can be obtained by measuring 3 points and taking the average value; 3) Average crack length of coating and number of micropores on the coating surface can be obtained by sampling 600mm... 2Within the area, metallographic analysis was performed using a microscope. It should be noted that the coating parameters can be further adjusted based on actual conditions, including additional indicators reflecting the surface condition such as the length of the insulating coating scratch and surface roughness.

[0055] The above-mentioned transformer statistics include transformer operating years, operating load, ambient temperature and winding temperature. Additional data reflecting the actual operating status of the transformer, such as transformer oil temperature, may be added as needed. All of these can be obtained through a database or system that stores the status data collected during transformer operation. No specific restrictions are imposed here.

[0056] S13. Based on the transformer statistical data, obtain the transformer hot spot temperature, and based on the transformer hot spot temperature and the transformer statistical data, obtain the transformer thermal life loss;

[0057] Specifically, the step of obtaining the transformer hot spot temperature based on the transformer statistical data includes:

[0058] Based on the operating load, the ambient temperature, and the winding temperature, the transformer hot spot temperature is obtained through finite element analysis or the difference equation method. Specific methods for obtaining the transformer hot spot temperature using finite element analysis or the difference equation method can be found in existing technologies and will not be elaborated here.

[0059] The process of obtaining transformer thermal life loss can be understood as inputting the obtained transformer hot spot temperature and transformer statistical data into a pre-established transformer thermal life loss model; the transformer thermal life loss model construction process can be based on the obtained thermal life loss related variable data through nonlinear fitting or other similar modeling methods, which are not limited here; specifically, the steps of obtaining transformer thermal life loss based on the transformer hot spot temperature and the transformer statistical data include:

[0060] Obtain the rated load of the transformer, and based on the operating load, obtain the average load of the transformer; wherein, the rated load of the transformer and the service life of the transformer can be provided by the transformer manufacturer or the transformer equipment procurement documents;

[0061] The transformer thermal life loss is obtained by taking the transformer's rated load, the transformer's operating years, the transformer's average load, and the transformer's hot spot temperature. Specifically, the transformer thermal life loss can be understood as the dependent variable value of the obtained transformer rated load, transformer operating years, transformer average load, and transformer hot spot temperature input into a preset transformer thermal life loss model, expressed as follows:

[0062]

[0063] Among them, F AIndicates the transformer's thermal life loss; P a P0, T, and θ represent the transformer's average load, rated load, service life, and hot spot temperature, respectively.

[0064] S14. Based on the coating parameters and the transformer thermal life loss, resistance-related parameters are selected, and a multiple regression model is established based on the resistance-related parameters and the coating resistivity.

[0065] The process of obtaining resistance-related parameters can be understood as a process of selecting parameters that have a reliable correlation with the coating resistivity by calculating the correlation between coating adhesion, average crack length of coating, number of micropores on the coating surface, and transformer thermal life loss and the coating resistivity. Specifically, the step of selecting resistance-related parameters based on the coating parameters and the transformer thermal life loss includes:

[0066] The correlation coefficients between the coating adhesion, the average crack length of the coating, the number of micropores on the coating surface, and the transformer thermal life loss and the coating resistivity are calculated respectively; the calculation method of the correlation coefficient can be referred to the existing technology and will not be elaborated here.

[0067] Based on each correlation coefficient, the resistance-related parameters are obtained through a correlation test method. Preferably, the correlation test method employs a statistical t-test, and based on the t-test results of the correlation coefficients, variables correlated with the coating resistance coefficient are retained, while those without statistical correlation are discarded. Specifically, the step of obtaining the resistance-related parameters based on each correlation coefficient through a correlation test method includes:

[0068] Based on each correlation coefficient, the corresponding one-sided t-test characteristic values ​​are obtained; the one-sided t-test characteristic values ​​are expressed as:

[0069]

[0070] Where t, ρ, and n represent the eigenvalues ​​of the one-sided t-test, the correlation coefficient, and the number of samples in the sample set of strip transformers of the same model, respectively;

[0071] Based on the characteristic values ​​of each one-sided t-test, the corresponding confidence probabilities are obtained, and it is determined whether the confidence probabilities are less than a preset significance level. If so, the corresponding parameter is determined as the resistance-related parameter; where the confidence probability P... s The standard t-value limit table for the t-test can be obtained by referring to the characteristic values ​​of the one-sided t-test. The preset significance level H0 can be understood as a fixed significance level set according to general statistical assumptions. In this embodiment, the preset significance level H0 is preferably set to 0.01. If the P-value is obtained by referring to a certain correlation coefficient...s If H0, it is considered that the variable parameter corresponding to the correlation coefficient is correlated with the coating resistance coefficient and can be used as a resistance-related parameter; otherwise, the variable parameter corresponding to the correlation coefficient needs to be excluded.

[0072] After obtaining the resistance-related parameters through the above method, based on the variable data obtained previously, taking the resistance-related parameters as independent variables and the coating resistance coefficient as the dependent variable, the least squares fitting method is used for regression analysis to obtain a multiple regression model for fitting the coating resistance coefficient, as shown in Equation (1):

[0073]

[0074] where, R f represents the fitted coating resistance coefficient; X i and w i represent the i-th resistance-related parameter and the corresponding regression coefficient respectively; m represents the number of resistance-related parameters.

[0075] S15. Obtain the resistance-related parameters of the strip to be evaluated and the corresponding true coating resistance coefficient, and according to the multiple regression model and the resistance-related parameters of the strip to be evaluated, obtain the fitted coating resistance coefficient; where, the strip to be evaluated can be understood as the amorphous alloy soft magnetic strip on the service transformer, and the corresponding resistance-related parameters are consistent with the variable parameters screened in step S14, and the true coating resistance coefficient is the insulation resistance coefficient actually measured for the strip to be evaluated;

[0076] The fitted coating resistance coefficient can be understood as the value of the dependent variable of the multiple regression model obtained by inputting the resistance-related parameters of the strip to be evaluated. The acquisition of this value is considered in view of the situation that when only using the single index of the true coating resistance coefficient obtained by actual measurement to determine the end of life, due to the single information and the lack of multi-dimensional state information of the strip, it is easy to produce evaluation deviations. Therefore, another life termination criterion index considering other indicators that can reflect the coating state is preferably introduced. The method of fitting the indicators that can reflect the coating state other than the insulation resistance coefficient into the predicted value of the coating resistance coefficient not only meets the requirement of simplifying the conditions for determining the end of life, but also supplements the true coating resistance coefficient and conducts the strip life assessment through the following method, which can effectively improve the reliability of the strip life termination assessment.

[0077] S16. According to the true coating resistance coefficient and the fitted coating resistance coefficient, obtain the corresponding true failure probability and fitted failure probability under the preset failure mode, and according to the true failure probability, the fitted failure probability and the preset critical value, obtain the life assessment result;

[0078] The true failure probability can be understood as the single-parameter failure probability, and the fitted failure probability can be understood as the multi-parameter failure probability. Specifically, the step of obtaining the true failure probability and the fitted failure probability corresponding to the preset failure mode based on the true coating resistivity and the fitted coating resistivity includes:

[0079] The actual coating resistivity and the fitted coating resistivity are respectively input into the coating failure probability function under the preset failure mode to obtain the corresponding actual failure probability and fitted failure probability. The preset failure mode includes step failure mode and progressive failure mode. In practical applications, the appropriate mode can be selected according to the requirements to determine the reliability of the coating. The corresponding coating failure probability functions are expressed as follows:

[0080]

[0081]

[0082] Where P1(R) and P2(R) represent the failure probabilities of the insulating coating under step failure mode and progressive failure mode, respectively; R represents the actual coating resistivity or the fitted coating resistivity; R 1,d This represents the minimum permissible resistivity of the coating under step failure mode, a value that can be determined according to industry standards; R 2,d This represents the minimum permissible resistivity of the coating under progressive failure mode. This value can also be determined according to industry standards, and is generally set to 30 Ω / mm. 2 ;R 2,w This represents the warning value for the resistivity under progressive failure mode, and its value ranges from the minimum allowable value R of the coating resistivity. 2,d It is 2 to 8 times that of the coating resistivity, preferably set as the minimum allowable value R. 2,d 5 times;

[0083] It should be noted that in practical applications, when assessing lifetime under step failure mode, the actual coating resistivity and the fitted coating resistivity can be substituted into equation (2) to obtain the corresponding actual failure probability and fitted failure probability. Similarly, when assessing lifetime under progressive failure mode, the actual coating resistivity and the fitted coating resistivity can be substituted into equation (3) to obtain the actual failure probability and fitted failure probability under this failure mode.

[0084] After obtaining the true failure probability and the fitted failure probability using the above method, the lifespan of the strip coating can be assessed based on the coating reliability requirements. Specifically, the step of obtaining the lifespan assessment result based on the true failure probability, the fitted failure probability, and a preset critical value includes:

[0085] If both the actual failure probability and the fitted failure probability are less than the preset critical value, the life assessment result is determined to be the end of the coating life; otherwise, if both are less than the preset critical value, the actual failure probability and the fitted failure probability are determined to be greater than the preset critical value. The preset critical value can be understood as the critical value P0(R) of the failure probability of the insulating coating set according to the reliability index requirements of the coating. In this embodiment, it is preferably set to 0.03.

[0086] If both the actual failure probability and the fitted failure probability are greater than the preset critical value, the lifetime assessment result is determined to be that the coating has remaining lifetime; otherwise, the lifetime assessment result is determined to be that the coating lifetime has reached a warning state.

[0087] This application embodiment obtains several sample sets of amorphous alloy soft magnetic tape materials from decommissioned transformers by classifying them according to model, and corresponding sample sets of transformers with the same model of tape. Then, it obtains coating parameters for the sample sets of the same model of tape, including coating resistivity, coating adhesion, average crack length of the coating, and the number of micropores on the coating surface, and transformer statistical data for the corresponding sample sets of transformers with the same model of tape, including transformer operating years, operating load, ambient temperature, and winding temperature. Based on the transformer hot spot temperature obtained from the transformer statistical data, the transformer thermal life loss is calculated. Finally, based on the correlation analysis between coating parameters and transformer thermal life loss, resistance-related parameters and coating resistivity are selected, and a multiple regression model is established to obtain the tape to be evaluated. This method involves analyzing the resistance-related parameters of the material and the corresponding true coating resistivity, obtaining the fitted coating resistivity based on a multiple regression model and the resistance-related parameters of the strip to be evaluated, and obtaining the true failure probability and fitted failure probability corresponding to the preset failure mode based on the true coating resistivity and fitted coating resistivity. Finally, it uses the true failure probability, fitted failure probability, and preset critical values ​​to obtain the life assessment results. This method, based on the analysis of the influence of thermal, mechanical stress, and chemical corrosion on the coating performance degradation in transformers, provides a simple and effective criterion for directly determining the end of the life of amorphous alloy strip coatings and corresponding reliability indicators. This effectively improves the reliability and accuracy of life assessment for amorphous alloy soft magnetic material insulating coatings, thereby providing a reliable guarantee for the safe operation of transformers.

[0088] In one embodiment, such as Figure 3 As shown, a life assessment system for insulating coatings of amorphous alloy soft magnetic materials is provided, the system comprising:

[0089] Sample set acquisition module 1 is used to acquire amorphous alloy soft magnetic tape materials in decommissioned transformers, and classify the amorphous alloy soft magnetic tape materials according to their models to obtain several sample sets of the same model of tape materials and corresponding sample sets of transformers with the same model of tape materials.

[0090] The basic data acquisition module 2 is used to acquire the coating parameters of the same type of strip sample set, and to perform statistical analysis on the corresponding same type of strip transformer sample set to obtain transformer statistical data; the coating parameters include coating resistivity, coating adhesion, average crack length of coating cracks and number of micropores on the coating surface; the transformer statistical data includes transformer operating years, operating load, ambient temperature and winding temperature;

[0091] The life loss calculation module 3 is used to obtain the transformer hot spot temperature based on the transformer statistical data, and to obtain the transformer thermal life loss based on the transformer hot spot temperature and the transformer statistical data.

[0092] The regression model construction module 4 is used to select resistance-related parameters based on the coating parameters and the transformer thermal life loss, and to establish a multiple regression model based on the resistance-related parameters and the coating resistivity.

[0093] The evaluation data acquisition module 5 is used to acquire the resistance-related parameters of the strip to be evaluated and the corresponding true coating resistance coefficient, and to obtain the fitted coating resistance coefficient based on the multiple regression model and the resistance-related parameters of the strip to be evaluated.

[0094] The life assessment module 6 is used to obtain the actual failure probability and the fitted failure probability corresponding to the preset failure mode based on the actual coating resistivity and the fitted coating resistivity, and to obtain the life assessment result based on the actual failure probability, the fitted failure probability and the preset critical value.

[0095] Specific limitations regarding the life assessment system for an amorphous alloy soft magnetic material insulating coating can be found in the limitations of the life assessment method for an amorphous alloy soft magnetic material insulating coating described above, and will not be repeated here. Each module in the aforementioned life assessment system for an amorphous alloy soft magnetic material insulating coating can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0096] Figure 4 An internal structural diagram of a computer device is shown in one embodiment. This computer device may specifically be a terminal or a server. Figure 4As shown, the computer device includes a processor, memory, network interface, display, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a method for evaluating the lifespan of an insulating coating on an amorphous alloy soft magnetic material. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0097] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computing devices may include more or fewer components than those shown in the figure, or combine certain components, or have the same component arrangement.

[0098] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0099] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0100] In summary, the present invention provides a method and system for assessing the lifespan of an amorphous alloy soft magnetic material insulating coating. The method involves classifying the amorphous alloy soft magnetic magnetic materials from decommissioned transformers into several sample sets of the same type of material and corresponding sample sets of transformers with the same type of material. Then, it acquires coating parameters from the sample sets of the same type of material, including coating resistivity, coating adhesion, average crack length, and number of micropores on the coating surface. It also acquires transformer statistical data from the corresponding sample sets of transformers with the same type of material, including transformer operating years, operating load, ambient temperature, and winding temperature. Based on the transformer hotspot temperature obtained from the transformer statistical data, it calculates the transformer thermal life loss. Finally, it selects resistance-related parameters based on correlation analysis between coating parameters and transformer thermal life loss. After establishing a multiple regression model based on the coating resistivity, the resistance-related parameters of the strip to be evaluated and the corresponding true coating resistivity are obtained. A fitted coating resistivity is then obtained based on the multiple regression model and the resistance-related parameters of the strip to be evaluated. Furthermore, based on the true coating resistivity and the fitted coating resistivity, the true failure probability and the fitted failure probability corresponding to the preset failure mode are obtained. Finally, the life assessment results are obtained based on the true failure probability, the fitted failure probability, and the preset critical value. This technical solution, based on the analysis of the influence of thermal, mechanical stress, and chemical corrosion on the coating performance degradation in transformers, simply and effectively provides a valid criterion for directly judging the end of the life of amorphous alloy strip coatings and corresponding reliability indicators. This effectively improves the reliability and accuracy of life assessment of amorphous alloy soft magnetic material insulating coatings, thereby providing a reliable guarantee for the safe operation of transformers.

[0101] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0102] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the technical principles of this invention, and these improvements and substitutions should also be considered within the scope of protection of this application. Therefore, the scope of protection of this patent application should be determined by the scope of the claims.

Claims

1. A method for assessing the lifetime of an insulating coating on an amorphous alloy soft magnetic material, characterized in that, The method includes the following steps: Amorphous alloy soft magnetic tape materials were obtained from the returned transformers, and the amorphous alloy soft magnetic tape materials were classified according to their models to obtain several sample sets of tape materials of the same model and corresponding sample sets of transformers with the same model of tape materials. The coating parameters of the same type of strip sample set are obtained, and statistical analysis is performed on the corresponding transformer sample set of the same type of strip to obtain transformer statistical data. The coating parameters include coating resistivity, coating adhesion, average crack length of coating cracks, and number of micropores on the coating surface. The transformer statistical data includes transformer operating years, operating load, ambient temperature, and winding temperature. Based on the transformer statistics, the transformer hot spot temperature is obtained, and based on the transformer hot spot temperature and the transformer statistics, the transformer thermal life loss is obtained. Based on the coating parameters and the transformer thermal life loss, resistance-related parameters are selected, and a multiple regression model is established based on the resistance-related parameters and the coating resistivity. Obtain the resistance-related parameters of the strip to be evaluated and the corresponding true coating resistance coefficient, and obtain the fitted coating resistance coefficient based on the multivariate regression model and the resistance-related parameters of the strip to be evaluated. Based on the actual coating resistivity and the fitted coating resistivity, the actual failure probability and the fitted failure probability corresponding to the preset failure mode are obtained, and the life assessment result is obtained based on the actual failure probability, the fitted failure probability and the preset critical value. The step of obtaining the actual failure probability and the fitted failure probability corresponding to the preset failure mode based on the actual coating resistivity and the fitted coating resistivity includes: The actual coating resistivity and the fitted coating resistivity are respectively input into the coating failure probability function under the preset failure mode to obtain the corresponding actual failure probability and fitted failure probability; the preset failure mode includes step failure mode and progressive failure mode, and the corresponding coating failure probability functions are expressed as follows: in, P 1( R )and P 2( R ) represent the failure probability of the insulating coating under step failure mode and progressive failure mode, respectively; R This represents the actual or fitted coating resistivity. R 1,d and R 2,d These represent the minimum allowable values ​​of coating resistivity under step failure mode and progressive failure mode, respectively. R 2,w This represents the warning value of the resistance coefficient under progressive failure mode.

2. The life assessment method for the insulating coating of amorphous alloy soft magnetic materials as described in claim 1, characterized in that, The step of obtaining the transformer hot spot temperature based on the transformer statistical data includes: Based on the operating load, the ambient temperature, and the winding temperature, the hot spot temperature of the transformer is obtained through finite element analysis or the difference equation method.

3. The life assessment method for the insulating coating of amorphous alloy soft magnetic materials as described in claim 1, characterized in that, The step of obtaining the transformer thermal life loss based on the transformer hot spot temperature and the transformer statistical data includes: Obtain the rated load of the transformer, and based on the operating load, obtain the average load of the transformer; The transformer's thermal life loss is obtained by using the transformer's rated load, the transformer's service life, the transformer's average load, and the transformer's hot spot temperature; the transformer's thermal life loss is expressed as: in, F A This indicates the thermal life loss of the transformer; P a , P 0、 T and These represent the transformer's average load, rated load, service life, and hot spot temperature, respectively.

4. The lifespan assessment method for the insulating coating of amorphous alloy soft magnetic materials as described in claim 1, characterized in that, The step of selecting resistance-related parameters based on the coating parameters and the transformer thermal life loss includes: Calculate the correlation coefficients between the coating adhesion, the average crack length of the coating, the number of micropores on the coating surface, and the transformer thermal life loss and the coating resistivity, respectively. Based on the correlation coefficients, the resistance-related parameters are obtained through correlation testing methods.

5. The life assessment method for the insulating coating of amorphous alloy soft magnetic materials as described in claim 4, characterized in that, The step of obtaining the resistance-related parameters based on each correlation coefficient and through a correlation test method includes: Based on each correlation coefficient, the corresponding one-sided t-test characteristic values ​​are obtained; the one-sided t-test characteristic values ​​are expressed as: in, t、 and n These represent the eigenvalues, correlation coefficients, and sample size of the sample set of strip transformers of the same model, respectively; Based on the characteristic values ​​of each one-sided t-test, the corresponding confidence probabilities are obtained, and it is determined whether the confidence probabilities are less than the preset significance level. If so, the corresponding parameters are determined as the resistance-related parameters.

6. The lifespan assessment method for the insulating coating of amorphous alloy soft magnetic materials as described in claim 1, characterized in that, The step of obtaining the lifetime assessment result based on the actual failure probability, the fitted failure probability, and the preset critical value includes: If both the actual failure probability and the fitted failure probability are less than the preset threshold, then the life assessment result is determined to be the end of the coating life; otherwise, it is determined whether both the actual failure probability and the fitted failure probability are greater than the preset threshold. If both the actual failure probability and the fitted failure probability are greater than the preset critical value, the lifetime assessment result is determined to be that the coating has remaining lifetime; otherwise, the lifetime assessment result is determined to be that the coating lifetime has reached a warning state.

7. A life assessment system for insulating coatings of amorphous alloy soft magnetic materials, characterized in that, The system employing the lifetime assessment method for the insulating coating of amorphous alloy soft magnetic materials as described in claim 1, comprises: The sample set acquisition module is used to acquire amorphous alloy soft magnetic tape materials in decommissioned transformers, and classify the amorphous alloy soft magnetic tape materials according to their models to obtain several sample sets of the same model of tape materials and corresponding sample sets of transformers with the same model of tape materials. The basic data acquisition module is used to acquire the coating parameters of the same type of strip sample set, and to perform statistical analysis on the corresponding same type of strip transformer sample set to obtain transformer statistical data; the coating parameters include coating resistivity, coating adhesion, average crack length of coating cracks and number of micropores on the coating surface; the transformer statistical data includes transformer operating years, operating load, ambient temperature and winding temperature; The life loss calculation module is used to obtain the transformer hot spot temperature based on the transformer statistical data, and to obtain the transformer thermal life loss based on the transformer hot spot temperature and the transformer statistical data. The regression model construction module is used to select resistance-related parameters based on the coating parameters and the transformer thermal life loss, and to establish a multiple regression model based on the resistance-related parameters and the coating resistivity. The evaluation data acquisition module is used to acquire the resistance-related parameters of the strip to be evaluated and the corresponding true coating resistance coefficient, and to obtain the fitted coating resistance coefficient based on the multivariate regression model and the resistance-related parameters of the strip to be evaluated. The life assessment module is used to obtain the actual failure probability and the fitted failure probability corresponding to the preset failure mode based on the actual coating resistivity and the fitted coating resistivity, and to obtain the life assessment result based on the actual failure probability, the fitted failure probability and the preset critical value.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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