Time curve fitting method, corona resistance time calculation method and storage device

Abstract

The present application is about a time curve fitting method, a corona resistance time calculation method and a storage device. The curve fitting method includes: (1) obtaining N measured data groups {Ui, Ti} of the sample to be tested; (2) performing linear fitting according to the measured data group {Ui, Ti}, calculating the first voltage-corona resistance time fitting curve and the first multiple regression coefficient R1; (3) performing exponential fitting according to the measured data group {Ui, Ti} and the first exponential model, calculating the second voltage-corona resistance time fitting curve and the second multiple regression coefficient R2; (4) selecting the fitting curve corresponding to the larger of the first multiple regression coefficient R1 and the second multiple regression coefficient R2 as the voltage-corona resistance time standard curve. The voltage-corona resistance time standard curve provided in the present application can be effectively used to characterize the insulation performance of the surface film of the self-adhesive enameled wire.

Description

Technical Field

[0001] The present application relates to the technical field of variable frequency motor manufacturing, and in particular to a time curve fitting method, a corona resistance time calculation method and a storage device. Background Art

[0002] In the related art, self-adhesive enameled wire is usually used as the winding of the stator coil of the motor. The surface film of the self-adhesive enameled wire has both adhesiveness and insulation. On the one hand, the self-adhesive enameled wire can be wound layer by layer and adhered to the stator bracket to form the coil winding of the stator; on the other hand, it has insulation properties, so that adjacent wires in the coil are insulated from each other.

[0003] Therefore, the insulation performance of the surface film of the self-adhesive enameled wire determines the performance of the stator coil. However, in the variable frequency motor, the surface film of the self-adhesive enameled wire will be broken down by the high-frequency voltage and lose its insulation performance. In order to explore whether the surface film of the self-adhesive enameled wire meets the application requirements of the variable frequency motor, the voltage-corona resistance time curve of the surface film of the self-adhesive enameled wire needs to be calculated before it leaves the factory. The staff only needs to substitute the working voltage of the variable frequency motor into the voltage-corona resistance time curve to obtain the corona resistance time of the self-adhesive enameled wire under the working voltage. If the sum of the corona resistance times corresponding to different working voltages is greater than the sum of the duration of each working voltage of the variable frequency motor, it proves that the self-adhesive enameled wire meets the application requirements of the variable frequency motor.

[0004] Under the existing technology, the staff needs to repeatedly test the corona resistance time of the self-adhesive enameled wire under different voltages, and substitute the curve equation T = K*U -n In the above, K is a known constant, and n needs to be solved to calculate the voltage-corona resistance time curve of the self-adhesive enameled wire. However, in practical applications, n needs to be accurate to 4 decimal places, so the staff needs to spend a lot of time to collect data on the corona resistance time of the self-adhesive enameled wire under different voltages.

[0005] The method of collecting the corona resistance time of a large number of self-adhesive enameled wires at different voltages takes too long. Therefore, there is an urgent need for a voltage-corona resistance time curve fitting method that can accurately reflect the relationship between the voltage and corona resistance time of the self-adhesive enameled wire and has low requirements on the accuracy of solving unknown constants. Summary of the invention

[0006] In order to overcome the problems existing in the related art, the first aspect of the present application provides a time curve fitting method, characterized in that it includes the following steps:

[0007] S1. Obtain N measured data sets of samples to be tested {U i , T i}; U is the voltage across the sample to be tested, in kilovolts; T is the corona resistance time, in hours; N is a positive integer greater than 3, and i is an integer greater than 1;

[0008] S2, according to the measured data set {U i , T i} Perform linear fitting to calculate the first voltage-corona resistance time fitting curve and the first multivariate regression coefficient R1;

[0009] S3, according to the measured data set {U i , T i} and the first exponential model are exponentially fitted to calculate the second voltage-corona resistance time fitting curve and the second multivariate regression coefficient R2; the first exponential model is T = A*e B / U ; A and B are fitting constant terms.

[0010] S4. Select the fitting curve corresponding to the larger one of the first multiple regression coefficient R1 and the second multiple regression coefficient R2 as the voltage-corona resistance time standard curve.

[0011] In one embodiment, the measured data set {U i , T i} and the first exponential model are exponentially fitted to calculate the second voltage-corona resistance time fitting curve and the second multivariate regression coefficient R2, including:

[0012] For the measured data set {U i , T i} is logarithmically transformed to convert the voltage Ui into a reciprocal form to obtain the transformed data set {1 / U i , ln T i};

[0013] Taking the logarithm of both sides of the first exponential model, the second exponential model is: ln T = ln (A) + B / U ;

[0014] According to the transformation data set {1 / U i , ln T i} and the second exponential model are fitted to calculate a second voltage-corona resistance time fitting curve and a second multivariate regression coefficient R1.

[0015] In one embodiment, before selecting the fitting curve corresponding to the larger one of the first multiple regression coefficient R1 and the second multiple regression coefficient R2 as the voltage-corona resistance time standard curve, the method further includes:

[0016] Get the preset multiple regression coefficient R S ;

[0017] If the first multiple regression coefficient R1 is less than or equal to the preset multiple regression coefficient R S , and the second multiple regression coefficient R2 is less than or equal to the preset multiple regression coefficient R S , then execute step S1 to refit;

[0018] If the first multiple regression coefficient R1 is greater than the preset multiple regression coefficient R S , or the second multiple regression coefficient R2 is greater than the preset multiple regression coefficient R S , then continue to step S4.

[0019] In one embodiment, if the first multiple regression coefficient R1 is less than or equal to the preset multiple regression coefficient R S , and the second multiple regression coefficient R2 is less than or equal to the preset multiple regression coefficient R S , then execute step S1 to refit, including:

[0020] If the first multiple regression coefficient R1 or the second multiple regression coefficient R2 is less than the preset multiple regression coefficient R S , then execute steps S1, S2, S31 and S4;

[0021] S31, according to the measured data set {U i , T i} and the third exponential model are exponentially fitted to calculate the second voltage-corona resistance time fitting curve and the second multivariate regression coefficient R2; the third exponential model is The A', the B' and the C are constant terms of the fitting curve.

[0022] In one embodiment, the method further comprises selecting the fitting curve corresponding to the larger one of the first multiple regression coefficient R1 and the second multiple regression coefficient R2 as the voltage-corona resistance time standard curve:

[0023] If the second voltage-corona resistance time fitting curve is selected as the voltage-corona resistance time standard curve, the constant form of the second voltage-corona resistance time fitting curve is calculated: T = A*e B / U And record it as the voltage-corona resistance time standard curve.

[0024] In one embodiment, the comparing the first multiple regression coefficient R1 and the second multiple regression coefficient R2, and selecting the fitting curve with the larger multiple regression coefficient as the voltage-corona resistance time standard curve further includes:

[0025] The voltage U is calculated according to the voltage-corona resistance time standard curve i and the corona resistance time T i , and get the fitting data set {U' i , T' i};

[0026] According to the fitting data set {U' i , T' i}, the measured data set {U i , T i} Calculate the standard error of the corona resistance time;

[0027] Calculate the voltage U based on the standard error and the corona resistance time i The confidence interval below.

[0028] In one embodiment, the sample to be tested is an enameled wire, the diameter of the enameled wire is 0.60 mm, and the paint film thickness of the enameled wire is 74-76 μm.

[0029] The second aspect of the present application provides a method for calculating corona resistance time, based on the time fitting curve, comprising:

[0030] Get a series of voltages U to be tested for the enameled wire x and the voltage-corona resistance time standard curve; wherein x is a positive integer greater than 0;

[0031] According to the voltage to be measured U x The theoretical corona resistance time of the enameled wire under a series of test voltages is calculated using the voltage-corona resistance time standard curve.

[0032] In one embodiment, the voltage to be measured U x After calculating the theoretical corona resistance time of the enameled wire under a series of voltages to be tested using the voltage-corona resistance time standard curve, the following further comprises:

[0033] Obtaining an actual voltage-time curve and the theoretical corona resistance time; the voltage-time curve reflects the relationship between the voltage applied to both ends of the enameled wire and the time during actual application;

[0034] Obtain a series of the voltages to be measured U according to the actual voltage-time curve x The corresponding corona duration is as follows;

[0035] Determine whether the enameled wire meets the standard based on the corona duration and the theoretical corona resistance time:

[0036] At the voltage to be measured U xIf the corona duration is greater than the theoretical corona resistance time of the enameled wire, it is judged that the enameled wire does not meet the standard, otherwise it meets the standard.

[0037] A third aspect of the present application provides a storage device having executable code stored thereon. When the executable code is executed by a processor of an electronic device, the processor is caused to execute the method as described in the first and second aspects of the present application.

[0038] Through a time curve fitting method provided in the present application, the voltage and corona resistance time data corresponding to the surface film of the self-adhesive enameled wire, that is, the measured data group, are obtained. The linear fitting and exponential fitting of the voltage-corona resistance time fitting curve are performed through the measured data group, and the multivariate regression coefficients of the two fitting methods are compared. When the multivariate regression coefficients of the two voltage-corona resistance time fitting curves are greater than the preset values, it means that the fitting effect is better, and then the multivariate regression coefficient is selected as the voltage-corona resistance time standard curve for characterizing the insulation performance of the surface film of the self-adhesive enameled wire.

[0039] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The above and other objects, features and advantages of the present application will become more apparent through a more detailed description of exemplary embodiments of the present application in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments of the present application.

[0041] Figure 1 It is a schematic flow chart of a time curve fitting method shown in an embodiment of the present application;

[0042] Figure 2 It is a flow chart of a method for calculating corona resistance time shown in an embodiment of the present application. DETAILED DESCRIPTION

[0043] The preferred embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to make the present application more thorough and complete, and to fully convey the scope of the present application to those skilled in the art.

[0044] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0045] It should be understood that although the terms "first", "second", "third", etc. may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0046] Embodiment 1

[0047] In order to calculate a fitting curve that can accurately reflect the voltage-corona resistance time relationship of the self-adhesive enameled wire, the embodiment of the present application provides a time curve fitting method, such as Figure 1 As shown, the following steps are included:

[0048] S1. Obtain N measured data sets of samples to be tested {U i , T i}; U is the voltage across the sample to be tested, in kilovolts; T is the corona resistance time, in hours; N is a positive integer greater than 3, and i is an integer greater than 1;

[0049] Furthermore, the sample to be tested is an enameled wire, the conductor portion of the enameled wire has a diameter of 0.60 mm, the paint film thickness of the enameled wire is 74-76 μm, and the paint film is a corona-resistant film.

[0050] Specifically, the enameled wire is prepared by a vertical enameling machine.

[0051] In the embodiment of the present application, the measured data set {U i , T i The corona resistance test is performed by applying different voltages U at both ends of the enameled wire in the length direction using a corona resistance tester. i , in kilovolts (kv), and the corona resistance tester starts timing; when the enameled wire is broken down by corona, the timing is stopped and the corona resistance time T is recorded. i , the unit is hour (h).

[0052] Specifically, each set of measured data needs to be tested at least three times. The method of selecting the corona resistance data refers to the national standard GB / T 4074.21-2018, that is, at voltage U i After repeated experiments, the corona resistance time T i The average of the minimum and middle values.

[0053] In actual operation, when the number of repeated tests is an even number and the intermediate values ​​include 2, the average value of the 2 intermediate values ​​is taken.

[0054] Specifically, the measured data set {U i , T i The voltage range of} is (2.5, 3.7), and the voltage U i The test interval is 0.2, the voltage U i The unit is kilovolt (kv).

[0055] In the embodiment of the present application, the measured data set {U i , T i}As shown in the following table:

[0056] Voltage U / kV 3.7 3.5 3.3 3.1 2.9 2.7 2.5 Corona resistance time T / h 1.341 1.873 2.457 4.927 7.343 12.923 25.164

[0057] S2, according to the measured data set {U i , T i} Perform linear fitting to calculate the first voltage-corona resistance time fitting curve and the first multivariate regression coefficient R1;

[0058] Specifically, according to the measured data set {U i , T i} Perform linear fitting, and the calculated first voltage-corona resistance time fitting curve is: T=62.5-17.85U, and then the first multivariate regression coefficient R1 is obtained to be equal to 78.44%.

[0059] S3, according to the measured data set {U i , T i} and the first exponential model are exponentially fitted to calculate the second voltage-corona resistance time fitting curve and the second multivariate regression coefficient R2; the first exponential model is T = A*e B / U ; A and B are fitting constant terms.

[0060] Specifically, in order to obtain a voltage-corona resistance time fitting curve with a higher fitting effect, it is necessary to change the fitting model to the first exponential model T=A*e B / U .

[0061] Further, in order to reduce the difficulty of data processing before using the first exponential model to perform curve fitting, the measured data set {U i , T i The corona resistance time T i Perform logarithmic transformation and obtain the voltage U i The inverse of the transformation data set {1 / U i , ln T i};

[0062] Specifically, the transformation data set {1 / U i , ln T i As shown in Table 2:

[0063] Voltage 1 / U 0.270 0.286 0.303 0.323 0.345 0.370 0.400 Corona resistance time Ln(T) 0.293 0.628 0.899 1.595 1.994 2.559 3.225

[0064] At the same time, the first exponential model also needs to be changed to a logarithmic form; taking the logarithm of both sides of the first exponential model, the second exponential model is obtained as follows: ln T = ln (A) + B / U ;

[0065] In the embodiment of the present application, according to the transformation data set {ln U i , ln T i} and the second exponential model to fit and calculate the second voltage-corona resistance time fitting curve and the second multivariate regression coefficient R1. i , ln T i The fitting relationship between ln T and 1 / U was calculated to be ln T = -5.911 + 22.888 / U, and the second multiple regression coefficient R2 was obtained to be 99.54%.

[0066] Furthermore, before step S4, the method further includes: obtaining a preset multiple regression coefficient R S ;

[0067] If the first multiple regression coefficient R1 is less than or equal to the preset multiple regression coefficient R S , and the second multiple regression coefficient R2 is less than or equal to the preset multiple regression coefficient R S , then execute step S1 to refit.

[0068] In the embodiment of the present application, in order to ensure the accuracy of the voltage-corona resistance time standard curve, the first multiple regression coefficient R1 and the second multiple regression coefficient R2 need to be greater than the preset multiple regression coefficient R S If the first multiple regression coefficient R1 and the second multiple regression coefficient R2 are both less than the preset multiple regression coefficient R S, then delete the record of the second voltage-corona resistance time fitting curve, and then change the exponential model of the second voltage-corona resistance time fitting curve and re-fit it.

[0069] Specifically, the refitting steps include:

[0070] If the first multiple regression coefficient R1 or the second multiple regression coefficient R2 is less than the preset multiple regression coefficient R S , then execute steps S1, S2, S31 and S4;

[0071] S31, according to the measured data set {U i , T i} and the third exponential model are exponentially fitted to calculate the second voltage-corona resistance time fitting curve and the second multivariate regression coefficient R2; the third exponential model is The A′, the B′ and the C are constant terms of the fitting curve.

[0072] Specifically, the preset multiple regression coefficient R S is 90%.

[0073] Furthermore, if the first multiple regression coefficient R1 is greater than the preset multiple regression coefficient R S , or the second multiple regression coefficient R2 is greater than the preset multiple regression coefficient R S , then continue to step S4.

[0074] S4. Select the fitting curve corresponding to the larger one of the first multiple regression coefficient R1 and the second multiple regression coefficient R2 as the voltage-corona resistance time standard curve.

[0075] Furthermore, after selecting the fitting curve corresponding to the larger one of the first multiple regression coefficient R1 and the second multiple regression coefficient R2 as the voltage-corona resistance time standard curve, the method further includes:

[0076] In the embodiment of the present application, the second voltage-corona resistance time fitting curve is logarithmically transformed before fitting. To facilitate daily use habits, if the second voltage-corona resistance time fitting curve is selected as the voltage-corona resistance time standard curve, the constant form of the second voltage-corona resistance time fitting curve is calculated: T = A*e B / U And record it as the voltage-corona resistance time standard curve.

[0077] Specifically, the constant form of the second voltage-corona resistance time fitting curve obtained in the embodiment of the present application is: T=0.0027e 22.888 / U .

[0078] Furthermore, after refitting, the first multiple regression coefficient R1 and the second multiple regression coefficient R2 are both less than the preset multiple regression coefficient R S , then stop fitting and send to reacquire the measured data set {U i , T i}ask.

[0079] Furthermore, after step S4, comparing the first multiple regression coefficient R1 and the second multiple regression coefficient R2, and selecting the fitting curve with the larger multiple regression coefficient as the voltage-corona resistance time standard curve, the method further includes:

[0080] The voltage U is calculated according to the voltage-corona resistance time standard curve i and the corona resistance time T i , and get the fitting data set {U' i , T' i}.

[0081] Specifically, the fitting data set {U' i , T' i} and the measured data set {U i , T i}As shown in the following table:

[0082]

[0083] Further, according to the fitting data set {U' i , T' i}, the measured data set {U i , T i}Calculate the standard error of the corona resistance time; calculate the confidence interval under the voltage Ui based on the standard error and the corona resistance time.

[0084] Exemplarily, the confidence level of the confidence interval is 95%.

[0085] Exemplarily, when U=3.1kV, it is calculated that the confidence interval of the corona resistance time with a confidence level of 95% is (3.535, 5.480). In other words, when U=3.1kV, when the corona resistance time falls within the interval (3.535, 5.480), there is a 95% confidence that the test result is accurate.

[0086] In the embodiment of the present application, the voltage and corona resistance time data corresponding to the enameled wire are obtained by the corona resistance tester, that is, the measured data set {U i , T i According to the measured data set {U i , T i}Perform linear fitting and exponential fitting of the voltage-corona resistance time fitting curve, compare the multiple regression coefficients of the two fitting methods, and when the multiple regression coefficients of the two voltage-corona resistance time fitting curves are greater than the preset values, it means that the fitting effect is good, and then select the larger of the multiple regression coefficients in the voltage-corona resistance time fitting curve as the voltage-corona resistance time standard curve for characterizing the insulation properties of the surface film of self-adhesive enameled wire.

[0087] Embodiment 2

[0088] In order to more intuitively compare whether the enameled wire can work normally under the set voltage, the embodiment of the present application provides a method for calculating the corona resistance time, based on the voltage-corona resistance time standard curve described in Example 1, such as Figure 2 As shown, the following steps are included:

[0089] S21. Obtain a series of voltages U to be tested for the enameled wire x and the voltage-corona resistance time standard curve; wherein x is a positive integer greater than 0.

[0090] S22, according to the voltage to be measured U x Calculate the theoretical corona resistance time of the enameled wire under a series of voltages to be tested using the voltage-corona resistance time standard curve;

[0091] Further, the voltage to be measured U x After calculating the theoretical corona resistance time of the enameled wire under a series of voltages to be tested using the voltage-corona resistance time standard curve, the following further comprises:

[0092] S23, obtaining an actual voltage-time curve and the theoretical corona resistance time. The voltage-time curve reflects the relationship between the voltage applied to both ends of the enameled wire and the time during actual application;

[0093] Obtain a series of the voltages to be measured U according to the actual voltage-time curve x The corresponding corona duration is as follows;

[0094] Determine whether the enameled wire meets the standard according to the corona duration and the theoretical corona resistance time: x If the corona duration is greater than the theoretical corona resistance time of the enameled wire, it is judged that the enameled wire does not meet the standard, otherwise it meets the standard.

[0095] In an embodiment of the present application, a voltage-time curve in an application scenario of a surface film of a self-adhesive enameled wire is obtained, and the maximum voltage is substituted into the voltage-corona resistance time standard curve to calculate the corona resistance time. If the corona resistance time is less than the duration of the maximum voltage in the voltage-time curve, it means that the self-adhesive enameled wire can operate well in a scenario that conforms to the voltage-time curve.

[0096] Embodiment 3

[0097] A storage device stores executable codes thereon. When the executable codes are executed by a processor of an electronic device, the processor is caused to execute the methods described in the first and second embodiments.

[0098] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated again here.

[0099] The scheme of the present application has been described in detail above with reference to the accompanying drawings. In the above embodiments, the description of each embodiment has its own emphasis. For the part that is not described in detail in a certain embodiment, refer to the relevant description of other embodiments. Those skilled in the art should also know that the actions and modules involved in the description are not necessarily required for the present application. In addition, it can be understood that the steps in the method of the embodiment of the present application can be adjusted in order, merged and deleted according to actual needs, and the modules in the device of the embodiment of the present application can be merged, divided and deleted according to actual needs.

[0100] In addition, the method according to the present application may also be implemented as a computer program or a computer program product, which includes computer program code instructions for executing some or all of the steps in the above method of the present application.

[0101] Alternatively, the present application can also be implemented as a non-temporary machine-readable storage medium (or computer-readable storage medium, or machine-readable storage medium) on which executable code (or computer program, or computer instruction code) is stored. When the executable code (or computer program, or computer instruction code) is executed by a processor of an electronic device or an electronic device, server, etc., the processor executes part or all of the steps of the above-mentioned method according to the present application.

[0102] Those skilled in the art will further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the application herein may be implemented as electronic hardware, computer software, or combinations of both.

[0103] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system and method according to multiple embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a part of a module, a program segment or a code, and the part of the module, the program segment or the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or the flow chart, and the combination of the square boxes in the block diagram and / or the flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0104] The embodiments of the present application have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A time curve fitting method, characterized in that: The following steps are involved: S1. Obtain N measured data sets of samples to be tested {U i , T i }; U is the voltage across the sample to be tested, in kilovolts; T is the corona resistance time, in hours; N is a positive integer greater than 3, and i is an integer greater than 1; S2, according to the measured data set {U i , T i } Perform linear fitting to calculate the first voltage-corona resistance time fitting curve and the first multivariate regression coefficient R1; S3, according to the measured data set {U i , T i } and the first exponential model are exponentially fitted to calculate the second voltage-corona resistance time fitting curve and the second multivariate regression coefficient R2; the first exponential model is T = A*e B / U; A and B are fitting constant terms; S4. Select the fitting curve corresponding to the larger one of the first multiple regression coefficient R1 and the second multiple regression coefficient R2 as the voltage-corona resistance time standard curve.

2. A time curve fitting method according to claim 1, characterized in that: The measured data set {U i , T i } and the first exponential model are exponentially fitted to calculate the second voltage-corona resistance time fitting curve and the second multivariate regression coefficient R2, including: For the measured data set {U i , T i } is logarithmically transformed to convert the voltage Ui into a reciprocal form to obtain the transformed data set {lnU i , lnT i }; Taking the logarithm of both sides of the first exponential model, the second exponential model is obtained as follows: lnT = ln(A) + B / U; According to the transformation data set {lnU i , lnT i } and the second exponential model are fitted to calculate a second voltage-corona resistance time fitting curve and a second multivariate regression coefficient R1.

3. A time curve fitting method according to claim 1, characterized in that: Before selecting the fitting curve corresponding to the larger one of the first multiple regression coefficient R1 and the second multiple regression coefficient R2 as the voltage-corona resistance time standard curve, the following further comprises: Get the preset multiple regression coefficient R S ; If the first multiple regression coefficient R1 is less than or equal to the preset multiple regression coefficient R S , and the second multiple regression coefficient R2 is less than or equal to the preset multiple regression coefficient R S , then execute step S1 to refit; If the first multiple regression coefficient R1 is greater than the preset multiple regression coefficient R S , or the second multiple regression coefficient R2 is greater than the preset multiple regression coefficient R S , then continue to step S4.

4. A time curve fitting method according to claim 3, characterized in that: If the first multiple regression coefficient R1 is less than or equal to the preset multiple regression coefficient R S , and the second multiple regression coefficient R2 is less than or equal to the preset multiple regression coefficient R S , then execute step S1 to refit, including: If the first multiple regression coefficient R1 or the second multiple regression coefficient R2 is less than the preset multiple regression coefficient R S , then execute steps S1, S2, S31 and S4; S31, according to the measured data set {U i , T i } and the third exponential model are exponentially fitted to calculate the second voltage-corona resistance time fitting curve and the second multivariate regression coefficient R2; the third exponential model is The A', the B' and the C are constant terms of the fitting curve.

5. A time curve fitting method according to claim 1, characterized in that: After selecting the fitting curve corresponding to the larger one of the first multiple regression coefficient R1 and the second multiple regression coefficient R2 as the voltage-corona resistance time standard curve, the method further includes: If the second voltage-corona resistance time fitting curve is selected as the voltage-corona resistance time standard curve, the constant form of the second voltage-corona resistance time fitting curve is calculated: T = A*e B / U and recorded as the voltage-corona resistance time standard curve.

6. A time curve fitting method according to claim 1, characterized in that: After comparing the first multiple regression coefficient R1 and the second multiple regression coefficient R2 and selecting the fitting curve with the larger multiple regression coefficient as the voltage-corona resistance time standard curve, the method further includes: The voltage U is calculated according to the voltage-corona resistance time standard curve i and the corona resistance time T i , and get the fitting data set {U' i , T' i }; According to the fitting data set {U' i , T' i }, the measured data set {U i , T i } Calculate the standard error of the corona resistance time; Calculate the voltage U based on the standard error and the corona resistance time i The confidence interval below.

7. A time curve fitting method according to claim 1, characterized in that: The sample to be tested is an enameled wire, the diameter of the enameled wire is 0.60 mm, and the thickness of the paint film of the enameled wire is 74-76 μm.

8. A method for calculating corona resistance time, characterized in that: The time curve fitting method according to any one of claims 1 to 7 comprises: Get a series of voltages U to be tested for the enameled wire x and the voltage-corona resistance time standard curve; wherein x is a positive integer greater than 0; According to the voltage to be measured U x The theoretical corona resistance time of the enameled wire under a series of test voltages is calculated using the voltage-corona resistance time standard curve.

9. A method for calculating corona resistance time according to claim 8, characterized in that: According to the voltage to be measured U x After calculating the theoretical corona resistance time of the enameled wire under a series of voltages to be tested using the voltage-corona resistance time standard curve, the following further comprises: Obtaining an actual voltage-time curve and the theoretical corona resistance time; the voltage-time curve reflects the relationship between the voltage applied to both ends of the enameled wire and the time during actual application; Obtain a series of the voltages to be measured U according to the actual voltage-time curve x The corresponding corona duration is as follows; Determine whether the enameled wire meets the standard based on the corona duration and the theoretical corona resistance time: At the voltage to be measured U x If the corona duration is greater than the theoretical corona resistance time of the enameled wire, it is judged that the enameled wire does not meet the standard, otherwise it meets the standard.

10. A storage device having executable codes stored thereon, wherein when the executable codes are executed by a processor of an electronic device, the processor is caused to execute the method according to any one of claims 1 to 9.

Images