Copper wire selection method and system for arc striking of high-capacity short-circuit arc test

Abstract

According to the method, on the basis of the correlation between the high-capacity short-circuit arc test condition and the steady-state pressure wave amplitude in the arc process and the impact wave amplitude in the copper wire electric explosion process, a reliable quantitative rule of a pressure peak value and influence factors can be obtained by performing grey correlation analysis and multivariate function regression fitting on a large amount of test data; and then a selection scheme of the diameter and the length of the copper wire is made, so that the influence of extra shock waves generated by arc striking of the copper wire on a test result is reduced to a controllable range, the probability of misoperation of the pressure relief valve is reduced, and the reliability and the safety of the pressure relief valve for protecting power equipment are greatly improved.

Description

Technical Field

[0001] The invention relates to the field of high voltage simulation test of electric power equipment, and in particular to a copper wire selection method and system for arc initiation in a large-capacity short-circuit arc test. Background Art

[0002] Oil-immersed power transformers are one of the key equipment in the power system. The normal operation of oil-immersed power transformers is an important guarantee for the safe, reliable, high-quality and economical operation of the power system. The occurrence of transformer failures and accidents must be prevented and reduced to the greatest extent possible. Oil-immersed transformers need to operate for a long time, and it is impossible to completely avoid failures and accidents. There are many reasons for transformer failures and accidents, especially the insulation aging and material deterioration caused by long-term operation of the transformer, which have become the main factors leading to transformer failures.

[0003] The faults of oil-immersed power transformers involve a wide range of areas, the fault mechanism is complex, and there are many ways to classify the fault types. According to the circuit, it can be divided into circuit faults, magnetic circuit faults and oil circuit faults; according to the main structure of the transformer, it can be divided into winding faults, core faults, oil quality faults and accessory faults; according to the common faults, it can be divided into insulation faults, core faults, tap changer faults, etc. Internal defects, insulation degradation and mechanical faults of the transformer will eventually turn into thermal and electrical faults. Electrical faults are internal faults of power transformers caused by insulation degradation under the action of high-voltage electric fields. Usually, electrical faults divide the internal discharge faults of power transformers into three categories: partial discharge, spark discharge and arc discharge according to the different energy density of the discharge. Partial discharge is a precursor to spark discharge and arc discharge. The energy density of partial discharge is not large, but once it develops, it will form a high-energy discharge and cause insulation breakdown or damage. Spark discharge is a kind of intermittent discharge, generally a low-energy discharge. Arc discharge has a high energy density and is a high-energy discharge. It is characterized by large and rapid gas production, which can easily cause perforation, burning or carbonization of insulating paper, and deformation or melting of metal materials.

[0004] In order to study the dynamic change law of the internal pressure of oil-immersed power transformers when an arc fault occurs, it is necessary to carry out arc fault simulation tests. However, the problem of arc initiation is involved in the long gap arc test in oil. In the current large-capacity short-circuit arc test of oil-immersed power equipment, copper wire is often used for arc initiation. Then, when the power frequency current with an amplitude of tens of kiloamperes passes through the metal wire, it will cause the copper wire to explode.

[0005] Although the introduction of copper wire can easily solve the problem of arc striking, in fact, the process of copper wire melting under the action of large current is the electric explosion of metal wire in liquid. This process itself will produce extremely strong shock waves. When superimposed with the shock wave of the arc fault caused by it, it will cause the shock wave generated by the arc test method of metal wire arc striking to be different from the shock wave in the short-circuit arc fault under actual working conditions. This difference may cause the malfunction of the pressure relief valve, which will inevitably affect the test results and even make some test conditions too harsh, excessively increasing the requirements for the structural strength of oil-immersed power equipment.

[0006] In summary, copper wire arc initiation has become the arc initiation method adopted by almost all short-circuit arc tests of oil-immersed power equipment. It is still unclear how much influence the existing copper wire selection scheme will have on the test results. The amplitude of the shock wave generated by the electric explosion of the copper wire is unknown, and the extent of the influence on the pressure amplitude generated by the arc process is still unclear. Summary of the invention

[0007] In order to solve the above technical problems, the purpose of the present invention is to provide a copper wire selection method and system for arc initiation in a large-capacity short-circuit arc test.

[0008] The steady-state pressure amplitude generated by the arc process is related to the discharge energy, power frequency current amplitude, power frequency current duration, and whether the test equipment has a reliable pressure relief device. These parameters jointly determine the steady-state pressure amplitude during the arc process. The shock wave amplitude generated by the copper wire electric explosion is related to the copper wire diameter, length, and current rise rate. These parameters jointly determine the shock wave amplitude during the copper wire electric explosion. By selecting the appropriate copper wire diameter and length, the shock wave amplitude generated by the copper wire electric explosion can be made smaller than the pressure amplitude generated by the arc process.

[0009] Therefore, based on the above principle, the present invention identifies and grasps the characteristics of the arc development process and the copper wire electric explosion process, and links them with the steady-state pressure amplitude and the shock wave amplitude through grey correlation analysis and regression analysis. The diameter and length of the copper wire are determined according to the prediction results to ensure that the shock wave amplitude generated by the copper wire electric explosion has little effect on the steady-state pressure amplitude generated by the arc process.

[0010] In order to achieve the above object, the present invention adopts the following scheme.

[0011] A method for selecting a copper wire for arc initiation in a large-capacity short-circuit arc test, the method comprising the following steps:

[0012] S100, obtaining test data through a large-capacity short-circuit arc test, the test data including: test condition data;

[0013] S200, performing grey correlation analysis on the test data and establishing a grey correlation model;

[0014] S300, obtaining the correlation between the test condition data and the steady-state pressure wave amplitude generated by the arc process, and screening out the main influencing factors;

[0015] S400, establishing a relationship between the test condition data and the steady-state pressure wave amplitude generated by the arc process; establishing a corresponding relationship between two size parameters, the length and diameter of the metal wire, and the shock wave amplitude generated by the electric explosion of the copper wire;

[0016] S500. In actual use, the steady-state pressure wave amplitude ultimately generated by the arc process is predicted using the test condition data set in the test, and the shock wave amplitude is set to 1 / x of the steady-state pressure wave amplitude generated by the arc process, where x is determined based on the tolerance level for shock waves caused by the copper wire;

[0017] S600, by predicting the steady-state pressure wave amplitude and determining x, an upper limit value of the shock wave amplitude generated by the metal wire explosion is obtained, and then a selection range of the metal wire size parameter is obtained inversely using the corresponding relationship between the metal wire size parameter and the shock wave amplitude.

[0018] Optionally, in step S100, the test condition data includes: discharge energy, power frequency current amplitude, power frequency current duration, and whether the test equipment has a pressure relief device.

[0019] Optionally, in step S100, the test data also includes test result data, and the test result data includes: the amplitude of the steady-state pressure wave generated by the arc process and the amplitude of the shock wave generated by the electric explosion of the copper wire.

[0020] Optionally, in step S200, establishing a grey relational model includes:

[0021] S201, determining a reference sequence based on the test result data ; The reference sequence is: ;

[0022] S202: Determine the factors that affect the peak value of pressure based on the test condition data and the copper wire diameter and copper wire length to form a comparison sequence ;

[0023] The comparison sequence is: , , , ;

[0024] S203, finding the initial value image of each comparison sequence, that is, performing dimensionless processing on each comparison sequence;

[0025] make ,

[0026] in, get ;

[0027] S204, find reference sequence Compare with sequence The absolute value sequence of the difference between the initial value image and the corresponding components of , and calculate the minimum and maximum values ​​of the absolute value sequence;

[0028] remember , , .

[0029] S205, calculating the correlation coefficient and correlation degree;

[0030] Correlation coefficient: ,

[0031] Among them, p is the resolution coefficient, generally p=0.5; is the minimum value of the absolute value sequence, is the maximum value of the absolute value sequence;

[0032] Relevance: .

[0033] Optionally, in step S300, the correlation is analyzed, and in the presence of a pressure relief valve, the arc energy is a key factor affecting the final steady-state pressure wave amplitude.

[0034] Optionally, in step S400, the relationship between the arc energy E and the steady-state pressure amplitude P is fitted by combining a multivariate function regression method, and the formula is: .

[0035] Optionally, in step S400, a multivariate function regression method is used to fit the relationship between the shock wave amplitude Pj and the wire length l and diameter d, and the formula is: Pj = -0.3514 + 0.0574* l + 23.9719* d.

[0036] A copper wire selection system for arc initiation in a large-capacity short-circuit arc test, the system comprising:

[0037] A data acquisition module, which is used to obtain test data through a large-capacity short-circuit arc test, wherein the test data includes: test condition data;

[0038] A model building module, which is used to perform grey correlation analysis on the test data and build a grey correlation model;

[0039] The analysis module obtains the correlation between the test condition data and the steady-state pressure wave amplitude generated by the arc process, and screens out the main influencing factors;

[0040] A fitting module is used to establish the relationship between the test condition data and the steady-state pressure wave amplitude generated by the arc process; establish the corresponding relationship between the two size parameters of the metal wire length and diameter and the shock wave amplitude generated by the copper wire electric explosion;

[0041] A target setting module is used to predict the amplitude of the steady-state pressure wave generated by the arc process in actual use by using the test condition data set by the test, and set the shock wave amplitude to 1 / x of the steady-state pressure wave amplitude generated by the arc process, where x is determined according to the tolerance of the shock wave caused by the copper wire;

[0042] The preferred module is used to obtain the upper limit of the shock wave amplitude generated by the explosion of the wire by predicting the steady-state pressure wave amplitude and determining x, and then use the corresponding relationship between the wire size parameter and the shock wave amplitude to reversely obtain the reasonable selection range of the wire size parameter.

[0043] A computer-readable storage medium is used to store a computer program, wherein the computer program is configured to implement the method when called by a processor.

[0044] An electronic device comprises: a memory, a processor and a computer program stored in the memory and executable on the processor; wherein the method is implemented when the processor executes the program.

[0045] Compared with the prior art, the present invention has the following beneficial technical effects:

[0046] The present invention is based on the correlation between large-capacity short-circuit arc test conditions and the steady-state pressure wave amplitude of the arc process and the shock wave amplitude of the copper wire electric explosion process. By performing grey correlation analysis and multivariate function regression fitting on a large amount of test data, a quantitative and reliable law between the pressure peak value and the influencing factors can be obtained, and then a selection scheme for the copper wire diameter and the copper wire length can be made, so that the influence of the additional shock wave generated by the copper wire arcing on the test results is reduced to a controllable range, the probability of malfunction of the pressure relief valve is reduced, and the reliability and safety of the pressure relief valve in protecting power equipment are greatly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings illustrate exemplary embodiments of the present invention and together with the description serve to explain the principles of the present invention. These drawings are included to provide a further understanding of the present invention and are incorporated in and constitute a part of this specification.

[0048] Figure 1 It is a flow chart of a copper wire selection method for arc initiation in a large-capacity short-circuit arc test in one embodiment of the present invention;

[0049] Figure 2It is a flow chart of grey relational analysis and multivariate function regression fitting in one embodiment of the present invention;

[0050] Figure 3 It is a correlation diagram between the test condition parameters of the embodiment of the present invention and the steady-state pressure wave amplitude in one embodiment of the present invention. DETAILED DESCRIPTION

[0051] The following is combined with Figures 1 to 3 The present invention is further described in detail with reference to the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant contents, rather than to limit the present invention. It is also necessary to explain that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings.

[0052] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0053] Unless otherwise specified, the exemplary embodiments / embodiments shown will be understood as exemplary features that provide various details of some ways in which the technical concept of the present invention can be implemented in practice. Therefore, unless otherwise specified, the features of the various embodiments / embodiments can be combined, separated, interchanged and / or rearranged without departing from the technical concept of the present invention.

[0054] The use of cross-hatching and / or shading in the accompanying drawings is generally used to make the boundaries between adjacent components clear. As such, unless otherwise specified, the presence or absence of cross-hatching or shading does not convey or indicate any preference or requirement for the specific materials, material properties, dimensions, proportions, commonalities between the components shown, and / or any other characteristics, attributes, properties, etc. of the components. In addition, in the accompanying drawings, the sizes and relative sizes of the components may be exaggerated for clarity and / or descriptive purposes. When the exemplary embodiments can be implemented differently, the specific process sequence can be performed in a different order than described. For example, two successively described processes can be performed substantially simultaneously or in an order opposite to the described order. In addition, the same figure numbers represent the same components.

[0055] When a component is referred to as being "on" or "over," "connected to," or "coupled to" another component, the component may be directly on, directly connected to, or directly coupled to the other component, or intervening components may be present. However, when a component is referred to as being "directly on," "directly connected to," or "directly coupled to" another component, there are no intervening components. For this purpose, the term "connected" may refer to a physical connection, an electrical connection, etc., with or without intervening components.

[0056] For descriptive purposes, the present invention may use spatially relative terms such as "below", "beneath", "under", "lower", "above", "upper", "above", "higher", and "side (e.g., as in "sidewall")" to describe the relationship of one component to another (other) component as shown in the accompanying drawings. The spatially relative terms are intended to encompass different orientations of the device in use, operation, and / or manufacture in addition to the orientation depicted in the accompanying drawings. For example, if the device in the accompanying drawings is turned over, components described as "below" or "beneath" other components or features would then be positioned "above" the other components or features. Thus, the exemplary term "below" can encompass both the "above" and "below" orientations. Furthermore, the device may be otherwise oriented (e.g., rotated 90 degrees or at other orientations), and the spatially relative descriptors used herein should be interpreted accordingly.

[0057] The terms used here are for the purpose of describing specific embodiments, and are not intended to be restrictive. As used here, unless the context clearly indicates otherwise, the singular forms "one (kind, person)" and "said (the)" are also intended to include plural forms. In addition, when the terms "comprise" and / or "include" and their variations are used in this specification, it is explained that there are stated features, integral bodies, steps, operations, parts, assemblies and / or their groups, but it is not excluded that there are or add one or more other features, integral bodies, steps, operations, parts, assemblies and / or their groups. It should also be noted that, as used here, the terms "substantially", "approximately" and other similar terms are used as approximate terms and not as degree terms, so that they are used to explain the inherent deviations of the measured values, calculated values ​​and / or the values ​​provided that will be recognized by those of ordinary skill in the art.

[0058] In one embodiment, the present invention discloses a method for selecting a copper wire for arc initiation in a large-capacity short-circuit arc test, the method comprising the following steps:

[0059] S100, obtaining test data through a large-capacity short-circuit arc test, the test data including: test condition data;

[0060] S200, performing grey correlation analysis on the test data and establishing a grey correlation model;

[0061] S300, obtaining the correlation between the test condition data and the steady-state pressure wave amplitude generated by the arc process, and screening out the main influencing factors;

[0062] S400, establishing a relationship between the test condition data and the steady-state pressure wave amplitude generated by the arc process; establishing a corresponding relationship between two size parameters, the length and diameter of the metal wire, and the shock wave amplitude generated by the electric explosion of the copper wire;

[0063] S500. In actual use, the steady-state pressure wave amplitude ultimately generated by the arc process is predicted using the test condition data set in the test, and the shock wave amplitude is set to 1 / x of the steady-state pressure wave amplitude generated by the arc process, where x is determined based on the tolerance level for shock waves caused by the copper wire;

[0064] S600, by predicting the steady-state pressure wave amplitude and determining x, an upper limit value of the shock wave amplitude generated by the metal wire explosion is obtained, and then a selection range of the metal wire size parameter is obtained inversely using the corresponding relationship between the metal wire size parameter and the shock wave amplitude.

[0065] Optionally, in step S100, the test condition data includes: discharge energy, power frequency current amplitude, power frequency current duration, and whether the test equipment has a pressure relief device.

[0066] Optionally, in step S100, the test data also includes test result data, and the test result data includes: the amplitude of the steady-state pressure wave generated by the arc process and the amplitude of the shock wave generated by the electric explosion of the copper wire.

[0067] Optionally, in step S200, establishing a grey relational model includes:

[0068] S201, determining a reference sequence based on the test result data ; The reference sequence is: ;

[0069] S202: Determine the factors that affect the peak value of pressure based on the test condition data and the copper wire diameter and copper wire length to form a comparison sequence ;

[0070] The comparison sequence is: , , , ;

[0071] S203, finding the initial value image of each comparison sequence, that is, performing dimensionless processing on each comparison sequence;

[0072] make ,

[0073] in, get ;

[0074] S204, find reference sequence Compare with sequence The absolute value sequence of the difference between the initial value image and the corresponding components of , and calculate the minimum and maximum values ​​of the absolute value sequence;

[0075] remember , , .

[0076] S205, calculating the correlation coefficient and correlation degree;

[0077] Correlation coefficient: ,

[0078] Among them, p is the resolution coefficient, generally p=0.5; is the minimum value of the absolute value sequence, is the maximum value of the absolute value sequence;

[0079] Relevance: .

[0080] Optionally, in step S300, the correlation is analyzed, and in the presence of a pressure relief valve, the arc energy is a key factor affecting the final steady-state pressure wave amplitude.

[0081] Optionally, in step S400, the relationship between the arc energy E and the steady-state pressure amplitude P is fitted by combining a multivariate function regression method, and the formula is: .

[0082] Optionally, in step S400, a multivariate function regression method is used to fit the relationship between the shock wave amplitude Pj and the wire length l and diameter d, and the formula is: Pj = -0.3514 + 0.0574* l + 23.9719* d.

[0083] In another embodiment, the present invention also discloses a copper wire selection system for arc initiation in a large-capacity short-circuit arc test, the system comprising:

[0084] A data acquisition module, which is used to obtain test data through a large-capacity short-circuit arc test, wherein the test data includes: test condition data;

[0085] A model building module, which is used to perform grey correlation analysis on the test data and build a grey correlation model;

[0086] The analysis module obtains the correlation between the test condition data and the steady-state pressure wave amplitude generated by the arc process, and screens out the main influencing factors;

[0087] A fitting module is used to establish the relationship between the test condition data and the steady-state pressure wave amplitude generated by the arc process; establish the corresponding relationship between the two size parameters of the metal wire length and diameter and the shock wave amplitude generated by the copper wire electric explosion;

[0088] A target setting module is used to predict the amplitude of the steady-state pressure wave generated by the arc process in actual use by using the test condition data set by the test, and set the shock wave amplitude to 1 / x of the steady-state pressure wave amplitude generated by the arc process, where x is determined according to the tolerance of the shock wave caused by the copper wire;

[0089] The preferred module is used to obtain the upper limit of the shock wave amplitude generated by the explosion of the wire by predicting the steady-state pressure wave amplitude and determining x, and then use the corresponding relationship between the wire size parameter and the shock wave amplitude to reversely obtain the reasonable selection range of the wire size parameter.

[0090] In another embodiment, the present invention further discloses a computer-readable storage medium, wherein the computer-readable storage medium is used to store a computer program, wherein the computer program is configured to implement the method when called by a processor.

[0091] In another embodiment, the present invention also discloses an electronic device, the electronic device comprising: a memory, a processor and a computer program stored in the memory and executable on the processor; wherein the processor implements the method when executing the program. In one embodiment, the present invention provides a copper wire selection method for arc initiation in a large-capacity short-circuit arc test, by analyzing the characteristics of steady-state pressure waves generated by the arc process and shock waves generated by the electric explosion of the copper wire, predicting the steady-state pressure amplitude and shock wave amplitude, and determining the diameter and length of the copper wire, so as to achieve that the shock wave amplitude generated by the electric explosion of the copper wire has little effect on the steady-state pressure amplitude generated by the arc process, thereby reducing the interference of the copper wire arc initiation on the short-circuit arc test results of oil-immersed power equipment.

[0092] like Figure 1 As shown, a method for selecting a copper wire for arc initiation in a large-capacity short-circuit arc test comprises the following steps:

[0093] S100, obtaining test data through a large-capacity short-circuit arc test, the test data including: test condition data and test result data; the test data constitutes a data set;

[0094] Among them, the test condition data include: discharge energy, power frequency current amplitude, power frequency current duration, and whether the test equipment has a pressure relief device; the test result data include: the amplitude of the steady-state pressure wave generated by the arc process and the amplitude of the shock wave generated by the electric explosion of the copper wire.

[0095] S200, performing grey correlation analysis on the test data and establishing a grey correlation model.

[0096] S300, training the grey correlation model on the data set, and respectively obtaining the correlation between the discharge energy, power frequency current amplitude, power frequency current duration, and whether the test equipment has a pressure relief device and each test condition data and the steady-state pressure wave amplitude to screen out the main influencing factors;

[0097] Among them, the test condition data such as discharge energy, power frequency current amplitude, power frequency current duration and whether the test equipment has a pressure relief device are determined before the test.

[0098] S400, using the data set, combined with multivariate function regression fitting, to establish the relationship between the above test condition data and the steady-state pressure wave amplitude; at the same time, using multivariate function regression fitting, to establish the corresponding relationship between the two dimensional parameters of the wire length and diameter and the generated shock wave amplitude; thus, the corresponding relationship between each parameter and the steady-state pressure wave amplitude and the shock wave amplitude is established.

[0099] S500. In actual use, the discharge energy, power frequency current amplitude, power frequency current duration and whether the test equipment has a pressure relief device set in the upcoming test are first used to predict the final steady-state pressure amplitude, and the shock wave amplitude is set to (1 / x) of the steady-state pressure wave amplitude generated by the arc process according to the requirements, x to be determined, so that the influence of copper wire arcing on the test results is controlled within the required range, and the two corresponding relationships are linked at the same time;

[0100] S600, obtaining the upper limit of the shock wave amplitude generated by the explosion of the metal wire through the prediction of the steady-state pressure amplitude and the determined x, and then obtaining the reasonable selection range of the metal wire size parameter in reverse by using the corresponding relationship between the metal wire size parameter and the shock wave amplitude.

[0101] In one embodiment, the present invention provides a method for selecting a copper wire for arc initiation in a large-capacity short-circuit arc test, the method comprising the following steps:

[0102] S100. Conduct multiple simulation tests to obtain test condition data including discharge energy, power frequency current amplitude, power frequency current duration, whether the test equipment has a pressure relief device, and test result data of the steady-state pressure wave amplitude generated by the arc fault, as shown in Table 1 below:

[0103] Table 1 Test results data

[0104]

[0105] S200, establish a grey correlation model; Figure 2 As shown. Includes:

[0106] 1. Determine the reference sequence based on the test result data ; The reference sequence is: ;

[0107] 2. Based on the test condition data and the copper wire diameter and copper wire length, determine the factors that affect the pressure peak value to form a comparison sequence ;

[0108] The comparison sequence is: , , , ;

[0109] 3. Find the initial value image of each comparison sequence, that is, perform dimensionless processing on each comparison sequence;

[0110] make ,

[0111] in, get ;

[0112] 4. Find the reference sequence Compare with sequence The absolute value sequence of the difference between the initial value image and the corresponding components of , and calculate the minimum and maximum values ​​of the absolute value sequence;

[0113] remember , , .

[0114] 5. Calculate the correlation coefficient and degree of correlation;

[0115] Correlation coefficient: ,

[0116] Among them, p is the resolution coefficient, generally p=0.5; is the minimum value of the absolute value sequence, is the maximum value of the absolute value sequence;

[0117] Relevance: .

[0118] S300, analyzing the correlation by using a MATLAB function, screening out the main influencing factor as parameter 1, that is, in the presence of a pressure relief valve, arc energy is a key factor affecting the final pressure amplitude; screening the main influencing factors includes the following steps:

[0119] Step 1: Input the sample data of each parameter and corresponding results obtained from each test row by row;

[0120] Taking the data in Table 1 as an example, when inputting experimental data, the parameters obtained from each test are input row by row. In Table 1, "discharge energy", "current amplitude", "current duration" and "whether there is a pressure relief device" are respectively used as parameters 1 to 4.

[0121] Step 2, extract the input parameter matrix and result vector, and classify the parameters of each test into categories;

[0122] Parameters 1 to 4 are used as conditional parameters to form a conditional parameter matrix; and the steady-state pressure amplitude is the test result, which is organized to form a result vector.

[0123] Step 3: Perform normalization processing and use range normalization;

[0124] Step 4: Calculate the grey correlation degree of each parameter based on the grey correlation algorithm;

[0125] Run the grey correlation algorithm to obtain the correlation between parameters 1 to 4 and the results in turn. The greater the correlation, the greater the influence of the parameter on the result. The grey correlation algorithm function must be placed at the end of the script.

[0126] Step 5, calculate the average grey relational degree, obtain the influence of each parameter on the result, and output the grey relational degree of each parameter;

[0127] Step 6: Find the key parameter, that is, the parameter with the largest grey correlation.

[0128] The key parameter affecting the final output result is parameter 1, which means that in this embodiment, the parameter 1 has the greatest impact on the steady-state pressure amplitude: the level of discharge energy.

[0129] This example only extracts four parameter variables for research, and finds the parameter with the greatest correlation with the result to fit the result. However, in fact, the number of research parameters and extracted key parameters is not limited to the situation demonstrated in this example. When more parameters are considered, parameters with a correlation greater than a certain value can be selected as key parameters to fit the result. It can be foreseen that the more key parameters, the more accurate the fitting result.

[0130] In this embodiment, execute the following command:

[0131] % Sample data of three input parameters and results (sample data of 4 parameters and results)

[0132] % Assume that the data is in the following format, with each line being entered once:

[0133] % The result y corresponding to the 4 parameters [x1, x2, x3, x4]

[0134] input_data = [

[0135] 0.5,15,40,1,2.95; % First input parameters and results

[0136] 1.5,63,20,1,4.21; % Second input parameters and results

[0137] 3,63,40,1,7.03 % The third input parameters and results ];

[0139] % Extract input parameter matrix and result vector

[0140] X = input_data(:, 1:4); % 4 parameter input

[0141] Y = input_data(:, 5); % result vector

[0142] % Normalization - Use range normalization

[0143] X_norm = (X - min(X)) . / (max(X) - min(X));

[0144] Y_norm = (Y - min(Y)) . / (max(Y) - min(Y));

[0145] % Calculate the grey correlation degree of each parameter

[0146] gamma = grey_relation(X_norm, Y_norm);

[0147] % Calculate the average grey correlation degree to get the influence of each parameter on the result

[0148] mean_gamma = mean(gamma);

[0149] % Output the grey correlation degree of each parameter

[0150] disp('Grey correlation between each parameter and result:');

[0151] disp(mean_gamma);

[0152] % Find the key parameter, that is, the parameter with the largest grey correlation

[0153] [~, key_param_index] = max(mean_gamma);

[0154] fprintf('The key parameters that affect the results are: parameters , key_param_index);

[0155] % Grey correlation calculation function, must be placed at the end of the script

[0156] function gamma = grey_relation(X_norm, Y_norm)

[0157] m = size(X_norm, 1); % Number of samples

[0158] n = size(X_norm, 2); % Number of parameters

[0159] % Calculate the grey correlation coefficient matrix

[0160] delta = zeros(m, n);

[0161] for i = 1:m

[0162] for j = 1:n

[0163] delta(i, j) = abs(X_norm(i, j) - Y_norm(i)); % Absolute difference

[0164] end

[0165] end

[0166] % Calculate the maximum and minimum values

[0167] delta_min = min(min(delta));

[0168] delta_max = max(max(delta));

[0169] % Set the resolution factor rho, usually 0.5

[0170] rho = 0.5;

[0171] % Calculate the grey correlation

[0172] gamma = (delta_min + rho * delta_max) . / (delta + rho * delta_max);

[0173] end

[0174] Command window output:

[0175] Grey correlation between each parameter and the result:

[0176] 0.9486 0.8066 0.6505 NaN

[0177] The key parameters that affect the results are: Parameter 1

[0178] S400. The relationship between arc energy E (MJ) and steady-state pressure amplitude P (MPa) is fitted by combining the multivariate function regression method, and the following results are obtained. Thus, the correlation between the test condition parameters and the steady-state pressure wave amplitude is established, such as Figure 3 shown.

[0179] (1)

[0180] S500. Through multiple simulation tests, the shock wave amplitude after changing the wire parameters is measured and counted, and the results are shown in Table 2.

[0181] Table 2 Shock wave amplitude

[0182]

[0183] S600. By using a multivariate function regression method, the relationship between the shock wave amplitude (Pj) and the wire length (l) and diameter (d) is fitted as follows. Thus, the corresponding relationship between the wire size parameters and the shock wave amplitude is established.

[0184] Pj = -0.3514 + 0.0574* l + 23.9719* d (2)

[0185] S700. Use the above model to select the wire size for the arc simulation test to be conducted in the laboratory.

[0186] The laboratory has several metal wires with a length of 15 mm and different diameters. The arc energy of the test to be carried out is about 80 J. According to formula (1), the corresponding steady-state pressure amplitude is about 2.4592 MPa. At this time, x=2 is set, that is, the shock wave amplitude is 1 / 2 of the steady-state pressure amplitude, and the upper limit of the shock wave pressure amplitude is 1.2296 MPa. Substituting Pj=2.4 and l=15 into formula (2), the diameter d of the metal wire is not more than 0.02 mm. The actual measurement shows that the peak value of the shock wave pressure generated under the conditions of a metal wire diameter of 0.02 mm, a length of 15 mm and an arc energy of 80 J does not exceed 1 MPa, proving that the prediction is accurate.

[0187] In the description of this specification, the description with reference to the terms "one embodiment / method", "some embodiments / methods", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment / method or example are included in at least one embodiment / method or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment / method or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments / methods or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments / methods or examples described in this specification and the features of the different embodiments / methods or examples, unless they are contradictory.

[0188] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0189] It should be understood by those skilled in the art that the above embodiments are only for the purpose of clearly illustrating the present invention, and are not intended to limit the scope of the present invention. For those skilled in the art, other changes or modifications may be made based on the above disclosure, and these changes or modifications are still within the scope of the present invention.

Claims

1. A method for selecting copper wire for arc initiation in large-capacity short-circuit arc test, characterized in that: The method comprises the following steps: S100, obtaining test data through a large-capacity short-circuit arc test, the test data including: test condition data; S200, performing grey correlation analysis on the test data and establishing a grey correlation model; S300, obtaining the correlation between the test condition data and the steady-state pressure wave amplitude generated by the arc process, and screening out the main influencing factors; S400, establishing a relationship between the test condition data and the steady-state pressure wave amplitude generated by the arc process; establishing a corresponding relationship between two size parameters, the length and diameter of the metal wire, and the shock wave amplitude generated by the electric explosion of the copper wire; S500. In actual use, the steady-state pressure wave amplitude ultimately generated by the arc process is predicted using the test condition data set in the test, and the shock wave amplitude is set to 1 / x of the steady-state pressure wave amplitude generated by the arc process, where x is determined based on the tolerance level for shock waves caused by the copper wire; S600, by predicting the steady-state pressure wave amplitude and determining x, an upper limit value of the shock wave amplitude generated by the metal wire explosion is obtained, and then a selection range of the metal wire size parameter is obtained inversely using the corresponding relationship between the metal wire size parameter and the shock wave amplitude.

2. The method according to claim 1, characterized in that Preferably, in step S100, the test condition data includes: discharge energy, power frequency current amplitude, power frequency current duration, and whether the test equipment has a pressure relief device.

3. The method according to claim 1, characterized in that In step S100, the test data also includes test result data, and the test result data includes: the amplitude of the steady-state pressure wave generated by the arc process and the amplitude of the shock wave generated by the electric explosion of the copper wire.

4. The method according to claim 1, characterized in that: In step S200, establishing a grey relational model includes: S201, determining a reference sequence based on the test result data ; The reference sequence is: ; S202: Determine the factors that affect the peak value of pressure based on the test condition data and the copper wire diameter and copper wire length to form a comparison sequence ; The comparison sequence is: , , , ; S203, finding the initial value image of each comparison sequence, that is, performing dimensionless processing on each comparison sequence; make , in, get ; S204, find reference sequence Compare with sequence The absolute value sequence of the difference between the initial value image and the corresponding components of , and calculate the minimum and maximum values ​​of the absolute value sequence; remember , , , S205, calculating the correlation coefficient and correlation degree; Correlation coefficient: , Among them, p is the resolution coefficient, generally p=0.5; is the minimum value of the absolute value sequence, is the maximum value of the absolute value sequence; Relevance: .

5. The method according to claim 1, characterized in that In step S300, the correlation is analyzed. In the presence of a pressure relief valve, arc energy is a key factor affecting the final steady-state pressure wave amplitude.

6. The method according to claim 1, characterized in that In step S400, the relationship between the arc energy E and the steady-state pressure amplitude P is fitted by combining the multivariate function regression method, and the formula is: 。 7. The method according to claim 1, characterized in that In step S400, the relationship between the shock wave amplitude Pj and the wire length l and diameter d is fitted by a multivariate function regression method, and the formula is: Pj = -0.3514 + 0.0574*l + 23.9719*d.

8. A copper wire selection system for arc initiation in large-capacity short-circuit arc test, characterized in that: The system comprises: A data acquisition module, which is used to obtain test data through a large-capacity short-circuit arc test, wherein the test data includes: test condition data; A model building module, which is used to perform grey correlation analysis on the test data and build a grey correlation model; The analysis module obtains the correlation between the test condition data and the steady-state pressure wave amplitude generated by the arc process, and screens out the main influencing factors; A fitting module is used to establish the relationship between the test condition data and the steady-state pressure wave amplitude generated by the arc process; establish the corresponding relationship between the two size parameters of the metal wire length and diameter and the shock wave amplitude generated by the copper wire electric explosion; A target setting module is used to predict the amplitude of the steady-state pressure wave generated by the arc process in actual use by using the test condition data set by the test, and set the shock wave amplitude to 1 / x of the steady-state pressure wave amplitude generated by the arc process, where x is determined according to the tolerance of the shock wave caused by the copper wire; The preferred module is used to obtain the upper limit of the shock wave amplitude generated by the explosion of the wire by predicting the steady-state pressure wave amplitude and determining x, and then use the corresponding relationship between the wire size parameter and the shock wave amplitude to reversely obtain the reasonable selection range of the wire size parameter.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store a computer program, and the computer program is configured to implement any one of the methods of claims 1-7 when called by a processor.

10. An electronic device, characterized in that: The electronic device comprises: a memory, a processor and a computer program stored in the memory and executable on the processor; wherein the processor implements any one of the methods of claims 1-7 when executing the program.

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