A switch cabinet insulation failure probability assessment method, system and switch cabinet

Abstract

The present invention discloses a switch cabinet insulation failure probability assessment method, system and switch cabinet, the method comprising: establishing a three-dimensional finite element electric field simulation model based on the entire switch cabinet, and simulating and obtaining the highest surface field strength at different positions in the switch cabinet; obtaining the field strength margin coefficients corresponding to different positions according to the control field strength inside the switch cabinet; correcting the field strength margin coefficients at different positions in combination with the operating years of the insulating parts inside the switch cabinet, and obtaining the corrected field strength margin coefficients; constructing arrays representing different risk states according to the magnitude of the field strength margin coefficients at different positions inside the corrected switch cabinet; applying the insulation failure probability equation group, respectively obtaining the insulation failure probabilities corresponding to the field strength margin coefficients contained in different arrays; obtaining the insulation failure probability of the entire switch cabinet according to the insulation failure probabilities corresponding to the field strength margin coefficients in different arrays. The present invention overcomes the problem that the existing methods do not consider the aging characteristics of the insulating parts.

Description

Technical Field

[0001] The present invention relates to the technical field of high-voltage switch cabinets, and in particular to a switch cabinet insulation failure probability assessment method, system and switch cabinet. Background Art

[0002] Solid insulators are key components of high-voltage switchgear, mainly including contact boxes, bushings, insulators, etc. They usually play the role of electrical insulation, separation of compartments, and conductor support. They are widely used in power grids and have a wide impact, and are the weakest link in the entire switchgear. Operation experience shows that fault tripping caused by insulation failures of insulating parts such as contact boxes and bushings accounts for a large proportion of the total fault tripping, and faults caused by defects in solid insulators account for a large proportion in switchgear.

[0003] In the prior art, the paper "Research on the Evaluation Method of Insulation State of Solid Switchgear" conducts three-dimensional modeling of the solid-sealed pole based on the operating conditions, obtains the main area of ​​insulation stress distribution of the epoxy resin material, and conducts abstract modeling based on the structural characteristics of the above parts to design electrode samples with different structures. Through equivalence verification analysis, solid-sealed insulating parts of corresponding structures are prepared, and accelerated electrical aging tests are carried out on them to obtain the electrical aging life of the test pieces and measure the insulation parameters at different aging stages, focusing on the corresponding relationship between the insulation parameters of the test pieces and the aging life. However, the paper evaluates the insulation state levels of solid insulated switchgear, such as healthy, normal, mild fault, moderate fault, and severe fault, but does not evaluate the insulation failure probability of the entire switchgear.

[0004] At present, the evaluation of the insulation failure probability of switchgear at home and abroad does not take into account the aging characteristics of insulation parts, the impact of field strength distribution at different positions inside the switchgear on the insulation failure probability, and the weight coefficient of the impact of different positions on the insulation failure of the switchgear, which directly affects the evaluation accuracy of the insulation failure probability of the switchgear. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method for evaluating the insulation failure probability of a switch cabinet taking into account the aging characteristics of insulating parts.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] A switch cabinet insulation failure probability assessment method, comprising:

[0008] A three-dimensional finite element electric field simulation model based on the entire switch cabinet is established, and the maximum surface field strength at different locations of the circuit breaker room, busbar room, and outgoing line room in the switch cabinet is obtained by simulation;

[0009] According to the control field strength inside the switch cabinet, the field strength margin coefficients at different positions of the circuit breaker room, busbar room, and outgoing line room are obtained respectively;

[0010] Combined with the service life of the insulation parts inside the switch cabinet, the field strength margin coefficients at different positions of the circuit breaker room, busbar room and outgoing line room are corrected to obtain the corrected field strength margin coefficients;

[0011] According to the magnitude of the field strength margin coefficient at different positions inside the switch cabinet after correction, an array representing different risk states is constructed;

[0012] Apply the insulation failure probability equation group to obtain the insulation failure probability corresponding to the field strength margin coefficient contained in different arrays;

[0013] The insulation failure probability of the entire switch cabinet is obtained according to the insulation failure probability corresponding to the field strength margin coefficient of different arrays.

[0014] In one embodiment of the present invention, the field strength margin coefficients at different positions of the circuit breaker room, busbar room, and outgoing line room are obtained by the following formula:

[0015] ; ; ;

[0016] In the formula, , , They are respectively expressed as the field strength margin coefficients of the high-voltage conductor surface, metal insert surface, and contact box surface inside the circuit breaker room. , , They are respectively expressed as the field strength margin coefficients of the high-voltage conductor surface inside the busbar room, the metal insert surface, and the bushing surface. , , They are respectively expressed as the field strength margin coefficients of the high-voltage conductor surface, metal insert surface, and grounding switch insulator surface in the outlet room. , , They represent the highest field strength on the surface of the high-voltage conductor, metal insert, and contact box inside the circuit breaker chamber. , , They represent the highest field strength on the surface of the high-voltage conductor inside the busbar room, the surface of the metal insert, and the surface of the bushing through the cabinet. , , They represent the highest field strength on the surface of the high-voltage conductor inside the outlet room, the surface of the metal insert, and the surface of the grounding switch insulator. , , They are respectively expressed as the control field strength of the high-voltage conductor surface, the metal insert surface, and the insulating surface. , , They represent the gradients of the highest electric field strength on the surface of the high-voltage conductor, metal insert, and contact box inside the circuit breaker chamber. , , They are respectively represented by the gradient of the highest electric field strength on the surface of the high-voltage conductor inside the busbar room, the surface of the metal insert, and the surface of the bushing through the cabinet. , , They are respectively represented by the gradient of the highest electric field strength on the surface of the high-voltage conductor inside the outlet room, the surface of the metal insert, and the surface of the grounding switch insulator. , , They are respectively expressed as the gradients of the controlled electric field strength on the surface of the high-voltage conductor, the surface of the metal insert, and the surface of the insulating part.

[0017] In one embodiment of the present invention, the corrected field strength margin coefficient is obtained by the following formula:

[0018] ; ; ;

[0019] In the formula, , They are respectively expressed as the field strength margin coefficients of the metal insert surface and contact box surface inside the circuit breaker chamber after correction, , They represent the field strength margin coefficients of the metal insert surface and contact box surface inside the circuit breaker chamber before correction, , They are respectively expressed as the field strength margin coefficients of the metal insert surface inside the busbar room and the bushing surface after correction. , They represent the field strength margin coefficients of the metal insert surface and the bushing surface in the busbar room before correction. , They are respectively expressed as the field strength margin coefficients of the metal insert surface inside the outgoing line room and the grounding switch insulator surface after correction, , They are respectively the field strength margin coefficients of the metal insert surface inside the outgoing line room and the grounding switch insulator surface before correction, It is expressed as a function of the internal breakdown voltage of the insulation material inside the switch cabinet as a function of the operating years. It is expressed as the function of the flashover voltage on the surface of the insulating material inside the switch cabinet as a function of the operating years. , , They are respectively represented by the service life of the contact box inside the circuit breaker room, the service life of the bushing inside the busbar room, and the service life of the grounding switch insulator inside the outgoing line room.

[0020] In one embodiment of the present invention, the internal breakdown voltage of the insulating material inside the switch cabinet varies with the operating years. , the function of the flashover voltage on the surface of the insulating material inside the switch cabinet with the operating years They are all obtained by fitting the test results of switchgear insulation materials with different operating years on site.

[0021] In one embodiment of the present invention, the array of different risk states includes the array u、 Arrays v、 Arrays w ;in,

[0022] Arrays u The corresponding field strength margin coefficient range is (0,0.8], indicating that the insulation system is in a high-risk state;

[0023] Arrays v The corresponding field strength margin coefficient range is (0.8, 1.2), indicating that the insulation system is in a medium risk state;

[0024] Arrays w The corresponding field strength margin coefficient range is [1.2, +∞), indicating that the insulation system is in a low-risk state.

[0025] In one embodiment of the present invention, the arrays are obtained respectively u、 Arrays v The insulation failure probability corresponding to the field strength margin coefficient contained in the equation group corresponding to the insulation failure probability is as follows:

[0026] ;

[0027] In the formula, Represented as an array u Internal field strength margin factor The corresponding insulation failure probability is, Represented as an array v Internal field strength margin factor The corresponding insulation failure probability is, , , Represented as arrays u Internal field strength margin factor Corresponding to different parameters of insulation failure probability, , , Represented as arrays v Internal field strength margin factor Corresponding parameters of insulation failure probability.

[0028] In one embodiment of the present invention, the insulation failure probability of the entire switch cabinet is obtained by the following formula:

[0029] ;

[0030] In the formula, It is expressed as the insulation failure probability of the entire switch cabinet, , Represented as arrays u Neidi The insulation failure probability and weighting coefficient corresponding to each field strength margin coefficient, , Represented as arrays v Neidi The insulation failure probability and weighting coefficient corresponding to each field strength margin coefficient, m , n Represented as arrays u , v The number of elements contained.

[0031] In one embodiment of the present invention, the weighting coefficient is obtained , by:

[0032] S100, pair array u The field strength margin coefficient within the filter is standardized to obtain a standardized data set;

[0033] S200, constructs a correlation coefficient matrix by calculating the Pearson correlation coefficient between each pair of variables in the standardized data set;

[0034] S300, solving the correlation coefficient matrix, obtaining the eigenvalues ​​and eigenvectors of the correlation coefficient matrix;

[0035] S400, determining the number of principal components according to the size of the eigenvalues;

[0036] S500, constructing a principal component loading matrix according to the number of principal components and the eigenvectors;

[0037] S600, calculating the contribution rate of each principal component according to the principal component load matrix, and obtaining a weighted coefficient;

[0038] Similarly, obtain the weighting coefficient The steps reuse steps S100 to S600, except that the data used in the initial step S100 is an array. v Field strength margin factor within.

[0039] In one embodiment of the present invention, the weighting coefficient is obtained , through the following formula:

[0040] ;

[0041] In the formula, a Representing an array u Any principal component retained in Expressed as a The variance contribution of the principal components is Expressed as a The eigenvalues ​​of the principal components, is expressed as the sum of all eigenvalues, m represents the total number of principal components, Expressed as i The variable in a The loadings on the principal components are Represented as a variable i With principal component a The relevant standardized score, Represents the number of principal components retained, Represented as a variable The weighting coefficient of .

[0042] The present invention also provides a switch cabinet insulation failure probability assessment system, which applies the switch cabinet insulation failure probability assessment method described above and comprises:

[0043] The maximum field strength module is used to establish a three-dimensional finite element electric field simulation model based on the entire switch cabinet, and simulate and obtain the surface maximum field strength at different locations of the circuit breaker room, busbar room, and outgoing line room in the switch cabinet;

[0044] The field strength margin coefficient module is used to obtain the field strength margin coefficients at different locations of the circuit breaker room, busbar room, and outgoing line room according to the control field strength inside the switch cabinet;

[0045] The correction module is used to correct the field strength margin coefficients at different positions of the circuit breaker room, busbar room and outgoing line room according to the service life of the internal insulating parts of the switch cabinet, and obtain the corrected field strength margin coefficients;

[0046] An array module is used to construct arrays representing different risk states according to the magnitude of the field strength margin coefficients at different positions inside the switch cabinet after correction;

[0047] An array insulation failure probability module is used to apply the insulation failure probability equation group to obtain the insulation failure probability corresponding to the field strength margin coefficient contained in different arrays;

[0048] The whole cabinet insulation failure probability module is used to obtain the insulation failure probability of the whole switch cabinet according to the insulation failure probability corresponding to the field strength margin coefficient of different arrays.

[0049] The present invention also provides a switch cabinet, which uses the above switch cabinet insulation failure probability assessment method to perform insulation failure probability assessment.

[0050] Compared with the prior art, the present invention has the following beneficial effects: the present invention reasonably evaluates the insulation failure probability of the switch cabinet, and overcomes the problems that the prior method does not consider the aging characteristics of the insulation parts, the influence of the field strength distribution at different positions inside the switch cabinet on the insulation failure probability, and the weight coefficient of the influence of different positions on the insulation failure of the switch cabinet. The introduction of the field strength gradient can more accurately evaluate the uniformity of the insulation system and avoid the problem of relying only on the absolute value of the field strength and ignoring the local high-risk area.

[0051] The present invention combines the operating years of different insulations inside the switch cabinet, comprehensively considers the field strength margin coefficient range and insulation failure probability at different positions, and introduces a weighting coefficient to obtain the insulation failure probability of the switch cabinet considering the aging characteristics of the insulation parts, which is of great significance for ensuring the safe operation of the switch cabinet. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 The present invention is a flow chart of a method for evaluating the insulation failure probability of a switch cabinet according to an embodiment of the present invention.

[0053] Figure 2 This is an overall electric field distribution diagram of the switch cabinet according to an embodiment of the present invention.

[0054] Figure 3 This is a diagram of the electric field distribution of a circuit breaker chamber in a switch cabinet according to an embodiment of the present invention.

[0055] Figure 4 This is a diagram of the electric field distribution of the busbar chamber in the switch cabinet according to an embodiment of the present invention.

[0056] Figure 5 This is a diagram of the electric field distribution in the outgoing line room of the switch cabinet according to an embodiment of the present invention.

[0057] Figure 6 The present invention is a flowchart of using the principal component analysis method to obtain weighting coefficients.

[0058] Figure 7 The present invention is a block diagram of a switch cabinet insulation failure probability assessment system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to facilitate those skilled in the art to understand the technical solution of the present invention, the technical solution of the present invention is further described in conjunction with the accompanying drawings of the specification.

[0060] 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. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0061] See also Figure 1 As shown, the present invention provides a method for evaluating the probability of insulation failure of a switch cabinet, comprising:

[0062] S10, establishing a three-dimensional finite element electric field simulation model based on the entire switch cabinet, and simulating to obtain the highest surface field strength at different positions of the circuit breaker room, busbar room, and outgoing line room in the switch cabinet.

[0063] See also Figures 2 to 5 As shown, in one embodiment of the present invention, in a finite element analysis environment, a three-dimensional finite element electric field simulation model based on the entire switch cabinet is established to simulate and calculate the maximum electric field strength on the surface of the high-voltage conductor in the circuit breaker room. , the highest field strength on the metal insert surface and the highest field strength on the contact box surface , the highest field strength on the surface of the high-voltage conductor in the busbar room , the highest field strength on the metal insert surface , the highest field strength on the surface of the cabinet bushing , the highest field strength on the surface of the high-voltage conductor inside the outlet room , the highest field strength on the metal insert surface , Maximum field strength on the surface of the grounding switch insulator .

[0064] S20, according to the control field strength inside the switch cabinet, the field strength margin coefficients of different positions of the circuit breaker room, the busbar room, and the outgoing line room are respectively obtained.

[0065] In one embodiment of the present invention, the field strength margin coefficients at different positions of the circuit breaker room, busbar room, and outgoing line room are obtained by the following formula:

[0066] ; ; ;

[0067] In the formula, , , They are respectively expressed as the field strength margin coefficients of the high-voltage conductor surface, metal insert surface, and contact box surface inside the circuit breaker room. , , They are respectively expressed as the field strength margin coefficients of the high-voltage conductor surface inside the busbar room, the metal insert surface, and the bushing surface. , , They are respectively expressed as the field strength margin coefficients of the high-voltage conductor surface, metal insert surface, and grounding switch insulator surface in the outlet room. , , They represent the highest field strength on the surface of the high-voltage conductor, metal insert, and contact box inside the circuit breaker chamber. , , They represent the highest field strength on the surface of the high-voltage conductor inside the busbar room, the surface of the metal insert, and the surface of the bushing through the cabinet. , , They represent the highest field strength on the surface of the high-voltage conductor inside the outlet room, the surface of the metal insert, and the surface of the grounding switch insulator. , , They are respectively expressed as the control field strength of the high-voltage conductor surface, the metal insert surface, and the insulating surface. , , They represent the gradients of the highest electric field strength on the surface of the high-voltage conductor, metal insert, and contact box inside the circuit breaker chamber. , , They are respectively represented by the gradient of the highest electric field strength on the surface of the high-voltage conductor inside the busbar room, the surface of the metal insert, and the surface of the bushing through the cabinet. , , They are respectively represented by the gradient of the highest electric field strength on the surface of the high-voltage conductor inside the outlet room, the surface of the metal insert, and the surface of the grounding switch insulator. , , They are respectively represented by the gradients of the controlled electric field strength on the surface of the high-voltage conductor, the surface of the metal insert, and the surface of the insulating part.

[0068] In this embodiment, the control field strength of the surface of the high-voltage conductor inside the switch cabinet, the surface of the metal insert, and the surface of the insulating parts such as the contact box, the cabinet bushing, and the grounding switch insulator is no longer divided according to the circuit breaker room, the busbar room, and the outgoing line room, because the meanings of different compartments are different, especially the insulating parts. For the circuit breaker room, the insulating part is the contact box. For the busbar room, the insulating part is the cabinet bushing.

[0069] In this embodiment, a specific example is given. When the rated lightning impulse voltage of the switch cabinet is 75kV, E cr1 =9.37kV / mm, E cr2 =18.75kV / mm, E cr3=5.32kV / mm, when the rated lightning impulse voltage of the switch cabinet is 185kV, E cr1 =6.85kV / mm, E cr2 =13.7kV / mm, E cr3 =3.89kV / mm.

[0070] S30, based on the service life of the insulating parts inside the switch cabinet, the field strength margin coefficients at different positions of the circuit breaker room, the busbar room, and the outgoing line room are corrected to obtain the corrected field strength margin coefficients.

[0071] In one embodiment of the present invention, in combination with the service life of the insulating parts such as the contact box, the bushing, and the grounding switch insulator inside the switch cabinet, the field strength margin coefficient of the surface of the metal inserts inside the circuit breaker chamber, the busbar chamber, and the outlet chamber of the switch cabinet, as well as the surface of the insulating parts such as the contact box, the bushing, and the grounding switch insulator are corrected. The corrected field strength margin coefficient is expressed by the following formula:

[0072] ; ; ;

[0073] In the formula, , They are respectively expressed as the field strength margin coefficients of the metal insert surface and contact box surface inside the circuit breaker chamber after correction, , They represent the field strength margin coefficients of the metal insert surface and contact box surface inside the circuit breaker chamber before correction, , They are respectively expressed as the field strength margin coefficients of the metal insert surface inside the busbar room and the bushing surface after correction. , They represent the field strength margin coefficients of the metal insert surface and the bushing surface in the busbar room before correction. , They are respectively expressed as the field strength margin coefficients of the metal insert surface inside the outgoing line room and the grounding switch insulator surface after correction, , They are respectively the field strength margin coefficients of the metal insert surface inside the outgoing line room and the grounding switch insulator surface before correction, It is expressed as a function of the internal breakdown voltage of the insulation material inside the switch cabinet as a function of the operating years. It is expressed as the function of the flashover voltage on the surface of the insulating material inside the switch cabinet as a function of the operating years. , , They are respectively represented by the service life of the contact box inside the circuit breaker room, the service life of the bushing inside the busbar room, and the service life of the grounding switch insulator inside the outgoing line room.

[0074] In one embodiment of the present invention, the internal breakdown voltage of the insulating material inside the switch cabinet varies with the operating years. , the function of the flashover voltage on the surface of the insulating material inside the switch cabinet with the operating years They are all obtained by fitting the test results of switchgear insulation materials with different operating years on site.

[0075] S40, constructing arrays representing different risk states according to the magnitudes of the corrected field strength margin coefficients at different positions inside the switch cabinet.

[0076] In one embodiment of the present invention, according to the field strength margin coefficients at different positions inside the switch cabinet, k D1 , k′ D2 , k′ D3 , k M1 , k′ M2 , k′ M3 , k C1 , k′ C2 , k′ C3 The size of the array is constructed differently u , v , w The magnitude of the field strength margin coefficient is directly related to the risk of insulation failure. Therefore, according to the magnitude of the field strength margin coefficient at different positions inside the switch cabinet, it is divided into u , v , w In the array. u The corresponding field strength margin coefficient range is (0,0.8], which means that the electric field strength is close to or equal to the breakdown electric field strength of the material, and the insulation system is in a high-risk state. v The corresponding field strength margin coefficient range is (0.8, 1.2). The field strength margin coefficient within this range indicates that the electric field strength is lower than the breakdown electric field strength of the material, but is close to the safety threshold. w The corresponding field strength margin coefficient range is [1.2, +∞). The field strength margin coefficient within this range indicates that the electric field strength is much lower than the breakdown electric field strength of the material, and the insulation system is in a low-risk state.

[0077] S50, applying the insulation failure probability equation group to obtain insulation failure probabilities corresponding to the field strength margin coefficients included in different arrays.

[0078] In one embodiment of the present invention, the arrays are calculated respectively u , v , w The insulation failure probability corresponding to the field strength margin coefficient element contained in the array is w The field strength margin factor included in the insulation failure probability is recorded as 0%. u With array v The field strength margin coefficient included in it, and the insulation failure probability calculation formula is as follows:

[0079] ;

[0080] In the formula, Represented as an array u Internal field strength margin factor The corresponding insulation failure probability is, Represented as an array v Internal field strength margin factor The corresponding insulation failure probability is, , , Represented as arrays u Internal field strength margin factor Corresponding to different parameters of insulation failure probability, , , Represented as arrays v Internal field strength margin factor Corresponding parameters of insulation failure probability.

[0081] In one embodiment of the present invention, specifically, , , Arrays u The amplitude, reference value and power term of the insulation failure probability corresponding to the field strength margin coefficient contained in it. Among them, the amplitude of the exponential function is affected by ,Decide Changes to The extent of the impact. hour, Takes a specific value. Decide and The degree of nonlinearity of the difference.

[0082] In one embodiment of the present invention, specifically, , , Arrays v The amplitude, reference value and power term of the insulation failure probability corresponding to the internal field strength margin coefficient. Affects the magnitude of the exponential function in the denominator and determines Changes to The extent of the impact. hour, Takes a specific value. Decide and The degree of nonlinearity of the difference.

[0083] In this embodiment, a specific example is given. For an array u With array v The field strength margin factor included in it has the following insulation failure probability:

[0084] ;

[0085] S60, obtaining the insulation failure probability of the entire switch cabinet according to the insulation failure probabilities corresponding to the field strength margin coefficients in different arrays.

[0086] In one embodiment of the present invention, the insulation failure probability of the entire switch cabinet is obtained by the following formula:

[0087] ;

[0088] In the formula, It is expressed as the insulation failure probability of the entire switch cabinet, , Represented as arrays u Neidi The insulation failure probability and weighting coefficient corresponding to each field strength margin coefficient, , Represented as arrays v Neidi The insulation failure probability and weighting coefficient corresponding to each field strength margin coefficient, m , n Represented as arrays u , v The number of elements contained.

[0089] See also Figure 6 As shown, the present invention provides a method for determining weighted coefficients using principal component analysis (PCA) , When determining the weighting coefficient, the principal component analysis method can adapt to multivariate and high-dimensional data scenarios, identify the main influencing factors through dimensionality reduction and information extraction, reduce model complexity, improve evaluation efficiency and model generalization ability, and provide an objective weight allocation method, thereby improving the accuracy and practicality of the evaluation. Specifically include:

[0090] Get weighting coefficient hour:

[0091] S100, pair array u The field strength margin coefficient within is standardized to obtain a standardized data set.

[0092] In one embodiment of the present invention, in order to eliminate the influence of different dimensions, the Z-score standardization method is used to process the data:

[0093] ;

[0094] In the formula, z is the standardized data, x is the original data, μ is the mean, σ is the standard deviation.

[0095] S200, constructs a correlation coefficient matrix by calculating the Pearson correlation coefficient between each pair of variables in the standardized data set.

[0096] In one embodiment of the present invention, the Pearson correlation coefficient is obtained by the following formula:

[0097] ;

[0098] In the formula, r xy is the field strength margin coefficient variable x and y The correlation coefficient between and is the field strength margin coefficient variable x and y No. i Observations, and is the field strength margin coefficient variable x and y The mean of the array, m is the number of observations, that is, u The number of elements contained.

[0099] According to the Pearson correlation coefficient formula, the correlation coefficient between each pair of variables is calculated to form a correlation coefficient matrix R .

[0100] S300, solving the correlation coefficient matrix, and obtaining the eigenvalues ​​and eigenvectors of the correlation coefficient matrix.

[0101] In one embodiment of the present invention, the eigenvalue and eigenvector can be obtained by solving the following characteristic equation:

[0102] ;

[0103] In the formula, det represents the determinant, R is the correlation coefficient matrix, λ is the characteristic value, I is the identity matrix, and R Same level, is the feature vector.

[0104] S400, determining the number of principal components according to the size of the eigenvalues.

[0105] In one embodiment of the present invention, the Kaiser's Rule is used to determine the number of principal components in the process of determining weighted coefficients using principal component analysis, that is, if the eigenvalue of a principal component is greater than 1, the principal component is retained, and the weighted coefficients corresponding to the variables not retained are set to 0.

[0106] S500, constructing a principal component loading matrix according to the number of principal components and the eigenvectors.

[0107] In one embodiment of the present invention, a principal component loading matrix is ​​constructed according to the selected number of principal components, and the matrix is ​​composed of eigenvectors corresponding to the principal components.

[0108] S600, calculating the contribution rate of each principal component according to the principal component load matrix, and obtaining a weighted coefficient.

[0109] In one embodiment of the present invention, the weighting coefficient is calculated by the following formula:

[0110] ;

[0111] In the formula, a Representing an array u Any principal component retained in Expressed as a The variance contribution of the principal components is Expressed as a The eigenvalues ​​of the principal components, is expressed as the sum of all eigenvalues, m represents the total number of principal components, Expressed as i The variable in a The loadings on the principal components are Represented as a variable iWith principal component a The relevant standardized score, Represents the number of principal components retained, Represented as a variable In this embodiment, the principal component can be understood as the variable after the composite requirement is screened. Therefore Only if the array u When all variables in are retained and .

[0112] Similarly, get the weighting coefficient The steps of the method reuse steps S100 to S600, except that, starting from step S100, the data applied is an array. v Field strength margin factor within.

[0113] Example 2

[0114] See also Figure 7 As shown, the present invention also provides a switch cabinet insulation failure probability assessment system, which applies the switch cabinet insulation failure probability assessment method described above, and comprises:

[0115] The maximum field strength module 10 is used to establish a three-dimensional finite element electric field simulation model based on the entire switch cabinet, and simulate and obtain the surface maximum field strength at different positions of the circuit breaker room, busbar room, and outgoing line room in the switch cabinet.

[0116] The field strength margin coefficient module 20 is used to obtain the field strength margin coefficients of different positions of the circuit breaker room, the busbar room, and the outgoing line room according to the control field strength inside the switch cabinet.

[0117] The correction module 30 is used to correct the field strength margin coefficients at different positions of the circuit breaker room, the busbar room and the outgoing line room in combination with the service life of the insulating parts inside the switch cabinet, and obtain the corrected field strength margin coefficients.

[0118] The array module 40 is used to construct arrays representing different risk states according to the magnitudes of the field strength margin coefficients at different positions inside the switch cabinet after correction.

[0119] The array insulation failure probability module 50 is used to apply the insulation failure probability equation group to obtain the insulation failure probabilities corresponding to the field strength margin coefficients contained in different arrays.

[0120] The whole cabinet insulation failure probability module 60 is used to obtain the insulation failure probability of the whole switch cabinet according to the insulation failure probabilities corresponding to the field strength margin coefficients in different arrays.

[0121] Example 3

[0122] The present invention also provides a switch cabinet, which uses the above-mentioned switch cabinet insulation failure probability assessment method to perform insulation failure probability assessment.

[0123] It is obvious to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, the embodiments should be regarded as exemplary and non-limiting from any point of view, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention, and any reference numerals in the claims should not be regarded as limiting the claims involved.

[0124] The above-described embodiments merely represent implementation methods of the invention. The protection scope of the present invention is not limited to the above-described embodiments. For those skilled in the art, several modifications and improvements may be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A method for evaluating the probability of insulation failure of a switch cabinet, characterized in that: include: A three-dimensional finite element electric field simulation model based on the entire switch cabinet is established, and the maximum surface field strength at different locations of the circuit breaker room, busbar room, and outgoing line room in the switch cabinet is obtained by simulation; According to the control field strength inside the switch cabinet, the field strength margin coefficients at different positions of the circuit breaker room, busbar room, and outgoing line room are obtained respectively; Combined with the service life of the insulation parts inside the switch cabinet, the field strength margin coefficients at different positions of the circuit breaker room, busbar room and outgoing line room are corrected to obtain the corrected field strength margin coefficients; According to the magnitude of the field strength margin coefficient at different positions inside the switch cabinet after correction, an array representing different risk states is constructed; Apply the insulation failure probability equation group to obtain the insulation failure probability corresponding to the field strength margin coefficient contained in different arrays; According to the insulation failure probability corresponding to the field strength margin coefficient of different arrays, the insulation failure probability of the entire switch cabinet is obtained; Arrays of different risk states include arrays u、 Arrays v、 Arrays w ; Get arrays separately u、 Arrays v The insulation failure probability corresponding to the field strength margin coefficient contained in the equation group corresponding to the insulation failure probability is as follows: ； In the formula, For array u Internal field strength margin factor The corresponding insulation failure probability is, For array v Internal field strength margin factor The corresponding insulation failure probability is, , , Arrays u Internal field strength margin factor Corresponding to different parameters of insulation failure probability, , , Arrays v Internal field strength margin factor Different parameters corresponding to the probability of insulation failure; The insulation failure probability of the entire switch cabinet is obtained by the following formula: ； In the formula, is the insulation failure probability of the entire switch cabinet, , Separate arrays u Neidi The insulation failure probability and weighting coefficient corresponding to each field strength margin coefficient, , Arrays v Neidi The insulation failure probability and weighting coefficient corresponding to each field strength margin coefficient, m , n Separate arrays u , v The number of elements contained.

2. The switch cabinet insulation failure probability assessment method according to claim 1, characterized in that: Obtain the field strength margin coefficients at different locations of the circuit breaker room, busbar room, and outgoing line room using the following formula: ； ； ； In the formula, , , They are respectively expressed as the field strength margin coefficients of the high-voltage conductor surface, metal insert surface, and contact box surface inside the circuit breaker room. , , They are respectively expressed as the field strength margin coefficients of the high-voltage conductor surface inside the busbar room, the metal insert surface, and the bushing surface. , , They are respectively expressed as the field strength margin coefficients of the high-voltage conductor surface, metal insert surface, and grounding switch insulator surface in the outlet room. , , They represent the highest field strength on the surface of the high-voltage conductor, metal insert, and contact box inside the circuit breaker chamber. , , They represent the highest field strength on the surface of the high-voltage conductor inside the busbar room, the surface of the metal insert, and the surface of the bushing through the cabinet. , , They represent the highest field strength on the surface of the high-voltage conductor inside the outlet room, the surface of the metal insert, and the surface of the grounding switch insulator. , , They are respectively expressed as the control field strength of the high-voltage conductor surface, the metal insert surface, and the insulating surface. , , They represent the gradients of the highest electric field strength on the surface of the high-voltage conductor, metal insert, and contact box inside the circuit breaker chamber. , , They are respectively represented by the gradient of the highest electric field strength on the surface of the high-voltage conductor inside the busbar room, the surface of the metal insert, and the surface of the bushing through the cabinet. , , They are respectively represented by the gradient of the highest electric field strength on the surface of the high-voltage conductor inside the outlet room, the surface of the metal insert, and the surface of the grounding switch insulator. , , They are respectively expressed as the gradients of the controlled electric field strength on the surface of the high-voltage conductor, the surface of the metal insert, and the surface of the insulating part.

3. The switch cabinet insulation failure probability assessment method according to claim 1, characterized in that: Get the corrected field strength margin coefficient through the following formula: ； ； ； In the formula, , They are respectively expressed as the field strength margin coefficients of the metal insert surface and contact box surface inside the circuit breaker chamber after correction, , They represent the field strength margin coefficients of the metal insert surface and contact box surface inside the circuit breaker chamber before correction, , They are respectively expressed as the field strength margin coefficients of the metal insert surface inside the busbar room and the bushing surface after correction. , They represent the field strength margin coefficients of the metal insert surface and the bushing surface in the busbar room before correction. , They are respectively expressed as the field strength margin coefficients of the metal insert surface inside the outgoing line room and the grounding switch insulator surface after correction, , They are respectively the field strength margin coefficients of the metal insert surface inside the outgoing line room and the grounding switch insulator surface before correction, It is expressed as a function of the internal breakdown voltage of the insulation material inside the switch cabinet as a function of the operating years. It is expressed as the function of the flashover voltage on the surface of the insulating material inside the switch cabinet as a function of the operating years. , , They are respectively represented by the service life of the contact box inside the circuit breaker room, the service life of the bushing inside the busbar room, and the service life of the grounding switch insulator inside the outgoing line room.

4. The switch cabinet insulation failure probability assessment method according to claim 3 is characterized in that: Function of internal breakdown voltage of insulation material inside switch cabinet as a function of operating years , the function of the flashover voltage on the surface of the insulating material inside the switch cabinet with the operating years They are all obtained by fitting the test results of switchgear insulation materials with different operating years on site.

5. The switch cabinet insulation failure probability assessment method according to claim 1, characterized in that: Arrays u The corresponding field strength margin coefficient range is (0,0.8], indicating that the insulation system is in a high-risk state; Arrays v The corresponding field strength margin coefficient range is (0.8, 1.2), indicating that the insulation system is in a medium risk state; Arrays w The corresponding field strength margin coefficient range is [1.2, +∞), indicating that the insulation system is in a low-risk state.

6. The switch cabinet insulation failure probability assessment method according to claim 1, characterized in that: Get weighting coefficient , by: S100, pair array u The field strength margin coefficient within the filter is standardized to obtain a standardized data set; S200, constructs a correlation coefficient matrix by calculating the Pearson correlation coefficient between each pair of variables in the standardized data set; S300, solving the correlation coefficient matrix, obtaining the eigenvalues ​​and eigenvectors of the correlation coefficient matrix; S400, determining the number of principal components according to the size of the eigenvalue; S500, constructing a principal component loading matrix according to the number of principal components and the eigenvectors; S600, calculating the contribution rate of each principal component according to the principal component load matrix, and obtaining a weighted coefficient; Similarly, obtain the weighting coefficient , reuse steps S100~S600, the difference is that the data applied in the starting step S100 is an array v Field strength margin factor within.

7. The switch cabinet insulation failure probability assessment method according to claim 6, characterized in that: Get weighting coefficient , through the following formula: ； In the formula, a Representing an array u Any principal component retained in Expressed as a The variance contribution of the principal components is Expressed as a The eigenvalues ​​of the principal components, is expressed as the sum of all eigenvalues, m represents the total number of principal components, Expressed as i The variable in a The loadings on the principal components are Represented as a variable i With principal component a The relevant standardized score, Represents the number of principal components retained, Represented as a variable The weighting coefficient of .

8. A switch cabinet insulation failure probability assessment system, characterized in that: The method for evaluating the insulation failure probability of a switch cabinet according to any one of claims 1 to 7 comprises: The maximum field strength module is used to establish a three-dimensional finite element electric field simulation model based on the entire switch cabinet, and simulate and obtain the surface maximum field strength at different locations of the circuit breaker room, busbar room, and outgoing line room in the switch cabinet; The field strength margin coefficient module is used to obtain the field strength margin coefficients at different positions of the circuit breaker room, busbar room, and outgoing line room according to the control field strength inside the switch cabinet; The correction module is used to correct the field strength margin coefficients at different positions of the circuit breaker room, busbar room and outgoing line room according to the service life of the internal insulating parts of the switch cabinet, and obtain the corrected field strength margin coefficients; An array module is used to construct arrays representing different risk states according to the magnitude of the field strength margin coefficients at different positions inside the switch cabinet after correction; An array insulation failure probability module is used to apply the insulation failure probability equation group to obtain the insulation failure probability corresponding to the field strength margin coefficient contained in different arrays; The whole cabinet insulation failure probability module is used to obtain the insulation failure probability of the whole switch cabinet according to the insulation failure probability corresponding to the field strength margin coefficient of different arrays.

9. A switch cabinet, characterized in that: The insulation failure probability assessment method for a switch cabinet as described in any one of claims 1 to 7 is used to perform insulation failure probability assessment.

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