Method and system for evaluating internal insulation performance of circuit breaker

Abstract

The invention discloses a method and system for evaluating the internal insulation performance of a circuit breaker, and the method comprises the steps: determining a key evaluation index based on the evaluation principle of an evaluation index system for the internal insulation performance of the circuit breaker; determining a subjective weight and an objective weight of the key evaluation index, and calculating a comprehensive weight of the key evaluation index based on the subjective weight and the objective weight; based on a clustering cloud model, establishing a comprehensive judgment matrix of the key evaluation indexes; and based on the comprehensive weight and the comprehensive judgment matrix, obtaining a comprehensive judgment value of the internal insulation performance of the circuit breaker, and based on the comprehensive judgment value, determining the internal insulation performance of the circuit breaker.

Description

Technical Field

[0001] The present invention relates to the technical field of insulation performance evaluation in circuit breakers, and more specifically, to a method and system for evaluating insulation performance in circuit breakers. Background Art

[0002] At present, sensor technology has been widely used in the evaluation of the internal insulation performance of circuit breakers, realizing real-time monitoring of the operating status of circuit breakers. Sensors can obtain key parameters such as current, voltage, and temperature in real time, providing rich data support for the evaluation of internal insulation performance. With the development of information technology and artificial intelligence technology, the evaluation of the internal insulation performance of circuit breakers is gradually developing towards intelligence and automation.

[0003] With the increasing complexity of power systems and the intelligence of power equipment, circuit breakers, as important control and protection equipment in power systems, are of great significance for ensuring the stable operation of power systems and reducing accident risks. However, since the insulation performance of circuit breakers is affected by many factors, such as electrical characteristics, operating years and working environment, its performance evaluation has always been a complex issue. Traditional internal insulation performance evaluation methods often rely on a single indicator or a simple mathematical model, which is difficult to fully and accurately reflect the true state of the circuit breaker.

[0004] Therefore, a technology is needed to evaluate the insulation performance in the circuit breaker. Summary of the invention

[0005] The technical solution of the present invention provides a method and system for evaluating the insulation performance in a circuit breaker, so as to solve the problem of how to evaluate the insulation performance in a circuit breaker.

[0006] In order to solve the above problem, the present invention provides a method for evaluating the insulation performance in a circuit breaker, the method comprising:

[0007] Determine key evaluation indicators based on the evaluation principle of the insulation performance evaluation index system in the circuit breaker; determine the subjective weight and objective weight of the key evaluation indicators respectively, and calculate the comprehensive weight of the key evaluation indicators based on the subjective weight and the objective weight;

[0008] Based on the clustering cloud model, a comprehensive judgment matrix of the key evaluation indicators is established;

[0009] Based on the comprehensive weight and the comprehensive judgment matrix, a comprehensive judgment value of the insulation performance in the circuit breaker is obtained, and based on the comprehensive judgment value, the insulation performance in the circuit breaker is determined.

[0010] Preferably, the key evaluation indicators include:

[0011] UHF partial discharge monitoring, ultrasonic partial discharge monitoring, metal particle monitoring, SF6 gas pressure monitoring, gas composition analysis, operating years, and number of operations.

[0012] Preferably, the subjective weight and the objective weight of the key evaluation indicators are determined separately, wherein the subjective weight is determined based on the extension hierarchical analysis method, including:

[0013] Based on the evaluation index system, an extensible judgment matrix A is established. ij (i,j=1,2,...,n):

[0014]

[0015] Among them, 1 / 9≤a ij - ≤a ij + ≤9, a ij - =1 / a ij + ,a ij + =1 / a ij - , a ij - is the minimum value of the importance of index i relative to index j, a ij + is the maximum value of the importance of indicator i relative to indicator j, i is the i-th indicator, j is the j-th indicator, and n is the number of indicators evaluated;

[0016] Based on the scalable judgment matrix, the subjective weights of key evaluation indicators are calculated according to the extension analytic hierarchy process:

[0017] Assume that there are N decision makers participating in the scoring, and the nth person gives the extension interval digit a ij n = ij -n , a ij n >(n=1,2,3.....,N); where a ij n is the maximum importance of indicator i relative to indicator j for the nth decision maker, which is the upper limit of the number of extension intervals. ij -n is the minimum importance of indicator i relative to indicator j for the nth decision maker, which is the lower limit of the number of extension intervals;

[0018] The calculation formula for the number of expandable intervals in the kth layer is:

[0019] ​

[0020] Among them, Aij k is the weighted average of the number of extension intervals of indicator i relative to indicator j of decision maker k, aij 1 …aij N is the judgment of the importance of indicator i relative to indicator j by N decision makers, which is an extensible interval number;

[0021] Obtain the extension judgment matrix of all key evaluation indicators of the kth layer for the k-1th layer;

[0022] Obtain multiple key evaluation indicators and compare and score them in pairs, and establish a score table based on the average of the key evaluation indicators and compare and score them in pairs; based on the score table, obtain the left judgment matrix A respectively - and the right judgment matrix A + , where A= - ,A + >,A - =[a ij - ] nk×nk ,A + =[a ij + ] nk×nk ;

[0023] Based on the left judgment matrix A - and the right judgment matrix A + , calculate the normalized eigenvectors with positive components, denoted as x - ,x + ;

[0024] Determine the addition coefficients k and m, and the calculation formula is:

[0025]

[0026] Among them, nk is the order of the k-th layer extension judgment matrix;

[0027] Calculate the weight vector of the extension judgment matrix:

[0028] S k =(S1 k ,S2 k ,...,S n k ) T = < kx - ,mx + > (4)

[0029] Among them, S1 k ,S2 k ,...,S n k ​is the relative importance of indicator k relative to other indicators, kx - is the lower limit of the weight vector of index k, mx + is the upper limit of the weight vector of index k;

[0030] Calculate the single sorting weight S of each element i k =<S i k- ,S i k+ >greater than S j k =<S j k- ,S j k+ The probability of > is calculated according to formula (5):

[0031]

[0032] Among them, S i k+ is the maximum possible weight of indicator i relative to indicator k, S j k+ is the maximum possible weight of indicator j relative to indicator k, S j k- is the minimum possible weight of index j relative to index k, S i k- is the maximum possible weight of index i relative to index k;

[0033] If for task i, j = 1, 2, ..., n k ; i≠j, V(S i k ≥S j k )≥0, then P j k =1, V(S i k ≥S j k ), where P j k It represents the single sorting of the i-th element of the k-th layer on the previous layer, which is normalized as follows:

[0034] P k =(P1 k , P2 k , ..., P n k ) T (6)

[0035] Among them, P1 k , P2 k , ..., Pn k is the single sorting weight of element n in the kth layer for the previous layer;

[0036] After obtaining the weight coefficient of each level to the upper level, we calculate upward layer by layer according to the series connection principle to obtain the relative importance of the bottom-level key evaluation indicators to the target level, which is the total weight of each key evaluation indicator:

[0037] W k =P k P k-1 ...P 3 W 2 (7)

[0038] Among them, W 2 The weight vector of all elements in the second layer to the target layer, P k is a single-ranked weight vector of all indicators in the k-th layer.

[0039] Preferably, the respectively determining of the subjective weight and the objective weight of the key evaluation indicator, wherein the determining of the subjective weight based on the entropy weight method comprises:

[0040] Suppose there are m groups to be evaluated and n evaluation indicators, forming a judgment matrix X

[0041]

[0042] Among them, x ij (i=1,2,...,m;j=1,2,...,n) is the jth indicator data of the i-th evaluation object;

[0043] Establish a dimensionless processing judgment matrix:

[0044]

[0045] Among them, a1 and a2 are the numerical thresholds of the indicators to be evaluated, and x i is the value of the indicator to be evaluated, and after dimensionless transformation, it forms the decision matrix Y ij ;

[0046] Normalize the i-th alternative under the j-th indicator, that is, take P ij , and then take the information entropy E of the jth index j :

[0047]

[0048] in, P ij is the normalized result of the importance of indicator i relative to indicator j;

[0049]

[0050] Among them, j = 1, 2, ..., m, satisfying And 0≤W′ j ≤1, which is the objective weight.

[0051] Preferably, the calculating of the comprehensive weight of the key evaluation indicator based on the subjective weight and the objective weight includes:

[0052] Based on the variable weight principle, the subjective weight and objective weight are comprehensively weighted, and the calculation method is:

[0053] W=aW S +bW0 (13)

[0054]

[0055] b=1-a (15)

[0056] Where: W s is the subjective weight; W0 is the objective weight; P i is the corresponding component of the subjective weight vector after being arranged in ascending order, i = 1, 2, ..., n; n is the number of indicators; a is the subjective weight coefficient; b is the objective weight coefficient.

[0057] Preferably, the comprehensive judgment matrix of the key evaluation indicators is established based on the clustering cloud model, including:

[0058] A forward cloud generator is established through a clustering cloud model, and a cloud droplet sample image feature value is generated based on the forward cloud generator;

[0059] The states of the circuit breakers are divided, and the hierarchical clustering center values ​​of the circuit breakers in different states are used as the expected values ​​of the clustering cloud model. The expected values ​​of the cloud models of the circuit breakers in different states are normalized to establish a membership selector of the clustering cloud model. Based on the membership selector, the membership of each clustering cloud model is obtained;

[0060] The comprehensive judgment matrix is ​​obtained based on the mean value of the key evaluation index, the characteristic value of the cloud droplet sample image and the degree of membership.

[0061] Preferably, the obtaining a comprehensive judgment value of the insulation performance in the circuit breaker based on the comprehensive weight and the comprehensive judgment matrix, and determining the insulation performance in the circuit breaker based on the comprehensive judgment value includes:

[0062] Multiplying the comprehensive weight by the comprehensive judgment matrix to obtain a comprehensive judgment vector D;

[0063] Based on the comprehensive judgment vector, a comprehensive judgment value r is obtained by weighted average:

[0064]

[0065] Among them, d i is the component of the comprehensive judgment vector D; f i It is the corresponding score of each circuit breaker status level i. The circuit breaker status is confirmed according to the value of the comprehensive judgment value r and the grading interval of the preset evaluation score.

[0066] According to another aspect of the present invention, the present invention provides a system for evaluating insulation performance in a circuit breaker, the system comprising:

[0067] An initial unit, used to determine key evaluation indicators based on the evaluation principle of the insulation performance evaluation indicator system within the circuit breaker;

[0068] A calculation unit, used to respectively determine the subjective weight and the objective weight of the key evaluation indicator, and calculate the comprehensive weight of the key evaluation indicator based on the subjective weight and the objective weight;

[0069] An establishing unit, used for establishing a comprehensive judgment matrix of the key evaluation indicators based on a clustering cloud model;

[0070] The result unit is used to obtain a comprehensive judgment value of the insulation performance in the circuit breaker based on the comprehensive weight and the comprehensive judgment matrix, and determine the insulation performance in the circuit breaker based on the comprehensive judgment value.

[0071] Preferably, the key evaluation indicators include:

[0072] UHF partial discharge monitoring, ultrasonic partial discharge monitoring, metal particle monitoring, SF6 gas pressure monitoring, gas composition analysis, operating years, and number of operations.

[0073] Preferably, the calculation unit is used to respectively determine the subjective weight and the objective weight of the key evaluation indicator, wherein the subjective weight is determined based on the extension hierarchical analysis method, and is also used to:

[0074] Based on the evaluation index system, an extensible judgment matrix A is established. ij (i,j=1,2,...,n):

[0075]

[0076] Among them, 1 / 9≤a ij - ≤a ij + ≤9, a ij - =1 / a ij + ,a ij + =1 / aij - , a ij - is the minimum value of the importance of index i relative to index j, a ij + is the maximum value of the importance of indicator i relative to indicator j, i is the i-th indicator, j is the j-th indicator, and n is the number of indicators evaluated;

[0077] Based on the scalable judgment matrix, the subjective weights of key evaluation indicators are calculated according to the extension analytic hierarchy process:

[0078] Assume that there are N decision makers participating in the scoring, and the nth person gives the extension interval digit a ij n = ij -n , a ij n >(n=1,2,3.....,N); where a ij n is the maximum importance of indicator i relative to indicator j for the nth decision maker, which is the upper limit of the number of extension intervals. ij -n is the minimum importance of indicator i relative to indicator j for the nth decision maker, which is the lower limit of the number of extension intervals;

[0079] The calculation formula for the number of expandable intervals in the kth layer is:

[0080]

[0081] Among them, Aij k is the weighted average of the number of extension intervals of indicator i relative to indicator j of decision maker k, aij 1 …aij N is the judgment of the importance of indicator i relative to indicator j by N decision makers, which is an extensible interval number. ;

[0082] Obtain the extension judgment matrix of all key evaluation indicators of the kth layer for the k-1th layer;

[0083] Obtain multiple key evaluation indicators and compare and score them in pairs, and establish a score table based on the average of the key evaluation indicators and compare and score them in pairs; based on the score table, obtain the left judgment matrix A respectively - and the right judgment matrix A + , where A= - ,A + >,A - =[a ij - ] nk×nk ,A + =[a​​ij + ] nk×nk ;

[0084] Based on the left judgment matrix A - and the right judgment matrix A + , calculate the normalized eigenvectors with positive components, denoted as x - ,x + ;

[0085] Determine the addition coefficients k and m, and the calculation formula is:

[0086]

[0087] Among them, nk is the order of the k-th layer extension judgment matrix;

[0088] Calculate the weight vector of the extension judgment matrix:

[0089] S k =(S1 k ,S2 k ,...,S n k ) T = < kx - ,mx + > (4)

[0090] Among them, S1 k ,S2 k ,...,S n k is the relative importance of indicator k relative to other indicators, kx - is the lower limit of the weight vector of index k, mx + is the upper limit of the weight vector of index k;

[0091] Calculate the single sorting weight S of each element i k =<S i k- ,S i k+ >greater than S j k =<S j k- ,S j k+ The probability of > is calculated according to formula (5):

[0092]

[0093] Among them, S i k+ is the maximum possible weight of indicator i relative to indicator k, S jk+ is the maximum possible weight of indicator j relative to indicator k, S j k- is the minimum possible weight of index j relative to index k, S i k- is the maximum possible weight of index i relative to index k;

[0094] If for task i, j = 1, 2, ..., n k ; i≠j, V(S i k ≥S j k )≥0, then P j k =1, V(S i k ≥S j k ), where P j k It represents the single sorting of the i-th element of the k-th layer on the previous layer, which is normalized as follows:

[0095]

[0096] in, is the single sorting weight of element n in the kth layer for the previous layer;

[0097] After obtaining the weight coefficient of each level to the upper level, we calculate upward layer by layer according to the series connection principle to obtain the relative importance of the bottom-level key evaluation indicators to the target level, which is the total weight of each key evaluation indicator:

[0098] W k =P k P k-1 ...P 3 W 2 (7)

[0099] Among them, W 2 The weight vector of all elements in the second layer to the target layer, P k is a single-ranked weight vector of all indicators in the k-th layer.

[0100] Preferably, the calculation unit is used to respectively determine the subjective weight and the objective weight of the key evaluation indicator, wherein the subjective weight is determined based on the entropy weight method, and is also used to:

[0101] Suppose there are m groups to be evaluated and n evaluation indicators, forming a judgment matrix X

[0102]

[0103] Among them, x ij(i=1,2,...,m;j=1,2,...,n) is the jth indicator data of the i-th evaluation object;

[0104] Establish a dimensionless processing judgment matrix:

[0105]

[0106] Among them, a1 and a2 are the numerical thresholds of the indicators to be evaluated, and x i is the index value to be evaluated, and the decision matrix Y is formed after tempering without quantity ij ;

[0107] Normalize the i-th alternative under the j-th indicator, that is, take P ij , and then take the information entropy E of the jth index j :

[0108]

[0109] in, P ij is the normalized result of the importance of indicator i relative to indicator j;

[0110]

[0111] Among them, j = 1, 2, ..., m, satisfying And 0≤W′ j ≤1, which is the objective weight.

[0112] Preferably, the calculation unit is used to calculate the comprehensive weight of the key evaluation indicator based on the subjective weight and the objective weight, and is also used to:

[0113] Based on the variable weight principle, the subjective weight and objective weight are comprehensively weighted, and the calculation method is:

[0114] W=aW S +bW0 (13)

[0115]

[0116] b=1-a(15)

[0117] Where: W s is the subjective weight; W0 is the objective weight; P i is the corresponding component of the subjective weight vector after being arranged in ascending order, i = 1, 2, ..., n; n is the number of indicators; a is the subjective weight coefficient; b is the objective weight coefficient.

[0118] Preferably, the establishing unit is used to establish a comprehensive judgment matrix of the key evaluation indicators based on the clustering cloud model, and is also used to:

[0119] A forward cloud generator is established through a clustering cloud model, and a cloud droplet sample image feature value is generated based on the forward cloud generator;

[0120] The states of the circuit breakers are divided, and the hierarchical clustering center values ​​of the circuit breakers in different states are used as the expected values ​​of the clustering cloud model. The expected values ​​of the cloud models of the circuit breakers in different states are normalized to establish a membership selector of the clustering cloud model. Based on the membership selector, the membership of each clustering cloud model is obtained;

[0121] The comprehensive judgment matrix is ​​obtained based on the mean value of the key evaluation index, the characteristic value of the cloud droplet sample image and the degree of membership.

[0122] Preferably, the result unit is used to obtain a comprehensive judgment value of the insulation performance in the circuit breaker based on the comprehensive weight and the comprehensive judgment matrix, and determine the insulation performance in the circuit breaker based on the comprehensive judgment value, and is also used to:

[0123] Multiplying the comprehensive weight by the comprehensive judgment matrix to obtain a comprehensive judgment vector D;

[0124] Based on the comprehensive judgment vector, a comprehensive judgment value is obtained by weighted average:

[0125]

[0126] Among them, d i is the component of the comprehensive judgment vector D; f i It is the corresponding score of each circuit breaker status level i. The circuit breaker status is confirmed according to the value of r and the grading interval of the preset evaluation score.

[0127] According to another aspect of the present invention, the present invention provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, and the computer program is used to execute a method for evaluating insulation performance in a circuit breaker.

[0128] Based on another aspect of the present invention, the present invention provides an electronic device, characterized in that the electronic device comprises: a processor and a memory; wherein,

[0129] The memory is a memory for storing instructions executable by the processor;

[0130] The processor is used to read the executable instructions from the memory and execute the instructions to implement a method for evaluating insulation performance in a circuit breaker.

[0131] The technical solution of the present invention provides a method and system for evaluating the insulation performance in a circuit breaker, wherein the method includes: determining key evaluation indicators based on the evaluation principle of the insulation performance evaluation index system in the circuit breaker; respectively determining the subjective weight and objective weight of the key evaluation indicators, and calculating the comprehensive weight of the key evaluation indicators based on the subjective weight and the objective weight; establishing a comprehensive judgment matrix of the key evaluation indicators based on the clustering cloud model; obtaining the comprehensive judgment value of the insulation performance in the circuit breaker based on the comprehensive weight and the comprehensive judgment matrix, and determining the insulation performance in the circuit breaker based on the comprehensive judgment value. The present invention proposes a method and system for evaluating the insulation performance in a circuit breaker based on comprehensive weighting and clustering cloud models, which improves the accuracy and reliability of the insulation performance evaluation in the circuit breaker. BRIEF DESCRIPTION OF THE DRAWINGS

[0132] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0133] Figure 1 A flow chart of a method for evaluating insulation performance in a circuit breaker according to a preferred embodiment of the present invention;

[0134] Figure 2 A schematic diagram of a circuit breaker evaluation index structure according to a preferred embodiment of the present invention;

[0135] Figure 3 A schematic diagram of cloud droplet distribution of a cloud model according to a preferred embodiment of the present invention; and

[0136] Figure 4 The figure is a structural diagram of a system for evaluating insulation performance in a circuit breaker according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0137] Now, exemplary embodiments of the present invention are described with reference to the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the accompanying drawings are not intended to limit the present invention. In the accompanying drawings, the same units / elements are marked with the same reference numerals.

[0138] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0139] Figure 1The present invention is a flowchart of a method for evaluating insulation performance in a circuit breaker according to a preferred embodiment of the present invention.

[0140] The present invention proposes a circuit breaker internal insulation performance evaluation method based on comprehensive weighting and clustering cloud model, aiming to improve the accuracy and reliability of the evaluation.

[0141] In view of the current technical problems of low efficiency and poor accuracy in circuit breaker status analysis and evaluation, the present invention proposes a circuit breaker internal insulation performance evaluation method based on comprehensive weighting and clustering cloud model.

[0142] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for evaluating the insulation performance of a circuit breaker based on a comprehensive weighting and clustering cloud model. By constructing an evaluation index system and combining the comprehensive weighting method and the clustering cloud model, a comprehensive and accurate evaluation of the status of the circuit breaker is performed.

[0143] like Figure 1 As shown, the present invention provides a method for evaluating the insulation performance in a circuit breaker, the method comprising:

[0144] Step 101: Determine key evaluation indicators based on the evaluation principle of the circuit breaker internal insulation performance evaluation indicator system;

[0145] Preferably, key evaluation indicators include:

[0146] UHF partial discharge monitoring, ultrasonic partial discharge monitoring, metal particle monitoring, SF6 gas pressure monitoring, gas composition analysis, operating years, and number of operations.

[0147] The present invention first constructs an evaluation index system for the insulation performance of the circuit breaker to ensure that the evaluation results are accurate and reliable. When constructing the evaluation index system, it is necessary to comprehensively consider various performance indicators and influencing factors of the circuit breaker. And follow the following principles:

[0148] A) Satisfy the principle of completeness, that is, the indicators should reflect the working status of the circuit breaker as a whole.

[0149] B) To meet the principle of simplicity, while meeting the principle of safety, the number of indicators should be reduced as much as possible to facilitate operation and management.

[0150] C) Satisfy the principle of relative independence, each indicator is independent of each other, and avoid inclusiveness.

[0151] D) To meet the objectivity principle, the evaluation indicators must be comparable and universal among different circuit breakers to avoid differences in evaluation results.

[0152] By counting the existing circuit breaker monitoring data and following the above principles, the present invention selects seven important indicators, including ultra-high frequency partial discharge monitoring, ultrasonic partial discharge monitoring, metal particle monitoring, SF6 gas pressure monitoring, gas composition analysis, operating years, and number of operations, to construct an evaluation system.

[0153] Step 102: respectively determining the subjective weight and the objective weight of the key evaluation indicators, and calculating the comprehensive weight of the key evaluation indicators based on the subjective weight and the objective weight;

[0154] Preferably, the subjective weight and the objective weight of the key evaluation indicators are determined respectively, wherein the subjective weight is determined based on the extension hierarchical analysis method, including:

[0155] Based on the evaluation index system, an extensible judgment matrix A is established ij (i,j=1,2,...,n):

[0156]

[0157] Among them, 1 / 9≤a ij - ≤a ij + ≤9, a ij - =1 / a ij + ,a ij + =1 / a ij - , a ij - is the minimum value of the importance of index i relative to index j, a ij + is the maximum value of the importance of indicator i relative to indicator j, i is the i-th indicator, j is the j-th indicator, and n is the number of indicators evaluated;

[0158] Based on the scalable judgment matrix, the subjective weights of key evaluation indicators are calculated according to the extension hierarchical analysis method:

[0159] Assume that there are N decision makers participating in the scoring, and the nth person gives the extension interval digit a ij n = ij -n , a ij n >(n=1,2,3.....,N); where a ij n is the maximum importance of indicator i relative to indicator j for the nth decision maker, which is the upper limit of the number of extension intervals. ij -n ​is the minimum importance of indicator i relative to indicator j for the nth decision maker, which is the lower limit of the number of extension intervals;

[0160] The calculation formula for the number of expandable intervals in the kth layer is:

[0161]

[0162] Among them, Aij k is the weighted average of the number of extension intervals of indicator i relative to indicator j of decision maker k, aij 1 …aij N is the judgment of the importance of indicator i relative to indicator j by N decision makers, which is an extensible interval number;

[0163] Obtain the extension judgment matrix of all key evaluation indicators of the kth layer for the k-1th layer;

[0164] Obtain multiple key evaluation indicators and compare them in pairs, and establish a score table based on the average of the scores of the key evaluation indicators. Based on the score table, obtain the left judgment matrix A respectively. - and the right judgment matrix A + , where A= - ,A + >,A - =[a ij - ] nk×nk ,A + =[a ij + ] nk×nk ;

[0165] Based on the left judgment matrix A - and the right judgment matrix A + , calculate the normalized eigenvectors with positive components, denoted as x - ,x + ;

[0166] Determine the addition coefficients k and m, and the calculation formula is:

[0167]

[0168] Among them, nk is the order of the k-th layer extension judgment matrix;

[0169] Calculate the weight vector of the extension judgment matrix:

[0170] S k =(S1 k ,S2 k ,...,S n k ) T = < kx​- ,mx + > (4)

[0171] Among them, S1 k ,S2 k ,...,S n k is the relative importance of indicator k relative to other indicators, kx - is the lower limit of the weight vector of index k, mx + is the upper limit of the weight vector of index k;

[0172] Calculate the single sorting weight S of each element i k =<S i k- ,S i k+ >greater than S j k =<S j k- ,S j k+ The probability of > is calculated according to formula (5):

[0173]

[0174] Among them, S i k+ is the maximum possible weight of indicator i relative to indicator k, S j k+ is the maximum possible weight of indicator j relative to indicator k, S j k- is the minimum possible weight of index j relative to index k, S i k- is the maximum possible weight of index i relative to index k;

[0175] If for task i, j = 1, 2, ..., n k ; i≠j, V(S i k ≥S j k )≥0, then P j k =1, V(S i k ≥S j k ), where P j k It represents the single sorting of the i-th element of the k-th layer on the previous layer, which is normalized as follows:

[0176]

[0177] in, is the single sorting weight of element n in the kth layer for the previous layer;

[0178] After obtaining the weight coefficient of each level to the upper level, we calculate upward layer by layer according to the series connection principle to obtain the relative importance of the bottom-level key evaluation indicators to the target level, which is the total weight of each key evaluation indicator:

[0179] W k =P k P k-1 ...P 3 W 2 (7)

[0180] Among them, W 2 The weight vector of all elements in the second layer to the target layer, P k is a single-ranked weight vector of all indicators in the k-th layer.

[0181] Preferably, the subjective weight and the objective weight of the key evaluation indicators are determined respectively, wherein the subjective weight is determined based on the entropy weight method, including:

[0182] Suppose there are m groups to be evaluated and n evaluation indicators, forming a judgment matrix X

[0183]

[0184] Among them, x ij (i=1,2,...,m;j=1,2,...,n) is the jth indicator data of the i-th evaluation object;

[0185] Establish a dimensionless processing judgment matrix:

[0186]

[0187] Among them, a1 and a2 are the numerical thresholds of the indicators to be evaluated, and x i is the index value to be evaluated, and the decision matrix Y is formed after tempering without quantity ij ;

[0188] Normalize the i-th alternative under the j-th indicator, that is, take P ij , and then take the information entropy E of the jth index j :

[0189]

[0190] in, P ij is the normalized result of the importance of indicator i relative to indicator j;

[0191]

[0192] Among them, j = 1, 2, ..., m, satisfying And 0≤W′ j ≤1, which is the objective weight.

[0193] Preferably, based on the subjective weight and the objective weight, the comprehensive weight of the key evaluation indicators is calculated, including:

[0194] Based on the variable weight principle, the subjective weight and objective weight are comprehensively weighted, and the calculation method is:

[0195] W=aW S +bW0 (13)

[0196]

[0197] b=1-a (15)

[0198] Where: W s is the subjective weight; W0 is the objective weight; P i is the corresponding component of the subjective weight vector after being arranged in ascending order, i = 1, 2, ..., n; n is the number of indicators; a is the subjective weight coefficient; b is the objective weight coefficient.

[0199] The present invention determines weights based on the comprehensive weighting method. The comprehensive weighting method is a weight determination strategy that combines the subjective weighting method and the objective weighting method. The present invention aims to more comprehensively reflect the actual situation of the evaluation object by integrating different weighting methods, and improve the accuracy and reliability of weight determination, and includes the following steps:

[0200] The subjective weight is determined by the extension hierarchical analysis method, and the subjective weight factor Ws is obtained.

[0201] The entropy weight method determines the objective weight and obtains the objective weight factor W0.

[0202] Based on the variable weight principle, the subjective weight and objective weight are comprehensively weighted to obtain the comprehensive weight W.

[0203] Step 103: Based on the clustering cloud model, a comprehensive judgment matrix of key evaluation indicators is established;

[0204] Preferably, based on the clustering cloud model, a comprehensive judgment matrix of key evaluation indicators is established, including:

[0205] A forward cloud generator is established through a clustering cloud model, and the characteristic values ​​of a cloud droplet sample image are generated based on the forward cloud generator;

[0206] The states of the circuit breakers are divided, and the hierarchical clustering center values ​​of the circuit breakers in different states are used as the expected values ​​of the clustering cloud model. The expected values ​​of the cloud models of the circuit breakers in different states are normalized to establish the membership selector of the clustering cloud model. Based on the membership selector, the membership of each clustering cloud model is obtained.

[0207] Based on the mean of key evaluation indicators, the characteristic value of cloud droplet sample image and the degree of membership, a comprehensive judgment matrix is ​​obtained.

[0208] Step 104: Based on the comprehensive weight and the comprehensive judgment matrix, a comprehensive judgment value of the insulation performance in the circuit breaker is obtained, and based on the comprehensive judgment value, the insulation performance in the circuit breaker is determined.

[0209] Preferably, based on the comprehensive weight and the comprehensive judgment matrix, a comprehensive judgment value of the insulation performance in the circuit breaker is obtained, and based on the comprehensive judgment value, the insulation performance in the circuit breaker is determined, including:

[0210] Multiply the comprehensive weight by the comprehensive judgment matrix to obtain a comprehensive judgment vector D;

[0211] Based on the comprehensive judgment vector, the comprehensive judgment value is obtained by weighted average:

[0212]

[0213] Among them, d i is the component of the comprehensive judgment vector D; f i It is the corresponding score of each circuit breaker status level i. The circuit breaker status is confirmed according to the value of the comprehensive judgment value r and the grading interval of the preset evaluation score.

[0214] The present invention evaluates the circuit breaker status based on the clustering cloud model, comprising the following steps:

[0215] A forward cloud generator is constructed through the cloud model, and cloud droplets generated by the three digital characteristics of the cloud are used to achieve the mutual conversion between the qualitative information expressed by language values ​​and the distribution law of quantitative data.

[0216] A clustering cloud model membership selector is constructed. Combining the relevant standards of State Grid Corporation and the fuzzy C-means clustering principle, the circuit breaker status is divided into four cluster centers, namely normal, caution, abnormal and severe. The cluster center value of the circuit breaker status is used as the expected value of the cloud model. The expected values ​​of the cloud models in different states are normalized by the extreme difference to construct the clustering cloud model membership selector.

[0217] Determine the comprehensive judgment matrix R.

[0218] 3.4 Evaluation of insulation performance in circuit breakers. The obtained weight vector is multiplied by the comprehensive judgment matrix to obtain the comprehensive judgment vector. The weighted average method is used to obtain the comprehensive judgment score. The circuit breaker status can be confirmed according to the graded interval of the score.

[0219] The circuit breaker internal insulation performance evaluation method based on comprehensive weighting and clustering cloud model provided by the present invention has the following advantages:

[0220] 1. High accuracy: Through the comprehensive weighting method, the importance and interrelationship of each evaluation indicator are fully considered, which improves the accuracy of the evaluation results.

[0221] 2. Strong adaptability: The clustering cloud model can handle state parameters with fuzziness and randomness, and is suitable for various complex insulation performance evaluation scenarios in circuit breakers.

[0222] 3. Easy to operate: The evaluation method is easy to construct and can easily realize automated and intelligent evaluation, thus improving the evaluation efficiency.

[0223] The following is an example of the implementation of the present invention with reference to the accompanying drawings:

[0224] The present invention provides a circuit breaker internal insulation performance evaluation based on comprehensive weighting and clustering cloud model, comprising the following steps:

[0225] 1. Construct an evaluation index system for the insulation performance of circuit breakers to ensure that the evaluation results are accurate and reliable. When constructing the evaluation index system, it is necessary to comprehensively consider the various performance indicators and influencing factors of the circuit breaker. And follow the following principles:

[0226] A) Satisfy the principle of completeness, that is, the indicators should reflect the working status of the circuit breaker as a whole.

[0227] B) To meet the principle of simplicity, while meeting the principle of safety, the number of indicators should be reduced as much as possible to facilitate operation and management.

[0228] C) Satisfy the principle of relative independence, each indicator is independent of each other, and avoid inclusiveness.

[0229] D) To meet the objectivity principle, the evaluation indicators must be comparable and universal among different circuit breakers to avoid differences in evaluation results.

[0230] By collecting statistics on existing circuit breaker monitoring data and following the above principles, the present invention selects seven important indicators, including ultra-high frequency partial discharge monitoring, ultrasonic partial discharge monitoring, metal particle monitoring, SF6 gas pressure monitoring, gas composition analysis, operating years, and number of operations, to construct an evaluation system. Figure 2 shown.

[0231] The present invention determines weights based on the comprehensive weighting method, which is a weight determination strategy that combines the subjective weighting method and the objective weighting method. The present invention aims to improve the accuracy and reliability of weight determination by integrating different weighting methods to more comprehensively reflect the actual situation of the evaluation object.

[0232] Step 2 of the present invention comprises the following steps:

[0233] 2.1 The steps of determining subjective weights by extension hierarchical analysis are as follows.

[0234] According to the insulation performance evaluation system in the circuit breaker, an expandable judgment matrix A is constructed. ij (i,j=1,2,...,n)

[0235]

[0236] In formula (1), 1 / 9≤a ij - ≤a ij + ≤9, a ij - =1 / a ij + ,a ij + =1 / a ij - .

[0237] The steps to calculate the subjective weights of key evaluation indicators according to the extension hierarchical analysis method are as follows:

[0238] 1) Assume that there are N decision makers participating in the scoring, and the nth person gives the extension interval digit a ij n = ij -n , a ij n >(n=1,2,3.....,N), then the calculation formula for the number of expandable intervals in the kth layer is

[0239]

[0240] Thus, the extension judgment matrix of all the indicators of the kth layer to the k-1th layer can be obtained. The present invention compares and scores each indicator in pairs, takes the average value, and obtains the data to build Table 1.

[0241] Table 1 Extension judgment matrix of index system

[0242]

[0243]

[0244] 2) According to the table, the left and right judgment matrix A can be obtained separately - and A + , where A= - ,A + >,A - ​​=[a ij - ] nk×nk ,A + =[a ij + ] nk×nk Then, according to the left and right judgment matrices, MATLAB can be used to calculate the normalized eigenvectors with positive components, which are denoted as x - ,x + .

[0245] 3) In order to match the consistency, coefficients k and m need to be added, and the calculation formula is:

[0246]

[0247] In formula (3), nk is the order of the k-th layer judgment matrix.

[0248] 4) Calculate the weight vector of the matrix

[0249] S k =(S1 k ,S2 k ,...,S nk k ) T = < kx - ,mx + > (4)

[0250] 5) Calculate the single ranking weight of each element. i k =<S i k- ,S i k+ >greater than S j k =<S j k- ,S j k+ The probability of > can be calculated according to formula (5)

[0251]

[0252] If for task i, j = 1, 2, ..., n k ; i≠j, all have V(S i k ≥S j k )≥0, then P j k =1, V(S i k ≥S j k ), where P jk represents the single ordering of the i-th element of the k-th layer on the previous layer. After normalization, we can get

[0253]

[0254] 6) After obtaining the weight coefficient of each level to the upper level, we can calculate upward layer by layer according to the series principle to obtain the relative importance of the lowest level indicator to the target level, which is the total weight of each indicator.

[0255] W k =P k P k-1 ...P 3 W 2 (7)

[0256] In formula (7), W 2 This is the result of the previously calculated hierarchical single ranking, and thus the subjective weight factor is obtained.

[0257] 2.2 The entropy weight method is used to determine the objective weights. The steps are as follows.

[0258] 1) Suppose there are m groups to be evaluated and n evaluation indicators, forming a judgment matrix X

[0259]

[0260] In formula (8), x ij (i=1,2,...,m;j=1,2,...,n) is the jth indicator data of the i-th evaluation object.

[0261] 2) Dimensionless processing of judgment matrix. Evaluation factors are of numerical and non-numerical types, which are not mutually commensurable. Therefore, before evaluation, it is necessary to use the ascending and descending semi-trapezoidal function to quantify the indicators uniformly.

[0262]

[0263] In formula (9), a1 and a2 are the numerical thresholds of the indicators to be evaluated. After tempering without quantity, the decision matrix Y ij .

[0264] 3) Normalize the i-th alternative under the j-th indicator, that is, take P ij , and then take the information entropy E of the jth index j :

[0265]

[0266] in

[0267]

[0268] In formula (12), j = 1, 2, ..., m, satisfying And 0≤W′ j ≤1, and obtain the objective weight.

[0269] 2.3 Based on the variable weight principle, the subjective weight and objective weight are comprehensively weighted. The calculation method is:

[0270] W=aW S +bW0 (13)

[0271]

[0272] B=1-a (15)

[0273] In formulas (13)-(15): W s is the subjective weight; W0 is the objective weight; P i is the corresponding component of the subjective weight vector after being arranged in ascending order; n is the number of indicators.

[0274]

[0275] In formula (16), x j is the dimensionless evaluation index value, 0 <x j <1. The variable weight vector is the Hardarmard product of the constant weight vector and the state variable weight vector

[0276]

[0277] In formula (17): W 0 is a constant weight vector; w(x) is a variable weight vector.

[0278] 3. Evaluate the circuit breaker status based on the clustering cloud model. The steps are as follows.

[0279] 3.1 Cloud Model By constructing a forward cloud generator, a cloud droplet sample map with Ex = 10, En = 5, He = 0.6 is generated, and the number of cloud droplets is 1000. Figure 3 shown.

[0280] 3.2 Construct the membership selector of clustering cloud model. Combined with the relevant standards of State Grid Corporation and the fuzzy C-means clustering principle, the circuit breaker status is divided into four cluster centers, namely normal, caution, abnormal and severe. The cluster center value of the circuit breaker status is used as the expected value of the cloud model. The expected values ​​of the cloud models of different states are normalized by extreme values ​​to construct the membership selector of the clustering cloud model.

[0281] Table 2 Membership limits

[0282]

[0283] 3.3 Determine the comprehensive judgment matrix R. Use formula (18) to calculate the mean and the membership of each clustering cloud model multiple times, and summarize to obtain the comprehensive judgment matrix R. The calculation formula is

[0284]

[0285] In formula (18): x i is the index value; E x For expectation; n To take En as the expectation, H e is a normal random number with standard deviation .

[0286] 3.4 Evaluation of insulation performance in circuit breakers. Multiply the comprehensive weight vector W and the comprehensive judgment matrix R to obtain the comprehensive judgment vector D. The weighted average method is used to obtain the comprehensive judgment score r:

[0287]

[0288] In formula (19): d i is the component of the comprehensive judgment vector D; f i is the corresponding score of each circuit breaker status level i. Assume that the scores corresponding to levels normal, caution, abnormal and severe are 1, 2, 3 and 4. The circuit breaker status can be confirmed according to the value of r and the grading interval of the evaluation score in Table 2.

[0289] Table 3 Evaluation score grading range

[0290]

[0291] The present invention constructs a circuit breaker evaluation index system and adopts a comprehensive weighting method to ensure the rationality and accuracy of the weights. The clustering cloud model is used to perform cluster analysis on the evaluation indicators, avoiding possible misjudgments and missed judgments in traditional methods, making the evaluation process more intelligent and accurate. The evaluation results are presented in a quantitative form, which is convenient for users to intuitively understand the operating status of the circuit breaker.

[0292] Figure 4 The figure is a structural diagram of a system for evaluating insulation performance in a circuit breaker according to a preferred embodiment of the present invention.

[0293] like Figure 4 As shown, the present invention provides a system for evaluating insulation performance in a circuit breaker, the system comprising:

[0294] The initial unit 401 is used to determine the key evaluation index based on the evaluation principle of the insulation performance evaluation index system in the circuit breaker;

[0295] A calculation unit 402 is used to determine the subjective weight and the objective weight of the key evaluation indicator respectively, and calculate the comprehensive weight of the key evaluation indicator based on the subjective weight and the objective weight;

[0296] An establishing unit 403 is used to establish a comprehensive judgment matrix of the key evaluation indicators based on the clustering cloud model;

[0297] The result unit 404 is used to obtain a comprehensive judgment value of the insulation performance in the circuit breaker based on the comprehensive weight and the comprehensive judgment matrix, and determine the insulation performance in the circuit breaker based on the comprehensive judgment value.

[0298] Preferably, the key evaluation indicators include:

[0299] UHF partial discharge monitoring, ultrasonic partial discharge monitoring, metal particle monitoring, SF6 gas pressure monitoring, gas composition analysis, operating years, and number of operations.

[0300] Preferably, the calculation unit is used to respectively determine the subjective weight and the objective weight of the key evaluation indicator, wherein the subjective weight is determined based on the extension hierarchical analysis method, and is also used to:

[0301] Based on the evaluation index system, an extensible judgment matrix A is established. ij (i,j=1,2,...,n):

[0302]

[0303] Among them, 1 / 9≤a ij - ≤a ij + ≤9, a ij - =1 / a ij + ,a ij + =1 / a ij - , a ij - is the minimum value of the importance of index i relative to index j, a ij + is the maximum value of the importance of indicator i relative to indicator j, i is the i-th indicator, j is the j-th indicator, and n is the number of indicators evaluated;

[0304] Based on the scalable judgment matrix, the subjective weights of key evaluation indicators are calculated according to the extension analytic hierarchy process:

[0305] Assume that there are N decision makers participating in the scoring, and the nth person gives the extension interval digit a ij n= ij -n , a ij n >(n=1,2,3.....,N); where a ij n is the maximum importance of indicator i relative to indicator j for the nth decision maker, which is the upper limit of the number of extension intervals. ij -n is the minimum importance of indicator i relative to indicator j for the nth decision maker, which is the lower limit of the number of extension intervals;

[0306] The calculation formula for the number of expandable intervals in the kth layer is:

[0307]

[0308] Among them, Aij k is the weighted average of the number of extension intervals of indicator i relative to indicator j of decision maker k, aij 1 …aij N is the judgment of the importance of indicator i relative to indicator j by N decision makers, which is an extensible interval number;

[0309] Obtain the extension judgment matrix of all key evaluation indicators of the kth layer for the k-1th layer;

[0310] Obtain multiple key evaluation indicators and compare and score them in pairs, and establish a score table based on the average of the key evaluation indicators and compare and score them in pairs; based on the score table, obtain the left judgment matrix A respectively - and the right judgment matrix A + , where A= - ,A + >,A - =[a ij - ] nk×nk ,A + =[a ij + ] nk×nk ;

[0311] Based on the left judgment matrix A - and the right judgment matrix A + , calculate the normalized eigenvectors with positive components, denoted as x - ,x + ;

[0312] Determine the addition coefficients k and m, and the calculation formula is:

[0313]

[0314] Among them, nk is the order of the k-th layer extension judgment matrix;​​

[0315] Calculate the weight vector of the extension judgment matrix:

[0316] S k =(S1 k ,S2 k ,...,S n k ) T = < kx - ,mx + > (4)

[0317] Among them, S1 k ,S2 k ,...,S n k is the relative importance of indicator k relative to other indicators, kx - is the lower limit of the weight vector of index k, mx + is the upper limit of the weight vector of index k;

[0318] Calculate the single sorting weight S of each element i k =<S i k- ,S i k+ >greater than S j k =<S j k- ,S j k+ The probability of > is calculated according to formula (5):

[0319]

[0320] Among them, S i k+ is the maximum possible weight of indicator i relative to indicator k, S j k+ is the maximum possible weight of indicator j relative to indicator k, S j k- is the minimum possible weight of index j relative to index k, S i k- is the maximum possible weight of index i relative to index k;

[0321] If for task i, j = 1, 2, ..., n k ; i≠j, V(S i k ≥S j k )≥0, then P j k =1, V(S ik ≥S j k ), where P j k It represents the single sorting of the i-th element of the k-th layer on the previous layer, which is normalized as follows:

[0322]

[0323] in, is the single sorting weight of element n in the kth layer for the previous layer;

[0324] After obtaining the weight coefficient of each level to the upper level, we calculate upward layer by layer according to the series connection principle to obtain the relative importance of the bottom-level key evaluation indicators to the target level, which is the total weight of each key evaluation indicator:

[0325] W k =P k P k-1 ...P 3 W 2 (7)

[0326] Among them, W 2 The weight vector of all elements in the second layer to the target layer, P k is a single-ranked weight vector of all indicators in the k-th layer.

[0327] Preferably, the calculation unit is used to respectively determine the subjective weight and the objective weight of the key evaluation indicator, wherein the subjective weight is determined based on the entropy weight method, and is also used to:

[0328] Suppose there are m groups to be evaluated and n evaluation indicators, forming a judgment matrix X

[0329]

[0330] Among them, x ij (i=1,2,...,m;j=1,2,...,n) is the jth indicator data of the i-th evaluation object;

[0331] Establish a dimensionless processing judgment matrix:

[0332]

[0333] Among them, a1 and a2 are the numerical thresholds of the indicators to be evaluated, and x i is the index value to be evaluated, and the decision matrix Y is formed after tempering without quantity ij ;

[0334] Normalize the i-th alternative under the j-th indicator, that is, take P ij , and then take the information entropy E of the jth index j:

[0335]

[0336] in, P ij is the normalized result of the importance of indicator i relative to indicator j;

[0337]

[0338] Among them, j = 1, 2, ..., m, satisfying And 0≤W′ j ≤1, which is the objective weight.

[0339] Preferably, the calculation unit is used to calculate the comprehensive weight of the key evaluation indicator based on the subjective weight and the objective weight, and is also used to:

[0340] Based on the variable weight principle, the subjective weight and objective weight are comprehensively weighted, and the calculation method is:

[0341] W=aW S +bW0 (13)

[0342]

[0343] b=1-a (15)

[0344] Where: W s is the subjective weight; W0 is the objective weight; P i is the corresponding component of the subjective weight vector after being arranged in ascending order, i = 1, 2, ..., n; n is the number of indicators; a is the subjective weight coefficient; b is the objective weight coefficient.

[0345] Preferably, the establishing unit is used to establish a comprehensive judgment matrix of the key evaluation indicators based on the clustering cloud model, and is also used to:

[0346] A forward cloud generator is established through a clustering cloud model, and a cloud droplet sample image feature value is generated based on the forward cloud generator;

[0347] The states of the circuit breakers are divided, and the hierarchical clustering center values ​​of the circuit breakers in different states are used as the expected values ​​of the clustering cloud model. The expected values ​​of the cloud models of the circuit breakers in different states are normalized to establish a membership selector of the clustering cloud model. Based on the membership selector, the membership of each clustering cloud model is obtained;

[0348] The comprehensive judgment matrix is ​​obtained based on the mean value of the key evaluation index, the characteristic value of the cloud droplet sample image and the degree of membership.

[0349] Preferably, the result unit is used to obtain a comprehensive judgment value of the insulation performance in the circuit breaker based on the comprehensive weight and the comprehensive judgment matrix, and determine the insulation performance in the circuit breaker based on the comprehensive judgment value, and is also used to:

[0350] Multiplying the comprehensive weight by the comprehensive judgment matrix to obtain a comprehensive judgment vector D;

[0351] Based on the comprehensive judgment vector, a comprehensive judgment value is obtained by weighted average:

[0352]

[0353] Among them, d i is the component of the comprehensive judgment vector D; f i It is the corresponding score of each circuit breaker status level i. The circuit breaker status is confirmed according to the value of r and the grading interval of the preset evaluation score.

[0354] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0355] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0356] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0357] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0358] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0359] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

[0360] The invention has been described above with reference to a few embodiments. However, it is readily apparent to a person skilled in the art that other embodiments than the ones disclosed above are equally within the scope of the invention, as defined by the appended patent claims.

[0361] Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise herein. All references to "a / said / the [means, components, etc.]" are to be openly interpreted as at least one instance of the means, components, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not necessarily have to be performed in the exact order disclosed, unless explicitly stated otherwise.

Claims

1. A method for evaluating insulation performance in a circuit breaker, the method comprising: Determine key evaluation indicators based on the evaluation principles of the circuit breaker internal insulation performance evaluation index system; Determining the subjective weight and the objective weight of the key evaluation indicators respectively, and calculating the comprehensive weight of the key evaluation indicators based on the subjective weight and the objective weight; Based on the clustering cloud model, a comprehensive judgment matrix of the key evaluation indicators is established; Based on the comprehensive weight and the comprehensive judgment matrix, a comprehensive judgment value of the insulation performance in the circuit breaker is obtained, and based on the comprehensive judgment value, the insulation performance in the circuit breaker is determined.

2. According to the method of claim 1, the key evaluation indicators include: UHF partial discharge monitoring, ultrasonic partial discharge monitoring, metal particle monitoring, SF6 gas pressure monitoring, gas composition analysis, operating years, and number of operations.

3. The method according to claim 1, wherein the subjective weight and the objective weight of the key evaluation indicators are determined separately, wherein: Determine the subjective weight based on the extension hierarchical analysis method, including: Based on the evaluation index system, an extensible judgment matrix A is established. ij (i,j=1,2,...,n): Among them, 1 / 9≤a ij - ≤a ij + ≤9, a ij - =1 / a ij + ,a ij + =1 / a ij - , a ij - is the minimum value of the importance of index i relative to index j, a ij + is the maximum value of the importance of indicator i relative to indicator j, i is the i-th indicator, j is the j-th indicator, and n is the number of indicators evaluated; Based on the scalable judgment matrix, the subjective weights of key evaluation indicators are calculated according to the extension analytic hierarchy process: Assume that there are N decision makers participating in the scoring, and the nth person gives the extension interval digit a ij n= ij -n , a ij n >(n=1,2,3.....,N); where a ij n is the maximum importance of indicator i relative to indicator j for the nth decision maker, which is the upper limit of the number of extension intervals. ij -n is the minimum importance of indicator i relative to indicator j for the nth decision maker, which is the lower limit of the number of extension intervals;​ The calculation formula for the number of expandable intervals in the kth layer is: Among them, Aij k is the weighted average of the number of extension intervals of indicator i relative to indicator j of decision maker k, aij 1 …aij N is the judgment of the importance of indicator i relative to indicator j by N decision makers, which is an extensible interval number; Obtain the extension judgment matrix of all key evaluation indicators of the kth layer for the k-1th layer; Obtain multiple key evaluation indicators and compare and score them in pairs, and establish a score table based on the average of the key evaluation indicators and compare and score them in pairs; based on the score table, obtain the left judgment matrix A respectively - and the right judgment matrix A + , where A= - ,A + >,A - =[a ij - ] nk×nk ,A + =[a ij + ] nk×nk ;​ Based on the left judgment matrix A - and the right judgment matrix A + , calculate the normalized eigenvectors with positive components, denoted as x - ,x + ; Determine the addition coefficients k and m, and the calculation formula is: Among them, nk is the order of the k-th layer extension judgment matrix; Calculate the weight vector of the extension judgment matrix: S k =(S1 k ,S2 k ,...,S n k ) T =<kx - ,mx + > (4) Among them, S1 k ,S2 k ,...,S n k is the relative importance of indicator k relative to other indicators, kx - is the lower limit of the weight vector of index k, mx + is the upper limit of the weight vector of index k; Calculate the single sorting weight S of each element i k =<S i k- ,S i k+ >greater than S j k =<S j k- ,S j k+ The probability of > is calculated according to formula (5): Among them, s i k+ is the maximum possible weight of indicator i relative to indicator k, S j k+ is the maximum possible weight of indicator j relative to indicator k, S j k- is the minimum possible weight of index j relative to index k, S i k- is the maximum possible weight of index i relative to index k; If for task i, j = 1, 2, ..., n k ; i≠j, V(S i k ≥S j k )≥0, then in It represents the single sorting of the i-th element of the k-th layer on the previous layer, which is normalized as follows: in, is the single sorting weight of element n in the kth layer for the previous layer; After obtaining the weight coefficient of each level to the upper level, we calculate upward layer by layer according to the series connection principle to obtain the relative importance of the bottom-level key evaluation indicators to the target level, which is the total weight of each key evaluation indicator: W k =P k P k-1 ...P 3 W 2 (7) Among them, W 2 The weight vector of all elements in the second layer to the target layer, P k is a single-ranked weight vector of all indicators in the k-th layer.

4. The method according to claim 1, wherein the subjective weight and the objective weight of the key evaluation indicators are determined separately, wherein: The subjective weights are determined based on the entropy weight method, including: Suppose there are m groups to be evaluated and n evaluation indicators, forming a judgment matrix X Among them, x ij (i=1,2,...,m;j=1,2,...,n) is the jth indicator data of the i-th evaluation object; Establish a dimensionless processing judgment matrix: Among them, a1 and a2 are the numerical thresholds of the indicators to be evaluated, and x i is the index value to be evaluated, and the decision matrix Y is formed after tempering without quantity ij ; Normalize the i-th alternative under the j-th indicator, that is, take P ij , and then take the information entropy E of the jth index j : in, P ij is the normalized result of the importance of indicator i relative to indicator j; Among them, j = 1, 2, ..., m, satisfying And 0≤W j ′≤1, which is the objective weight.

5. The method according to claim 1, wherein the comprehensive weight of the key evaluation indicator is calculated based on the subjective weight and the objective weight, comprising: Based on the variable weight principle, the subjective weight and objective weight are comprehensively weighted, and the calculation method is: <h2 style=";text-align:left;direction:ltr">W=aW<h2 style=";text-align:left;direction:ltr"> S <h2 style=";text-align:left;direction:ltr"> +bW0 (13) Where: W s is the subjective weight; W0 is the objective weight; P i is the corresponding component of the subjective weight vector after being arranged in ascending order, i = 1, 2, ..., n; n is the number of indicators; a is the subjective weight coefficient; b is the objective weight coefficient.

6. The method according to claim 1, wherein the comprehensive judgment matrix of the key evaluation indicators is established based on the clustering cloud model, comprising: A forward cloud generator is established through a clustering cloud model, and a cloud droplet sample image feature value is generated based on the forward cloud generator; The states of the circuit breakers are divided, and the hierarchical clustering center values ​​of the circuit breakers in different states are used as the expected values ​​of the clustering cloud model. The expected values ​​of the cloud models of the circuit breakers in different states are normalized to establish a membership selector of the clustering cloud model. Based on the membership selector, the membership of each clustering cloud model is obtained; The comprehensive judgment matrix is ​​obtained based on the mean value of the key evaluation index, the characteristic value of the cloud droplet sample image and the degree of membership.

7. The method according to claim 1, wherein the step of obtaining a comprehensive judgment value of the insulation performance in the circuit breaker based on the comprehensive weight and the comprehensive judgment matrix, and determining the insulation performance in the circuit breaker based on the comprehensive judgment value comprises: Multiplying the comprehensive weight by the comprehensive judgment matrix to obtain a comprehensive judgment vector D; Based on the comprehensive judgment vector, a comprehensive judgment value r is obtained by weighted average: Among them, d i is the component of the comprehensive judgment vector D; f i It is the corresponding score of each circuit breaker status level i. The circuit breaker status is confirmed according to the value of the comprehensive judgment value r and the grading interval of the preset evaluation score.

8. A system for evaluating insulation performance in a circuit breaker, the system comprising: An initial unit, used to determine key evaluation indicators based on the evaluation principle of the insulation performance evaluation indicator system within the circuit breaker; A calculation unit, used to respectively determine the subjective weight and the objective weight of the key evaluation indicator, and calculate the comprehensive weight of the key evaluation indicator based on the subjective weight and the objective weight; An establishing unit, used for establishing a comprehensive judgment matrix of the key evaluation indicators based on a clustering cloud model; The result unit is used to obtain a comprehensive judgment value of the insulation performance in the circuit breaker based on the comprehensive weight and the comprehensive judgment matrix, and determine the insulation performance in the circuit breaker based on the comprehensive judgment value.

9. The system according to claim 8, wherein the key evaluation indicators include: UHF partial discharge monitoring, ultrasonic partial discharge monitoring, metal particle monitoring, SF6 gas pressure monitoring, gas composition analysis, operating years, and number of operations.

10. The system according to claim 8, wherein the calculation unit is used to determine the subjective weight and the objective weight of the key evaluation indicator respectively, wherein: Determining subjective weights based on the extension hierarchical analysis method is also used for: Based on the evaluation index system, an extensible judgment matrix A is established. ij (i,j=1,2,...,n): Among them, 1 / 9≤a ij - ≤a ij + ≤9, a ij - =1 / a ij + ,a ij + =1 / a ij - , a ij - is the minimum value of the importance of index i relative to index j, a ij + is the maximum value of the importance of indicator i relative to indicator j, i is the i-th indicator, j is the j-th indicator, and n is the number of indicators evaluated; Based on the scalable judgment matrix, the subjective weights of key evaluation indicators are calculated according to the extension analytic hierarchy process: Assume that there are N decision makers participating in the scoring, and the nth person gives the extension interval digit a ij n = ij -n , a ij n >(n=1,2,3.....,N); where a ij n is the maximum importance of indicator i relative to indicator j for the nth decision maker, which is the upper limit of the number of extension intervals. ij -n is the minimum importance of indicator i relative to indicator j for the nth decision maker, which is the lower limit of the number of extension intervals;​ The calculation formula for the number of expandable intervals in the kth layer is: Among them, Aij k is the weighted average of the number of extension intervals of indicator i relative to indicator j of decision maker k, aij 1 …aij N is the judgment of the importance of indicator i relative to indicator j by N decision makers, which is an extensible interval number; Obtain the extension judgment matrix of all key evaluation indicators of the kth layer for the k-1th layer; Obtain multiple key evaluation indicators and compare and score them in pairs, and establish a score table based on the average of the key evaluation indicators and compare and score them in pairs; based on the score table, obtain the left judgment matrix A respectively - and the right judgment matrix A + , where A= - ,A + >,A - =[a ij - ] nk×nk ,A + =[a ij + ] nk×nk ;​ Based on the left judgment matrix A - and the right judgment matrix A + , calculate the normalized eigenvectors with positive components, denoted as x - ,x + ; Determine the addition coefficients k and m, and the calculation formula is: Among them, nk is the order of the k-th layer extension judgment matrix; Calculate the weight vector of the extension judgment matrix: S k =(S1 k ,S2 k ,...,S n k ) T =<kx - ,mx + > (4) Among them, S1 k ,S2 k ,...,S n k is the relative importance of indicator k relative to other indicators, kx - is the lower limit of the weight vector of index k, mx + is the upper limit of the weight vector of index k; Calculate the single sorting weight S of each element i k =<S i k- ,S i k+ >greater than S j k =<S j k- ,S j k+ The probability of > is calculated according to formula (5): Among them, s i k+ is the maximum possible weight of indicator i relative to indicator k, S j k+ is the maximum possible weight of indicator j relative to indicator k, S j k- is the minimum possible weight of index j relative to index k, S i k- is the maximum possible weight of index i relative to index k; If for task i, j = 1, 2, ..., n k ; i≠j, V(S i k ≥S j k )≥0, then in It represents the single sorting of the i-th element of the k-th layer on the previous layer, which is normalized as follows: in, is the single sorting weight of element n in the kth layer for the previous layer; After obtaining the weight coefficient of each level to the upper level, we calculate upward layer by layer according to the series connection principle to obtain the relative importance of the bottom-level key evaluation indicators to the target level, which is the total weight of each key evaluation indicator: W k =P k P k-1 ...P 3 W 2 (7) Among them, W 2 The weight vector of all elements in the second layer to the target layer, P k is a single-ranked weight vector of all indicators in the k-th layer.

11. The system according to claim 8, wherein the calculation unit is used to determine the subjective weight and the objective weight of the key evaluation indicator respectively, wherein: The subjective weights are determined based on the entropy weight method and are also used for: Suppose there are m groups to be evaluated and n evaluation indicators, forming a judgment matrix X Among them, x ij (i=1,2,...,m;j=1,2,...,n) is the jth indicator data of the i-th evaluation object; Establish a dimensionless processing judgment matrix: Among them, a1 and a2 are the numerical thresholds of the indicators to be evaluated, and x i is the index value to be evaluated, and the decision matrix Y is formed after tempering without quantity ij ; Normalize the i-th alternative under the j-th indicator, that is, take P ij , and then take the information entropy E of the jth index j : in, P ij is the normalized result of the importance of indicator i relative to indicator j; Among them, j = 1, 2, ..., m, satisfying And 0≤W j ′≤1, which is the objective weight.

12. The system according to claim 8, wherein the calculation unit is used to calculate the comprehensive weight of the key evaluation indicator based on the subjective weight and the objective weight, and is also used to: Based on the variable weight principle, the subjective weight and objective weight are comprehensively weighted, and the calculation method is: <h2 style=";text-align:left;direction:ltr">W=aW<h2 style=";text-align:left;direction:ltr"> S <h2 style=";text-align:left;direction:ltr"> +bW0 (13) b=1-a (15) in: W s is the subjective weight; W0 is the objective weight; P i is the corresponding component of the subjective weight vector after being arranged in ascending order, i = 1, 2, ..., n; n is the number of indicators; a is the subjective weight coefficient; b is the objective weight coefficient.

13. The system according to claim 8, wherein the establishing unit is used to establish a comprehensive judgment matrix of the key evaluation indicators based on a clustering cloud model, and is also used to: A forward cloud generator is established through a clustering cloud model, and a cloud droplet sample image feature value is generated based on the forward cloud generator; The states of the circuit breakers are divided, and the hierarchical clustering center values ​​of the circuit breakers in different states are used as the expected values ​​of the clustering cloud model. The expected values ​​of the cloud models of the circuit breakers in different states are normalized to establish a membership selector of the clustering cloud model. Based on the membership selector, the membership of each clustering cloud model is obtained; The comprehensive judgment matrix is ​​obtained based on the mean value of the key evaluation index, the characteristic value of the cloud droplet sample image and the degree of membership.

14. The system according to claim 8, wherein the result unit is used to obtain a comprehensive judgment value of the insulation performance in the circuit breaker based on the comprehensive weight and the comprehensive judgment matrix, determine the insulation performance in the circuit breaker based on the comprehensive judgment value, and is also used to: Multiplying the comprehensive weight by the comprehensive judgment matrix to obtain a comprehensive judgment vector D; Based on the comprehensive judgment vector, a comprehensive judgment value r is obtained by weighted average: in, d i is the component of the comprehensive judgment vector D; f i It is the corresponding score of each circuit breaker status level i. The circuit breaker status is confirmed according to the value of the comprehensive judgment value r and the grading interval of the preset evaluation score.

15. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is used to execute the method according to any one of claims 1 to 7.

16. An electronic device, characterized in that: The electronic device comprises: a processor and a memory; wherein, The memory is a memory for storing instructions executable by the processor; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method according to any one of claims 1 to 7.

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