A method for evaluating the operating status of high-voltage cables

Abstract

This invention discloses a method for evaluating the operating status of high-voltage cables, mainly including: establishing an evaluation index system for the operating status of high-voltage cables, including: determining a set of factors with a three-layer structure (target layer, project layer, and index layer) from the operating parameters of the high-voltage cable according to enterprise standards; establishing a state set; collecting index data corresponding to the above factor set for the high-voltage cable to be evaluated; normalizing the collected index data; using grey fuzzy theory to achieve a first-level evaluation from the index layer to the project layer, obtaining the evaluation results of each project; and using D-S evidence theory to achieve a second-level evaluation from the project layer to the target layer, obtaining the operating status of the high-voltage cable. This invention evaluates the operating status of high-voltage cables based on grey fuzzy theory and D-S evidence theory, which can fully consider the greyness, fuzziness, and randomness of various state quantities characterizing the current operating status, thus obtaining evaluation results that are closer to the actual state of the equipment.

Description

Technical Field

[0001] This invention relates to a high-voltage cable operation system, and more particularly to a method for evaluating the operating status of high-voltage cables. Background Technology

[0002] In recent years, with the increasing rate of undergrounding overhead power lines, the crucial role of high-voltage cables in urban power supply systems has become increasingly prominent. Over the years, high-voltage cables experience insulation aging and damage. If these issues are not detected and remedial measures are not taken in time, this insulation deterioration will worsen over time, eventually leading to power line failures. This can range from affecting daily life and business operations to causing serious injuries or fatalities. Therefore, evaluating the operational status of high-voltage cables in the power grid is of paramount importance.

[0003] Currently, the evaluation of the operating status of high-voltage cables is mostly based on the status evaluation guidelines issued by power companies such as the State Grid Corporation of China. This involves scoring the cable based on the weight of individual cable characteristics and the degree of defect, and then accumulating the scores to determine the cable's status. However, this method relies heavily on expert-determined weights and deduction criteria, making it highly subjective and unable to fully consider the complex variations in different characteristics of different equipment, thus failing to effectively identify some potential faults. Summary of the Invention

[0004] In view of the above-mentioned prior art, the present invention provides a method for evaluating the operating status of high-voltage cables. The method described in the present invention evaluates the operating status of high-voltage cables based on grey fuzzy theory and DS evidence theory, which can fully consider the greyness, fuzziness and randomness of various state quantities characterizing the current operating status, thereby obtaining evaluation results that are closer to the actual state of the equipment.

[0005] To address the aforementioned technical problems, this invention proposes a method for evaluating the operating status of high-voltage cables, which mainly includes:

[0006] Step 1: Establish a high-voltage cable operation status evaluation index system, including: determining a set of factors with a three-layer structure (target layer, project layer, and index layer) from the high-voltage cable operation parameters according to enterprise standards; and establishing a status set;

[0007] Step 2: Collect the corresponding index data of the high-voltage cable to be evaluated and the above-mentioned factors. All index data are from original data, operation records, preventive tests, live-line testing and online monitoring systems.

[0008] Step 3: Normalize the index data collected in Step 2, including: dividing the high-voltage cable operation status evaluation index into quantitative indicators, descriptive indicators, and qualitative evaluation indicators; the quantitative indicators are obtained by online monitoring systems or on-site measurements. For quantitative indicators, the concept of relative degradation is used for normalization, and the normalization result is [0, 1]. The closer to 1, the better the cable operation status; the descriptive indicators refer to the indicators of cable components whose status classification standards can only be described in words. For descriptive indicators, an expert scoring method is used for normalization, and the scoring range is [0, 1]. The closer the score is to 1, the better the cable operation status; the expert scoring is based on the "Q / GDW 456-2010 Cable Line Status Evaluation Guidelines" issued by the State Grid Corporation of China; the qualitative evaluation indicators refer to the cable components whose status has two clear classification standards. For qualitative evaluation indicators, the normalization result is 0 or 1 according to the two classification standards, where 1 is the normal state and 0 is the severe state.

[0009] Step 4: Use grey fuzzy theory to achieve a first-level evaluation from the indicator layer to the project layer, thereby obtaining the evaluation results of each project;

[0010] Step 5: Use the DS evidence theory to achieve a two-level evaluation from the project level to the target level, and finally obtain the operating status of the high-voltage cable.

[0011] Furthermore, in the high-voltage cable operating status evaluation method of the present invention:

[0012] In step 1, the target layer refers to the operating status of the high-voltage cable. This target layer consists of five project layers: cable body Z1, line terminal Z2, intermediate joint Z3, auxiliary equipment Z4, and line channel Z5. Each project layer includes multiple indicator layers. Among them, the indicator layer included in the cable body Z1 is the withstand voltage test result Z of the cable body. 11 Insulation resistance Z of outer sheath 12 Partial discharge quantity Z 13 Line load Z 14 Appearance Z 15 familial defects Z 16 Inspection Record Z 17 Operating life Z 18 The line terminal Z2 includes the temperature Z of the metal connection of the line terminal. 21 , casing temperature Z 22 Partial discharge Z 23 Appearance Z 24 familial defects Z 25 Inspection Record Z 26 and operating years Z 27The intermediate joint Z3 includes the pressure resistance test results Z of the intermediate joint. 31 Partial discharge quantity Z 32 Intermediate joint temperature Z 33 Appearance Z 34 familial defects Z 35 Inspection Record Z 36 and operating years Z 37 The auxiliary equipment Z4 includes the pressure resistance test results of the auxiliary facilities. 41 Grounding current Z 42 Temperature Z at equipment connection point 43 and appearance Z 44 The line channel Z5 includes the main structure of the line channel Z. 51 External environment conditions of the channel Z52, channel markings Z 53 Internal facilities of the structure Z 54 Leakage and water accumulation Z 55 Fire prevention and anti-theft system status Z 56 and harmful gases and foreign objects Z 57 ;

[0013] Based on the enterprise standards for cable line condition evaluation and the actual operating conditions of high-voltage cables, condition quantities are selected, including the following: These selected condition quantities are used as evaluation factors:

[0014] U = {U1, U2, ..., U} m} (1)

[0015] In equation (1), U i (i = 1, 2, ..., 5) represent the five project layers that characterize the cable's operating status: cable body Z1, line terminal Z2, intermediate joint Z3, auxiliary equipment Z4, and line channel Z5.

[0016]

[0017] In equation (2), u ij (j=1,2,…,n) represents the j-th indicator in the i-th project layer that characterizes the cable's operating status;

[0018] Establish a state set:

[0019] V={v1,v2,v3,v4} (3)

[0020] The operating status of high-voltage cables is divided into four status levels: normal status, warning status, abnormal status, and critical status, which are represented by v1, v2, v3, and v4 respectively.

[0021] The normal state is that all indicators in the factor concentration index layer are within the attention values ​​specified in the enterprise standard for cable line condition evaluation;

[0022] The state of attention is that one or more indicators in the factor concentration index layer are the intermediate values ​​of the attention values ​​and abnormal values ​​specified in the enterprise standard for cable line condition evaluation.

[0023] An abnormal state is when one or more indicators in the factor set indicator layer exceed the warning value, but do not reach the abnormal value.

[0024] A severe state is when one or more indicators in the factor cluster indicator layer exceed the specified outlier value.

[0025] In step 2, the sources of all indicator layer data are:

[0026] The withstand voltage test result Z of the cable body Z1. 11 Insulation resistance value Z of outer sheath 12 Derived from preventative testing, partial discharge quantity Z 13 Originating from live-line testing, line load Z 14 Originating from an online monitoring system, appearance Z 15 Inspection Record Z 17 Derived from runtime logs, familial defect Z 16 Operating life Z 18 This information is derived from the original data. In the aforementioned line terminal Z2, the temperature Z at the metal connection of the line terminal is... 21 , casing temperature Z 22 Partial discharge quantity Z 23 Originating from live-line testing, appearance Z 24 Inspection Record Z 26 Derived from runtime logs, familial defect Z 25 Operating life Z 27 This information is derived from the original data. The pressure resistance test result Z of the intermediate joint Z3 is... 31 Derived from preventative testing, partial discharge quantity Z 32 Intermediate joint temperature Z 33 Originating from live-line testing, appearance Z 34 Inspection Record Z 36 Derived from runtime logs, familial defect Z 35 Operating life Z 37 Sourced from original data. The pressure resistance test results Z of the auxiliary equipment Z4 are as follows: 41 Derived from preventative testing, grounding current Z 42 Temperature Z at equipment connection point 43 Originating from live-line testing, appearance Z 44This information is derived from operational records. The main structural details of line channel Z5 are as follows: 51 External environment conditions of the passage Z 52 Channel Identifier Z 53 Internal facilities of the structure Z 54 Fire prevention and anti-theft system status Z 56 Based on operation records, information on water leakage and water accumulation is available. 55 Harmful gases and foreign objects Z 57 Sourced from the online monitoring system.

[0027] Step 4 utilizes grey fuzzy theory to achieve a first-level evaluation from the indicator layer to the project layer, thereby obtaining the evaluation results for each project, including:

[0028] Step 4-1) Determine the gray fuzzy weight matrix: The gray fuzzy weight matrix consists of a modulus and a gray part;

[0029] The modulus of the gray fuzzy weight matrix refers to the weights corresponding to the evaluation indicators. Determining the modulus of the gray fuzzy weight matrix includes determining subjective weights using the analytic hierarchy process (AHP), determining objective weights using the entropy weight method, and obtaining combined weights based on subjective and objective weights using game theory. The gray part of the gray fuzzy weight matrix is ​​determined by expert scoring based on the credibility of the weights. The gray part of the gray fuzzy weight matrix refers to the gray level of the point corresponding to the weight.

[0030] Step 4-2) Determine the gray fuzzy discrimination matrix: The gray fuzzy discrimination matrix consists of a modulus and a gray part; the modulus of the gray fuzzy discrimination matrix uses fuzzy membership degree to characterize the fuzzy membership relationship of the evaluation index to each evaluation state, and the membership degree is determined by cloud model theory; the gray part of the gray fuzzy discrimination matrix uses point gray points to characterize the reliability of the determination of the fuzzy membership degree of the evaluation index.

[0031] Step 4-3) Synthesize the gray fuzzy comprehensive evaluation and convert the gray fuzzy comprehensive evaluation matrix into a system result set according to the principle of maximum membership degree and minimum gray degree. The system result set is the evaluation result of each project.

[0032] Step 5 utilizes the DS evidence theory to achieve a two-level evaluation from the project level to the target level, ultimately obtaining the operational status of the high-voltage cable, including:

[0033] Step 5-1) Determination of the identification framework Θ: The identification framework Θ consists of four state levels v1, v2, v3, and v4 of the high-voltage cable's operating status: normal state, warning state, abnormal state, and severe state, and the uncertainty θ specified by the DS evidence theory, that is:

[0034] Θ = {v1, v2, v3, v4, θ}

[0035] Step 5-2) Determine the basic assignment function for each independent piece of evidence: Based on the established evaluation index system for the operating status of high-voltage cables, the five items at the project level—cable body Z1, line terminal Z2, intermediate joint Z3, auxiliary facilities Z4, and line corridor Z5—are taken as independent pieces of evidence. The evaluation results of each item obtained in Step 4 are used as the basic assignment function for each independent piece of evidence, using m... i (A) indicates that the formula is satisfied:

[0036]

[0037] Introducing a confidence parameter α to the basic assignment function m i (A) is modified to represent the relative importance between different items; α is derived from the following formula:

[0038]

[0039] In the formula, λ is the finite confidence coefficient, λ = 0.9; ω i Let ω be the weight of the i-th item in the weight vector of five independent pieces of evidence. max The maximum value in the weight vector;

[0040] The basic assignment function after credibility correction is defined as follows:

[0041]

[0042] Step 5-3) Evidence fusion: Fusion of evidence shall be performed according to the following formula.

[0043]

[0044] Where m(A) is the basic assignment function corresponding to each state level after evidence fusion; A i To identify a subset of frame Θ;

[0045] Step 5-4) Evaluation and Decision: The results of the basic assignment functions for each state level are judged using the maximum membership principle. The conditional formula is as follows:

[0046]

[0047] In the formula, m(ν0) represents the maximum value of the basic assignment function for the evaluation level, that is:

[0048] This represents the second largest value of the basic assignment function for the rating level, i.e. If the difference between the two exceeds the predetermined value ε1, then ε1 = 0.15;

[0049] If equation (36) is satisfied, then the evaluation result of the final operating state of the high-voltage cable is the level corresponding to ν0;

[0050] If equation (36) is not satisfied, a reliability criterion is used to judge the results of the basic assignment function for each state level. The formula is as follows:

[0051]

[0052] In equation (37), ε2 is the confidence level, ε2=0.5; if equation (37) is satisfied, then the evaluation result of the final operating status of the high-voltage cable is the level corresponding to ν0;

[0053] If equation (37) is not satisfied, return to step 4 to adjust the gray part of the cloud model, subjective weight, gray fuzzy weight matrix, and gray fuzzy discrimination matrix until equation (36) or equation (37) is satisfied.

[0054] In this invention, the enterprise standards mentioned include: "Q / GDW 456-2010 Guidelines for Cable Line Condition Evaluation" and "Q / GDW 11316-2014 Test Procedures for Power Cable Lines" issued by State Grid Corporation of China.

[0055] Compared with the prior art, the beneficial effects of the present invention are:

[0056] This invention, based on enterprise standards and actual high-voltage cable operation, fully analyzes the factors affecting the operating status of high-voltage cables, establishes an evaluation index system for the operating status of high-voltage cables, and utilizes grey fuzzy theory and DS evidence theory to achieve a comprehensive evaluation of the operating status of high-voltage cables. This effectively solves the problems of fuzziness, randomness, and greyness of evaluation factors, making the evaluation results more comprehensive and accurate, providing a basis for condition-based maintenance of high-voltage cables, reducing the occurrence of power outages due to equipment failures, and improving the reliability of cable power supply. Attached Figure Description

[0057] Figure 1 This is the flowchart of the high-voltage cable operation status evaluation method of the present invention;

[0058] Figure 2 This is the index system for the evaluation method of high-voltage cable operation status.

[0059] Figure 3 The evaluation method of this invention utilizes grey fuzzy theory to realize a first-level evaluation process from the indicator layer to the project layer;

[0060] Figure 4 The evaluation method of this invention utilizes the DS evidence theory to implement a two-level evaluation process from the project level to the target level. Detailed Implementation

[0061] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but the following embodiments are by no means intended to limit the present invention.

[0062] like Figure 1 As shown, the main steps of the high-voltage cable operation status evaluation method proposed in this invention are as follows:

[0063] Step 1: Establish an evaluation index system for the operating status of high-voltage cables;

[0064] Step 2: Collect the corresponding index data of the high-voltage cable to be evaluated and the above factor set;

[0065] Step 3: Normalize the indicator data collected in Step 2;

[0066] Step 4: Use grey fuzzy theory to achieve a first-level evaluation from the indicator layer to the project layer, and obtain the evaluation results at the project level;

[0067] Step 5: Use the DS evidence theory to achieve a two-level evaluation from the project level to the target level, and obtain the operating status of the evaluated high-voltage cable.

[0068] Step 1 includes: determining a set of factors with a three-layer structure (target layer, project layer, and indicator layer) from the operating parameters of high-voltage cables according to enterprise standards; and establishing a state set;

[0069] 1.1 Determine the evaluation index system

[0070] When establishing evaluation index systems for high-voltage cables, researchers typically divide the system vertically from four dimensions: raw data, operational data, maintenance data, and other data. However, because high-voltage cable lines are generally long, this index system is not conducive to fault location, identification, and subsequent condition-based maintenance. Furthermore, the components of a cable line are relatively independent, with significantly different functions and causes of faults, resulting in varying performance requirements in electrical and mechanical aspects. This invention fully considers the relative independence and differences between the components of a high-voltage cable, dividing it horizontally into five units: the cable body, line terminals, intermediate joints, auxiliary facilities, and line corridor. It analyzes the indicators representing the state of each unit, ultimately obtaining the current operational status evaluation results. This system architecture allows for different evaluation methods to be applied to different components, while also being structurally clear, hierarchically defined, and easy to understand.

[0071] High-voltage cable operation status evaluation index system, such as Figure 2 As shown, the target layer represents the operating state of the high-voltage cable. This target layer consists of five project layers, which include the cable body Z1, line terminal Z2, intermediate joint Z3, auxiliary equipment Z4, and line channel Z5.

[0072] Each project layer consists of multiple indicator layers, among which...

[0073] The cable body Z1 includes an index layer containing the withstand voltage test results Z of the cable body. 11 Insulation resistance Z of outer sheath 12 Partial discharge quantity Z 13 Line load Z 14 Appearance Z 15 familial defects Z 16 Inspection Record Z 17 Operating life Z 18 ; .

[0074] The line terminal Z2 includes the temperature Z of the metal connection of the line terminal. 21 , casing temperature Z 22 Partial discharge Z 23 Appearance Z 24 familial defects Z 25 Inspection Record Z 26 and operating years Z 27 .

[0075] The intermediate joint Z3 includes the pressure resistance test results of the intermediate joint Z. 31 Partial discharge quantity Z 32 Intermediate joint temperature Z 33 Appearance Z 34 familial defects Z 35 Inspection Record Z 36 and operating years Z 37 .

[0076] The auxiliary equipment Z4 includes the pressure resistance test results of the auxiliary facilities. 41 Grounding current Z 42 Temperature Z at equipment connection point 43 and appearance Z 44 .

[0077] The line channel Z5 includes the main structure of the line channel Z. 51 External environment conditions of the passage Z 52 Channel marker Z 53 Internal facilities of the structure Z 54 Leakage and water accumulation Z 55 Fire prevention and anti-theft system status Z 56 and harmful gases and foreign objects Z 57 .

[0078] 1.2 Determine the factor set and state set

[0079] The selected state quantities are based on the enterprise standards for cable line condition evaluation (typically including: "Q / GDW456-2010 Guidelines for Cable Line Condition Evaluation" and "Q / GDW 11316-2014 Test Procedures for Power Cable Lines" issued by State Grid Corporation of China) and the actual operating conditions of high-voltage cables. These selected state quantities are as follows:

[0080] Based on the evaluation index system for the operating status of high-voltage cables, the selected status indicators are used as evaluation factors:

[0081] U = {U1, U2, ..., U} m} (1)

[0082] In equation (1), U i (i = 1, 2, ..., 5) represent the five project layers that characterize the cable's operating status: cable body Z1, line terminal Z2, intermediate joint Z3, auxiliary equipment Z4, and line channel Z5.

[0083] U i ={u i1 u i2 ,…,u in} (2)

[0084] In equation (2), u ij (j=1,2,…,n) represents the j-th indicator in the i-th project layer characterizing the cable's operating status, and represents the i-th influencing factor in each unit of the cable. Taking the cable body as an example, where m=1, n=8, u 11 to u 18 These represent withstand voltage test, outer sheath insulation resistance, partial discharge, line load, appearance, family defects, maintenance records, and service life, respectively.

[0085] Establish a state set:

[0086] V={v1,v2,v3,v4} (3)

[0087] In this invention, the operating status of high-voltage cables is divided into four status levels, including normal status, warning status, abnormal status and critical status, which are represented by v1, v2, v3 and v4 respectively.

[0088] The normal state is characterized by all indicators at the factor-based index layer falling within the warning values ​​specified in the enterprise standard for cable line condition evaluation. When the cable is in a normal state, its overall operating condition meets the requirements. All indicators are within the specified warning and alert values, indicating normal operation.

[0089] The "Caution" state refers to a situation where one or more indicators in the factor-focused indicator layer are at the midpoint between the warning and abnormal values ​​specified in the enterprise standard for cable line condition evaluation. When a cable is in the "Caution" state, it indicates that one or more indicators are trending towards the standard limits. However, since they do not exceed the specified standard limits, or only some indicators with minimal impact on equipment performance and safe operation exceed the limits, the cable can still operate. Close monitoring of its operation is necessary.

[0090] An abnormal state occurs when one or more indicators in the factor-based indicator layer exceed the warning value, but do not reach the abnormal value. When the cable is in an abnormal state, it indicates that a certain indicator has changed significantly, approaching or slightly exceeding the standard. Its operation should be monitored, and maintenance should be scheduled within a certain period.

[0091] A critical condition occurs when one or more indicators in the factor concentration index layer exceed the specified abnormal values. When a cable is in a critical condition, it indicates that a certain indicator is seriously out of standard or that multiple indicators that have a significant impact on equipment performance and safe operation are close to or exceed the standard limits, and maintenance should be arranged promptly.

[0092] 2. Collect the corresponding index data of the high-voltage cable to be evaluated and the above-mentioned factor set. All index data are derived from original data, operation records, preventive tests, live-line testing, and online monitoring systems. The sources of all index-level data are:

[0093] The withstand voltage test result Z of the cable body Z1. 11 Insulation resistance value Z of outer sheath 12 Derived from preventative testing, partial discharge quantity Z 13 Originating from live-line testing, line load Z 14 Originating from an online monitoring system, appearance Z 15 Inspection Record Z 17 Derived from runtime logs, familial defect Z 16 Operating life Z 18 Sourced from original data.

[0094] In the aforementioned line terminal Z2, the temperature Z at the metal connection of the line terminal is... 21 , casing temperature Z 22 Partial discharge quantity Z 23 Originating from live-line testing, appearance Z 24 Inspection Record Z 26 Derived from runtime logs, familial defect Z 25 Operating life Z 27 Sourced from original data.

[0095] The pressure resistance test result of the intermediate joint Z3 is Z. 31 Derived from preventative testing, partial discharge quantity Z32 Intermediate joint temperature Z 33 Originating from live-line testing, appearance Z 34 Inspection Record Z 36 Derived from runtime logs, familial defect Z 35 Operating life Z 37 Sourced from original data.

[0096] Among the auxiliary equipment Z4, the pressure resistance test result Z of the auxiliary facilities 41 Derived from preventative testing, grounding current Z 42 Temperature Z at equipment connection point 43 Originating from live-line testing, appearance Z 44 Sourced from runtime logs.

[0097] The main structure of the line channel Z5 is as follows: 51 External environment conditions of the passage Z 52 Channel Identifier Z 53 Internal facilities of the structure Z 54 Fire prevention and anti-theft system status Z 56 Based on operation records, information on water leakage and water accumulation is available. 55 Harmful gases and foreign objects Z 57 Sourced from the online monitoring system.

[0098] 3. Evaluation index normalization

[0099] Because various indicators generally have different dimensions and orders of magnitude, direct calculations using them would inevitably lead to a mismatch in evaluation status levels, failing to accurately reflect the operating status of high-voltage cables. Therefore, normalization of each indicator is necessary. This invention categorizes high-voltage cable operating status evaluation indicators into three types: quantitative indicators, descriptive indicators, and qualitatively evaluable indicators. The normalization of each type of indicator is analyzed below.

[0100] (1) Quantitative indicators:

[0101] The quantitative indicators are obtained from online monitoring systems or on-site measurements, possessing precise measurement data and numerical scales, and can directly reflect the current operating status of the equipment. The quantitative indicators are normalized using the concept of relative degradation, with the normalization result being [0, 1]. The closer to 1, the better the cable's operating status. In the normalization process using the concept of relative degradation, the relative degradation degree L characterizes the degree of deviation of various indicators of the high-voltage cable from normal conditions, with a value range of [0, 1]. When L is 1, it indicates that the indicator is in its optimal state; when L is 0, it indicates that the indicator is in its worst state.

[0102] a) For indicators where larger is always better, the actual measured data should be normalized using the following formula:

[0103]

[0104] b) For indicators where smaller is better, the actual measured data should be normalized using the following formula:

[0105]

[0106] c) For moderate-sized indicators, the actual measured data are normalized using the following formula:

[0107]

[0108] In equations (4), (5), and (6), L represents the relative deterioration of the indicator, X represents the original value of the indicator, and X max X min X represents the upper and lower limits of the safe operation of the indicator, respectively. m This indicates the optimal value for the indicator.

[0109] (2) Descriptive indicators:

[0110] For indicators such as the appearance of various cable components, the criteria for classifying the degree of condition can only be described in words, which are greatly influenced by the subjective judgment of the evaluators. For descriptive indicators, an expert scoring method is adopted, and the expert scoring is based on the "Q / GDW 456-2010 Cable Line Condition Evaluation Guidelines" issued by the State Grid Corporation of China; in order to correspond with the relative deterioration degree, the scoring range is [0, 1], and the better the indicator reflects the cable's operating condition, the closer the score is to 1.

[0111] (3) Qualitative evaluation indicators: These indicators have clear distinctions between different status levels. For example, the withstand voltage test has only two results: pass and fail, corresponding to normal and severe status, respectively. That is, the qualitative evaluation indicators refer to cable components having two clear distinctions in status. For qualitative evaluation indicators, the normalized result is 0 or 1 based on the two distinctions, where 1 represents the normal status and 0 represents the severe status.

[0112] 4. Utilize grey fuzzy theory to achieve a first-level evaluation from the indicator layer to the project layer, such as... Figure 3 As shown.

[0113] 4.1 Determine the gray fuzzy weight matrix:

[0114] The gray fuzzy weight matrix consists of a modulus and a gray part; the modulus of the gray fuzzy weight matrix refers to the weight corresponding to the evaluation index, and the gray part of the gray fuzzy weight matrix is ​​determined by expert scoring based on the credibility of the weight; the gray part of the gray fuzzy weight matrix refers to the gray level of the point corresponding to the weight.

[0115] 4.1.1 Modulus of the Grey Fuzzy Weight Matrix

[0116] The module was obtained by combining the analytic hierarchy process (AHP) and the entropy weight method.

[0117] First, subjective weights are determined using the Analytic Hierarchy Process (AHP).

[0118] 1) Calculate the stratification weights

[0119] The establishment of the indicator system determines the hierarchical relationships between different levels. For an element at a certain level, using the relevant elements of the previous level as a benchmark, a 1-9 scale is used to represent the importance of that element compared to other elements. When each element in the same level has been compared pairwise, the resulting ratios are placed into a matrix to obtain the judgment matrix for that level. Judgment matrix A = (a ij ) n×m The numerical values ​​and their corresponding degrees are shown in Table 1.

[0120] Table 1 Scaling Method

[0121]

[0122] The expression for the judgment matrix A is as follows:

[0123]

[0124] In equation (7), n is the order of matrix A, a ij Representing the xth i The factor and the xth j The ratio of the weights of each element to A.

[0125] 2) Consistency check

[0126] The largest eigenvalue λ corresponding to the judgment matrix A max The result obtained after normalizing the eigenvectors is the weighted ranking of the importance of a factor at the same level to the corresponding factor at the next higher level, which is called hierarchical single ranking. When there are many system evaluation factors, the judgment matrix is ​​often complex and may contain contradictions, making it difficult to fully meet the consistency condition. Therefore, it is necessary to verify the judgment matrix according to the consistency check formula. The specific steps are as follows:

[0127] ① Calculate the product M of the elements in each row of the judgment matrix A. i :

[0128]

[0129] ② Calculate M for each row i The nth root W:

[0130]

[0131] ③ For vector W = (W1, W2, ..., W... n ) T After normalization, we obtain the weight coefficient value w for the desired indicator. i :

[0132]

[0133] ④ Perform consistency verification:

[0134]

[0135] Where CI is the consistency index, CR is the test coefficient, RI is the average random consistency index, n is the order of the judgment matrix, and λ is the random consistency index. max It is the largest eigenvalue of the judgment matrix A.

[0136] The average random consistency index (RI) values ​​are shown in Table 2.

[0137] Table 2. Average random consistency indices for orders 1-13

[0138]

[0139] When CR < 0.1, the consistency check is considered to be successful; when CR > 0.1, the consistency check does not meet the requirements, and the mutual importance between each indicator needs to be reassigned until it passes the consistency check of the judgment matrix.

[0140] (2) Use the entropy weight method to determine the objective weight.

[0141] 1) Construct the original evaluation matrix

[0142] If m objects are evaluated using n indicators, then the original evaluation matrix is:

[0143]

[0144] 2) Normalization of the judgment matrix

[0145] Since the evaluation indicators differ in nature and magnitude, the matrix R should be normalized using the critical value method to obtain a dimensionless index matrix R' = (r' ij ) m×n .

[0146]

[0147] 3) Determine the information entropy of each indicator.

[0148] Calculate the weight of the j-th indicator for the i-th object, i.e., the magnitude of variation of that indicator.

[0149]

[0150] The entropy value b of the j-th index of the sample j for:

[0151]

[0152] Then the entropy weight w' of the j-th index j for:

[0153]

[0154] Finally, the weight vector of the desired index is obtained as follows:

[0155] W' = (w'1, w'2, ..., w') n ) T (17)

[0156] (3) Use game theory to obtain the combined weights based on subjective and objective weights;

[0157] The comprehensive weight calculation process based on game theory is as follows:

[0158] 1) Calculate n weights using n different methods, and then establish a basic weight vector set W = {w1, w2, ..., w...} n A possible weight vector w is a combination of n vectors in any linear combination, as shown in the following expression:

[0159]

[0160] Where w is the weight vector, α k These are the weighting coefficients.

[0161] 2) The game theory-based combined weighting method calculates the optimal balanced weight vector w* from among the possible weight vectors, indicating a compromise among n weight calculation methods. This compromise can be considered as the weight coefficient α of a linear combination. k The optimization is achieved by using the following formula to make w and w k Minimize the deviation between:

[0162]

[0163] Based on the differentiation properties of matrices, the condition for the optimal first derivative in the above equation is:

[0164]

[0165] The corresponding linear equation is:

[0166]

[0167] 3) Calculate the weighting coefficients (α1, α2, ..., α) using the above formula. n Then, normalize the weighting coefficients using the following formula.

[0168]

[0169] 4) Calculate the final overall weight.

[0170]

[0171] 4.1.2 Gray area of ​​gray fuzzy weight matrix

[0172] The gray area of ​​the gray fuzzy weight matrix is ​​determined by averaging the expert scoring method. The gray area scoring criteria are shown in Table 3.

[0173] Table 3 Reference Table for Gray Area Scoring Standards

[0174]

[0175] Suppose there are n evaluation indicators a1, a2, ..., a n m experts were invited to give a comprehensive score, with corresponding scores of b1, b2, ..., b m To mitigate expert bias and fully consider the opinions of the majority of experts, the final score is obtained by averaging the highest and lowest scores after removing the highest and lowest scores. The calculation formula is as follows:

[0176]

[0177] Therefore, for n evaluation indicators a1, a2, ..., a at the same level n Construct a weight matrix whose modulus is [w*1, w*2, ..., w* n The gray part is [v1, v2, ..., v] n The gray fuzzy weight matrix is ​​then represented as follows:

[0178]

[0179] 4.2 Determine the gray fuzzy discrimination matrix

[0180] The gray fuzzy discrimination matrix also consists of a modal part and a gray part. The modal part uses fuzzy membership degrees to characterize the fuzzy membership relationship of the evaluation index to each evaluation state, while the gray part uses gray points to characterize the reliability of the determination of the fuzzy membership degree of the evaluation index.

[0181] For both quantitative and descriptive indicators, this invention uses cloud model theory to determine membership degrees (for qualitative indicators, normalization results are directly used). After normalization, the state variables are divided into levels based on references and expert experience, namely V1(c,d](d,∞), V2(b2,c1), V3(a1,b1), and V4(0,a). The three numerical characteristic values ​​of the cloud model are finally determined, and the determination method is shown in Table 4.

[0182] Table 4. Methods for Determining Feature Values ​​of Cloud Models

[0183]

[0184] Read indicator z i The corresponding raw data, after processing, yields the values ​​l0 and z. i =l0 intersects with the four clouds at M respectively. i A cloud droplet, then M i The mean membership degree of each cloud droplet is the index z. i Membership degree μ relative to each state level ij ,Right now

[0185]

[0186] In the formula, x0 represents the relative degradation degree of the index value, and E x E is the expected value. ni Therefore, E x For expected value, H e A normal random number with a standard deviation of .

[0187] For qualitative indicators, since there are clear criteria for classifying the different state levels, each indicator can only belong to one state. For example, a pressure test has only two results: pass and fail, corresponding to the normal and severe states, respectively. The degree of membership is represented by (1,0,0,0) and (0,0,0.1).

[0188] The gray part calculation method for the gray fuzzy discrimination matrix is ​​the same as that for the gray fuzzy weight matrix.

[0189] Therefore, for n evaluation indicators a1, a2, ..., a at the same level n Construct a gray fuzzy discrimination matrix, with membership degree μ ij The grayscale value ν represents the fuzzy membership relationship between evaluation indicators and evaluation status. ij Corresponding membership degree μ ij Given a certain level of confidence, the gray fuzzy discrimination matrix is ​​as follows:

[0190]

[0191] 4.3 Gray Fuzzy Comprehensive Evaluation

[0192] High-voltage cable operation status evaluation involves analyzing the dynamic changes in equipment operating conditions. However, the actual status of each evaluation indicator cannot be accurately grasped during the evaluation process. To retain as much information as possible, the comprehensive evaluation module uses the M(·,+) operator, and its gray part uses the M(⊙,+) operator. The synthesized gray fuzzy comprehensive evaluation formula is as follows:

[0193]

[0194] In the formula, Represents the weight matrix. Indicates and The corresponding gray fuzzy discrimination matrix, w i and v i The evaluation metrics have weights and corresponding point gray levels; μ kj and v kj This represents the membership value of the corresponding indicator and the corresponding grayscale value of the point.

[0195] Taking into account the fuzziness and grayness of state evaluation, and based on the principles of maximum membership degree and minimum grayness, it can be... Transform it into a system result set. Assume b ij yes The j-th vector, u j Indicates membership degree, v j If we represent grayscale, then the system result set is:

[0196]

[0197] The system's result set consists of the evaluation results for each project.

[0198] 5. Utilize the DS evidence theory to achieve a two-level evaluation from the project level to the target level, such as... Figure 4 As shown.

[0199] (1) Determining the identification frame Θ

[0200] This invention classifies the operating status of high-voltage cables into four levels: "normal," "caution," "abnormal," and "critical." The identification framework Θ consists of four status levels (v1, v2, v3, v4) of the high-voltage cable's operating status (normal, caution, abnormal, and critical) and the uncertainty θ specified by the DS evidence theory, namely:

[0201] Θ={v1,v2,v3,v4,θ} (30)

[0202] (2) Determine the basic assignment function for each independent piece of evidence.

[0203] Based on the established evaluation index system for the operating status of high-voltage cables, this invention uses five items at the project level—cable body Z1, line terminal Z2, intermediate joint Z3, auxiliary facilities Z4, and line corridor Z5—as independent evidence. The evaluation results of each item obtained using grey fuzzy theory are used as the basic assignment function for each independent evidence, with m... i (A) indicates that the formula is satisfied:

[0204]

[0205] Considering the varying importance of different project layers as evidence, and the differing precision in parameter data collection, the credibility of each piece of evidence at the project layer also varies. Therefore, the basic assignment functions for each project layer need to be modified before evidence synthesis. Here, a credibility parameter α is introduced. k α is used to represent the relative importance of different items. k The following formula is used to derive:

[0206]

[0207] In the formula, λ is the finite confidence coefficient. Extensive practical experience has shown that λ = 0.9 is the most effective, and in this invention, λ = 0.9; ω i Let ω be the weight of the i-th item in the weight vector of five independent pieces of evidence. max The maximum value in the weight vector;

[0208] The basic assignment function after credibility correction is defined as follows:

[0209]

[0210] (3) Evidence fusion

[0211] Evidence fusion shall be performed according to the following formula.

[0212]

[0213] Where m(A) is the basic assignment function corresponding to each state level after evidence fusion; A i To identify a subset of frame Θ;

[0214] (4) Evaluation of decision-making

[0215] In DS evidence theory, the maximum membership principle and the reliability criterion are generally used as evaluation and decision-making methods. In practical applications, the maximum membership principle requires a certain difference in membership between state levels; when the difference is less than a predetermined value, an accurate result cannot be given. Conversely, the reliability criterion, when dealing with situations where state levels are close to the confidence level, may incorrectly interpret the result as the previous one. Therefore, this invention organically integrates these two methods, employing a combined evaluation and decision-making method. The specific method is as follows.

[0216] The results of the basic assignment functions for each state level are judged using the maximum membership principle, and the condition formula is as follows:

[0217]

[0218] In the formula, m(ν0) represents the maximum value of the basic assignment function for the evaluation level, that is:

[0219] This represents the second largest value of the basic assignment function for the rating level, i.e. If the difference between the two exceeds the predetermined value ε1, then ε1 = 0.15;

[0220] If equation (36) is satisfied, then the evaluation result of the final operating state of the high-voltage cable is the level corresponding to ν0;

[0221] If equation (36) is not satisfied, and the maximum membership principle cannot be used, it indicates that the basic assignment function values ​​of the evaluation levels differ relatively little. In this case, the reliability criterion is used to judge the results of the basic assignment functions of each state level, and the formula is as follows:

[0222]

[0223] In equation (37), ε2 is the confidence level, ε2=0.5; if equation (37) is satisfied, then the evaluation result of the final operating status of the high-voltage cable is the level corresponding to ν0;

[0224] If equation (37) is not satisfied, return to step 4 to adjust the gray part of the cloud model, subjective weight, gray fuzzy weight matrix, and gray fuzzy discrimination matrix until equation (36) or equation (37) is satisfied.

[0225] Study examples:

[0226] Taking a certain 110kV line as an example, the state variables and normalization results are shown in Table 5:

[0227] Table 5

[0228]

[0229]

[0230] The subjective weights of the cable body are W1 = [0.1455 0.0442 0.2724 0.3585 0.0573 0.0753 0.0202 0.0266].

[0231] The objective weights are W2 = [0.0600 0.1161 0.1369 0.1056 0.0600 0.0600 0.3229]

[0232] The combined weights are W = [0.1116 0.0726 0.2193 0.2707 0.0764 0.0693 0.03359 0.1442]

[0233] The gray fuzzy weight of the cable body is:

[0234]

[0235] The membership degree is calculated using the cloud model, and the results are shown in Table 6.

[0236] Table 6

[0237]

[0238] Based on the sufficiency of information about the cable itself, the credibility of each evaluation index corresponding to each state level is determined by calculating the average value using the expert scoring method, thus obtaining the gray fuzzy discrimination matrix.

[0239]

[0240] The final evaluation result of the cable body is as follows:

[0241]

[0242] Similarly, the grey fuzzy evaluation results for the other four items—line terminals, intermediate joints, ancillary facilities, and line corridors—are as follows:

[0243]

[0244]

[0245]

[0246]

[0247] After obtaining the grey fuzzy evaluation results for the cable body, line terminals, intermediate joints, auxiliary facilities, and line channels, the system result set is calculated as follows:

[0248]

[0249] The final assessment of the cable body, auxiliary facilities, and line channel is "normal," while the assessment of the line terminals and intermediate joints is "caution."

[0250] The following uses the DS evidence theory to evaluate the overall status of the high-voltage cable, i.e., the evaluation from the project level to the target level. The gray fuzzy evaluation results of each project level are used as evidence and fused together to finally obtain the evaluation result of the high-voltage cable's operating status.

[0251] The gray fuzzy evaluation results of the five projects at the project level, which were calculated earlier, were used as the initial basic assignment function for the comprehensive evaluation, as shown in Table 7.

[0252] Table 7

[0253]

[0254] The weights of each project layer are calculated using the combined weighting method as follows:

[0255] W=[0.23, 0.281, 0.298, 0.099, 0.092]

[0256] The confidence parameter α can be calculated using the formula. k = [0.695, 0.849, 0.9, 0.299, 0.278, and then the uncertainty m(θ) = [0.305, 0.151, 0.1, 0.701, 0.722] is calculated by the formula. The corrected basic probability allocation results are shown in Table 8.

[0257] Table 8

[0258]

[0259] The basic probability allocation results were fused according to the formula, and the results are shown in Table 9.

[0260] Table 9

[0261]

[0262] The evaluation results are judged using the above method. First, the uncertainty is verified, and m(θ) = 0.0460.05, indicating that the uncertainty of the evaluation meets the requirements. Then, the maximum membership criterion is verified, and the formula is obtained. 0.189 > 0.15, which meets the maximum membership criterion requirement. Therefore, the high-voltage cable is in a "caution" state.

[0263] Although the present invention has been described above in conjunction with the accompanying drawings, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many modifications under the guidance of the present invention without departing from the spirit of the present invention, and these modifications are all within the protection scope of the present invention.

Claims

1. A method for evaluating the operating status of high-voltage cables, characterized in that, Includes the following steps: Step 1: Establish a high-voltage cable operation status evaluation index system, including: determining a set of factors with a three-layer structure (target layer, project layer, and index layer) from the high-voltage cable operation parameters according to enterprise standards; and establishing a status set; Step 2: Collect the corresponding index data of the high-voltage cable to be evaluated and the above-mentioned factors. All index data are from original data, operation records, preventive tests, live-line testing and online monitoring systems. Step 3: Normalize the indicator data collected in Step 2, including: The evaluation indicators for the operating status of high-voltage cables are divided into quantitative indicators, descriptive indicators, and qualitative evaluation indicators. The quantitative indicators are obtained by online monitoring systems or on-site measurements. The quantitative indicators are normalized using the concept of relative deterioration. The normalization result is [0, 1]. The closer to 1, the better the cable's operating condition. The descriptive indicators refer to the indicators of cable components whose condition can only be described in words according to the classification standards. For descriptive indicators, an expert scoring method is used for normalization. The scoring range is [0, 1]. The closer the score is to 1, the better the cable's operating condition. The expert scoring is based on the "Q / GDW 456-2010 Cable Line Condition Evaluation Guidelines" issued by the State Grid Corporation of China. The qualitative evaluation index refers to the cable component's condition having two clear classification criteria. For the qualitative evaluation index, based on the two classification criteria, the normalization result is 0 or 1, where 1 represents a normal state and 0 represents a severe state. Step 4: Use grey fuzzy theory to achieve a first-level evaluation from the indicator layer to the project layer, thereby obtaining the evaluation results of each project; Step 5: Utilize the DS evidence theory to achieve a two-level evaluation from the project level to the target level, and finally obtain the operating status of the high-voltage cable; Step 4 utilizes grey fuzzy theory to achieve a first-level evaluation from the indicator layer to the project layer, thereby obtaining the evaluation results for each project, including: Step 4-1) Determine the gray fuzzy weight matrix: The gray fuzzy weight matrix consists of a modulus and a gray part; The modulus of the gray fuzzy weight matrix refers to the weights corresponding to the evaluation indicators. Determining the modulus of the gray fuzzy weight matrix includes determining subjective weights using the analytic hierarchy process (AHP), determining objective weights using the entropy weight method, and obtaining combined weights based on subjective and objective weights using game theory. The gray part of the gray fuzzy weight matrix is ​​determined by expert scoring based on the credibility of the weights. The gray part of the gray fuzzy weight matrix refers to the gray level of the point corresponding to the weight. Step 4-2) Determine the gray fuzzy discrimination matrix: The gray fuzzy discrimination matrix consists of a modulus and a gray part; the modulus of the gray fuzzy discrimination matrix uses fuzzy membership degree to characterize the fuzzy membership relationship of the evaluation index to each evaluation state, and the membership degree is determined by cloud model theory; the gray part of the gray fuzzy discrimination matrix uses point gray points to characterize the reliability of the determination of the fuzzy membership degree of the evaluation index. Step 4-3) Synthesize the gray fuzzy comprehensive evaluation and convert the gray fuzzy comprehensive evaluation matrix into a system result set according to the principle of maximum membership degree and minimum gray degree. The system result set is the evaluation result of each project.

2. The method for evaluating the operating status of high-voltage cables according to claim 1, characterized in that, In step 1, The target layer refers to the operating status of the high-voltage cable. This target layer consists of five project layers, which include the cable body Z1, line terminal Z2, intermediate joint Z3, auxiliary equipment Z4, and line channel Z5. Each project layer comprises multiple indicator layers, wherein the indicator layer included in the cable body Z1 includes the withstand voltage test results Z of the cable body. 11 Insulation resistance Z of outer sheath 12 Partial discharge quantity Z 13 Line load Z 14 Appearance Z 15 familial defects Z 16 Inspection Record Z 17 Operating life Z 18 The line terminal Z2 includes the temperature Z of the metal connection of the line terminal. 21 , casing temperature Z 22 Partial discharge Z 23 Appearance Z 24 familial defects Z 25 Inspection Record Z 26 and operating years Z 27 The intermediate joint Z3 includes the pressure resistance test results Z of the intermediate joint. 31 Partial discharge quantity Z 32 Intermediate joint temperature Z 33 Appearance Z 34 familial defects Z 35 Inspection Record Z 36 and operating years Z 37 The auxiliary equipment Z4 includes the pressure resistance test results of the auxiliary facilities Z. 41 Grounding current Z 42 Temperature Z at equipment connection point 43 and appearance Z 44 The line channel Z5 includes the main structure of the line channel Z. 51 External environment conditions of the passage Z 52 Channel marker Z 53 Internal facilities of the structure Z 54 Leakage and water accumulation Z 55 Fire prevention and anti-theft system status Z 56 and harmful gases and foreign objects Z 57 ; The selected state quantities are based on the enterprise standards for cable line condition evaluation and the actual operating conditions of high-voltage cables. These selected state quantities include the following: The selected state quantity is used as the evaluation factor: （1）； In equation (1), The five project layers representing the cable's operating status are: cable body Z1, line terminal Z2, intermediate joint Z3, auxiliary equipment Z4, and line channel Z5. （2）； In equation (2), The first characterization of the cable's operating status The first project layer One indicator; Establish a state set: （3）； The operating status of high-voltage cables is divided into four levels: normal, warning, abnormal, and critical. These are respectively represented by... express; The normal state is that all indicators in the factor concentration index layer are within the attention values ​​specified in the enterprise standard for cable line condition evaluation; The state of attention is that one or more indicators in the factor concentration index layer are the intermediate values ​​of the attention values ​​and abnormal values ​​specified in the enterprise standard for cable line condition evaluation. An abnormal state is when one or more indicators in the factor set indicator layer exceed the warning value, but do not reach the abnormal value. A severe state is when one or more indicators in the factor cluster indicator layer exceed the specified outlier value.

3. The method for evaluating the operating status of high-voltage cables according to claim 2, characterized in that, The aforementioned enterprise standards include: "Q / GDW 456-2010 Guidelines for Cable Line Condition Evaluation" and "Q / GDW 11316-2014 Test Procedures for Power Cable Lines" issued by State Grid Corporation of China.

4. The method for evaluating the operating status of high-voltage cables according to claim 2, characterized in that, In step 2, the sources of all indicator layer data are: The withstand voltage test result Z of the cable body Z1. 11 Insulation resistance value Z of outer sheath 12 Derived from preventative testing, partial discharge quantity Z 13 Originating from live-line testing, line load Z 14 Originating from an online monitoring system, appearance Z 15 Inspection Record Z 17 Derived from runtime logs, familial defect Z 16 Operating life Z 18 Sourced from original data; In the aforementioned line terminal Z2, the temperature Z at the metal connection of the line terminal is... 21 , casing temperature Z 22 Partial discharge quantity Z 23 Originating from live-line testing, appearance Z 24 Inspection Record Z 26 Derived from runtime logs, familial defect Z 25 Operating life Z 27 Sourced from original data; The pressure resistance test result of the intermediate joint Z3 is Z. 31 Derived from preventative testing, partial discharge quantity Z 32 Intermediate joint temperature Z 33 Originating from live-line testing, appearance Z 34 Inspection Record Z 36 Derived from runtime logs, familial defect Z 35 Operating life Z 37 Sourced from original data; Among the auxiliary equipment Z4, the pressure resistance test result Z of the auxiliary facilities 41 Derived from preventative testing, grounding current Z 42 Temperature Z at equipment connection point 43 Originating from live-line testing, appearance Z 44 Sourced from runtime logs; The main structure of the line channel Z5 is as follows: 51 External environment conditions of the passage Z 52 Channel Identifier Z 53 Internal facilities of the structure Z 54 Fire prevention and anti-theft system status Z 56 Based on operation records, information on water leakage and water accumulation is available. 55 Harmful gases and foreign objects Z 57 Sourced from the online monitoring system.

5. The method for evaluating the operating status of high-voltage cables according to claim 1, characterized in that, Step 5 utilizes the DS evidence theory to achieve a two-level evaluation from the project level to the target level, ultimately obtaining the operational status of the high-voltage cable, including: Step 5-1) Identify the frame Determination of the identification framework The operating status of high-voltage cables is classified into four levels: normal status, warning status, abnormal status, and critical status. Uncertainty specified by DS evidence theory Composition, namely: Step 5-2) Determine the basic assignment function for each independent piece of evidence: Based on the established evaluation index system for the operating status of high-voltage cables, the five items at the project level—cable body Z1, line terminal Z2, intermediate joint Z3, auxiliary facilities Z4, and line corridor Z5—are taken as independent pieces of evidence. The evaluation results of each item obtained in Step 4 are used as the basic assignment function for each independent piece of evidence. This means that the following formula is satisfied: （31）； Introducing credibility parameters For the basic assignment function The corrections are made to indicate the relative importance of different items; The following formula is used to derive: （32）； In the formula, It is a finite confidence coefficient. ; Let be the weight of the i-th term in the weight vector of the five independent pieces of evidence. The maximum value in the weight vector; The basic assignment function after credibility correction is defined as follows: （33）； Step 5-3) Evidence Fusion: Fusion of evidence shall be performed according to the following formula. （34）； in, It is the basic assignment function corresponding to each state level after evidence fusion; For identification framework A subset of; Step 5-4) Evaluation and Decision: The basic assignment function results for each state level are judged using the maximum membership principle. The condition formula is as follows: （36）； In the formula, This represents the maximum value of the basic assignment function for the rating level, i.e.: ; This represents the second largest value of the basic assignment function for the rating level, i.e. If the difference between the two exceeds the predetermined value , ; If equation (36) is satisfied, then the evaluation result of the final operating state of the high-voltage cable is: The corresponding level; If equation (36) is not satisfied, the reliability criterion is used to judge the results of the basic assignment function for each state level. The formula is as follows: （37）； In equation (37), For confidence level, If equation (37) is satisfied, then the evaluation result of the final operating state of the high-voltage cable is: The corresponding level; If equation (37) is not satisfied, return to step 4 to adjust the gray part of the cloud model, subjective weight, gray fuzzy weight matrix, and gray fuzzy discrimination matrix until equation (36) or equation (37) is satisfied.

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