Cable state evaluation method and system

Abstract

The invention provides a power cable state evaluation method and system based on an AHP-Critic method and improved TOPSIS grey correlation analysis, and the method comprises the steps: obtaining the index data of a to-be-evaluated cable based on a predetermined index affecting the state of the power cable, carrying out the corresponding preprocessing, and building a power cable state evaluation system; respectively calculating subjective and objective weights of the indexes based on an analytic hierarchy process and a Critic method, obtaining a combined weight through a game theory method, and determining a system health level; based on FCE, namely a fuzzy comprehensive evaluation method, a membership function is determined, a fuzzy evaluation matrix is calculated, and fuzzy comprehensive evaluation is carried out; and on the basis of improved TOPSIS grey correlation analysis, calculating a weighted mahalanobis distance and a comprehensive grey correlation degree between the state of the to-be-evaluated cable and the positive and negative ideal states, and combining the weighted mahalanobis distance and the comprehensive grey correlation degree to obtain the state of the to-be-evaluated cable so as to complete cable state evaluation.

Description

Technical Field

[0001] The present invention belongs to the technical field of cable status assessment, and in particular relates to a power cable status assessment method and system based on combined weighting. Background Art

[0002] In recent years, with the continuous development of online monitoring technology, my country has mostly applied a periodic inspection maintenance system in power cable detection. This method has played a huge role in preventing equipment accidents and ensuring safe and reliable power supply, but these methods also have some obvious shortcomings. These methods are periodic inspections and maintenance. Even if the equipment is in good condition, it is necessary to test and repair it according to the plan, which may lead to excessive maintenance. On the other hand, since periodic inspections are not continuous real-time monitoring, the insulation condition of the cable may still have problems during the inspection interval. More importantly, these systems are only used for short-term online monitoring on site, and cannot monitor the operating status of the cable for a long time, resulting in serious omissions and false alarms. After long-term online monitoring of partial discharge of the cable and analysis of the discharge data, the development law of aging can be found, so that the insulation condition of the cable can be estimated and a reliable reference can be provided for maintenance.

[0003] High-voltage power cables have the advantages of low line loss, large transmission capacity, and high power supply reliability, and have been widely used in the upgrading and transformation of urban power grids. However, during operation, high-voltage power cables will be affected by multiple factors such as electricity, heat, machinery, and environment, causing insulation aging, reduced service life, and other faults. Timely detection and elimination of abnormalities and faults are the key to ensuring the safe operation of cables. At present, high-voltage trunk lines mostly adopt a regular maintenance mode, which has problems such as lack of emphasis, low efficiency, and serious "disrepair" and "over-repair". In addition, general faults of high-voltage power cables laid underground are difficult to be directly discovered, and troubleshooting cable defects and faults requires a lot of personnel, material resources, and financial resources. With the increase in the mileage of high-voltage power cables, the problems of insufficient maintenance manpower and unreasonable allocation of maintenance resources have become increasingly prominent. Therefore, it is necessary to accurately evaluate the operating status of high-voltage power cables and formulate differentiated maintenance strategies to improve the operational reliability of the power system. Summary of the invention

[0004] The object of the present invention is to provide a method and system for evaluating the state of a power cable based on an AHP-Critic method and improved TOPSIS grey relational analysis.

[0005] To achieve the above object, the present invention adopts the following technical solution:

[0006] A method and system for evaluating the state of a power cable based on the AHP-Critic method and improved TOPSIS grey correlation analysis, comprising a cable characteristic quantity sensor unit, a data acquisition unit, a state evaluation unit, and a storage unit. Including:

[0007] Based on the pre-determined indicators affecting the power cable status, the indicator data of the cable to be evaluated is obtained and pre-processed accordingly to establish a power cable status evaluation system;

[0008] Based on the analytic hierarchy process and the critic method, the subjective and objective weights of the indicators are calculated respectively, and the combined weights are obtained through the game theory method to determine the system health level;

[0009] Based on FCE, i.e. fuzzy comprehensive evaluation method, the membership function is determined, the fuzzy evaluation matrix is ​​calculated, and fuzzy comprehensive evaluation is performed;

[0010] Based on the improved TOPSIS grey correlation analysis, the weighted Mahalanobis distance and comprehensive grey correlation degree between the cable state to be evaluated and the positive and negative ideal states are calculated, and the two are combined to obtain the state of the cable to be evaluated, thus completing the cable state evaluation.

[0011] The establishment of the power cable status assessment system is specifically as follows: establishing an assessment index system for the power cable status; listing the basis for judging each assessment index of the power cable status; standardizing the assessment index of the degree of degradation; and determining the comment set of the cable.

[0012] Furthermore, by using the existing detection and monitoring methods, an evaluation index system for the power cable status is established from three aspects: test data, online monitoring data and historical data.

[0013] Among them, the test data include insulation resistance, dielectric loss tangent value and leakage current; the online monitoring data include ultrasonic data, transient ground voltage data, infrared temperature measurement data and ambient temperature and humidity data; the historical data include operation inspections and years of operation.

[0014] Furthermore, the basis for judging each evaluation index of power cable status is listed by using existing technical specifications, general standards, guidelines and research.

[0015] Furthermore, the degradation evaluation index is standardized to standardize each state parameter to solve the error caused by the difference between different dimensions and units.

[0016] Furthermore, the evaluation factors of the power cable are used to determine the comment set of the cable.

[0017] The determination of the indicator weights is specifically as follows: based on the establishment of the AHP structural model and the comparison judgment matrix, the judgment matrix is ​​tested according to the consistency verification formula, the eigenvector obtained by the judgment matrix is ​​normalized to obtain the weight value; based on the Critic method, an m×n evaluation indicator matrix is ​​established, and the indicators are normalized to obtain the objective weights; and the combined weights are obtained through the game theory method.

[0018] Among them, the establishment of the fuzzy evaluation matrix is ​​specifically as follows: based on the evaluation basis, the degradation values ​​of each level of evaluation indicators after standardized processing are substituted into the membership function respectively, and then the fuzzy relationship matrix is ​​obtained; based on the fuzzy relationship matrix and the corresponding weighted average synthesis operation, an evaluation is performed to determine the level of the operating status.

[0019] Furthermore, using the evaluation basis, the degradation values ​​of the standardized evaluation indicators at all levels are substituted into the membership function, and then the fuzzy relationship matrix is ​​obtained as follows:

[0020]

[0021] The cable characteristic quantity sensor unit adopts the online monitoring data of the online monitoring sensor to obtain ultrasonic data, transient ground voltage data, infrared temperature measurement data and ambient temperature and humidity data.

[0022] Wherein, the data acquisition unit predetermines the indexes that affect the state of the power cable, obtains the index data of the cable to be evaluated and performs corresponding preprocessing;

[0023] The state evaluation unit calculates the weighted Mahalanobis distance and the comprehensive grey correlation degree between the state of the cable to be evaluated and the positive and negative ideal states based on the improved TOPSIS grey correlation analysis, and combines the two to obtain the state of the cable to be evaluated, thereby completing the cable state evaluation;

[0024] The storage unit is characterized in that the speed of storing operating data is determined by the comprehensive safety factor of the power cable; low-speed storage is selected for good working conditions, and full-speed storage is selected for abnormal and dangerous working conditions.

[0025] Compared with the prior art, the cable status assessment method and system provided by the present application have the following beneficial effects:

[0026] The present application provides a cable status assessment method and system, which uses the combination of AHP and Critic methods to calculate the subjective and objective weights of indicators, which can comprehensively consider subjective judgment and objective data, reduce the deviation caused by a single method, and improve the comprehensiveness and reliability of the assessment results. Combining through game theory can not only improve the scientificity and rationality of weight allocation, but also promote a more effective decision-making process.

[0027] The present application provides a cable status assessment method and system, which uses fuzzy comprehensive evaluation to assess the health status of a power supply system, and can solve the problem of multiple factors affecting the system and unclear boundaries. At the same time, the Gaussian function as a membership function greatly improves the accuracy of the assessment.

[0028] The combination of AHP and Critic methods can clearly understand the relationship between various factors, determine which factors have a greater impact on the system, and facilitate the formulation of reasonable response plans. In practice, many problems are not very certain or clear, and fuzziness needs to be controlled and considered. At this time, fuzzy comprehensive evaluation can play a role. The combination of the two can make the assessment of the problem more accurate and comprehensive.

[0029] The present application provides a cable status assessment method and system, wherein the system uses improved TOPSIS grey correlation analysis to calculate the weighted Mahalanobis distance and comprehensive grey correlation between the cable status to be assessed and the positive and negative ideal statuses, so as to directly derive the priority of the cable status. Combining grey correlation and Mahalanobis distance calculation can reduce the uncertainty and error in the assessment and improve the credibility of the results compared to applying a single method alone. Grey correlation analysis can effectively capture the implicit relationships in the data, thereby supplementing the information that may be omitted by traditional methods and improving the accuracy and reliability of the assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a flow chart of a power cable condition assessment method based on AHP-Critic method and improved TOPSIS grey relational analysis;

[0031] Figure 2 This is a schematic diagram of the power cable status assessment index system;

[0032] Figure 3 Combined weight graph for cable indicators. DETAILED DESCRIPTION

[0033] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0034] It should be noted that the terms used herein are for describing specific embodiments only and are not intended to be limiting of exemplary embodiments according to the present invention.

[0035] The purpose of this embodiment is to provide a power cable status assessment method and system based on AHP-Critic method and improved TOPSIS grey correlation analysis, which includes a cable characteristic quantity sensor unit, a data acquisition unit, a status assessment unit, and a storage unit. Including:

[0036] Based on the pre-determined indicators affecting the power cable status, the indicator data of the cable to be evaluated is obtained and pre-processed accordingly to establish a power cable status evaluation system;

[0037] Based on the analytic hierarchy process and the critic method, the subjective and objective weights of the indicators are calculated respectively, and the combined weights are obtained through the game theory method to determine the system health level;

[0038] Based on FCE, i.e. fuzzy comprehensive evaluation method, the membership function is determined, the fuzzy evaluation matrix is ​​calculated, and fuzzy comprehensive evaluation is performed;

[0039] Based on the improved TOPSIS grey correlation analysis, the weighted Mahalanobis distance and comprehensive grey correlation degree between the cable state to be evaluated and the positive and negative ideal states are calculated, and the two are combined to obtain the state of the cable to be evaluated, thus completing the cable state evaluation.

[0040] The establishment of the power cable status assessment system is specifically as follows: establishing an assessment index system for the power cable status; listing the basis for judging each assessment index of the power cable status; standardizing the assessment index of the degree of degradation; and determining the comment set of the cable.

[0041] In the specific implementation, the existing detection and monitoring means are used to establish an evaluation index system for the power cable status from three aspects: test data, online monitoring data and historical data.

[0042] Among them, the test data include insulation resistance, dielectric loss tangent value and leakage current; the online monitoring data include ultrasonic data, transient ground voltage data, infrared temperature measurement data and ambient temperature and humidity data; the historical data include operation inspections and years of operation.

[0043] In the specific implementation, the existing technical specifications, general standards, guidelines and research are used to list the evaluation basis of various evaluation indicators of power cable status.

[0044] In the specific implementation, the degradation evaluation index is standardized and each state parameter is standardized to solve the error caused by the difference between different dimensions and units.

[0045] In a specific implementation, the evaluation factors of the power cable are used to determine the comment set of the cable.

[0046] The determination of indicator weights is as follows: based on the establishment of the AHP structural model and the comparative judgment matrix, the judgment matrix is ​​tested according to the consistency verification formula, the eigenvector obtained by the judgment matrix is ​​normalized to obtain the weight value; based on the Critic method, an m×n evaluation indicator matrix is ​​established, and the indicators are normalized to obtain the objective weights; the combined weights are obtained through the game theory method.

[0047] Among them, the establishment of the fuzzy evaluation matrix is ​​specifically as follows: based on the evaluation basis, the degradation values ​​of each level of evaluation indicators after standardized processing are substituted into the membership function respectively, and then the fuzzy relationship matrix is ​​obtained; based on the fuzzy relationship matrix and the corresponding weighted average synthesis operation, an evaluation is performed to determine the level of the operating status.

[0048] In the specific implementation, the judgment basis is used to substitute the degradation degree values ​​of each level of evaluation indicators after standardization into the membership function, and then the fuzzy relationship matrix is ​​obtained as follows:

[0049]

[0050] Specifically, for ease of understanding, the solution described in this embodiment is described in detail below with reference to the accompanying drawings:

[0051] At present, the evaluation guidelines for the operation status of cables are relatively rough, and it is difficult to clearly define the differences in the impact of various indicators on cables. In order to solve the problems existing in the existing technology, such as Figure 1 As shown, this embodiment provides a method and system for evaluating the state of a power cable based on the AHP-Critic method and improved TOPSIS grey relational analysis. The solution described in this embodiment specifically adopts the following technical concepts:

[0052] Firstly, according to the operating characteristics of power cables, the evaluation index system of power cable status is established from three aspects: test data, online monitoring data and historical data. The evaluation basis of each evaluation index of power cable status is listed, the evaluation index of degradation degree is standardized, and the comment set of cable is determined. Secondly, the AHP structural model and comparative judgment matrix are used to check the judgment matrix according to the consistency check formula, and the eigenvector obtained by the judgment matrix is ​​normalized to obtain the weight value; based on the Critic method, an m×n evaluation index matrix is ​​established, and the index is normalized to obtain the objective weight; the combined weight is obtained by the game theory method. Secondly, using the judgment basis, the degradation degree values ​​of each level of evaluation index after standardization are substituted into the membership function respectively, and then the fuzzy relationship matrix is ​​obtained; based on the fuzzy relationship matrix and the corresponding weighted average synthesis operation, the evaluation is carried out to determine the level of the operating status. Finally, the weighted Mahalanobis distance and comprehensive grey correlation degree between the cable status to be evaluated and the positive and negative ideal states are calculated by using the improved TOPSIS grey correlation analysis, and the two are combined to obtain the status of the cable to be evaluated, and the cable status evaluation is completed.

[0053] The following is a detailed description of the scheme:

[0054] (1) Establishing a cable status assessment system

[0055] 1) Evaluation Metric Selection

[0056] Based on the existing detection and monitoring methods, an evaluation index system for cable terminal joints is established from three aspects: test data, online monitoring data and historical data. The first is test data, including insulation resistance, dielectric loss tangent value and leakage current; the second is online monitoring data, including ultrasonic data, transient ground voltage data, infrared temperature measurement data and ambient temperature and humidity data; the third is historical data, including operation inspection and commissioning years. Figure 2 shown.

[0057] 2) Evaluation criteria

[0058] Use the test data, online monitoring data and historical data of the cable terminal joint to evaluate its operating status. According to the existing technical specifications, general standards, guidelines and research, the evaluation basis of each evaluation index of the cable terminal joint is listed.

[0059] 3) Standardization of evaluation indicators

[0060] The degree of degradation indicates that the equipment is between the normal state and the abnormal state, a value between 0 and 1, which describes how close the current state of the equipment is to the fault state. The smaller the value, the more normal the equipment is, and the larger the value, the more serious the degradation. The system indicators can be divided into three categories, namely, the larger the better type, the smaller the better type, and the intermediate type.

[0061] 4) Determine the cable review set

[0062] After determining the evaluation factors of power cables, each factor should be graded. The classification should be based on the actual operating conditions and is generally divided into four or five levels. In this paper, it is divided into four levels, namely {normal (V1), caution (V2), abnormal (V3), severe (V4)}.

[0063] Normal status: Indicates that the power cable is in good operating condition and the values ​​of various parameters are within the normal range. Caution status: Some parameters of the power cable are at the boundary values ​​of the normal range, or exceed the normal range, but it can continue to operate overall and does not need to be replaced. However, the probability of failure is very high and the cable needs to be monitored at any time. Abnormal status: Indicates that several main state quantities of the power cable state quantity exceed the standard boundary values, or one main state quantity exceeds the boundary value specified by the standard, and other state quantities also have obvious abnormalities, which have affected the performance of the equipment. Severe status: One or several state quantities of the power cable seriously exceed the normal range of various parameters, and the equipment can only operate for a short period of time or needs to stop operating immediately.

[0064] (2) Determine the weight coefficient of evaluation index

[0065] Establish the AHP structure model and comparative judgment matrix, test the judgment matrix according to the consistency check formula, obtain the eigenvector of the judgment matrix, normalize the eigenvector to get the weight value; establish the m×n evaluation index matrix based on the Critic method, normalize the index to get the objective weight; get the combination weight through the game theory method. Figure 3 shown.

[0066] 1) Calculate subjective weight based on analytic hierarchy process

[0067] The analytic hierarchy process (AHP) is a multi-criteria and multi-factor decision-making method. It decomposes a complex system into several components according to certain principles or structures, and then establishes these components into a hierarchical structure with certain logical relationships. The analytic hierarchy process analysis flow chart is as follows: Figure 3 Shown

[0068] ① Establish AHP structural model

[0069] The system to be analyzed is hierarchical and organized. The system is divided into several levels from top to bottom according to the different attributes of each component. The top layer has only one element, which is the target of the system. The number of levels is directly related to the complexity of the problem.

[0070] ② Establish a comparative judgment matrix

[0071] The judgment matrix is ​​a matrix obtained by comparing the relative importance of each indicator at this level with respect to the evaluation indicators at the previous level. It is the basis of the hierarchical analysis method. According to the multi-level indicator model, the importance of each indicator at the same level is compared according to the 9-level scaling method, and the elements of the judgment matrix are assigned values. The 1-9 scaling method is used to establish the importance of each other.

[0072] Table 1 Importance scale table

[0073]

[0074] The expression of judgment matrix A is as follows:

[0075]

[0076] Where n is the order of matrix A. Element a ij Has the following properties:

[0077]

[0078] ③Consistency test

[0079] When there are many system evaluation factors, the judgment matrix is ​​often more complicated and may be inconsistent, making it difficult to fully meet the consistency conditions. Therefore, the judgment matrix needs to be tested according to the consistency verification formula.

[0080]

[0081] Table 2 Average random consistency index values ​​of the second-order judgment matrix

[0082]

[0083] For different orders of judgment matrices, people will have different errors when making judgments, so the value of the average random consistency index RI will also be different. When RI < 0.1, the consistency check is qualified; when RI > 0.1, the consistency check does not meet the requirements, and the mutual importance between the indicators needs to be reassigned until it passes the consistency check of the judgment matrix.

[0084] ④ Calculate the weight of each indicator

[0085] After the consistency check is completed, the judgment matrix eigenvector is calculated:

[0086]

[0087] After the eigenvector is normalized, it becomes the weight of each indicator.

[0088] 2) Calculate objective weights based on the Critic method

[0089] The Critic method is an objective weighting method based on the intensity of data comparison and the conflict between data.

[0090] ①Construct an m×n evaluation index matrix, where m is the number of evaluation objects and n is the number of evaluation indicators.

[0091]

[0092] ②In order to eliminate the differences in the dimensions and value ranges between indicators, the indicator data are normalized. Due to the different nature of the effects of indicators, the indicators are divided into two categories: positive indicators and negative indicators. Among them, positive indicators represent excellent indicators of the evaluation object, and the larger the value, the better. Use the following formula to normalize them:

[0093]

[0094] Negative indicators represent poor quality indicators of the evaluation object. The smaller the value, the better. Use the following formula to normalize it:

[0095]

[0096] ③Calculation index I j The objective weight of:

[0097]

[0098] in,

[0099]

[0100] 3) Combination weighting based on game theory

[0101] By introducing the game combination weighting method, the advantages of subjective weight and objective weight are combined to find the optimal weight. Game combination weighting is based on the idea of ​​game theory, which treats subjective weight and objective weight as game participants, so that the deviation between the combined weight and the weight obtained by each method is minimized, so that both parties can reach a consensus or compromise.

[0102] ① Assume that there are m ways (m=2 in this paper) to assign weights, and the weight vector obtained by the assignment is ω i , the weight vector ω obtained by different methods i , make any linear combination to form the combination weight, and the linear combination expression is

[0103]

[0104] Where: β i is the linear correlation coefficient and also the weight coefficient.

[0105] ② In order to obtain the optimal weight, with the goal of minimizing the deviation, a game strategy model is established as

[0106]

[0107] ③ Obtain the weight coefficients (β1, β2, ...., β i ), calculate the combined weight:

[0108]

[0109] in,

[0110]

[0111] Where: is the result of normalizing the weight coefficient.

[0112] (3) Establishing the fuzzy relationship matrix

[0113] 1) Determine the membership function

[0114] The relative degradation degree is used to divide the status level, that is, the fuzzy dividing intervals corresponding to the relative degradation degree index of the cable and the normal, warning, abnormal and severe states are set to [0, 0.2], [0.2, 0.5], [0.5, 0.75] and [0.75, 1], that is, the corresponding values ​​of X1, X2, X3 and X4 are 0.2, 0.5, 0.75 and 1.

[0115] 2) Establish fuzzy relationship matrices at all levels

[0116] Based on the evaluation criteria, the degradation values ​​of each level of evaluation indicators after standardization are substituted into the membership function, and then the fuzzy relationship matrix is ​​obtained as follows:

[0117]

[0118] 3) Fuzzy comprehensive evaluation

[0119] According to the status of the cable, the criterion layer is first analyzed from the indicator layer, and the fuzzy relationship matrix R of the cable test data, online monitoring data and historical data is i (=1, 2, 3) and the corresponding weighted average synthesis operation are performed to perform the first-level evaluation, as shown in the formula:

[0120] B i =W i R i

[0121] Then, the target layer is analyzed from the criterion layer of the cable evaluation index system, that is, the secondary evaluation is carried out based on the results of the primary evaluation:

[0122]

[0123] Finally, the level r to which the cable status belongs is determined based on the maximum membership degree.

[0124] (4) Improved TOPSIS grey correlation analysis method

[0125] 1) Construct positive and negative ideal samples

[0126] The mechanism of TOPSIS is to select positive and negative optimal solutions, and score the samples to be evaluated by detecting the degree of proximity between the evaluated object and the optimal solution and the worst solution. represents a positive ideal sample, represents a negative ideal sample, then:

[0127]

[0128] 2) Determine the correlation coefficient

[0129] The calculation method of grey correlation coefficient is as follows:

[0130]

[0131] Then the sample X i With the optimal solution The grey correlation degree is:

[0132]

[0133] 3) Set the dynamic resolution coefficient

[0134] The correlation coefficient and the resolution coefficient are closely related. The resolution coefficient affects the correlation between the evaluation sequences and the sensitivity of the evaluation model to data differences. Therefore, if the resolution coefficient is assumed to be unchanged, it will have a certain impact on the comprehensive calculation results. This paper proposes a dynamic resolution coefficient selection method to make up for the above defects. Define the resolution coefficient segmented discriminant number β i for:

[0135]

[0136] 4) Calculate the comprehensive grey relational degree

[0137] Traditional grey relational degree β i It is to quantitatively measure the closeness between factors through the closeness of the shape of the sequence curve of each factor; while the grey absolute correlation degree τ i It reflects the similarity of the data change rate, that is, it describes the similarity of the derivatives. i Improve the traditional grey relational degree ε i Forming comprehensive grey relational degree η i , which can better describe the similarity between two sequences.

[0138] initialization:

[0139]

[0140] Difference calculation:

[0141]

[0142] y ij =c ij -c i1

[0143]

[0144] Absolute grey relational degree:

[0145]

[0146] Comprehensive grey relational degree:

[0147]

[0148] 5) Calculate the weighted Mahalanobis distance

[0149] Assume that the positive covariance matrix is ​​the covariance matrix of the matrix composed of the positive grey incidence matrix and the optimal solution, and the negative covariance matrix is ​​the covariance matrix of the matrix composed of the negative grey incidence matrix and the worst solution. Then the weighted Mahalanobis distance between the i-th evaluation sequence and the optimal solution and the worst solution is:

[0150]

[0151] 6) Calculate the comprehensive state closeness

[0152] The state closeness of comprehensive grey relational closeness and weighted Mahalanobis distance are:

[0153]

[0154] The overall comprehensive state closeness of the equipment is:

[0155] A i =a1P i +(1-a1)Q i

[0156] In the formula, the weight value a1=0.5.

Claims

1. A power cable status assessment method and system based on AHP-Critic method and improved TOPSIS grey relational analysis, characterized in that: The overall structure consists of a cable characteristic quantity sensor unit, a data acquisition unit, a state evaluation unit, and a storage unit.

2. A method and system for evaluating power cable status based on AHP-Critic method and improved TOPSIS grey correlation analysis as described in claim 1, characterized in that: include: Based on the pre-determined indicators affecting the power cable status, the indicator data of the cable to be evaluated is obtained and pre-processed accordingly to establish a power cable status evaluation system; Based on the analytic hierarchy process and the critic method, the subjective and objective weights of the indicators are calculated respectively, and the combined weights are obtained through the game theory method to determine the system health level; Based on FCE, i.e. fuzzy comprehensive evaluation method, the membership function is determined, the fuzzy evaluation matrix is ​​calculated, and fuzzy comprehensive evaluation is performed; Based on the improved TOPSIS grey correlation analysis, the weighted Mahalanobis distance and comprehensive grey correlation degree between the cable state to be evaluated and the positive and negative ideal states are calculated, and the two are combined to obtain the state of the cable to be evaluated, thus completing the cable state evaluation.

3. A method and system for evaluating power cable status based on AHP-Critic method and improved TOPSIS grey correlation analysis as described in claim 2, characterized in that: Based on the predetermined indicators that affect the power cable status, the indicator data of the cable to be evaluated is obtained and preprocessed accordingly to establish a power cable status evaluation system, specifically: Based on the existing detection and monitoring methods, an evaluation index system for the power cable status is established from three aspects: test data, online monitoring data and historical data; based on the existing technical specifications, general standards, guidelines and research, the evaluation basis of each evaluation index of the power cable status is listed; based on the standardization of the evaluation index of degradation degree, each state parameter is standardized to solve the errors caused by the differences between different dimensions and units; based on the evaluation factors of power cables, the comment set of cables is determined.

4. A method and system for evaluating power cable status based on AHP-Critic method and improved TOPSIS grey correlation analysis as described in claim 2, characterized in that: The subjective and objective weights of the indicators are calculated using the analytic hierarchy process and the critic method respectively. The combined weights are obtained through the game theory method to determine the system health level, which is as follows: Based on the establishment of AHP structural model and comparison judgment matrix, the judgment matrix is ​​tested according to the consistency check formula, the eigenvector obtained by the judgment matrix is ​​normalized to obtain the weight value; Based on the Critic method, an m×n evaluation index matrix is ​​established, and the indicators are normalized to obtain objective weights; the combined weights are obtained through game theory methods.

5. A method and system for evaluating power cable status based on AHP-Critic method and improved TOPSIS grey correlation analysis as described in claim 2, characterized in that: Based on FCE, i.e. fuzzy comprehensive evaluation method, the membership function is determined, the fuzzy evaluation matrix is ​​calculated, and the fuzzy comprehensive evaluation is performed, specifically: Based on the judgment basis, the degradation degree values ​​after standardized processing of the evaluation indicators at each level are substituted into the membership function respectively, and then the fuzzy relationship matrix is ​​obtained; based on the fuzzy relationship matrix and the corresponding weighted average synthesis operation, the evaluation is carried out to determine the level of the operating status.

6. A method and system for evaluating power cable status based on AHP-Critic method and improved TOPSIS grey relational analysis as described in claim 3, characterized in that: The test data include insulation resistance, dielectric loss tangent value and leakage current; the online monitoring data include ultrasonic data, transient ground voltage data, infrared temperature measurement data and ambient temperature and humidity data; the historical data include operation inspection and years of operation.

7. The cable characteristic quantity sensor unit according to claim 1, characterized in that: Based on the online monitoring data of the online monitoring sensor, ultrasonic data, transient ground voltage data, infrared temperature measurement data and ambient temperature and humidity data are obtained.

8. The data acquisition unit according to claim 1, characterized in that: Based on the predetermined indicators affecting the status of the power cable, the indicator data of the cable to be evaluated is obtained and preprocessed accordingly.

9. The state evaluation unit according to claim 1, characterized in that: Based on the improved TOPSIS grey correlation analysis, the weighted Mahalanobis distance and comprehensive grey correlation degree between the cable state to be evaluated and the positive and negative ideal states are calculated, and the two are combined to obtain the state of the cable to be evaluated, thus completing the cable state evaluation.

10. The storage unit according to claim 1, characterized in that The speed of storing operating data is determined by the comprehensive safety factor of the power cable; low-speed storage is selected for good working conditions, and full-speed storage is selected for abnormal and dangerous working conditions.

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