Oil paper insulation state evaluation method

Abstract

The invention proposes an oil paper insulation state evaluation method, and relates to the technical field of transformer oil paper insulation, and the method comprises the steps: S1, obtaining the AC breakdown voltage, DC conductivity and partial discharge characteristic quantity of oil paper insulation at different aging temperatures and time, and enabling the obtained values to serve as insulation characteristic evaluation indexes; s2, establishing an evaluation index set according to the insulation characteristic evaluation indexes, and determining a subjective weight of the evaluation index set based on an order relation analysis method; s3, determining an objective weight of the evaluation index set based on an entropy weight method; s4, combining the supervisor weight and the objective weight to determine a combined weight; s5, establishing a combined empowerment-improved TOPSIS model according to the combined weight, and evaluating the aging state of oil paper insulation by using the model to obtain an evaluation result; according to the method, the oil paper insulation state of the transformer can be effectively evaluated, the insulation level of the high-voltage transformer is improved, and safe and stable operation of the high-voltage transformer is maintained.

Description

Technical Field

[0001] The invention relates to the technical field of transformer oil-paper insulation, and in particular to a method for evaluating the state of oil-paper insulation. Background Art

[0002] With the rapid development of my country's power industry, power transmission and transformation equipment is developing towards larger capacity and higher voltage. 500kV AC and DC electrical equipment has been widely used and has become the core part of the power grid system. Oil-immersed high-voltage transformers are one of the most important equipment, so they must have high reliability to ensure the safe and efficient operation of the power grid. This insulation system is mainly composed of oil and paper to form a composite insulating medium. Traditional oil-immersed high-voltage transformers use cellulose insulation paper. Therefore, more and more high-voltage transformers are beginning to use new insulation paper to ensure optimal operation.

[0003] At present, most of the specific research on insulation aging by experts and scholars is based on the index results obtained from various insulation tests to estimate the in-service status of various cable insulation properties, so as to evaluate the degree of cable insulation aging. These experts and scholars have conducted in-depth research on the changes in one or more insulation mechanisms, but have only achieved breakthroughs in theory, and have not been able to effectively evaluate the state of oil-paper insulation in experimental practice. The comprehensive evaluation of the state of transformer oil-paper insulation or life evaluation still needs to be deepened and innovated. Summary of the invention

[0004] The purpose of the present invention is to provide an oil-paper insulation status assessment method, which can effectively assess the oil-paper insulation status of a transformer, and is of great significance for improving the insulation level of a water high-voltage transformer and maintaining the safe and stable operation of the high-voltage transformer.

[0005] The technical solution of the present invention is: The present application provides a method for evaluating the state of oil-paper insulation, which comprises the following steps: S1. Obtain the AC breakdown voltage, DC conductivity and partial discharge characteristic quantity of the oil-paper insulation at different aging temperatures and times as insulation property evaluation indicators; S2. Establish an evaluation index set based on the insulation characteristic evaluation index, and establish the subjective weight of the evaluation index set based on the order relationship analysis method; S3. Establish the objective weight of the evaluation index set based on the entropy weight method; S4. Combine the subjective weight and the objective weight to establish the combined weight; S5. A combined weighting-improved TOPSIS model is established according to the combined weights, and the model is used to evaluate the aging state of oil-paper insulation to obtain the evaluation results.

[0006] Furthermore, in step S1, the above-mentioned partial discharge characteristic quantities include the average discharge amount, the maximum discharge amount, the total discharge amount, the total number of discharges and the discharge repetition rate of the partial discharge.

[0007] Further, step S2 includes: S21, establish a sequence relationship, and establish an evaluation index set according to the insulation characteristic evaluation index; S22. Rank the evaluation indicators in the evaluation indicator set according to their importance, conduct quantitative analysis on the importance of adjacent indicators, and calculate weight coefficients; The process of quantitatively analyzing the importance of adjacent indicators and calculating the weight coefficients includes: , , , In the formula, is the relative importance ratio between the kth adjacent evaluation index and the k-1th adjacent evaluation index, and They represent the subjective weight of the kth adjacent evaluation index and the subjective weight of the k-1th adjacent evaluation index respectively, and i represents the i-th evaluation object.

[0008] Further, step S3 includes: S31, establishing an initial evaluation matrix based on the evaluation indicators in the evaluation indicator set and the corresponding original data; wherein the expression of the initial evaluation matrix is: ; S32, performing forward normalization processing on the initial evaluation matrix to obtain a forward normalized matrix; the calculation process is: Standardization of positive indicators of good insulation condition: , Standardization of negative indicators of good insulation condition: , It should be noted that for a positive indicator indicating a good insulation condition, the larger the indicator, the better the insulation property; for a negative indicator indicating a good insulation condition, the smaller the indicator, the better the insulation property; Forward normalization matrix: ; S33, calculating the evaluation index information entropy after normalizing the forward normalized matrix; The calculation formula for normalization is: , , The calculation formula for calculating the information entropy of the evaluation index is: ; S34, calculating the corresponding entropy weight of the evaluation index according to the information entropy of the evaluation index; wherein the formula for calculating the corresponding entropy weight of the evaluation index is: In the formula, is the initial evaluation matrix, represents the value of the original data corresponding to the jth indicator of the ith evaluation object, i represents the ith evaluation object, j represents the jth indicator, m is the number of evaluation objects, n is the number of evaluation indicators, and v ij is the original data after forward transformation, V is the forward normalization matrix, Z ij represents the positive standard normalized matrix parameter of the i-th evaluation object corresponding to the j-th indicator, Z represents the positive standard normalized matrix, represents the information entropy corresponding to the jth evaluation index, That is, it represents the corresponding entropy weight of the j-th evaluation index.

[0009] Furthermore, in step S4, the calculation process of the combined weight includes: Establish the combined weights of each insulation characteristic index: , The degree of deviation of subjective and objective weighted attributes: , The minimum deviation degree of subjective and objective weighted attributes is used as the objective function to calculate the subjective and objective weight participation coefficient: , , In the formula, w j is the combined weight of each insulation characteristic index, L is the Lagrangian function, is a constant; is the subjective weight coefficient, satisfying × ≥0, + =1; is the subjective weight of the jth evaluation index, is the objective weight coefficient, is the entropy weight corresponding to the jth evaluation index, v ij is the original data after positive transformation, It represents the degree of deviation of the corresponding subjective and objective weighted attributes of the i-th evaluation object, m is the number of evaluation objects, and n is the number of evaluation indicators.

[0010] Furthermore, in step S5, the calculation process of establishing the combined weighted-improved TOPSIS model is: Multiplying the forward standard normalization matrix with the combination weights yields the weighted normalization matrix: , According to the weighted normalization matrix, the corresponding positive ideal solution and negative ideal solution of each insulation evaluation index are determined: , , Calculate the Euclidean distance of each insulation aging evaluation object: , Calculate the grey correlation coefficient of the positive and negative ideal solutions of each insulation aging evaluation object: , The Euclidean distance and the grey correlation coefficient are dimensionlessly normalized: , Combine Euclidean distance and grey relational degree: , Calculate the relative closeness of each insulation aging sample: , Where Y is the weighted normalization matrix, w j is the combined weight of each insulation characteristic index, y ij represents the weighted normalized matrix parameter of the i-th evaluation object corresponding to the j-th indicator, z ij represents the normalized matrix parameter of the positive standard corresponding to the jth indicator of the ith evaluation object, m is the number of evaluation objects, n is the number of evaluation indicators, Y + is the set of positive ideal solutions, Y - is the set of negative ideal solutions, y + n is the nth positive ideal solution, y - n is the nth negative ideal solution, y + j is the positive ideal solution of the jth index, y - j is the negative ideal solution of the jth index, d i + is the Euclidean distance of the insulation aging evaluation object relative to the positive ideal solution, d i - h is the Euclidean distance of the insulation aging evaluation object relative to the negative ideal solution, + ij 、h- ij are the grey correlation coefficients of the positive and negative ideal solutions under each insulation aging cycle for the jth insulation characteristic index, is the resolution coefficient; g i + , g i - are the grey correlation degrees of each insulation aging evaluation object relative to the positive and negative ideal solutions, D i + is the Euclidean distance of the positive ideal solution, D i - is the Euclidean distance of the negative ideal solution, G i + is the grey relational degree of the positive ideal solution, G i - is the grey relational degree of the negative ideal solution, A i + It is expressed as the degree of closeness between the characteristic indexes of the ith insulation aging sample and the ideal optimal insulation operating state; A i - It is expressed as the degree of distance between each characteristic index of the i-th insulation aging sample and the ideal optimal insulation operating state. and are the relative participation coefficients of Euclidean distance and grey relational degree, which add up to one; T i + is the relative closeness of each insulation aging sample.

[0011] Compared with the prior art, the present invention has at least the following advantages or beneficial effects: (1) The present invention provides an oil-paper insulation state assessment method, which establishes a combined weighted-improved TOPSIS model that considers subjective and objective weights to assess the aging state of insulation samples at different temperatures, thereby significantly improving the assessment accuracy, which is of great significance for improving the insulation level of high-voltage transformers and maintaining the safe and stable operation of high-voltage transformers. (2) The present invention introduces the grey correlation coefficient while calculating the relative closeness based on the TOPSIS algorithm, which significantly improves the utilization rate of data information and reduces costs; (3) The model of the present invention uses the electrical characteristic quantities measured at different temperatures as the evaluation object, uses the order relationship analysis method as the subjective weighting method, and the entropy weight method as the objective weighting method, and performs subjective and objective combined weighting on the evaluation object of oil-paper insulation, which can further realize the effective evaluation of the oil-paper insulation status of the transformer. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 A step diagram of an oil-paper insulation status evaluation method of the present invention; Figure 2 The AC breakdown field strength results of thermal aging at 90℃ and 110℃ for different times; Figure 3 The graph shows the DC conductivity results of thermal aging at 0℃ and 110℃ for different times. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0015] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0016] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0017] It should be noted that, in this article, the term "comprises" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprises..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0018] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0019] Example 1 See also Figure 1 , Figure 1 Shown is a step diagram of an oil-paper insulation status evaluation method provided in an embodiment of the present application.

[0020] The present invention provides an oil-paper insulation state evaluation method, which comprises the following steps: S1. Obtain the AC breakdown voltage, DC conductivity and partial discharge characteristic quantity of the oil-paper insulation at different aging temperatures and times as insulation property evaluation indicators; S2. Establish an evaluation index set based on the insulation characteristic evaluation index, and establish the subjective weight of the evaluation index set based on the order relationship analysis method; S3. Establish the objective weight of the evaluation index set based on the entropy weight method; S4. Combine the subjective weight and the objective weight to establish the combined weight; S5. A combined weighting-improved TOPSIS model is established according to the combined weights, and the model is used to evaluate the aging state of oil-paper insulation to obtain the evaluation results.

[0021] As a preferred implementation, in step S1, the local discharge characteristic quantities include the local discharge average discharge amount, the maximum discharge amount, the total discharge amount, the total number of discharges and the discharge repetition rate.

[0022] As a preferred implementation, step S2 includes: S21, establish a sequence relationship, and establish an evaluation index set according to the insulation characteristic evaluation index; S22. Rank the evaluation indicators in the evaluation indicator set according to their importance, conduct quantitative analysis on the importance of adjacent indicators, and calculate weight coefficients; The process of quantitatively analyzing the importance of adjacent indicators and calculating the weight coefficients includes: , , , In the formula, is the relative importance ratio between the kth adjacent evaluation index and the k-1th adjacent evaluation index, and They represent the subjective weight of the kth adjacent evaluation index and the subjective weight of the k-1th adjacent evaluation index respectively.

[0023] As a preferred implementation, step S3 includes: S31, establishing an initial evaluation matrix based on the evaluation indicators in the evaluation indicator set and the corresponding original data; wherein the expression of the initial evaluation matrix is: ; S32, performing forward normalization processing on the initial evaluation matrix to obtain a forward normalized matrix; the calculation process is: Standardization of positive indicators of good insulation condition: , Standardization of negative indicators of good insulation condition: , It should be noted that for a positive indicator indicating a good insulation condition, the larger the indicator, the better the insulation property; for a negative indicator indicating a good insulation condition, the smaller the indicator, the better the insulation property; Forward normalization matrix: ; S33, calculating the evaluation index information entropy after normalizing the forward normalized matrix; The calculation formula for normalization is: , , The calculation formula for calculating the information entropy of the evaluation index is: ; S34, calculating the corresponding entropy weight of the evaluation index according to the information entropy of the evaluation index; wherein the formula for calculating the corresponding entropy weight of the evaluation index is: In the formula, is the initial evaluation matrix, represents the value of the original data corresponding to the jth indicator of the i-th evaluation object, m is the number of evaluation objects, n is the number of evaluation indicators, and v ij is the original data after forward transformation, V is the forward normalization matrix, Z ij represents the positive standard normalized matrix parameter of the i-th evaluation object corresponding to the j-th indicator, Z represents the positive standard normalized matrix, represents the information entropy corresponding to the jth evaluation index, That is, it represents the corresponding entropy weight of the j-th evaluation index.

[0024] As a preferred implementation, in step S4, the calculation process of the combination weight includes: Establish the combined weights of each insulation characteristic index: , The degree of deviation of subjective and objective weighted attributes: , The minimum deviation degree of subjective and objective weighted attributes is used as the objective function to calculate the subjective and objective weight participation coefficient: , , In the formula, w j is the combined weight of each insulation characteristic index, L is the Lagrangian function, is a constant; is the subjective weight coefficient, satisfying × ≥0, + =1; is the subjective weight of the jth evaluation index, is the objective weight coefficient, is the entropy weight corresponding to the jth evaluation index, v ij is the original data after positive transformation, It represents the degree of deviation of the corresponding subjective and objective weighted attributes of the i-th evaluation object, m is the number of evaluation objects, and n is the number of evaluation indicators.

[0025] It should be noted that for the values ​​of the subjective weight coefficient and the objective weight coefficient, a Lagrangian function can be constructed to solve them. The smaller the corresponding value is, the more consistent the subjective and objective weighted attributes of the evaluation object i are. Therefore, in the process of constructing the Lagrangian function, the minimum deviation of the subjective and objective weighted attributes can be used as the objective function to calculate the subjective and objective weight participation coefficients.

[0026] As a preferred implementation, in step S5, the calculation process of establishing the combined weighted-improved TOPSIS model is: Multiplying the forward standard normalization matrix with the combination weights yields the weighted normalization matrix: , According to the weighted normalization matrix, the corresponding positive ideal solution and negative ideal solution of each insulation evaluation index are determined: , , Calculate the Euclidean distance of each insulation aging evaluation object: , Calculate the grey correlation coefficient of the positive and negative ideal solutions of each insulation aging evaluation object: , The Euclidean distance and the grey correlation coefficient are dimensionlessly normalized: , Combine Euclidean distance and grey relational degree: , Calculate the relative closeness of each insulation aging sample: , Where Y is the weighted normalization matrix, w j is the combined weight of each insulation characteristic index, y ij represents the weighted normalized matrix parameter of the i-th evaluation object corresponding to the j-th indicator, z ij represents the normalized matrix parameter of the positive standard corresponding to the jth indicator of the ith evaluation object, m is the number of evaluation objects, n is the number of evaluation indicators, Y + is the set of positive ideal solutions, Y - is the set of negative ideal solutions, y + n is the nth positive ideal solution, y - n is the nth negative ideal solution, y + j is the positive ideal solution of the jth index, y - j is the negative ideal solution of the jth index, d i + is the Euclidean distance of the insulation aging evaluation object relative to the positive ideal solution, d i - h is the Euclidean distance of the insulation aging evaluation object relative to the negative ideal solution, + ij 、h - ij are the grey correlation coefficients of the positive and negative ideal solutions under each insulation aging cycle for the jth insulation characteristic index, is the resolution coefficient; g i + , g i - are the grey correlation degrees of each insulation aging evaluation object relative to the positive and negative ideal solutions, D i + is the Euclidean distance of the positive ideal solution, D i - is the Euclidean distance of the negative ideal solution, G i + is the grey relational degree of the positive ideal solution, G i - is the grey relational degree of the negative ideal solution, A i +It is expressed as the degree of closeness between the characteristic indexes of the ith insulation aging sample and the ideal optimal insulation operating state; A i - It is expressed as the degree of distance between each characteristic index of the i-th insulation aging sample and the ideal optimal insulation operating state. and are the relative participation coefficients of Euclidean distance and grey relational degree, which add up to one; T i + is the relative closeness of each insulation aging sample.

[0027] It should be noted that when judging by relative proximity, the insulation status of each insulation aging sample is sorted according to the size of the relative proximity. The insulation aging sample with a larger relative proximity value has a better insulation operating state, and conversely, the insulation aging sample with a smaller relative proximity value has a worse insulation operating state.

[0028] Example 2 In the embodiments of the present invention, ordinary Kraft insulation paper and Nomex insulation paper produced by DuPont of the United States are first selected for testing, KI25X transformer oil is selected as the insulating oil, 90°C, 110°C and 130°C are selected as the aging temperature, 10, 20, 30, 40 and 50 days are selected as the aging time, and the AC breakdown field strength and DC conductivity of the oil-paper insulation are measured in time after aging, and a partial discharge experiment under AC is carried out, and the partial discharge characteristic quantities including the average discharge amount, maximum discharge amount, total discharge amount, total discharge number, and discharge repetition rate of partial discharge are obtained through the experiment. The electrical performance indicators obtained above are used as evaluation objects to establish a combined weighted-improved TOPSIS model that considers subjective and objective weights, and the final evaluation results are obtained through evaluation by the model. Figure 2-3 shown.

[0029] It is understood that the structure shown in the figure is only for illustration, and an oil-paper insulation state assessment method may also include more or fewer components than those shown in the figure, or have a different configuration than those shown in the figure. Each component shown in the figure may be implemented by hardware, software or a combination thereof.

[0030] In the embodiments provided in the present application, it should be understood that the disclosed method can also be implemented in other ways. The embodiments described above are merely schematic, for example, the flowchart or block diagram in the accompanying drawings shows the possible implementation architecture, functions and operations of the method and computer program product according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or the flowchart, and the combination of the boxes in the block diagram and / or the flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0031] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0032] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0033] In summary, an oil-paper insulation status evaluation method provided in an embodiment of the present application performs corresponding evaluation on the aging status of insulation samples at different temperatures by establishing a combined weighting-improved TOPSIS model considering subjective and objective weights. The model takes the electrical characteristic quantities measured at different temperatures as evaluation objects, and uses the order relationship analysis method as a subjective weighting method and the entropy weight method as an objective weighting method to perform subjective and objective combined weighting on the evaluation object of oil-paper insulation. While calculating the relative closeness on the basis of the TOPSIS algorithm, the grey correlation coefficient is introduced to improve the utilization rate of data information. The transformer oil-paper insulation status can be effectively evaluated, which is of great significance for improving the insulation level of high-voltage transformers and maintaining the safe and stable operation of high-voltage transformers.

[0034] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0035] It will be apparent to those skilled in the art that the present application is not limited to the details of the exemplary embodiments described above, and that the present application can be implemented in other specific forms without departing from the spirit or essential features of the present application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present application. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for evaluating the state of oil-paper insulation, characterized in that: The following steps are involved: S1. Obtain the AC breakdown voltage, DC conductivity and partial discharge characteristic quantity of the oil-paper insulation at different aging temperatures and times as insulation property evaluation indicators; S2. Establish an evaluation index set based on the insulation characteristic evaluation index, and establish the subjective weight of the evaluation index set based on the order relationship analysis method; S3. Establish the objective weight of the evaluation index set based on the entropy weight method; S4. Combine the subjective weight and the objective weight to establish the combined weight; S5. A combined weighting-improved TOPSIS model is established according to the combined weights, and the model is used to evaluate the aging state of oil-paper insulation to obtain the evaluation results.

2. The oil-paper insulation status evaluation method according to claim 1, characterized in that: In step S1, the local discharge characteristic quantities include local discharge average discharge quantity, maximum discharge quantity, total discharge quantity, total discharge times and discharge repetition rate.

3. The oil-paper insulation status evaluation method according to claim 1, characterized in that: Step S2 includes: S21, establish a sequence relationship, and establish an evaluation index set according to the insulation characteristic evaluation index; S22. Rank the evaluation indicators in the evaluation indicator set according to their importance, conduct quantitative analysis on the importance of adjacent indicators, and calculate weight coefficients; The process of quantitatively analyzing the importance of adjacent indicators and calculating the weight coefficients includes: ， ， , where is the relative importance ratio between the kth adjacent evaluation index and the k-1th adjacent evaluation index, and They represent the subjective weight of the kth adjacent evaluation index and the subjective weight of the k-1th adjacent evaluation index respectively.

4. The oil-paper insulation status evaluation method according to claim 1, characterized in that: Step S3 includes: S31, establishing an initial evaluation matrix based on the evaluation indicators in the evaluation indicator set and the corresponding original data; wherein the expression of the initial evaluation matrix is: ； S32, performing forward normalization processing on the initial evaluation matrix to obtain a forward normalized matrix; the calculation process is: Standardization of positive indicators of good insulation condition: ， Standardization of negative indicators of good insulation condition: ， Forward normalization matrix: ； S33, calculating the evaluation index information entropy after normalizing the forward normalized matrix; The calculation formula for normalization is: ， ， The calculation formula for calculating the information entropy of the evaluation index is: ； S34, calculating the corresponding entropy weight of the evaluation index according to the information entropy of the evaluation index; wherein the formula for calculating the corresponding entropy weight of the evaluation index is: ， In the formula, is the initial evaluation matrix, represents the value of the original data corresponding to the jth indicator of the ith evaluation object, m is the number of evaluation objects, n is the number of evaluation indicators, and v ij is the original data after forward transformation, V is the forward normalization matrix, Z ij represents the positive standard normalized matrix parameter of the i-th evaluation object corresponding to the j-th indicator, Z represents the positive standard normalized matrix, represents the information entropy corresponding to the jth evaluation index, That is, it represents the corresponding entropy weight of the j-th evaluation index.

5. The oil-paper insulation status evaluation method according to claim 1, characterized in that: In step S4, the calculation process of the combined weight includes: Establish the combined weights of each insulation characteristic index: ， The degree of deviation of subjective and objective weighted attributes: ， The minimum deviation degree of subjective and objective weighted attributes is used as the objective function to calculate the subjective and objective weight participation coefficient: ， ， In the formula, w j is the combined weight of each insulation characteristic index, L is the Lagrangian function, is a constant; is the subjective weight coefficient, satisfying × ≥0, + =1; is the subjective weight of the jth evaluation index, is the objective weight coefficient, is the entropy weight corresponding to the jth evaluation index, v ij is the original data after positive transformation, It represents the degree of deviation of the corresponding subjective and objective weighted attributes of the i-th evaluation object, m is the number of evaluation objects, and n is the number of evaluation indicators.

6. The oil-paper insulation status evaluation method according to claim 5, characterized in that: In step S5, the calculation process of establishing the combined weighted-improved TOPSIS model is: Multiplying the forward standard normalization matrix with the combination weights yields the weighted normalization matrix: ， According to the weighted normalization matrix, the corresponding positive ideal solution and negative ideal solution of each insulation evaluation index are determined: ， ， Calculate the Euclidean distance of each insulation aging evaluation object: ， Calculate the grey correlation coefficient of the positive and negative ideal solutions of each insulation aging evaluation object: ， The Euclidean distance and the grey correlation coefficient are dimensionlessly normalized: ， Combine Euclidean distance and grey relational degree: ， Calculate the relative closeness of each insulation aging sample: ， Where Y is the weighted normalization matrix, w j is the combined weight of each insulation characteristic index, y ij represents the weighted normalized matrix parameter of the i-th evaluation object corresponding to the j-th indicator, z ij represents the normalized matrix parameter of the positive standard corresponding to the jth indicator of the ith evaluation object, m is the number of evaluation objects, n is the number of evaluation indicators, Y + is the set of positive ideal solutions, Y - is the set of negative ideal solutions, y + n is the nth positive ideal solution, y - n is the nth negative ideal solution, y + j is the positive ideal solution of the jth index, y - j is the negative ideal solution of the jth index, d i + is the Euclidean distance of the insulation aging evaluation object relative to the positive ideal solution, d i - h is the Euclidean distance of the insulation aging evaluation object relative to the negative ideal solution, + ij 、h - ij are the grey correlation coefficients of the positive and negative ideal solutions under each insulation aging cycle for the jth insulation characteristic index, is the resolution coefficient; g i + , g i - are the grey correlation degrees of each insulation aging evaluation object relative to the positive and negative ideal solutions, D i + is the Euclidean distance of the positive ideal solution, D i - is the Euclidean distance of the negative ideal solution, G i + is the grey relational degree of the positive ideal solution, G i - is the grey relational degree of the negative ideal solution, A i + It is expressed as the degree of closeness between the characteristic indexes of the i-th insulation aging sample and the ideal optimal insulation operating state; A i - It is expressed as the degree of distance between each characteristic index of the i-th insulation aging sample and the ideal optimal insulation operating state. and are the relative participation coefficients of Euclidean distance and grey relational degree, which add up to one; T i + is the relative closeness of each insulation aging sample.

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