Method and device for state assessment of breather applied to transformer

Abstract

The present invention discloses a method and device for state evaluation of a breather applied to a transformer. The method includes: determining the to-be-used values corresponding to each breather under at least one evaluation dimension within the same time slice; determining an index normalization matrix based on the to-be-used values corresponding to each breather under at least one evaluation dimension; wherein, the rows in the normalization index matrix represent breathers, the columns represent evaluation dimensions, and the element values represent the normalized values corresponding to the breathers under the corresponding evaluation dimensions; determining a correlation coefficient matrix and the index weight values corresponding to each evaluation dimension according to the index normalization matrix; determining a grey correlation degree matrix based on the index weight values and the correlation coefficient matrix; determining the state information corresponding to each breather based on the grey correlation degree matrix, dividing multiple evaluation dimensions according to the operating characteristics of the breather and related influencing factors, realizing the comprehensive evaluation of the breather state, improving the breather evaluation efficiency and reducing the evaluation cost at the same time.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer breather maintenance, and particularly to a method and device for state evaluation of a breather applied to a transformer. Background Art

[0002] A breather, also known as a moisture absorber, is an auxiliary safety protection device of a main transformer, which plays a role in purifying the air inhaled by the transformer. Therefore, the performance evaluation of the breather is of great significance to the normal operation of the transformer.

[0003] The existing method relies on manual inspection to observe the discolored part of the silica gel inside the breather, so as to determine the health status of the breather.

[0004] The above solution has certain subjectivity in evaluating the health status of the breather, and the evaluation efficiency is low, the effect is not good, and the labor cost is high. Summary of the Invention

[0005] The present invention provides a method and device for state evaluation of a breather applied to a transformer, so as to realize a comprehensive evaluation of the health status of the breather, improve the evaluation efficiency, reduce the evaluation cost and improve the evaluation effect at the same time.

[0006] In a first aspect, an embodiment of the present invention provides a method for state evaluation of a breather applied to a transformer, the method including:

[0007] Determine the to-be-used values corresponding to each breather in at least one evaluation dimension within the same time slice;

[0008] Based on the to-be-used values corresponding to each breather in at least one evaluation dimension, determine an index normalization matrix; wherein, the rows in the normalization index matrix represent breathers, the columns represent evaluation dimensions, and the element values represent the normalized values corresponding to the breathers in the corresponding evaluation dimensions;

[0009] According to the index normalization matrix, determine a correlation coefficient matrix and the index weight values corresponding to each evaluation dimension;

[0010] Based on the index weight values and the correlation coefficient matrix, determine a grey correlation degree matrix;

[0011] Based on the grey correlation degree matrix, determine the state information corresponding to each breather.

[0012] In a second aspect, an embodiment of the present invention further provides a device for state evaluation of a breather applied to a transformer, the device including:

[0013] A value determination module, configured to determine the to-be-used values corresponding to each breather in at least one evaluation dimension within the same time slice;

[0014] A matrix determination module, configured to determine an index normalization matrix based on the to-be-used values corresponding to each respirator under at least one evaluation dimension; wherein, the rows in the normalization index matrix represent respirators, the columns represent evaluation dimensions, and the element values represent the normalized values corresponding to the respirators under the corresponding evaluation dimensions;

[0015] A weight value determination module, configured to determine a correlation coefficient matrix and the index weight values corresponding to each evaluation dimension according to the index normalization matrix;

[0016] A correlation degree matrix determination module, configured to determine a grey correlation degree matrix based on the index weight values and the correlation coefficient matrix;

[0017] A status information determination module, configured to determine the status information corresponding to each respirator based on the grey correlation degree matrix.

[0018] In a third aspect, the present invention further provides an electronic device, which includes:

[0019] At least one processor; and

[0020] A memory communicatively connected to the at least one processor; wherein,

[0021] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for evaluating the status of a respirator applied to a transformer according to any embodiment of the present invention.

[0022] According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the method for evaluating the status of a respirator applied to a transformer according to any embodiment of the present invention when executed.

[0023] The technical solution of the embodiment of the present invention determines the to-be-used values corresponding to each respirator under at least one evaluation dimension within the same time slice; determines an index normalization matrix based on the to-be-used values corresponding to each respirator under at least one evaluation dimension; wherein, the rows in the normalization index matrix represent respirators, the columns represent evaluation dimensions, and the element values represent the normalized values corresponding to the respirators under the corresponding evaluation dimensions; determines a correlation coefficient matrix and the index weight values corresponding to each evaluation dimension according to the index normalization matrix; determines a grey correlation degree matrix based on the index weight values and the correlation coefficient matrix; determines the status information corresponding to each respirator based on the grey correlation degree matrix, divides multiple dimensions to comprehensively evaluate the respirator, solves the problems of strong subjectivity, low evaluation efficiency, poor effect, and high labor cost in the evaluation of the health status of the respirator, realizes the comprehensive evaluation of the status of the respirator, improves the evaluation efficiency of the respirator and reduces the evaluation cost at the same time.

[0024] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. Brief Description of the Drawings

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0026] Figure 1 is a flowchart of a method for evaluating the state of a breather applied to a transformer according to Embodiment 1 of the present invention;

[0027] Figure 2 is a flowchart of a method for evaluating the state of a breather applied to a transformer according to Embodiment 2 of the present invention;

[0028] Figure 3 is a flowchart of a method for evaluating the state of a breather applied to a transformer according to Embodiment 3 of the present invention;

[0029] Figure 4 is a schematic structural diagram of a device for evaluating the state of a breather applied to a transformer according to Embodiment 4 of the present invention;

[0030] Figure 5 is a schematic structural diagram of an electronic device for implementing the method for evaluating the state of a breather applied to a transformer according to the embodiments of the present invention. Detailed Description of the Embodiments

[0031] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] Before introducing the technical solution of the embodiment of the present invention, the use scenario of the respirator is first explained: in the substation, the safe and stable operation of the power transformer plays an extremely important role in the normal operation of the entire power system. The respirator, as one of the necessary devices in the transformer, plays the role of purifying the air inhaled by the transformer, and can effectively filter the moisture in the air to reduce the moisture and oxidation of the transformer oil, thereby maintaining the insulation strength of the transformer oil. If the discolored part of the respirator silicone exceeds 2 / 3 of the total amount, the air filtering function of the respirator will be greatly reduced, and there is a risk of causing internal faults in the transformer. Therefore, timely detection and replacement of failed respirator silicone can effectively ensure the normal operation of the transformer.

[0034] Embodiment 1

[0035] Figure 1 This is a flow chart of a method for evaluating the state of a respirator in a transformer provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation of evaluating the state of a respirator in a transformer. The method can be executed by a device for evaluating the state of a respirator in a transformer. The device for evaluating the state of a respirator in a transformer can be implemented in the form of hardware and / or software. The device for evaluating the state of a respirator in a transformer can be configured in a hardware device.

[0036] like Figure 1 As shown, the method includes:

[0037] S110, determining a value to be used corresponding to each ventilator in at least one evaluation dimension in the same time slice.

[0038] Among them, the time slice can be a time period or a time range. The evaluation dimension refers to the evaluation dimension used for evaluating the health status of the respirator, such as: transformer oil temperature dimension, transformer load dimension, ambient temperature dimension, and ambient humidity dimension. The values to be used are the values used to quantify each evaluation dimension, which can be the values to be used corresponding to each evaluation dimension within a preset time period every other time period, or the values to be used corresponding to each evaluation dimension within a time range.

[0039] Specifically, since the determination methods of the values to be used for each respirator are the same, the determination method of the values to be used for one of the respirators will be described below: If the time slice is 5 minutes, the data corresponding to each evaluation dimension of the current respirator within the current five minutes can be obtained, and the values to be used can be obtained after data processing; or, taking five minutes as a cycle, the data under each evaluation within a fixed time range before can be obtained and processed every five minutes, and then the values to be used can be obtained.

[0040] Exemplarily, the current time is 2:00, the time slice is 5 minutes, and the evaluation dimension is the transformer oil temperature dimension. Since the determination methods of the values to be used for each respirator are the same, the determination method of the values to be used for one of the respirators will be described below: The transformer oil temperature data collected by the oil temperature measuring instrument in the transformer within the time range of 1:55 - 2:00 can be obtained, and then the values to be used under the transformer oil temperature dimension can be determined; if the data under each evaluation within a fixed time range before are obtained and processed every five minutes, then the transformer oil temperature data collected by the oil temperature measuring instrument in the transformer within the time range of 0:00 - 2:00 will be obtained at 2:00, and the transformer oil temperature data collected by the oil temperature measuring instrument in the transformer within the time range of 0:00 - 2:05 will be obtained at 2:05.

[0041] It should be noted that a transformer may contain multiple respirators at the same time. Therefore, the number of respirators to be evaluated can be one and / or more.

[0042] S120. Determine an index normalization matrix based on the values to be used corresponding to each respirator under at least one evaluation dimension.

[0043] Among them, normalization means making the values to be used dimensionless and processing the values into decimals between 0 and 1 through appropriate calculations, so as to make the evaluation dimensions comparable. The normalized index matrix refers to the matrix composed of the values obtained after normalizing each value to be used.

[0044] Exemplarily, for respirator 1, respirator 2, respirator 3, and respirator 4, the values to be used under the dimensions of transformer oil temperature, transformer load, ambient temperature, and ambient humidity are respectively [b 11 b 12 b 13 b 14 , [b 21 b 22 b 23 b 24 , [b 31 b 32 b 33 b 34 , [b 41 b 42 b 43 b 44 , then the normalized index matrix is:

[0045] Furthermore, the rows in the normalized index matrix represent respirators, the columns represent evaluation dimensions, and the element values represent the normalized values corresponding to the respirators under the corresponding evaluation dimensions.

[0046] S130. According to the index normalization matrix, determine the correlation coefficient matrix and the index weight values corresponding to each evaluation dimension.

[0047] Among them, the correlation coefficient matrix is a matrix that can represent the degree of association between each respirator and the respirator in the most ideal healthy state. The index weight value is a quantitative value that represents the level of value, relative importance, and proportion of the evaluation indicators corresponding to each evaluation dimension of the respirator in the whole.

[0048] Optionally, "According to the index normalization matrix, determine the correlation coefficient matrix" may include: according to the index normalization matrix, determine the maximum value and the minimum value corresponding to each evaluation dimension; for each normalized value, according to the maximum value and the minimum value of the evaluation dimension to which the current normalized value belongs, determine the correlation coefficient value corresponding to the current normalized value; based on the correlation coefficient values corresponding to each normalized value, determine the correlation coefficient matrix.

[0049] Among them, the maximum value is the largest normalized value of each respirator under the current evaluation dimension; the minimum value is the smallest normalized value of each respirator under the current evaluation dimension. The correlation coefficient value is a value that represents the correlation relationship between the current dimension corresponding to the current respirator and the maximum value.

[0050] Exemplarily, if the normalized index matrix is:

[0051] n represents the nth respirator, and m represents the mth evaluation dimension. By comparing the normalized values in each column, the maximum value in each column is obtained, which is the maximum value corresponding to each evaluation dimension. Similarly, by comparing the normalized values in each column, the minimum value in each column is obtained, which is the minimum value corresponding to each evaluation dimension. Since the processing method for each normalized value is the same, the processing method for one of the normalized values is described below: The sequence composed of the maximum values of all evaluation dimensions is z m = max(b 1m , b 2m , …, b nm ), and the sequence difference Δnm = |z m - b nm | is calculated. Then, the two-level minimum difference Δmin and the two-level maximum difference Δmax are obtained. The element ξ nm in the correlation coefficient matrix ξ is calculated as follows:

[0052] Substitute into the formula: Among them, ρ is the resolution coefficient, and when ρ = 0.5, the correlation coefficient matrix is:

[0053] Optionally, "determining the index weight values corresponding to each evaluation dimension according to the index normalization matrix" may include: for each evaluation dimension, based on the normalized values corresponding to each evaluation dimension in the index normalization matrix, determining the standard deviation and average value corresponding to the corresponding evaluation dimension; based on the standard deviation and average value corresponding to each evaluation dimension, determining the coefficient of variation matrix; based on the coefficient of variation matrix and the coefficient values in the coefficient of variation matrix, determining the index weight values of each evaluation dimension.

[0054] Among them, the standard deviation refers to the value that can characterize the degree of dispersion between each normalized value and the overall mean under the corresponding evaluation dimension. The average value refers to the average value of all element values under each evaluation dimension. The coefficient of variation is a coefficient that can characterize the importance degree of each evaluation dimension in the comprehensive evaluation process of the respirator.

[0055] Exemplarily, if the normalized index matrix is: represents the nth respirator, and m represents the mth evaluation dimension. Since the processing method for the normalized values under each evaluation dimension is the same, the processing method for the normalized values under one of the evaluation dimensions is described below: For the normalized values under the first evaluation dimension Standard deviation (i is the variable i = 1, 2, 3…n), coefficient of variation Similarly, the coefficient of variation corresponding to each evaluation dimension can be obtained Among them, σ m is the standard deviation of the mth index; x mis the average value of the m-th index. Then the coefficient of variation matrix is [V1 V2 V3…V m .

[0056] Furthermore, the formula for determining the index weight values of each evaluation dimension according to the coefficients in the coefficient of variation matrix is as follows:

[0057]

[0058] where j is the variable j = 1, 2, 3…m. Since the calculation methods of the index weight values for each evaluation dimension are the same, the calculation method for the index weight value of the first evaluation dimension is now described: Substitute the coefficient of variation corresponding to the first evaluation dimension into the formula to obtain the index weight value W1 of the first evaluation dimension.

[0059] S140. Determine the grey relational grade matrix based on the index weight values and the correlation coefficient matrix.

[0060] where the grey relational grade matrix is a matrix that can evaluate the states of each respirator.

[0061] Optionally, multiply the index weight values by the correlation coefficient matrix to determine the grey relational grade matrix; the grey relational grade matrix is an n×1 order matrix, where the number of rows corresponds to the number of respirators, and each element value is used to represent the evaluation value corresponding to the corresponding respirator.

[0062] Based on the above example, according to the obtained index weight values of each evaluation dimension, the index weight value matrix of the evaluation dimension can be obtained: W = [W1 W2 W3…W m . If the correlation coefficient matrix is: n represents the n-th respirator, and m represents the m-th evaluation dimension. Then the grey relational grade matrix: where the grey relational grade matrix has n rows, and s n represents the evaluation value corresponding to the n-th respirator.

[0063] S150. Determine the state information corresponding to each respirator based on the grey relational grade matrix.

[0064] where the state information of the respirator is the grade information describing the health state of the respirator, such as: excellent, good, poor, etc. Different grades can be divided according to different score segments, and the specific division rules are not limited in this embodiment.

[0065] Exemplarily, if the evaluation value is between 0 - 0.6, then determine that the health state information of the corresponding respirator is sub-healthy; if the evaluation value is within the range of 0.6 - 0.1, it indicates that the health state information of the corresponding respirator is healthy.

[0066] Optionally, based on the evaluation values corresponding to the elements in each row of the gray-scale correlation matrix and the preset state threshold range, determine the state information corresponding to the corresponding respirator, so as to maintain the corresponding respirator based on each state information.

[0067] Among them, the preset state threshold range refers to several ranges preset in advance. If the evaluation value is within a certain state threshold range, it means that the health state information of the respirator corresponding to the evaluation value is the health state information corresponding to the state threshold range.

[0068] Exemplarily, the preset state threshold ranges are 0 - 0.5, 0.5 - 0.8, and 0.8 - 1 respectively, and the corresponding state information is: poor, good, excellent. When the evaluation value is between 0 - 0.5, it means that the health state information of the corresponding respirator is poor; when the evaluation value is between 0.5 - 0.8, it means that the health state information of the corresponding respirator is good; when the evaluation value is between 0.8 - 1, it means that the health state information of the corresponding respirator is excellent.

[0069] The technical solution of the embodiment of the present invention determines the to-be-used values corresponding to each respirator in at least one evaluation dimension within the same time slice; based on the to-be-used values corresponding to each respirator in at least one evaluation dimension, determine the index normalization matrix; among them, the rows in the normalization index matrix represent respirators, the columns represent evaluation dimensions, and the element values represent the normalized values corresponding to the respirators in the corresponding evaluation dimensions; according to the index normalization matrix, determine the correlation coefficient matrix and the index weight values corresponding to each evaluation dimension; based on the index weight values and the correlation coefficient matrix, determine the gray correlation degree matrix; based on the gray correlation degree matrix, determine the state information corresponding to each respirator, and comprehensively evaluate the respirator by dividing multiple dimensions, solving the problems of strong subjectivity, low evaluation efficiency, poor effect, and high labor cost in the evaluation of the health state of the respirator, realizing the comprehensive evaluation of the state of the respirator, improving the evaluation efficiency of the respirator and reducing the evaluation cost at the same time.

[0070] Embodiment Two

[0071] Figure 2 It is a flowchart of a method for evaluating the state of a respirator applied to a transformer provided by the second embodiment of the present invention. On the basis of the foregoing embodiment, the determination of the to-be-used values corresponding to each respirator in at least one evaluation dimension within the same time slice and the determination of the index normalization matrix can be further refined. The specific implementation manner can refer to the detailed description of the embodiment of the present invention. Among them, the same or corresponding technical terms as those in the above embodiment will not be repeated here.

[0072] As Figure 2 shown, the method includes:

[0073] S210. For each respirator, obtain at least one raw value corresponding to the current respirator in each evaluation dimension within the current time slice.

[0074] Wherein, the raw value refers to the raw data measured by measuring instruments such as an oil temperature gauge and a temperature sensor for physical parameters in each evaluation dimension within the current time slice.

[0075] Exemplarily, since the acquisition methods of the raw values in each evaluation dimension of each respirator are the same, the acquisition method of the raw value corresponding to one evaluation dimension of one of the respirators is now described: The current time slice is 5 minutes. For the current respirator, use an oil temperature measuring instrument to measure the oil temperature in the transformer within the current 5 minutes, and obtain the raw value corresponding to the current respirator in the transformer oil temperature dimension within the current time slice. Further, since the number of measurements can be one and / or multiple, the number of raw values corresponding to the current respirator in each evaluation dimension within the current time slice can be one and / or multiple.

[0076] S220. For each evaluation dimension, determine the value to be used corresponding to the current evaluation dimension according to at least one raw value corresponding to the current evaluation dimension and the objective function corresponding to the current evaluation dimension.

[0077] Wherein, the objective function refers to a function that is preset to calculate the value to be used through data calculation of the raw values.

[0078] Optionally, in this embodiment, the "evaluation dimension" may include: transformer oil temperature dimension, transformer load dimension, ambient temperature dimension, and ambient humidity dimension; correspondingly, the objective functions corresponding to each evaluation dimension include at least one of the following:

[0079] The objective function corresponding to the transformer oil temperature dimension is:

[0080]

[0081] Wherein, m b is the total mass of the transformer oil in the transformer, ρ max is the density value of the transformer oil at the highest temperature within one time slice, and ρ min is the density value of the transformer oil at the lowest temperature within one time slice.

[0082] Specifically, ΔG is the value to be used corresponding to the transformer oil temperature dimension. The total mass of the transformer oil in the transformer can be calculated through a transformer oil level gauge and the density of the transformer oil. ρ max is the density value of the transformer oil measured by a density measuring instrument at the moment of the highest temperature within one time slice. ρ minIt is the density value of the transformer oil measured by the density measuring instrument at the moment of the lowest temperature within a time slice.

[0083] Exemplarily, the volume of the transformer oil can be determined according to the oil level gauge in the transformer. According to the density of the transformer oil under normal conditions, the total mass m of the transformer oil can be calculated. b , the total mass of the transformer oil can be obtained; the internal temperature of the transformer and the density of the transformer oil are collected within a time slice, and the corresponding time during the collection is recorded to obtain the density value ρ of the transformer oil corresponding to the moment of the highest temperature in the transformer. max and the density value ρ of the transformer oil corresponding to the moment of the lowest temperature in the transformer. min , then m b , ρ max and ρ min are the original values in the dimension of transformer oil temperature. Substituting them into the above objective function, the value ΔG to be used corresponding to the dimension of transformer oil temperature is obtained.

[0084] It should be noted that due to thermal expansion and contraction, the change in the temperature of the transformer oil will cause a change in the volume of the oil, and the breather will be used to balance the stability of the oil tank and the outside. Therefore, the temperature of the oil is one of the important factors affecting the health status of the breather.

[0085] The objective function corresponding to the dimension of transformer load is:

[0086]

[0087] where, L max is the maximum value of the load during overload operation within a time slice; L min is the minimum value of the load during operation; L0 is the load value during the normal operation of the transformer.

[0088] Specifically, the maximum value of the load during overload operation and the minimum value of the load during operation can be measured by a transformer load tester. The load value L0 during the normal operation of the transformer is a fixed value. Substituting it into the objective function, the value ΔL to be used in the dimension of transformer load can be obtained.

[0089] Exemplarily, the rated capacity of the transformer can be determined according to the parameters of the transformer. When the load of the transformer exceeds the rated capacity, the transformer is in overload operation. The load values of the transformer within a time slice are collected by a transformer load tester. Among them, the data with a value greater than the rated capacity and the largest load value is L max , and the smallest value among all the load values is L min . Substituting the original values L max , L min , and L0 into the objective function, the value ΔL to be used in the dimension of transformer load is obtained.

[0090] It should be noted that when the transformer is operating under overload or low load, the temperature of the transformer oil will also change. Similarly, thermal expansion and contraction will occur, which will affect the service life of the breather.

[0091] The objective function corresponding to the environmental temperature dimension is:

[0092]

[0093] where, T max and T min are the maximum temperature and the minimum temperature within a time slice; T0 is the most suitable operating environmental temperature of the disconnector; ΔT1 is the value to be used under the environmental temperature dimension.

[0094] Specifically, the maximum and minimum temperatures of the transformer operating environment within a time slice can be measured by temperature sensors in the transformer. The most suitable operating environmental temperature T0 of the disconnector is determined according to the parameters of the disconnector and substituted into the objective function to obtain the value ΔT1 to be used under the environmental temperature dimension.

[0095] Exemplarily, the operating environmental temperature inside the transformer within a time slice is collected by temperature sensors in the transformer, and each temperature value is compared to obtain the maximum temperature T max and the minimum temperature T min , and T0 can be determined according to the parameters of the disconnector. Substitute the original values T max , T min , and T0 into the objective function to obtain the value to be used under the environmental temperature dimension.

[0096] It should be noted that a large temperature difference will have a certain impact on the service life of the disconnector of the breather, and thus affect the service life of the breather.

[0097] The objective function corresponding to the environmental humidity dimension is:

[0098]

[0099] where, η is the relative humidity of the air; H S is the saturated water vapor pressure; H is the atmospheric pressure of the air.

[0100] Specifically, the relative humidity of the air can be measured using a humidity measuring instrument, and the saturated water vapor pressure and the atmospheric pressure of the air can be measured using a gas pressure measuring sensor. Substitute the above parameters into the objective function to obtain the value S to be used under the environmental humidity dimension.

[0101] Exemplarily, a hygrometer and a gas pressure measurement sensor in the working environment of the transformer are used to measure the relative humidity, saturated water vapor pressure, and atmospheric pressure of the air within the current time slice, obtaining the original values η, H S and H, and substituting them into the objective function corresponding to the environmental humidity dimension to obtain the value S to be used under the environmental humidity dimension.

[0102] S230. Determine the evaluation index matrix according to the values to be used corresponding to each respirator under at least one evaluation dimension.

[0103] Among them, the evaluation index matrix is a matrix composed of the values to be used corresponding to each respirator under each evaluation dimension.

[0104] Exemplarily, the evaluation index matrix A is a matrix composed of the values to be used by respirator 1, respirator 2, respirator 3, and respirator 4 under the transformer oil temperature dimension, transformer load dimension, environmental temperature dimension, and environmental humidity dimension: where a 11 、a 12 、a 13 、a 14 are the values to be used corresponding to respirator 1 under the transformer oil temperature dimension, transformer load dimension, environmental temperature dimension, and environmental humidity dimension; a 21 、a 22 、a 23 、a 24 are the values to be used corresponding to respirator 2 under the transformer oil temperature dimension, transformer load dimension, environmental temperature dimension, and environmental humidity dimension.

[0105] S240. According to the evaluation index matrix and the index function, perform normalization processing on the evaluation index matrix to obtain the index normalization matrix.

[0106] Among them, the index function refers to a function for performing normalization processing on the values to be used under each evaluation dimension, which can be a positive index function or an inverse index function.

[0107] Specifically, perform normalization processing on the values to be used in the evaluation index matrix through the corresponding index function to obtain the index normalization matrix.

[0108] Optionally, determine the target index function corresponding to each evaluation dimension in the evaluation index matrix, substitute the values to be used in the same column into the target index function to obtain the normalized values corresponding to the values to be used; based on the normalized values corresponding to the values to be used, determine the index normalization matrix.

[0109] Among them, the target index function refers to the index function selected according to the characteristics of the evaluation dimension, which can be a positive index function or an inverse index function. Further, since some of the indicators corresponding to the evaluation dimensions are of the type where the larger the value, the better, that is, the larger the indicator value, the better the state of the respirator; some of the indicators corresponding to the evaluation dimensions are of the type where the smaller the value, the better, that is, the smaller the indicator value, the better the state of the respirator. Therefore, for the indicators of the type where the larger the value, the better, a positive index function is used for normalization processing, and for the indicators of the type where the smaller the value, the better, an inverse index function is used for normalization processing.

[0110] Further, the formulas for the positive index function and the inverse index function are as follows:

[0111]

[0112] Among them, i = 1, 2,..., n; j = 1, 2,..., m. a ij is the value to be used for the i-th respirator under the j-th evaluation dimension; b ij is the normalized value corresponding to the value to be used for the i-th respirator under the j-th evaluation dimension. min(a 1m , a 2m ,... a nm ) represents the minimum value of all the values to be used under the m-th evaluation dimension corresponding to n respirators, and max(a 1m , a 2m ,... a nm ) represents the maximum value of all the values to be used under the m-th evaluation dimension corresponding to n respirators.

[0113] Exemplarily, the evaluation index matrix A is a matrix composed of the values to be used for respirator 1, respirator 2, respirator 3, and respirator 4 under the dimensions of transformer oil temperature, transformer load, ambient temperature, and ambient humidity Among them, each row represents the values to be used for each respirator under the four dimensions, and each column represents the values to be used for the four respirators under each evaluation dimension. The indicators corresponding to the evaluation dimensions in this embodiment are all of the type where the smaller the value, the better. Therefore, the target index function is an inverse index function. Since the processing methods for each value to be used in each column of the evaluation index matrix A are the same, the processing of a 11 and a 21 in the first column is used as an example for explanation: By comparing the magnitudes of the 4 values to be used in the first column, the maximum value is a 21 , and the minimum value is a 31 , then Performing the same processing on each value to be used can obtain the normalized values of each value to be used, and the index normalization matrix is:

[0114]

[0115] S250. Determine the correlation coefficient matrix and the index weight values corresponding to each evaluation dimension according to the index normalization matrix.

[0116] S260. Determine the grey correlation degree matrix based on the index weight values and the correlation coefficient matrix.

[0117] S270. Determine the status information corresponding to each respirator based on the grey correlation degree matrix.

[0118] In the technical solution of the embodiment of the present invention, for each respirator, at least one original value corresponding to the current respirator in each evaluation dimension within the current time slice is obtained; for each evaluation dimension, according to at least one original value corresponding to the current evaluation dimension and the objective function corresponding to the current evaluation dimension, the value to be used corresponding to the current evaluation dimension is determined; according to the values to be used corresponding to each respirator in at least one evaluation dimension, an evaluation index matrix is determined; according to the evaluation index matrix and the index function, the evaluation index matrix is normalized to obtain an index normalization matrix, and each value to be used is made dimensionless and processed into a decimal between 0 and 1 through appropriate calculations, so as to make each evaluation dimension comparable, and further realize the comprehensive evaluation of the status of the respirator and obtain a more scientific evaluation result.

[0119] Embodiment III

[0120] Figure 3 FIG. is a flowchart of a method for evaluating the status of a respirator applied to a transformer provided in Embodiment III of the present invention. On the basis of the foregoing embodiments, the method for evaluating the status of a respirator applied to a transformer can be further optimized. The specific implementation manner can refer to the detailed description of the embodiments of the present invention. Among them, the same or corresponding technical terms as those in the above embodiments will not be described herein again.

[0121] As Figure 3 shown, the method includes:

[0122] S310. For each respirator, obtain at least one original value corresponding to the current respirator in each evaluation dimension within the current time slice.

[0123] Exemplarily, since the acquisition methods of the original values of each respirator in each evaluation dimension are the same, the acquisition method of the original value of one respirator corresponding to one evaluation dimension is described below: The current time slice is 5 minutes. For the current respirator, an oil temperature measuring instrument is used to measure the oil temperature in the transformer within the current 5 minutes, and the original value corresponding to the current respirator in the transformer oil temperature dimension within the current time slice is obtained.

[0124] S320. For each evaluation dimension, determine the value to be used corresponding to the current evaluation dimension according to at least one original value corresponding to the current evaluation dimension and the objective function corresponding to the current evaluation dimension.

[0125] Exemplarily, according to the objective functions corresponding to the transformer oil temperature dimension, the transformer load dimension, the ambient temperature dimension, and the ambient humidity dimension, and find the corresponding values from the obtained original values, and substitute them into the corresponding objective functions to obtain the values to be used corresponding to each evaluation dimension.

[0126] S330. Determine the evaluation index matrix according to the values to be used corresponding to each breather under at least one evaluation dimension.

[0127] Exemplarily, obtain the values to be used corresponding to breather 1, breather 2, breather 3, and breather 4 under the transformer oil temperature dimension, the transformer load dimension, the ambient temperature dimension, and the ambient humidity dimension to obtain the evaluation index matrix

[0128] S340. Perform normalization processing on the evaluation index matrix to obtain the index normalization matrix.

[0129] Based on the above example, perform normalization processing on the evaluation index matrix A. Since the processing methods for each value to be used in each column of the evaluation index matrix are the same, the processing of the first value to be used in the first column is now described: By comparing the magnitudes of the 4 values to be used in the first column, the maximum value is obtained as a 21 , and the minimum value is a 31 , then Thus, the normalized values of each value to be used can be obtained, and the index normalization matrix is:

[0130]

[0131] S350. Determine the correlation coefficient matrix according to the index normalization matrix.

[0132] Exemplarily, since the processing methods for each normalized value are the same, the processing method for one of the normalized values is now described: The sequence formed by the maximum values of the 4 evaluation dimensions is z = [z1 z2 z3 z4] = [b 11 b 22 b 22 b 43 . For the current normalized value b 11 , calculate |z1 – b 1m | and Δ 11 = |b 11 – b 11|, and then obtain the two - level minimum difference Δmin and the two - level maximum difference Δmax, and substitute them into the element ξ in the correlation coefficient matrix ξ nm Calculation formula ρ is the resolution coefficient, take ρ = 0.5, and get b 11 The corresponding correlation coefficient ξ 11 , and similarly calculate the correlation coefficients corresponding to each value to be processed. Then the correlation coefficient matrix is:

[0133] S360. According to the index normalization matrix, determine the coefficient of variation matrix of the indicators corresponding to each evaluation dimension.

[0134] Exemplarily, if the normalization index matrix is: Since the processing methods of the normalized values under each evaluation dimension are the same, now take the normalized values under one of the evaluation dimensions as an example: For the normalized values under the first evaluation dimension Standard deviation Coefficient of variation Similarly, the coefficients of variation corresponding to each evaluation dimension can be obtained Among them, σ j is the standard deviation of the j - th indicator; x j is the average value of the j - th indicator. Then the coefficients of variation of the four evaluation dimensions are: V1, V2, V3, V4.

[0135] S370. Based on the coefficients of variation of the indicators corresponding to each evaluation dimension, determine the weight matrix of each indicator. On the basis of the above example, calculate the weights of the indicators corresponding to the 4 evaluation dimensions, then

[0136] Then Then the weight matrix is W = [W1 W2 W3 W4].

[0137] S380. Based on the indicator weight matrix and the correlation coefficient matrix, determine the grey correlation degree matrix.

[0138] On the basis of the above example, the grey correlation degree matrix is obtained by multiplying the indicator weight matrix and the correlation coefficient matrix: where s1, s2, s3, s4 are the evaluation values corresponding to the respirator 1, respirator 2, respirator 3, and respirator 4 respectively.

[0139] S390. Based on the grey correlation degree matrix, determine the state information corresponding to each respirator.

[0140] On the basis of the above example, if the evaluation value is between 0 - 0.6, then determine that the health state information of the corresponding respirator is sub - healthy; if the evaluation value is in the range of 0.6 - 0.1, it means that the health state information of the corresponding respirator is healthy.

[0141] In the technical solution of the embodiment of the present invention, for each respirator, at least one original value corresponding to the current respirator in each evaluation dimension within the current time slice is obtained; for each evaluation dimension, according to at least one original value corresponding to the current evaluation dimension and the objective function corresponding to the current evaluation dimension, the value to be used corresponding to the current evaluation dimension is determined; according to the values to be used corresponding to each respirator in at least one evaluation dimension, an evaluation index matrix is determined; the evaluation index matrix is normalized to obtain an index normalization matrix; according to the index normalization matrix, a correlation coefficient matrix is determined; according to the index normalization matrix, a coefficient of variation matrix of the indexes corresponding to each evaluation dimension is determined; based on the coefficient of variation of the indexes corresponding to each evaluation dimension, a weight matrix of each index is determined; based on the index weight matrix and the correlation coefficient matrix, a grey correlation degree matrix is determined; based on the grey correlation degree matrix, the state information corresponding to each respirator is determined, and the respirators are comprehensively evaluated by dividing into multiple dimensions, which solves the problems of strong subjectivity, low evaluation efficiency, poor effect, and high labor cost in the evaluation of the health state of the respirator, realizes the comprehensive evaluation of the state of the respirator, improves the evaluation efficiency of the respirator and reduces the evaluation cost at the same time.

[0142] Embodiment 4

[0143] Figure 4 FIG. is a schematic structural diagram of a state evaluation device for a respirator applied in a transformer provided in Embodiment 4 of the present invention.

[0144] As Figure 4 shown, the device includes:

[0145] A numerical value determination module 410, configured to determine the values to be used corresponding to each respirator in at least one evaluation dimension within the same time slice; a matrix determination module 420, configured to determine an index normalization matrix based on the values to be used corresponding to each respirator in at least one evaluation dimension; wherein, the rows in the normalized index matrix represent respirators, the columns represent evaluation dimensions, and the element values represent the normalized values corresponding to the respirators in the corresponding evaluation dimensions; a weight value determination module 430, configured to determine a correlation coefficient matrix and the index weight values corresponding to each evaluation dimension according to the index normalization matrix. A correlation degree matrix determination module 440, configured to determine a grey correlation degree matrix based on the index weight values and the correlation coefficient matrix; a state information determination module 450, configured to determine the state information corresponding to each respirator based on the grey correlation degree matrix.

[0146] Based on the above technical solutions, the numerical value determination module specifically includes:

[0147] An original numerical value acquisition unit, configured to obtain, for each respirator, at least one original value corresponding to the current respirator in each evaluation dimension within the current time slice;

[0148] A value to be used determination unit is configured to determine, for each evaluation dimension, a value to be used corresponding to the current evaluation dimension according to at least one original value corresponding to the current evaluation dimension and an objective function corresponding to the current evaluation dimension.

[0149] Based on the above technical solutions, at least one evaluation dimension includes a transformer oil temperature dimension, a transformer load dimension, an ambient temperature dimension, and an ambient humidity dimension; correspondingly, the objective functions corresponding to the respective evaluation dimensions include at least one of the following:

[0150] The objective function corresponding to the transformer oil temperature dimension is:

[0151]

[0152] where m b is the total mass of the transformer oil in the transformer, ρ max is the density value of the transformer oil at the highest temperature within a time slice, and ρ min is the density value of the transformer oil at the lowest temperature within a time slice;

[0153] The objective function corresponding to the transformer load dimension is:

[0154]

[0155] where L max is the maximum value of the load during overload operation within a time slice; L min is the minimum value of the load during operation; L0 is the load value during normal operation of the transformer;

[0156] The objective function corresponding to the ambient temperature dimension is:

[0157]

[0158] where T max and T min are the maximum temperature and the minimum temperature within a time slice; T0 is the most suitable operating ambient temperature for the disconnecting switch;

[0159] The objective function corresponding to the ambient humidity dimension is:

[0160]

[0161] where η is the relative humidity of the air; H S is the saturated water vapor pressure; H is the atmospheric pressure of the air.

[0162] Based on the above technical solutions, the matrix determination module specifically includes:

[0163] An evaluation index matrix determination unit, configured to determine an evaluation index matrix according to the to-be-used values corresponding to each breathing apparatus under at least one evaluation dimension;

[0164] An index normalization matrix determination unit, configured to perform a normalization process on the evaluation index matrix according to the evaluation index matrix and an index function to obtain an index normalization matrix.

[0165] Based on the above technical solutions, the index normalization matrix determination unit is specifically configured to:

[0166] Determine the target index function corresponding to each evaluation dimension in the evaluation index matrix, substitute the to-be-used values in the same column into the target index function to obtain the normalized values corresponding to the to-be-used values; and determine the index normalization matrix based on the normalized values corresponding to the to-be-used values.

[0167] Based on the above technical solutions, the weight value determination module includes:

[0168] A maximum and minimum value determination unit, configured to determine the maximum value and the minimum value corresponding to each evaluation dimension according to the index normalization matrix;

[0169] A correlation value determination unit, configured to, for each normalized value, determine the correlation value corresponding to the current normalized value according to the maximum value and the minimum value of the evaluation dimension to which the current normalized value belongs;

[0170] A correlation coefficient matrix determination unit, configured to determine a correlation coefficient matrix based on the correlation values corresponding to the normalized values.

[0171] Based on the above technical solutions, the weight value determination module further includes:

[0172] A standard deviation and average value determination unit, configured to, for each evaluation dimension, determine the standard deviation and the average value corresponding to the corresponding evaluation dimension based on the normalized values under each evaluation dimension in the index normalization matrix;

[0173] A coefficient of variation matrix determination unit, configured to determine a coefficient of variation matrix based on the standard deviation and the average value corresponding to each evaluation dimension;

[0174] An index weight value determination unit, configured to determine the index weight values of each evaluation dimension based on the coefficient of variation matrix and the coefficient values under the coefficient of variation matrix.

[0175] Based on the above technical solutions, the correlation degree matrix determination module is specifically configured to:

[0176] By multiplying the index weight values and the correlation coefficient matrix, the grey correlation degree matrix is determined; the grey correlation degree matrix is an n×1 order matrix, the number of rows corresponding to the number of respirators, and each element value is used to represent the evaluation value corresponding to the corresponding respirator.

[0177] Based on the above technical solutions, the status information determination module is specifically configured to:

[0178] Based on the evaluation values corresponding to each row element in the grey degree correlation matrix and the preset status threshold range, determine the status information corresponding to the corresponding respirator, so as to maintain the corresponding respirator based on each status information.

[0179] In the technical solution of the embodiment of the present invention, by determining the to-be-used values corresponding to each respirator in at least one evaluation dimension within the same time slice; based on the to-be-used values corresponding to each respirator in at least one evaluation dimension, determining an index normalization matrix; wherein, the rows in the normalization index matrix represent respirators, the columns represent evaluation dimensions, and the element values represent the normalized values corresponding to the respirators in the corresponding evaluation dimensions; according to the index normalization matrix, determining a correlation coefficient matrix and the index weight values corresponding to each evaluation dimension; based on the index weight values and the correlation coefficient matrix, determining a grey correlation degree matrix; based on the grey correlation degree matrix, determining the status information corresponding to each respirator, and comprehensively evaluating the respirator by dividing into multiple dimensions, which solves the problems of strong subjectivity in the health status evaluation of respirators, low evaluation efficiency, poor effect, high labor cost, etc., realizes the comprehensive evaluation of the status of respirators, improves the evaluation efficiency of respirators and reduces the evaluation cost at the same time.

[0180] The status evaluation device for a respirator in a transformer provided by the embodiment of the present invention can execute the status evaluation method for a respirator in a transformer provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0181] Embodiment Five

[0182] Figure 5 FIG. shows a schematic structural diagram of an electronic device 10 that can be used to implement the embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0183] As Figure 5As shown, the electronic device 10 includes at least one processor 11 and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. Among them, the memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0184] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disc, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0185] The processor 11 can be various general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include but are not limited to a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the state evaluation method applied to the breather in the transformer.

[0186] In some embodiments, the state evaluation method applied to the breather in the transformer can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the state evaluation method applied to the breather in the transformer described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the state evaluation method applied to the breather in the transformer by any other appropriate means (for example, by means of firmware).

[0187] The various embodiments of the systems and techniques described above in this specification can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), systems on a chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.

[0188] The computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer programs can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0189] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0190] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0191] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0192] The computing system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0193] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and this is not limited herein.

[0194] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for evaluating the state of a breather applied to a transformer, characterized in that, At least one breather is applied to a transformer, and the method includes: Determine the to-be-used values corresponding to each breather in at least one evaluation dimension within the same time slice; Based on the to-be-used values corresponding to each breather in at least one evaluation dimension, determine an index normalization matrix; wherein, the rows in the index normalization matrix represent breathers, the columns represent evaluation dimensions, and the element values represent the normalized values corresponding to the breathers in the corresponding evaluation dimensions; According to the index normalization matrix, determine a correlation coefficient matrix and the index weight values corresponding to each evaluation dimension; Based on the index weight values and the correlation coefficient matrix, determine a grey correlation degree matrix; Based on the grey correlation degree matrix, determine the state information corresponding to each breather; The step of determining the correlation coefficient matrix according to the index normalization matrix includes: According to the index normalization matrix, determine the maximum value and the minimum value corresponding to each evaluation dimension; For each normalized value, determine the correlation value corresponding to the current normalized value according to the maximum value and the minimum value of the evaluation dimension to which the current normalized value belongs; Based on the correlation values corresponding to each normalized value, determine the correlation coefficient matrix.

2. The method according to claim 1, wherein The step of determining the to-be-used values corresponding to each breather in at least one evaluation dimension within the same time slice includes: For each breather, obtain at least one original value corresponding to the current breather in each evaluation dimension within the current time slice; For each evaluation dimension, determine the to-be-used value corresponding to the current evaluation dimension according to at least one original value corresponding to the current evaluation dimension and the objective function corresponding to the current evaluation dimension.

3. The method according to claim 2, wherein The at least one evaluation dimension includes a transformer oil temperature dimension, a transformer load dimension, an ambient temperature dimension, and an ambient humidity dimension; correspondingly, the objective functions corresponding to each evaluation dimension include at least one of the following: The objective function corresponding to the transformer oil temperature dimension is: where m b is the total mass of the transformer oil in the transformer, ρ max is the density value of the transformer oil at the highest temperature within a time slice, and ρ min is the density value of the transformer oil at the lowest temperature within a time slice; The objective function corresponding to the transformer load dimension is: Among them, L max is the maximum value of the load during overload operation within a time slice; L min is the minimum value of the load during operation; L0 is the load value during normal operation of the transformer; The objective function corresponding to the ambient temperature dimension is: Among them, T max and T min are the maximum temperature and the minimum temperature within a time slice; T0 is the most suitable operating environment temperature for the disconnector; The objective function corresponding to the ambient humidity dimension is: where η is the relative humidity of the air; H S is the saturated water vapor pressure; H is the atmospheric pressure of the air.

4. The method according to claim 1, wherein The step of determining the index normalization matrix based on the to-be-used values corresponding to each breather in at least one evaluation dimension includes: According to the to-be-used values corresponding to each breather in at least one evaluation dimension, determine an evaluation index matrix; According to the evaluation index matrix and the index function, perform normalization processing on the evaluation index matrix to obtain the index normalization matrix.

5. The method according to claim 4, characterized in that The step of performing normalization processing on the evaluation index matrix according to the evaluation index matrix and the index function to obtain the index normalization matrix includes: Determine the target index function corresponding to each evaluation dimension in the evaluation index matrix, and substitute the to-be-used values in the same column into the target index function to obtain the normalized values corresponding to each to-be-used value; Based on the normalized values corresponding to each to-be-used value, determine the index normalization matrix.

6. The method according to claim 1, wherein According to the index normalization matrix, determining the index weight values corresponding to each evaluation dimension includes: For each evaluation dimension, based on the normalized values corresponding to each evaluation dimension in the index normalization matrix, determine the standard deviation and mean value corresponding to the corresponding evaluation dimension; Based on the standard deviation and mean value corresponding to each evaluation dimension, determine the coefficient of variation matrix; Based on the coefficient of variation matrix and each coefficient value in the coefficient of variation matrix, determine the index weight values of each evaluation dimension.

7. The method according to claim 1, characterized in that The determining of the grey correlation degree matrix based on the index weight values and the correlation coefficient matrix includes: Determine the grey correlation degree matrix by multiplying the index weight values and the correlation coefficient matrix; The grey correlation degree matrix is an n×1 order matrix, the number of rows corresponding to the number of the respirators, and each element value is used to represent the evaluation value corresponding to the corresponding respirator.

8. The method according to claim 1, characterized in that The determining of the status information corresponding to each respirator based on the grey correlation degree matrix includes Based on the evaluation values corresponding to each row element in the grey correlation degree matrix and the preset status threshold range, determine the status information corresponding to the corresponding respirator, so as to maintain the corresponding respirator based on each status information.

9. A state evaluation device applied to a breather in a transformer, characterized in that, At least one respirator is applied to a transformer, and the device includes: A numerical value determination module, configured to determine the to-be-used numerical values corresponding to each respirator in at least one evaluation dimension within the same time slice; A matrix determination module, configured to determine an index normalization matrix based on the to-be-used numerical values corresponding to each respirator in at least one evaluation dimension; wherein, the rows in the index normalization matrix represent respirators, the columns represent evaluation dimensions, and the element values represent the normalized values corresponding to the respirators in the corresponding evaluation dimensions; A weight value determination module, configured to determine a correlation coefficient matrix and the index weight values corresponding to each evaluation dimension according to the index normalization matrix; A correlation degree matrix determination module, configured to determine a grey correlation degree matrix based on the index weight values and the correlation coefficient matrix; A status information determination module, configured to determine the status information corresponding to each respirator based on the grey correlation degree matrix; The determining of the correlation coefficient matrix according to the index normalization matrix includes: According to the index normalization matrix, determine the maximum value and the minimum value corresponding to each evaluation dimension; For each normalized value, determine the correlation coefficient value corresponding to the current normalized value according to the maximum value and the minimum value of the evaluation dimension to which the current normalized value belongs; Based on the correlation coefficient values corresponding to each normalized value, determine the correlation coefficient matrix.

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