Isolation switch state evaluation method based on grey theory and combination weighting

Abstract

The invention discloses an isolating switch state evaluation method based on a grey theory and combined weighting. The method comprises the following steps: step 1, setting a bull's-eye degree range in the grey theory for each health level of an isolating switch; step 2, taking each component of the disconnecting switch as a state index in a gray scale theory, taking the health score of each component as an index parameter in the gray scale theory, and calculating to obtain a gray correlation coefficient of each state index in the to-be-evaluated time, namely the jth time, in the gray scale theory; 3, calculating the combination weight of each state index; 4, based on the grey correlation coefficient and the combined weight of each state index in the jth time, calculating to obtain the target center degree of the whole disconnecting switch in the jth time; and step 5, comparing the bull's-eye degree of the whole disconnecting switch with the bull's-eye degree range of each health level, thereby determining the health level of the disconnecting switch. According to the invention, the health state of the disconnecting switch can be graded by using a more scientific method.

Description

Technical Field

[0001] The present invention relates to the field of disconnector state evaluation methods, and specifically to a disconnector state evaluation method based on grey theory and combined weighting. Background Art

[0002] In recent years, the deterioration of the contact state of disconnectors has led to multiple insulation failures. During long-term operation, reasons such as equipment aging, corrosion, or frequent operation may cause changes in the mechanical state of disconnectors, resulting in continuous deterioration of their contact state, and ultimately may lead to serious equipment failures. For a long time, the research on disconnectors has mainly focused on fault diagnosis, such as fault diagnosis and analysis of overheating of contacts, incomplete closing and opening, etc., while the research on the reliability and overall state evaluation of disconnectors is not deep enough. The reasons for this phenomenon are that the structure and working principle of disconnectors are simple, and research institutions, university personnel, and system personnel do not pay enough attention to disconnectors and the investment cost is insufficient, etc.

[0003] The traditional operation and maintenance strategies of regular inspection and regular maintenance are difficult to detect the operation defects of disconnectors in a timely manner, while adopting condition-based maintenance will make the maintenance timely and efficient, reducing the power outage times and economic losses caused by disconnector failures. However, the prerequisite for condition-based maintenance of disconnectors is to be able to accurately evaluate the health state of disconnectors. Currently, methods for evaluating the health state of power equipment include neural networks, fuzzy theory, matter-element theory, grey theory, etc. Among them, the neural network method has disadvantages such as being easily trapped in local optima, while the fuzzy theory has subjective human factors in determining fuzzy rules and membership functions and lacks convincing objective basis. The matter-element theory transforms the evaluation of multiple variables into a single-objective evaluation, and a standard pattern of the object to be evaluated needs to be given during the evaluation process, but it is very difficult to obtain a general object standard pattern under insufficient information. Grey theory has great advantages in the state evaluation of disconnectors because it can abstract and establish a model from a system with unclear, insufficient overall information, and without the need to give a standard pattern.

[0004] Grey system theory is a new method for studying problems of uncertainty with few data and poor information. The state data of disconnectors obtained under the regular inspection and operation and maintenance strategy are not many, which have the characteristics of few data and poor information; and when a failure occurs, it is impossible to clearly determine which of the many failure factors are responsible, and the interaction relationship between various failure factors cannot be determined, which has the characteristics of uncertainty. Therefore, the disconnector failure system can be regarded as a "grey system". Therefore, using grey theory to evaluate the operation state of disconnectors is of great significance for improving its operation reliability and ensuring the reliability and safety of the primary line operation and maintenance of substations. Summary of the Invention

[0005] The present invention provides a method for evaluating the state of a disconnector based on grey theory and combined weighting, so as to solve the problems of large subjective factors and difficulty in obtaining accurate results under limited information existing in the prior art method for evaluating the state of a disconnector.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] A method for evaluating the state of a disconnector based on grey theory and combined weighting, comprising the following steps:

[0008] Step 1: Divide the health levels of the disconnector into multiple levels, and respectively set the corresponding range of the bull's-eye degree in grey theory for each health level;

[0009] Step 2: Consider the disconnector as composed of n components, and obtain the health scores of each component in a total of m historical times, where n and m are natural numbers greater than or equal to 2;

[0010] Take each component as a state index in grey theory, and take the health score of each component as the index parameter in grey theory. Based on grey theory, according to the index parameters of each state index in a total of m historical times, obtain the standard evaluation mode of each state index;

[0011] Then, set the time to be evaluated of the disconnector as the j-th time, j = m + N, N ≥ 1, obtain the health score, i.e., the index parameter, of each component, i.e., the state index, at the j-th time. Based on grey theory, according to the standard evaluation mode of each state index and the index parameters of each state index at the j-th time, calculate the grey correlation coefficient of each state index in grey theory at the j-th time;

[0012] Step 3: Calculate the subjective weight of each state index, calculate the objective weight of each state index according to the index parameters of all state indexes in a total of m historical times, and then combine the subjective weight and the objective weight to obtain the combined weight of each state index;

[0013] Step 4: Based on the grey correlation coefficients of each state index at the time to be evaluated, i.e., the j-th time, calculated in Step 2 and the combined weights of each state index obtained in Step 3, calculate the bull's-eye degree of the entire disconnector at the j-th time;

[0014] Step 5: Compare the bull's-eye degree of the entire disconnector calculated in Step 4 with the range of the bull's-eye degree of each health level divided in Step 1, and thereby determine the health level of the disconnector at the j-th time.

[0015] Further, in step 2, based on the polarities of the index parameters in the total of m historical times of each state index, the standard evaluation mode of each state index is obtained as follows:

[0016] When the index parameters in the total of m historical times of each state index have a maximum polarity, the maximum value among the index parameters in the total of m historical times of each state index is taken as the standard evaluation mode of this state index;

[0017] When the index parameters in the total of m historical times of each state index have a neutral polarity, the average value among the index parameters in the total of m historical times of each state index is taken as the standard evaluation mode of this state index;

[0018] When the index parameters in the total of m historical times of each state index have a minimum polarity, the minimum value among the index parameters in the total of m historical times of each state index is taken as the standard evaluation mode of this state index.

[0019] Further, in step 2, first, based on the grey theory, grey target transformation is performed on the evaluation mode of each state index and the index parameters of each state index at the j-th time, and the grey target transformation value of the evaluation mode of each state index relative to the index parameters of each state index at the j-th time is obtained. For the evaluation mode ω0(k) of the k-th state index, the grey target transformation calculation formula is as shown in formula (6):

[0020]

[0021] In formula (6): N≥1; Tω′ j (k) represents grey target transformation; ω′ j (k) represents the index parameter of the k-th state index at the j-th time; ω0(k) represents the evaluation mode of the k-th state index; x j (k) represents the grey target transformation result of the evaluation mode of the k-th state index relative to the index parameter of the k-th state index at the j-th time.

[0022] Based on formula (6), the grey target transformation value of the standard evaluation mode, that is, the standard target center, can be obtained as Tω0 = x0 = {x0(1), x0(2), …, x0(k), …, x0(n)} = (1, 1, …, 1, …, 1); where, x0(n) represents the standard target center value, and Tω0 = x0 represents the set of standard target center values;

[0023] B) Then, based on x0(k), x j (k), the grey correlation coefficient of each state index at the j-th time is calculated. For the grey correlation coefficient γ{x0(k), x j(k)}, calculate Δk as shown in Formulas (7) and (8):

[0024] Δ 0j (k) = |x0(k) - x j (k)| = |1 - x j (k)| (7)

[0025]

[0026] In Formulas (7) and (8): Δ 0j (k) represents the value of the grey relational difference information space; ζ represents the importance coefficient, which takes values within (0, 1), and takes 0.5 here.

[0027] Further, in Step 3, the Delphi - Analytic Hierarchy Process is used to calculate the subjective weight of each state index.

[0028] Further, in Step 3, the coefficient of variation method is used to calculate the objective weight of each state index.

[0029] Further, in Step 3, the Lagrange multiplier method and based on the principle of minimum information entropy are used to combine the subjective weight and the objective weight to obtain the combined weight of each state index.

[0030] Further, in Step 4, the grey relational coefficient of each state index at the j - th time calculated in Step 2 is multiplied by the combined weight of each state index obtained in Step 3 to obtain the weighted bull's - eye degree of each state index. Then, after accumulating the weighted bull's - eye degrees of each state index, the bull's - eye degree of the entire disconnector at the j - th time is calculated therefrom.

[0031] Compared with the prior art, the advantages of the present invention are as follows:

[0032] The present invention proposes a method for evaluating the state of a disconnector based on grey theory and combined weighting. For the problem that in the past, when judging the health state of a component, the subjective factor was too large by simply relying on whether the single - item deduction value and the total deduction value of each part were greater than or less than a certain artificially set value, the combined weighting method is introduced to comprehensively consider the subjective and objective weight values of the indicators, with less subjective human factors, and a more scientific method is used to classify the health state of the disconnector, and an accurate evaluation result can be obtained under limited information. Brief Description of the Drawings

[0033] Figure 1 is the flowchart of the method in the embodiment of the present invention. Detailed Embodiment

[0034] The present invention will be further described below in conjunction with the drawings and embodiments.

[0035] As Figure 1As shown in the figure, this embodiment discloses a method for evaluating the state of a disconnector based on the grey theory and combined weighting, including the following steps:

[0036] Step 1: Divide the health level of the disconnector into multiple levels, and set the corresponding range of the bull's-eye degree in the grey theory for each health level.

[0037] In this embodiment, based on the balance principle of the grey system theory, each target can be assigned a level every 0.1 between 0 and 1, that is, [0.9, 1], [0.8, 0.9],..., [0.0, 0.1]. Also, according to the relevant theorem of grey correlation degree, under the condition of ζ = 0.5, there is That is, γ(ω0(k), ω j (k)) ≥ 0.33. Therefore, each level below [0.3, 0.4] is meaningless, and the level where the bull's-eye degree is located is the grey evaluation level. At the same time, after combining the health state grading rules of power transformers and the on-site experience of relevant experts from the power supply bureau, the health level of the disconnector is divided into five levels with reference:

[0038] The first level is the healthy state, and the corresponding range of the bull's-eye degree in the grey target theory is [0.9, 1]. The maintenance suggestion is to extend the normal cycle by one year.

[0039] The second level is the normal state, and the corresponding range of the bull's-eye degree in the grey target theory is [0.7, 0.9]. The maintenance suggestion is normal maintenance. Since most disconnectors are in the normal state, the normal state interval is larger than other state intervals.

[0040] The third level is a minor fault, and the corresponding range of the bull's-eye degree in the grey target theory is [0.5, 0.7]. The maintenance suggestion is not to exceed the normal cycle.

[0041] The fourth level is a moderate fault, and the corresponding range of the bull's-eye degree in the grey target theory is [0.4, 0.5]. The maintenance suggestion is to arrange it as soon as possible.

[0042] The fifth level is a serious fault, and the corresponding range of the bull's-eye degree in the grey target theory is [0.33, 0.4]. The maintenance suggestion is to arrange it immediately.

[0043] Step 2: In this embodiment, the disconnector is regarded as composed of n components, and the health scores of each component in a total of m historical times are obtained, where n and m are natural numbers greater than or equal to 2.

[0044] In this embodiment, each component is used as the state index in the grey scale theory, and the health score of each component is used as the index parameter in the grey scale theory. Let the index parameters of the n state indexes at the i-th time in a total of m historical times be ωi (1), ω i (2), … ω i (n), then the object state pattern ω at the i-th time in the gray theory i is as shown in the following formula (1):

[0045]

[0046] In formula (1): ω i (k) is the index parameter of the k-th state index at the i-th time.

[0047] In this embodiment, based on the gray theory, according to the index parameters of each state index in a total of m historical times, the standard evaluation pattern of each state index is obtained. Specifically, based on the polarity of the index parameters of each state index in a total of m historical times, the standard evaluation pattern of each state index is obtained, and the process is as follows:

[0048] Let POL(min), POL(mem), POL(max) be the minimum polarity, the neutral polarity, and the maximum polarity respectively, and let ω(k) be the index parameter of the k-th state index in a total of m historical times. ω(l) is as shown in formula (2):

[0049]

[0050] When the index parameter of the k-th state index in a total of m historical times has the maximum polarity, that is, POLω(k) = POL(max), then take the maximum value among the index parameters of the k-th state index in a total of m historical times as the standard evaluation pattern ω0(k) of the k-th state index, as shown in formula (3):

[0051]

[0052] When the index parameter of the k-th state index in a total of m historical times has the neutral polarity, that is, POLω(k) = POL(mem), then take the average value among the index parameters of the k-th state index in a total of m historical times as the standard evaluation pattern ω0(k) of the k-th state index, as shown in formula (4):

[0053]

[0054] When the index parameter of the k-th state index in a total of m historical times has the minimum polarity, that is, POLω(k) = POL(min), then take the minimum value among the index parameters of the k-th state index in a total of m historical times as the standard evaluation pattern ω0(l) of the k-th state index, as shown in formula (5):

[0055]

[0056] In this embodiment, let the time to be evaluated of the disconnector be the j-th time, where j = m + N and N ≥ 1, indicating that the time to be evaluated is the (m + N)-th time after m historical times. Obtain the health score, i.e., the index parameter, of each component, i.e., the status index, at the j-th time, and thus obtain the object status pattern ω of the j-th time. j ={ω j (1)′, ω j (2)′,... ω j (n)′}, where ω j (1)′, ω j (2)′,... ω j (n)′ are the index parameters of the 1st to the n-th status indices at the j-th time respectively.

[0057] Based on the grey theory, according to the standard evaluation pattern of each status index and the index parameters of each status index at the j-th time, calculate the grey correlation coefficient of each status index in the grey theory. The specific process is as follows:

[0058] A1) First, based on the grey theory, perform grey target transformation on the standard evaluation pattern of each status index and the index parameters of each status index at the j-th time to obtain the grey target transformation value of each status index evaluation pattern relative to the index parameters of each status index at the j-th time. For the evaluation pattern ω0(k) of the k-th status index, the grey target transformation calculation formula is as shown in formula (6):

[0059]

[0060] In formula (6): N ≥ 1; Tω′ j (k) represents grey target transformation; ω′ j (k) represents the index parameter of the k-th status index at the j-th time; ω0(k) represents the evaluation pattern of the k-th status index; x j (k) represents the grey target transformation result of the k-th status index evaluation pattern relative to the index parameter of the k-th status index at the j-th time.

[0061] Based on formula (6), the grey target transformation value of the standard evaluation pattern, i.e., the standard target center, can be obtained as Tω0 = x0 = {x0(1), x0(2),..., x0(k),..., x0(n)} = (1, 1,..., 1,..., 1). Among them, x0(n) represents the standard target center value, and Tω0 = x0 represents the set of standard target center values.

[0062] A2) Then, based on x0(k), x j(k), the grey correlation coefficient of each state index at the j-th time is calculated. For the grey correlation coefficient γ{x0(k), x j (k)} at the k-th state index in the j-th time, the calculation is as shown in formulas (7) and (8):

[0063] Δ 0j (k) = |x0(k) - x j (k)| = |1 - x j (k)| (7)

[0064]

[0065] In formulas (7) and (8): Δ 0j (k) represents the grey correlation difference information space value; ζ represents the importance coefficient, which takes values within (0, 1), and here it takes 0.5.

[0066] Step 3: Calculate the subjective weight of each state index. Based on the index parameters of all state indexes in a total of m historical times, calculate the objective weight of each state index, and then combine the subjective weight and the objective weight to obtain the combined weight of each state index.

[0067] The subjective weight is calculated using the Delphi - Analytic Hierarchy Process. The Delphi - Analytic Hierarchy Process decomposes the elements related to the decision into levels such as goals, criteria, and solutions. It is a multi - objective decision - making analysis method that combines qualitative and quantitative analysis methods. The process of calculating the subjective weight using the Delphi - Analytic Hierarchy Process in this embodiment is as follows:

[0068] (B1) Establishment of m comparison matrices

[0069] m experts subjectively score and assign values to the correlation degree of pairwise elements of the comparison matrix, and the comparison matrix obtained by the k-th expert is as shown in formula (9):

[0070]

[0071] In the formula: B k represents the comparison matrix given by the k-th expert; k represents the order of the comparison matrix; represents the element in the i-th row and j-th column of matrix B k (indicating the importance degree of the i-th state index relative to the j-th state index).

[0072] The pairwise comparison matrix is a comparison of the relative importance of the elements in one layer and a certain factor in the upper layer. The element b ij in this matrix represents the comparison result of the i-th factor relative to the j-th factor, and its value takes 1 - 9 as the scale. The specific meaning of the scale is shown in Table 1:

[0073] Table 1 Specific meanings of scales

[0074]

[0075] (B2) Perform averaging calculation on m comparison matrices

[0076] The element value in the average comparison matrix obtained after averaging calculation is As shown in formula (10):

[0077]

[0078] The elements on the main diagonal of all comparison matrices are all 1. Only the elements in the upper triangular part of the matrix are processed, and the other parts are filled in through the formula to obtain a complete matrix

[0079] (B3) Through calculate the maximum eigenvalue λ of the comparison matrix max , where E is the identity matrix; and through calculate λ max corresponding eigenvector w.

[0080] (B4) Perform consistency check. Substitute the obtained maximum eigenvalue λ of the comparison matrix max into the formula CI = (λ max -n) / (n - 1), and according to the order n of the comparison matrix, obtain the value of CI, then obtain the corresponding RI value from Table 2, and obtain the consistency ratio CR = CI / RI. Table 2 is as follows:

[0081] Table 2 Consistency index values

[0082]

[0083] Thus, the consistency ratio CR = CI / RI is obtained. When the consistency ratio value is less than 0.1, the matrix meets the consistency check.

[0084] (B5) Perform normalization processing. After meeting the consistency check, normalize the eigenvector w to obtain the vector w i , which is the finally obtained subjective weight value.

[0085] The objective weight is calculated using the coefficient of variation method. The coefficient of variation method effectively utilizes the data of each index and avoids the complex weight calculation process. The process of calculating the objective weight using the coefficient of variation method in this embodiment is as follows:

[0086] (C1) Define m evaluation indicators, n disconnector evaluation objects, X is the initial data matrix, X ijis the value of the j-th evaluation index for the i-th disconnector evaluation object, as shown in formula (11):

[0087]

[0088] (C2) Index preprocessing

[0089] The disconnector status evaluation indexes are divided into positive, negative, and interval indexes. For positive indexes, the larger the value, the better; for negative indexes, the smaller the value, the better; for interval indexes, the closer to the middle, the better. Given that the dimensions and types of each index are different, it is necessary to first normalize each index to the interval [0,1] and then proceed with the processing.

[0090] ① The formula for the unification and dimensionless processing of positive indexes is shown in formula (12):

[0091]

[0092] ② The formula for the unification and dimensionless processing of interval indexes is shown in formula (13):

[0093]

[0094] ③ The formula for the unification and dimensionless processing of negative indexes is shown in formula (14):

[0095]

[0096] In formulas (12), (13), and (14): X i represents the value of the evaluation index of the i-th disconnector evaluation object under a certain index, X imax represents the maximum value of the evaluation index of the disconnector evaluation object under a certain index, X imin represents the minimum value of the evaluation index of the disconnector evaluation object under a certain index, X imid represents the middle value of the evaluation index of the disconnector evaluation object under a certain index.

[0097] (C3) Calculate the standard deviation S of each index j , which reflects the absolute variation degree of each index, as shown in formula (15):

[0098]

[0099] In formula (15): is the mean value of the j-th item of m indexes.

[0100] (C4) Calculate the coefficient of variation V of each index j , which reflects the relative variation degree of each index, as shown in formula (16):

[0101]

[0102] (C5) Normalize the coefficient of variation of each index to obtain the objective weight w of each index j , as shown in formula (17):

[0103]

[0104] The combined weight is obtained by combining the subjective weight and the objective weight using the Lagrange multiplier method and based on the principle of minimum information entropy. The calculation process of the combined weight in this embodiment is as follows:

[0105] Use the combined weighting method to determine the objective weight of each performance index, and combine the subjective weight w i and the objective weight w j to determine the comprehensive weight W i = as shown in formula (18):

[0106]

[0107] In order to make the comprehensive weight W i as close as possible to the subjective and objective weights w i and w j , according to the principle of minimum information entropy, as shown in formula (19):

[0108]

[0109] In formula (19), minE is the minimum information entropy.

[0110] Use the Lagrange multiplier method to optimize and obtain the comprehensive weight W i as shown in formula (20):

[0111]

[0112] Among them,

[0113] Step 4: Based on the grey correlation coefficient of each state index at the j-th time calculated in step 2 and the combined weight of each state index obtained in step 3, calculate the bull's-eye degree of the entire disconnector at the j-th time.

[0114] Specifically, let the combined weight of the k-th state index be λ k , k ∈ (1, 2,..., n), and the grey correlation coefficient γ{x0(k), x j (k)} of the k-th state index at the j-th time calculated in step 2 and the combined weight λ kMultiply to obtain the weighted bull's-eye degree of the k-th state index, and then accumulate the weighted bull's-eye degrees of all a total of n state indexes, and thus calculate the bull's-eye degree γ{x0,x j}, and the calculation is as shown in formula (21):

[0115]

[0116] In formula (21): x0(k) represents the standard bull's-eye value; x j (k) represents the grey target transformation result of the k-th state index evaluation mode with respect to the k-th state index parameter in the j-th time; γ{x0(k),x j (k)} represents the grey correlation coefficient of the k-th state index in the j-th time.

[0117] Step 5. Compare the bull's-eye degree γ{x0,x j} of the entire disconnector calculated in step 4 with the bull's-eye degree ranges of each health level divided in step 1, and thus determine the health level of the disconnector at the j-th time.

[0118] The performance of the evaluation method in this embodiment is further described below with specific examples.

[0119] According to the disconnector status evaluation guide, the disconnector status evaluation items are divided into four parts: operating mechanism, earthing switch, conducting part, and transmission part. The health index (HI) value of each part is set to 100 points, and the overall health index value of the disconnector can be obtained by multiplying the final score of each part by the corresponding weight of each part and adding them up. Taking a GW4-126 type disconnector put into operation in a substation in a certain area as an example, the state scoring data obtained in the four years of 2019, 2020, 2021, and 2022 are used to evaluate the health status of the disconnector in 2023. The data is shown in Table 3:

[0120] Table 3 State scoring data of a GW4-126 type disconnector in a substation in a certain area from 2019 to 2023

[0121]

[0122] According to the method of the grey target theory described above, the data from 2019 to 2022 is used to establish a standard mode, and the data in 2023 is used as the mode to be evaluated. According to the first step of the grey target theory knowledge above, establish a standard state evaluation mode. The four index scores all have the maximum value polarity, that is, POLω i (1) = POL(max).

[0123] Take ω i(k) ∈ ω(k), the value selected as the standard mode for the operating mechanism evaluation item is: Similarly, for the grounding switch, the conductive part, and the transmission part, the values selected as the standard mode are: ω0(2) = 90, ω0(3) = 85, ω0(4) = 90. Then the standard mode can be obtained as: ω0 = (90, 90, 85, 90).

[0124] Taking the state of the disconnecting switch in 23 years as the state to be evaluated, then: {ω′1(1), ω′1(2), ω′1(3), ω′1(4)} = {80, 80, 80, 85}, and the second-step grey target transformation is carried out as follows: Tω0 = x0 = (1, 1, 1, 1), Similarly, the other transformation quantities are obtained as x2 = 0.8889, x3 = 0.9412, x4 = 0.9444. Calculate the grey correlation coefficient from equation (5): Δ 01 = {Δ 01 (1), Δ 01 (2), Δ 01 (3), Δ 01 (4)} = (0.1111, 0.1111, 0.0588, 0.0556), and when ζ = 0.5, from equation (4), we can get:

[0125]

[0126]

[0127]

[0128]

[0129] The target center coefficient matrix can be obtained as (0.7146, 0.7146, 0.9775, 1.000). If we want to obtain the final target center degree, we need to calculate the weights of each part. Take the importance degree of the operating mechanism to the grounding switch The importance degrees to the conductive part and the transmission part are 5 and 7 respectively. The other matrix values are similar. The averaged comparison matrix is constructed as follows:

[0130]

[0131] The maximum eigenvalue of D is obtained as: λ max = 4.1646, λ max The corresponding eigenvector is: W = (-0.4401 -0.8863 -0.1290 -0.0646). Substitute the maximum eigenvalue λ max of the obtained comparison matrix into to get CI = 0.0557; then obtain the corresponding RI value from Table 2 to get the consistency ratio Then the matrix satisfies the consistency check. After normalizing the eigenvector w, the subjective weight value w is obtained. i =(0.2895 0.5831 0.0849 0.0425).

[0132] According to the disconnector status scoring data from 2019 to 2022 for the four indicators of the operating mechanism, earthing switch, conductive part, and transmission part, the initial data matrix is constructed as follows:

[0133]

[0134] Calculate the disconnector status evaluation indicators according to the positive indicators and normalize them to the interval [0,1]. The normalized matrix is as follows:

[0135]

[0136] Substitute into the objective weight calculation formula to obtain the objective weight value as:

[0137] w j =(0.1130 0.2289 0.3021 0.3560)

[0138] Finally, the combined weight is:

[0139] W=(0.2181 0.4405 0.1931 0.1483)

[0140] Combining the target center coefficient obtained from the grey target theory with the combined weight, the final target center degree of the entire disconnector is:

[0141] γ{x0,x5}=ωγ{x0(k),x5(k)}

[0142] =0.7146×0.2181 + 0.7146×0.4405 + 0.9775×0.1931 + 1×0.1483 = 0.8077

[0143] Checking the disconnector health status classification in the previous text shows that the status to be evaluated belongs to the normal state. Combining with the health index value of the disconnector in 2023, the scores of the four parts are basically normal compared with previous years. Judging according to expert experience, it belongs to the normal state, and the two are consistent. The maintenance suggestion corresponding to the normal state is normal maintenance, which can provide certain help for formulating the maintenance plan of the disconnector. The overall results show that the results of the disconnector status evaluation method and grading strategy based on grey theory and combined weighting have a high consistency with the actual operating state of the disconnector, verifying the effectiveness and scientificity of grey theory and combined weighting in the application of disconnector health status evaluation.

[0144] The preferred embodiments of the present invention have been described in detail above in conjunction with the accompanying drawings. The embodiments described in the present invention are only descriptions of the preferred embodiments of the present invention, and do not limit the concept and scope of the present invention. Among the various specific technical features described in the above specific embodiments, they can be combined in any suitable manner without contradiction. As long as such a combination does not violate the idea of the present invention, it should also be regarded as the content disclosed in this disclosure. To avoid unnecessary repetition, the present invention will not separately describe various possible combination methods.

[0145] The present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention and without departing from the design idea of the present invention, various modifications and improvements made by those skilled in the art to the technical solution of the present invention should fall within the protection scope of the present invention. The technical content claimed by the present invention has been fully recorded in the claims.

Claims

1. A disconnector status assessment method based on grey theory and combined weighting, characterized in that: The following steps are involved: Step 1: Divide the health level of the disconnector into multiple levels, and set the corresponding bull's eye range in the grey theory for each health level; Step 2: Consider the disconnector to be composed of n components, and obtain the health score of each component in a total of m historical times, where n and m are natural numbers greater than or equal to 2; Each component is used as a state indicator in the grayscale theory, and the health score of each component is used as an indicator parameter in the grayscale theory. Based on the grayscale theory and the indicator parameters of each state indicator in a total of m historical times, a standard evaluation model for each state indicator is obtained; Then, assuming that the time to be evaluated of the disconnector is the jth time, j=m+N, N≥1, obtain the health score of each component, i.e., the state indicator, in the jth time, i.e., the indicator parameter, and calculate the gray correlation coefficient of each state indicator in the jth time in the gray theory based on the standard evaluation mode of each state indicator and the indicator parameter of each state indicator in the jth time based on the gray theory; Step 3: Calculate the subjective weight of each state indicator, calculate the objective weight of each state indicator according to the indicator parameters of all state indicators in a total of m historical times, and then combine the subjective weight and the objective weight to obtain the combined weight of each state indicator; Step 4: Based on the grey correlation coefficient of each state indicator in the jth time to be evaluated calculated in step 2 and the combined weight of each state indicator obtained in step 3, the bull's eye degree of the entire disconnector in the jth time is calculated; Step 5: Compare the bull's-eye degree of the entire isolating switch calculated in step 4 with the bull's-eye degree range of each health level divided in step 1, thereby determining the health level of the isolating switch at the jth time.

2. The isolating switch status assessment method based on grey theory and combined weighting according to claim 1 is characterized in that: In step 2, based on the polarity of the indicator parameters of each status indicator in a total of m historical times, the standard evaluation mode of each status indicator is obtained, and the process is as follows: When the indicator parameters of each state indicator in a total of m historical times have a maximum polarity, the maximum value of the indicator parameters of each state indicator in a total of m historical times is taken as the standard evaluation mode of the state indicator; When the indicator parameters of each state indicator in a total of m historical times have moderate polarity, the average value of the indicator parameters of each state indicator in a total of m historical times is taken as the standard evaluation mode of the state indicator; When the indicator parameters of each state indicator in a total of m historical times have a minimum polarity, the minimum value of the indicator parameters of each state indicator in a total of m historical times is taken as the standard evaluation mode of the state indicator.

3. The isolating switch status assessment method based on grey theory and combined weighting according to claim 1 is characterized in that: In step 2, first, based on the grayscale theory, the evaluation mode of each state indicator and the indicator parameters of each state indicator at the jth time are gray-target transformed to obtain the gray-target transformation value of each state indicator evaluation mode relative to the indicator parameters of each state indicator at the jth time. For the evaluation mode ω0(k) of the kth state indicator, the gray-target transformation calculation formula is shown in formula (6): In formula (6): N≥1; Tω′ j (k) represents gray target transformation; ω′ j (k) represents the index parameter of the kth state index in the jth time; ω0(k) represents the evaluation mode of the kth state index; x j (k) represents the grey target transformation result of the k-th state index evaluation mode relative to the k-th state index parameter in the j-th time. Based on formula (6), the gray target transformation value of the standard evaluation mode, i.e., the standard bull's eye value, can be obtained as Tω0=x0={x0(1),x0(2),…,x0(k),…,x0(n)}=(1,1,…,1,…,1); wherein x0(n) represents the standard bull's eye value, and Tω0=x0 represents the set of standard bull's eye values; B) Then, based on x0(k), x j (k), calculate the grey correlation coefficient of each state indicator in the jth time. For the grey correlation coefficient γ{x0(k),x j (k)}, calculated as shown in formula (7) and (8): Δ 0j (k)=|x0(k)-x j (k)|=|1-x j (k)| (7) In formula (7) and (8): Δ 0j (k) represents the value of the grey relational difference information space; ζ represents the importance coefficient, which takes values ​​within (0,1) and is 0.5 here.

4. The isolating switch status assessment method based on grey theory and combined weighting according to claim 1 is characterized in that: In step 3, the Delphi-AHP method is used to calculate the subjective weight of each status indicator.

5. The isolating switch status assessment method based on grey theory and combined weighting according to claim 1 is characterized in that: In step 3, the coefficient of variation method is used to calculate the objective weight of each status indicator.

6. The isolating switch status assessment method based on grey theory and combined weighting according to claim 1 is characterized in that: In step 3, the Lagrange multiplier method is used and based on the principle of minimum information entropy, the subjective weight and the objective weight are combined to obtain the combined weight of each state indicator.

7. The isolating switch status assessment method based on grey theory and combined weighting according to claim 1 is characterized in that: In step 4, the grey correlation coefficient of each state indicator at the jth time calculated in step 2 and the combined weight of each state indicator obtained in step 3 are multiplied to obtain the weighted bull's-eye degree of each state indicator, and then the weighted bull's-eye degrees of each state indicator are accumulated to calculate the bull's-eye degree of the entire disconnector at the jth time.

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