Method and system for evaluating performance of synthetic ester insulating oil-compound additive system

Abstract

The invention discloses a synthetic ester insulating oil-compound additive system performance evaluation method and system.The method comprises the steps that a hierarchical structure model comprising an index layer and a scheme layer is constructed, the scheme layer comprises a plurality of synthetic ester insulating oil-compound additive systems, and the index layer comprises secondary indexes; obtaining secondary index original data corresponding to each system, and performing standardization processing on the original data to obtain a standardized evaluation index data matrix; obtaining a comprehensive weight based on the standardized evaluation index data matrix; and on the basis of the standardized evaluation index data matrix and the comprehensive weight, constructing a weighted decision matrix, and obtaining the relative proximity of each system, thereby determining the optimal synthetic ester insulating oil-compound additive system formula. The method not only can clearly distinguish the advantages and disadvantages of different formulas, but also can intuitively reflect the difference between the formula and the ideal performance level.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power systems, and relates to a synthetic ester insulating oil-compound additive system performance evaluation method and system. BACKGROUND

[0002] As the core equipment in the power system, the operation reliability of the transformer directly affects the safety and stability of the power grid. The insulating oil in the transformer bears multiple functions such as insulation, cooling and arc extinguishing, and is the key medium to ensure the long-term safe operation of the transformer. With the increasingly stringent environmental protection requirements and the continuous improvement of the performance requirements of the equipment, the limitations of the traditional mineral insulating oil in biodegradability, fire safety and the inhibition ability of the aging of insulating materials gradually appear.

[0003] As a new type of environmentally friendly insulating liquid, the synthetic ester insulating oil has been widely used and promoted in the power transformer in recent years due to its excellent environmental friendliness, high flash point, strong water absorption capacity and potential to inhibit the aging of cellulose insulating paper.

[0004] To further improve the comprehensive performance of the synthetic ester insulating oil, multiple functional additives are usually added to form a compound additive system. Common additives include phenolic or amine antioxidants to enhance oxidation stability, metal passivators to inhibit metal-catalyzed oxidation reactions, impact performance enhancers to improve dielectric strength, and anti-gas precipitation agents to control gas generation during discharge. By reasonable compounding, the physicochemical, dielectric and thermal stability and other multidimensional properties of the synthetic ester insulating oil can be significantly optimized without changing the molecular structure of the base oil.

[0005] However, different compounding schemes have different effects on the performance of the synthetic ester insulating oil, and even some performance may be improved while others are deteriorated. Therefore, how to scientifically and systematically evaluate the multidimensional comprehensive performance of different compound additive systems has become a key technical problem in selecting the optimal formula and realizing product standardization.

[0006] At present, the performance evaluation of the insulating oil-additive system mainly adopts single index testing or subjective weighting scoring method based on expert experience, such as analytic hierarchy process (AHP). This kind of method relies on human judgment to determine the weight of each performance index, and has strong subjectivity and one-sidedness, which is difficult to objectively reflect the inherent law of actual sample data, and is easy to lead to evaluation result deviation, and cannot meet the needs of precise and data-driven evaluation for the research and development of high-performance synthetic ester insulating oil.

[0007] Therefore, it is urgent to establish an objective, systematic and quantifiable multidimensional performance comprehensive evaluation method, which can automatically determine the reasonable weight of each evaluation index combined with the measured data, comprehensively measure the overall performance of different compound additive systems, and provide reliable technical support for the functional design and industrial application of synthetic ester insulating oil. SUMMARY

[0008] To overcome the above problems, the present application provides a synthetic ester insulating oil-compound additive system performance evaluation method and system. The method comprises: constructing a hierarchical structure model comprising an index layer and a scheme layer, wherein the scheme layer comprises a plurality of synthetic ester insulating oil-compound additive systems, and the index layer comprises secondary indicators for evaluating the performance of the systems; obtaining the original data of the secondary indicators corresponding to each system, and performing standardization processing on the original data to obtain a standardized evaluation index data matrix; based on the standardized evaluation index data matrix, obtaining a comprehensive weight; based on the standardized evaluation index data matrix and the comprehensive weight, constructing a weighted decision matrix, and obtaining the relative closeness of each system, thereby determining the optimal synthetic ester insulating oil-compound additive system formula. The method not only can clearly distinguish the advantages and disadvantages of different formulas, but also can intuitively reflect the gap with the ideal performance level, and the evaluation result has good comparability and interpretability, which is convenient for guiding actual formula optimization and selection decision.

[0009] Specifically, the present application aims to provide the following aspects:

[0010] In a first aspect, a synthetic ester insulating oil-compound additive system performance evaluation method is provided, comprising:

[0011] Step 1, constructing a hierarchical structure model comprising an index layer and a scheme layer, wherein the scheme layer comprises a plurality of synthetic ester insulating oil-compound additive systems, and the index layer comprises secondary indicators for evaluating the performance of the systems;

[0012] Step 2, obtaining the original data of the secondary indicators corresponding to each system, and performing standardization processing on the original data to obtain a standardized evaluation index data matrix;

[0013] Step 3, based on the standardized evaluation index data matrix, obtaining a comprehensive weight;

[0014] Step 4, based on the standardized evaluation index data matrix and the comprehensive weight, constructing a weighted decision matrix, and obtaining the relative closeness of each system, thereby determining the optimal synthetic ester insulating oil-compound additive system formula.

[0015] In step 1, the hierarchical structure model further comprises a target layer, which is used to clearly define the final evaluation target.

[0016] In step 1, the hierarchical structure model further comprises a criterion layer, which comprises a first-level indicator conforming to the evaluation criteria, and the first-level indicator comprises: physicochemical properties, dielectric properties, oxidation stability, and gas production characteristics.

[0017] In step 1, the secondary indicators include acid value, kinematic viscosity, and dielectric loss tangent.

[0018] In step 1, the compound additives include any one or more of phenolic antioxidants, amine antioxidants, metal passivators, impact performance enhancers, and anti-gas release agents.

[0019] In step 2, the raw data of the secondary indicators corresponding to each system are obtained through standard measurements. In step 2, the raw data are standardized using a forward normalization method and a centering method.

[0020] Step 3 includes:

[0021] Step 3-1: Extract feature values ​​from the standardized evaluation indicator data matrix to obtain the objective weights of the primary indicators;

[0022] Step 3-2: Objectively assign weights to the secondary indicators to obtain their objective weights;

[0023] Step 3-3: Obtain the comprehensive weight of the secondary indicators based on the objective weights of the primary indicators and the objective weights of the secondary indicators.

[0024] Secondly, a performance evaluation system for a synthetic ester insulating oil-compound additive system is provided, the system comprising:

[0025] The model module is used to construct a hierarchical model including an index layer and a scheme layer, wherein the scheme layer includes multiple synthetic ester insulating oil-compound additive systems, and the index layer includes secondary indices for evaluating the performance of the system.

[0026] The matrix analysis module is used to obtain the original data of the secondary indicators corresponding to each system, and to standardize the original data to obtain a standardized evaluation indicator data matrix.

[0027] The weighting module is used to obtain the comprehensive weight based on the standardized evaluation index data matrix.

[0028] The evaluation module is used to construct a weighted decision matrix based on the standardized evaluation index data matrix and the comprehensive weight, and to obtain the relative similarity of each system, thereby determining the optimal formulation of the synthetic ester insulating oil-compound additive system.

[0029] Thirdly, a computer-readable storage medium is provided, including a stored complete computer program that, when the computer program is run, implements the performance evaluation method for the synthetic ester insulating oil-compound additive system as described in the first aspect.

[0030] The beneficial effects of this invention include:

[0031] (1) The method of the present application constructs a hierarchical model including an index layer and a scheme layer, which comprehensively covers the key performance dimensions involved in the operation of synthetic ester insulating oil in power equipment such as transformers. Through a multi-dimensional and multi-level evaluation framework, the comprehensive performance of the compounded additive system is systematically evaluated, avoiding the one-sidedness caused by single index evaluation, and improving the scientificity and engineering applicability of the evaluation results; not only can the advantages and disadvantages of different formulations be clearly distinguished, but also the gap between the actual performance and the ideal performance level can be intuitively reflected, and the evaluation results have good comparability and interpretability, which is convenient for guiding the actual formulation optimization and selection decision.

[0032] (2) The method of the present application adopts a comprehensive evaluation system combining principal component analysis and entropy weight method, objectively weights based on system performance data to determine the comprehensive weight of each evaluation index, and objectively evaluates the performance of the synthetic ester insulating oil-compounded additive system according to the relative closeness determined by the comprehensive weight, without relying on expert judgment, avoiding subjective weighting, effectively improving the accuracy and scientificity of the evaluation system, and providing a theoretical basis for the standardization of the compounded additive formulation for synthetic ester insulating oil.

[0033] (3) The method of the present application can realize automatic processing through computer program at each link, supports batch data import, automatic weighting, rapid sorting and result output. This feature makes it easy to integrate into an insulating oil performance evaluation system or a material research and development platform, significantly improves the evaluation efficiency, reduces the cost of manual intervention, is suitable for rapid screening and formulation optimization of new environmentally friendly insulating oil, and has good industrial application prospect. BRIEF DESCRIPTION OF DRAWINGS

[0034] Various other advantages and benefits of the present application will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiment. The accompanying drawings are included to provide a better understanding of the preferred embodiment and are not to be considered as limitations of the present application. Obviously, the drawings described below are only some embodiments of the present application, and other drawings can be obtained from these drawings without creative labor for those of ordinary skill in the art.

[0035] In the drawings:

[0036] Figure 1 A flowchart showing the synthetic ester insulating oil-compounded additive system performance evaluation method is shown.

[0037] Figure 2 A structural diagram showing the hierarchical model is shown.

[0038] Figure 3 A comparison chart showing the Euclidean distance and relative closeness of each candidate system in Example 1 to the optimal solution and the worst solution is shown. DETAILED DESCRIPTION

[0039] The application will be described below with reference to the accompanying drawings. Figures 1 to 3 The specific embodiments of the present application will be described in more detail with reference to the accompanying drawings. Although specific embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present application can be more thoroughly understood and so that the scope of the present application can be conveyed to those skilled in the art.

[0040] It should be noted that certain terms are used throughout the present specification and claims which have particular meanings as set forth below. Those skilled in the art will understand that not all terms have the same meaning as they are used in different aspects of the present application. Unless otherwise defined, all terms used herein have the same meaning as they are commonly understood by one skilled in the art to which the present application pertains. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted in an overly literal or overly formal sense unless expressly so defined herein.

[0041] In the description of the present application, it should be noted that the terms "upper", "lower", "inner", "outer", "front", "back", etc. indicate the orientation or positional relationship based on the working state of the present application, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second", "third", "fourth" are only for the purpose of description and cannot be understood as indicating or implying relative importance.

[0042] In order to facilitate the understanding of the embodiments of the present application, the following will be further explained and described with specific examples in conjunction with the accompanying drawings, and each drawing does not constitute a limitation on the embodiments of the present application.

[0043] In one aspect, a method for evaluating the performance of a synthetic ester insulating oil-compound additive system is provided according to the present application, as shown in Figure 1 The method comprises the following steps:

[0044] Step 1: Construct a hierarchical structure model comprising an index layer and a scheme layer, wherein the scheme layer comprises a plurality of synthetic ester insulating oil-compound additive systems, and the index layer comprises secondary indexes for evaluating the performance of the systems;

[0045] Step 2: Obtain the corresponding secondary index original data of each system, and perform standardization processing on the original data to obtain a standardized evaluation index data matrix;

[0046] Step 3, obtaining a comprehensive weight based on the standardized evaluation index data matrix;

[0047] Step 4, constructing a weighted decision matrix based on the standardized evaluation index data matrix and the comprehensive weight, obtaining a relative closeness of each system, and determining an optimal synthetic ester insulating oil-compound additive system formula.

[0048] The above method is described in detail as follows.

[0049] Step 1, constructing a hierarchical structure model including an index layer and a scheme layer, wherein the scheme layer includes a plurality of synthetic ester insulating oil-compound additive systems, and the index layer includes secondary indexes for evaluating performances of the systems.

[0050] In step 1, the construction process of the hierarchical structure model is based on a systematic analysis of application requirements of synthetic ester insulating oil, and is determined comprehensively in combination with operating conditions of power equipment, industry standards, and laboratory performance test capabilities. Through the model, a complex multi-dimensional performance evaluation problem is decomposed into structured sub-problems, realizing a layer-by-layer refinement from “overall goal” to “specific index”, and laying a methodological foundation for subsequent objective weighting and comprehensive evaluation.

[0051] In step 1, the purpose is to establish an evaluation framework with clear structure and distinct levels, providing an organizational basis for subsequent data processing, weight calculation, and comprehensive evaluation.

[0052] In one embodiment, as shown in FIG. 1, the hierarchical structure model further includes a target layer and a criterion layer. Figure 2

[0053] The target layer is used for comprehensive evaluation of the multi-dimensional performance of the system, i.e., the final evaluation target of the method is determined. The criterion layer includes first-level indexes conforming to the evaluation criteria, including: physicochemical performance, dielectric performance, oxidation stability, and gas production characteristics; the four first-level indexes comprehensively cover the key performance dimensions involved in the actual operation of the system, ensuring the systematicness and integrity of the evaluation. The index layer is used to specifically represent the performance level of each first-level index. The scheme layer includes m synthetic ester insulating oil-compound additive systems, and each candidate system in the scheme layer corresponds to a complete set of secondary indexes as an input sample for subsequent evaluation.

[0054] Further, the secondary indexes corresponding to each first-level index are as follows:

[0055] ​Physical and chemical properties: including acid value (mgKOH / g) and kinematic viscosity (mm² / s). Acid value reflects the content of acidic substances in oil, which is an important indicator to measure the aging degree and corrosivity of oil; viscosity affects the flowability and heat dissipation performance of oil, which is directly related to the cooling efficiency of the transformer.

[0056] Dielectric properties: including dielectric loss tangent (tanδ, %), relative dielectric constant (ε r ), lightning impulse breakdown voltage (kV) and AC breakdown voltage (kV). The above indicators collectively reflect the energy loss, polarization response and voltage withstand capability of insulating oil under the action of electric field, which are the core parameters to ensure the safety of transformer insulation.

[0057] Oxidation stability: including oxidation induction time (OIT, min) and onset oxidation temperature (OOT, °C). Both are determined by differential scanning calorimetry (DSC) and used to evaluate the antioxidant capacity of oil under high temperature and oxygen environment, which is a key indicator to determine the service life of oil.

[0058] Gas production characteristics: including gas production (μL). This indicator reflects the tendency of oil to produce gas under partial discharge or overheating conditions, which is of great significance to transformer fault diagnosis and operational safety.

[0059] In the present application, each system is composed of a base synthetic ester insulating oil and different types and proportions of functional additives, not limited to any one on the market. The synthetic ester insulating oil is selected from any one or several of neopentyl glycol ester, trimethylolpropane ester and pentaerythritol ester; the functional additives are selected from any one or several of phenolic antioxidants (such as BHT, TBHQ, 1010), amine antioxidants (such as Irganox L06, Irganox L57, RHY536), metal deactivators (such as BTA, Irgamet39), impact performance enhancers (such as UV1, UVA PLUS) and anti-gas separation agents (such as alkylbenzene).

[0060] Step 2, obtain the secondary indicator raw data corresponding to each system, and standardize the raw data to obtain a standardized evaluation index data matrix.

[0061] In step 2, the purpose is to eliminate the incommensurability caused by the differences in dimension, order of magnitude and type of index between different performance indicators, to ensure the scientificity and fairness of subsequent weight calculation and comprehensive evaluation.

[0062] In step 2, the raw data of the secondary indicators corresponding to each system are obtained through standard measurements. Specifically: the acid value is determined by potentiometric titration; the kinematic viscosity is determined using a capillary viscometer; the dielectric loss tangent and relative permittivity are determined using a Schering bridge; the lightning impulse breakdown voltage and AC breakdown voltage are determined using a ball-to-ball electrode; the oxidation induction time and initial oxidation temperature are determined by differential scanning calorimetry; and the gas production is determined by gas chromatography.

[0063] In step 2, the standardized evaluation index data matrix is ​​obtained through the following steps:

[0064] Step 2-1: Based on the original data of the secondary indicators, construct the original data matrix;

[0065] Step 2-2: The original data matrix is ​​standardized by sequentially applying a forward processing method and a centering method to obtain a standardized evaluation index data matrix.

[0066] In step 2-1, there are m systems (i.e., m evaluation schemes / candidate schemes) and n secondary indicators. An original data matrix X, x, and n are constructed based on this matrix. ij It is the element in the i-th row and j-th column of the original data matrix, representing the j-th secondary indicator data of the i-th candidate scheme, where i=1,…,m; j=1,…,n; that is:

[0067]

[0068] In step 2-2, the forward processing method includes: converting extremely small indicators into extremely large indicators, wherein the extremely small indicators include acid value, kinematic viscosity, dielectric loss tangent, and gas production. (Extremely small indicator x) ij The expression for the forward processing is: , max x j For the first The largest element of each secondary indicator, y ij Let be the element in the i-th row and j-th column of the normalized matrix.

[0069] Furthermore, after normalization, the normalized matrix Y is obtained.

[0070] In step 2-2, the influence of different dimensions is eliminated through a centralization process, including:

[0071]

[0072] Among them, z ij The element in the i-th row and j-th column of the standardized evaluation index data matrix; y ij is the element in the i-th row and j-th column of the positiveized matrix Y; m is the total number of evaluation schemes.

[0073] Further, after the centralization processing, the standardized evaluation index data matrix Z of is obtained.

[0074] In step 2-2, since the physical meanings and dimensions of each secondary index are different (such as acid value unit mgKOH / g, viscosity unit mm² / s, breakdown voltage unit kV), and there are large-scale indexes (such as lightning impulse breakdown voltage, AC breakdown voltage, oxidation induction time, initial oxidation temperature, relative dielectric constant) and small-scale indexes (such as acid value, kinematic viscosity, dielectric loss tangent, gas production), directly using the original data for comprehensive evaluation will lead to the dominance of the evaluation results by the indexes with large dimensions, causing deviation.

[0075] Therefore, the original data matrix needs to be standardized to convert all indexes into dimensionless, comparable and consistent standardized values. The standardized evaluation index data matrix will be the input data basis for the subsequent steps, ensuring the objectivity and consistency of the evaluation process.

[0076] Step 3, based on the standardized evaluation index data matrix, the comprehensive weight is obtained.

[0077] In step 3, the comprehensive weight of each evaluation index is determined by principal component analysis-entropy weight method, avoiding the subjective deviation caused by the dependence on expert experience in traditional methods, and improving the accuracy and reliability of the evaluation system.

[0078] In one embodiment, the step 3 includes:

[0079] Step 3-1, extracting the characteristic value from the standardized evaluation index data matrix to obtain the objective weight of the primary index;

[0080] Step 3-2, objectively weighting the secondary index to obtain the objective weight of the secondary index;

[0081] Step 3-3, according to the objective weight of the primary index and the objective weight of the secondary index, the comprehensive weight of the secondary index is obtained.

[0082] In step 3-1, the characteristic covariance matrix S of the standardized evaluation index data matrix Z is calculated to describe the linear correlation between the secondary indexes. The covariance matrix S is a symmetric matrix, and the element s αβ of the αth row and βth column is calculated as follows:

[0083]

[0084] wherein, is the th candidate scheme; ziα Z is the standardized evaluation index data matrix, and z is the element in the αth column of the ρth row of the standardized evaluation index data matrix Z; z iβ is the element in the βth column of the ρth row of the standardized evaluation index data matrix Z.

[0085] Further, eigenvalues f of the covariance matrix S are solved, and the f eigenvalues are obtained, which are consistent with the number n of the secondary indexes, and are used to describe the information richness of each secondary index.

[0086]

[0087] wherein E is an f-order unit matrix, the main diagonal elements are 1, and the other elements are 0, that is:

[0088]

[0089] Further, the contribution rate of the eigenvalue is obtained according to the eigenvalue, that is: , k = 1, 2,..., f; the contribution rate is used to describe the information amount contained in each eigenvalue.

[0090] Further, the objective weight of the primary index is determined according to the contribution rate of the eigenvalue, that is: , t = 1, 2,..., h; g t is the contribution rate of the tth eigenvalue, and h is the number of the primary indexes (h = 4, that is, the physical and chemical properties, the dielectric properties, the oxidation stability, and the gas production characteristics).

[0091] In step 3-2, first, a secondary index data matrix under each primary index is constructed; then, the secondary index data matrix is standardized to obtain a standard matrix; then, the information entropy of each secondary index is obtained according to the standard matrix; finally, the objective weight of the secondary index is obtained according to the information entropy. Specifically:

[0092] (1) assuming that there are m systems, there are q secondary performance indexes under the hth primary index, and a secondary index data matrix A of m rows and q columns is constructed, a ρθ is the element in the θth column of the ρth row of the secondary index data matrix, which is the data of the θth secondary index of the ρth candidate scheme, ρ = 1,..., m; θ = 1,..., q;

[0093] Further, a secondary index data matrix A is constructed for each primary index, and a total of h secondary index data matrices A are constructed.

[0094] (2) the secondary index data matrix is standardized by the normalization processing method and the standardization processing.

[0095] ​​The positive processing method comprises: converting a minimum index into a maximum index, the minimum index comprising an acid value, a kinematic viscosity, a medium loss tangent, and a gas production rate. ρθ The expression of the positive processing of the minimum index a ρθ is an element in the ρth row and the θth column of the positive matrix; max a θ is a maximum element of the θth secondary index. The positive matrix B is obtained after the positive processing.

[0096] The expression of the standardization processing is as follows: ρθ is an element in the ρth row and the θth column of the standard matrix; b ρθ is an element in the ρth row and the θth column of the positive matrix; b iθ is an element in the ith row and the θth column of the positive matrix; i is the ith candidate scheme. The standard matrix C is obtained after the standardization processing.

[0097] (3) According to the standard matrix, first, the proportion d ρθ of the ρth system under the θth secondary performance index is obtained; then, the information entropy e θ of the θth secondary performance index is obtained.

[0098] The proportion is obtained as follows: m is the total number of evaluation schemes; c ρθ is an element in the ρth row and the θth column of the standard matrix; c iθ is an element in the ith row and the θth column of the standard matrix.

[0099] The information entropy is obtained as follows: d iθ is the proportion of the ith candidate scheme under the θth secondary performance index; m is the total number of evaluation schemes; e θ is the information entropy of the θth secondary performance index.

[0100] (4) The objective weight of the secondary index under each primary index is represented as: u θ is the objective weight of the θth secondary index under each primary index; e θ is the information entropy of the θth secondary performance index; γ is the γth secondary performance index; q is the number of secondary indexes under the hth primary index.

[0101] In step 3-3, the comprehensive weight of the secondary index is calculated by weighted fusion, which is represented as: .

[0102] ​​In step 3, the comprehensive weight will be the core parameter for constructing the weighted decision matrix in step 4, which is used for the subsequent calculation of relative proximity.

[0103] In step 4, based on the standardized evaluation index data matrix and the comprehensive weight, a weighted decision matrix is constructed, and the relative proximity of each system is obtained to determine the optimal synthetic ester insulating oil-compound additive system formula.

[0104] In step 4, based on the standardized evaluation index data matrix obtained in step 2 and the comprehensive weight determined in step 3, a weighted decision space is constructed. By quantifying the Euclidean distance of each candidate system from the ideal optimal solution and the ideal worst solution, the relative proximity is calculated, and the comprehensive performance of all candidate systems is sorted, and the optimal formula is finally determined.

[0105] In step 4, the expression of each element in the weighted decision matrix is as follows: , z ij is the element in the i-th row and j-th column of the matrix after standardization; is the comprehensive weight of the second-level index; r ij is the element in the i-th row and j-th column of the weighted decision matrix, satisfying n is the total number of second-level indicators; the elements in the weighted decision matrix constitute the weighted decision matrix R, which is represented as:

[0106]

[0107] In step 4, define: among all candidate solutions, the virtual optimal solution SP j (maximum value of all m candidate solutions under each index) is constructed; among all candidate solutions, the virtual worst solution SM j (minimum value of all m candidate solutions under each index) is constructed.

[0108] Further, the determination method of the optimal solution is: , m is the total number of evaluation solutions, and j is the jth evaluation index; the determination method of the worst solution is: , m is the total number of evaluation solutions, and j is the jth evaluation index.

[0109] In step 4, for the i-th candidate system, the Euclidean distance from the optimal solution and the worst solution is respectively:

[0110] ;

[0111] ​In the above formula, n is the total number of secondary indicators; j is the jth evaluation indicator; SdP i is the Euclidean distance of the ith candidate system from the optimal solution; SdM i is the Euclidean distance of the ith candidate system from the worst solution; r ij is the element in the ith row and jth column of the weighted decision matrix. The above distance measure reflects the deviation of each candidate system from the ideal performance level.

[0112] In step 4, the relative closeness represents the closeness of the ith system to the optimal solution relative to the distance from the worst solution, and the relative closeness η i is represented as: , η i ∈ [0, 1], η i closer to 1, the closer the system is to the optimal solution, and the better the overall performance; η i closer to 0, the closer the system is to the worst solution, and the worse the overall performance.

[0113] In step 4, the relative closeness of all candidate systems is arranged in descending order, and the relative closeness of the synthetic ester insulating oil-compound additive system optimal formula is the highest.

[0114] In step 4, by constructing a weighted decision matrix and introducing a relative closeness index, quantitative comparison of multiple indicators and multiple schemes is realized, subjective judgment is avoided, and the objectivity, comparability and engineering practicability of the evaluation results are ensured.

[0115] In a second aspect, the application provides a synthetic ester insulating oil-compound additive system performance evaluation system, which comprises:

[0116] a model module for constructing a hierarchical structure model comprising an indicator layer and a scheme layer, wherein the scheme layer comprises a plurality of synthetic ester insulating oil-compound additive systems, and the indicator layer comprises secondary indicators for evaluating the performance of the systems;

[0117] a matrix analysis module for obtaining the original data of the secondary indicators corresponding to each system, and performing standardization processing on the original data to obtain a standardized evaluation index data matrix;

[0118] a weight module for obtaining comprehensive weights based on the standardized evaluation index data matrix;

[0119] an evaluation module for constructing a weighted decision matrix based on the standardized evaluation index data matrix and the comprehensive weights, and obtaining the relative closeness of each system, thereby determining the optimal synthetic ester insulating oil-compound additive system formula.

[0120] The foregoing embodiment of the synthetic ester insulating oil-compound additive system multi-dimensional performance comprehensive evaluation method involves all relevant contents of each step, which can be cited to the function description of the corresponding module of the synthetic ester insulating oil-compound additive system multi-dimensional performance comprehensive evaluation system in the embodiment of the present application, and will not be described here.

[0121] In a third aspect, the present application provides a computer readable storage medium, comprising a complete computer program stored therein, and when the computer program is run, the synthetic ester insulating oil-compound additive system performance evaluation method of the first aspect is implemented.

[0122] The complete computer program can be stored in the computer readable storage medium, or can be loaded into a computer or other programmable data processing device, and when the computer program is executed, the steps of each method embodiment described above can be executed. The computer readable storage medium can include the built-in storage medium in the electronic device, and of course can also include the external expansion storage medium supported by the electronic device, such as a mobile hard disk, a U disk, an optical disk, a random access memory (RAM), and a read-only memory (ROM), etc.

[0123] The present application will be further described below through specific examples, but these examples are merely exemplary and do not constitute any limitation on the scope of protection of the present application.

[0124] Example 1

[0125] This example is directed to the performance evaluation of 8 synthetic ester insulating oil-compound additive systems (composition as shown in Table 1), and the steps are as follows:

[0126] (1) Construct a hierarchical structure model including a target layer, a criterion layer, an index layer, and a scheme layer:

[0127] Target layer: multi-dimensional performance comprehensive evaluation of 8 candidate systems, and screening of the optimal formula;

[0128] Criterion layer (primary index): including dielectric performance and oxidation stability (only part of the primary index is selected in this embodiment to simplify the demonstration);

[0129] Index layer (secondary index): dielectric loss tangent (tan δ, %), relative dielectric constant (ε r ), initial oxidation temperature (OOT, °C);

[0130] Scheme layer: including 8 candidate systems, the base oil of which is pentaerythritol ester, and the types and ratios of functional additives are shown in Table 1.

[0131] (2) The dielectric properties (including dielectric loss tangent and relative dielectric constant) of the above-mentioned 8 systems were measured by means of the Xilin bridge, and the initial oxidation temperature of the above-mentioned 8 systems was measured by means of the differential scanning calorimetry, the compositions of the 8 systems and the measurement results are shown in Table 1.

[0132] Table 1:

[0133]

[0134] According to the raw data in Table 1, an 8 (m) x 3 (n) raw data matrix (i.e. the last three columns of Table 1 constitute the raw matrix X) is formed, wherein the dielectric loss tangent is a minimum type index and needs to be processed by forward transformation: , The forward transformation matrix Y is obtained after forward transformation of the data of the 7th group of data shown in Table 1.

[0135] Table 2:

[0136]

[0137] The standardized evaluation index data matrix Z is obtained by centering the forward transformation matrix Y.

[0138] (3) The characteristic covariance matrix S of the standardized evaluation index data matrix Z is calculated;

[0139]

[0140] The eigenvalues of the covariance matrix S are solved, i.e.

[0141]

[0142] The , , .

[0143] The objective weight of the first-level index is finally determined according to the eigenvalues and the eigenvalue contribution rate: the dielectric property is 0.7030, and the oxidation stability is 0.2970.

[0144] The corresponding second-level index data matrix is constructed for each of the above-mentioned two first-level indexes. Taking the dielectric property as an example, the second-level index data matrix is constructed, and the second-level index data standard matrix is obtained after forward transformation and standardization; wherein the dielectric loss tangent is a minimum type index and needs to be processed by forward transformation: , The forward transformation matrix B is obtained after forward transformation of the data of the 7th group of data, and the dielectric loss tangent and the relative dielectric constant are standardized: , the standard matrix C is obtained, and the data composition is shown in Table 3.

[0145] Table 3:

[0146]

[0147] According to the secondary index data standard matrix, first, the proportion of each system under each secondary performance index is obtained, and then the information entropy is solved; the data in the secondary index data standard matrix and the information entropy data are shown in Table 4.

[0148] Table 4:

[0149]

[0150] The objective weights of the medium loss tangent, the relative dielectric constant and the initial oxidation temperature solved by the information entropy are 0.9994, 0.0006 and 1.0000 respectively.

[0151] The comprehensive weights of the secondary indexes are calculated by weighted fusion: the medium loss tangent 0.7030*0.9994≈0.7026, the relative dielectric constant 0.7030*0.0006≈0.0004, and the initial oxidation temperature 0.2970*1.0000=0.2970. Since the overall change range of the relative dielectric constant of the synthetic ester insulating oil-compound additive system in the original data is not large, its proportion in the comprehensive weight is small and can be ignored. Finally, the comprehensive weight is determined as the medium loss tangent 70% and the initial oxidation temperature 30%.

[0152] (4) Construct a weighted decision matrix R, and each element in the weighted decision matrix satisfies: ,

[0153]

[0154] Solve the optimal solution and the worst solution in all candidate solutions under the two indexes of the medium loss tangent and the initial oxidation temperature: SP1 is the maximum value of the eight candidate solutions under the medium loss tangent, SM1 is the minimum value of the eight candidate solutions under the medium loss tangent, , (medium loss tangent); SP2 is the maximum value of the eight candidate solutions under the initial oxidation temperature, and SM2 is the minimum value of the eight candidate solutions under the initial oxidation temperature, , (initial oxidation temperature).

[0155] Solve the Euclidean distance and the relative closeness of each candidate system to the optimal solution and the worst solution, Figure 3The contrast chart of Euclidean distance and relative closeness of each candidate system to the optimal solution and the worst solution (the substances represented by serial numbers 1-8 correspond to the substances in Table 1 respectively); the corresponding data shown in Table 5.

[0156] Table 5:

[0157]

[0158] The closer the relative closeness to 1, the closer the system to the optimal solution, and the better the comprehensive performance; the closer the relative closeness to 0, the closer the system to the worst solution, and the worse the comprehensive performance. Among the 8 groups of synthetic ester insulating oil-compounded additive systems measured, the relative closeness of serial number 4 (pentaerythritol ester + 0.3% 1010) is the largest, which is 0.873, and it is the optimal formula among the measured systems.

[0159] The above has described the present application in detail in combination with preferred embodiments and exemplary examples. However, it needs to be declared that these specific embodiments are only illustrative explanations of the present application, and do not constitute any limitation to the protection scope of the present application. Various improvements, equivalent replacements or modifications can be made to the technical content and the embodiments of the present application without exceeding the spirit and protection scope of the present application, and these all fall within the protection scope of the present application. The protection scope of the present application is subject to the appended claims.

Claims

1. A method for evaluating the performance of a synthetic ester insulating oil-compound additive system, characterized in that, The method includes: Step 1: Construct a hierarchical model including an index layer and a scheme layer, wherein the scheme layer includes multiple synthetic ester insulating oil-compound additive systems, and the index layer includes secondary indices for evaluating the performance of the system. Step 2: Obtain the original data of the secondary indicators corresponding to each system, and perform standardization processing on the original data to obtain a standardized evaluation indicator data matrix; Step 3: Obtain the comprehensive weight based on the standardized evaluation index data matrix; Step 4: Based on the standardized evaluation index data matrix and the comprehensive weight, construct a weighted decision matrix and obtain the relative similarity of each system, thereby determining the optimal synthetic ester insulating oil-compound additive system formulation.

2. The method according to claim 1, characterized in that, Preferably, in step 1, the hierarchical model further includes a target layer, which is used to define the final evaluation target.

3. The method according to claim 1, characterized in that, In step 1, the hierarchical model also includes a criteria layer, which includes primary indicators that meet the evaluation criteria. The primary indicators include: physicochemical properties, dielectric properties, oxidation stability, and gas generation characteristics.

4. The method according to claim 1, characterized in that, In step 1, the secondary indicators include acid value, kinematic viscosity, and dielectric loss tangent.

5. The method according to claim 1, characterized in that, In step 1, the compound additives include any one or more of phenolic antioxidants, amine antioxidants, metal passivators, impact performance enhancers, and anti-gas release agents.

6. The method according to claim 1, characterized in that, In step 2, the raw data of the secondary indicators corresponding to each system are obtained through standard measurement.

7. The method according to claim 1, characterized in that, In step 2, the original data is standardized using a forward processing method and a centralized processing method.

8. The method according to claim 1, characterized in that, Step 3 includes: Step 3-1: Extract feature values ​​from the standardized evaluation indicator data matrix to obtain the objective weights of the primary indicators; Step 3-2: Objectively assign weights to the secondary indicators to obtain their objective weights; Step 3-3: Obtain the comprehensive weight of the secondary indicators based on the objective weights of the primary indicators and the objective weights of the secondary indicators.

9. A performance evaluation system for a synthetic ester insulating oil-compound additive system, characterized in that, The system includes: The model module is used to construct a hierarchical model including an index layer and a scheme layer, wherein the scheme layer includes multiple synthetic ester insulating oil-compound additive systems, and the index layer includes secondary indices for evaluating the performance of the system. The matrix analysis module is used to obtain the original data of the secondary indicators corresponding to each system, and to standardize the original data to obtain a standardized evaluation indicator data matrix. The weighting module is used to obtain the comprehensive weight based on the standardized evaluation index data matrix. The evaluation module is used to construct a weighted decision matrix based on the standardized evaluation index data matrix and the comprehensive weight, and to obtain the relative similarity of each system, thereby determining the optimal formulation of the synthetic ester insulating oil-compound additive system.

10. A computer-readable storage medium comprising a stored complete computer program, characterized in that, When the computer program is run, it implements the performance evaluation method of the synthetic ester insulating oil-compound additive system as described in any one of claims 1 to 8.

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