Dry-type transformer health state assessment method and system based on improved fuzzy analytic hierarchy process and sudden change series method

Abstract

The invention provides a dry-type transformer health state assessment method and system based on an improved fuzzy analytic hierarchy process and an abrupt change series method, and belongs to the technical field of transformer state assessment. A health state assessment index system is established, and a health state comment set is constructed according to the health state deterioration degree of a transformer; dynamically adjusting the weight, and determining the index weight of each layer; calculating the membership degree of each index, and combining the membership degree with the weight matrix to obtain a total fuzzy evaluation matrix; calculating to obtain a system total mutation membership function value; and determining the health state grade of the dry-type transformer according to the maximum membership degree principle by adopting a weighted average method and integrating the evaluation results of the two. According to the method, the interaction and synergistic effect between indexes are considered, and the defects of existing single-angle evaluation are overcome; the method considers the influence of operation time, dynamically adjusts the weight, accurately reflects the influence degree of each index in different operation stages on the health state of the transformer, and achieves the accurate and comprehensive evaluation of the health state of the dry-type transformer.

Description

Technical Field

[0001] The present invention relates to the technical field of dry-type transformer condition assessment, and particularly relates to a method and system for assessing the health status of a dry-type transformer based on an improved fuzzy analytic hierarchy process and a mutation series method. Background Art

[0002] As a key component of the rail transit traction power supply system, the health status of a dry-type transformer is of great significance for ensuring the safe and stable operation of the entire system. However, during the actual operation process, the dry-type transformer is often affected by various voltage and currents, impact loads, and dust and dirt in the environment, resulting in inevitable aging and even breakdown damage of the insulation of the corresponding components of the dry-type transformer. At the same time, as the operation years increase, when approaching the designed service life of the dry-type transformer, its failure rate has a significant upward process, leading to frequent failures, and thus affecting the safety and reliability of the traction power supply system.

[0003] To ensure the normal operation of the dry-type transformer, currently, most adopt a condition-based maintenance method, and monitor and evaluate its health status by establishing various evaluation models. However, the existing evaluation methods have obvious limitations. First, when analyzing the health status of the transformer, some current evaluation methods are often limited to a single perspective, and it is difficult to comprehensively consider the interaction and synergy effects among multiple indicators, resulting in incomplete and inaccurate evaluation results; second, in determining the index weights of the existing evaluation models, a static weight allocation method is mostly used. As the operation time of the transformer increases, its internal structure and performance will change, and the influence degree of different indicators on the health status will also change accordingly. The static weight cannot adapt to this change, resulting in the evaluation results being difficult to accurately reflect the actual operation status of the equipment.

[0004] Therefore, the present invention provides a method and system for assessing the health status of a dry-type transformer based on an improved fuzzy analytic hierarchy process and a mutation series method. The invention comprehensively evaluates the health status of the transformer from different perspectives by organically combining the fuzzy analytic hierarchy process and the mutation series method. The fuzzy analytic hierarchy process is used to construct a comprehensive evaluation index system, while the mutation series method deeply mines and comprehensively calculates the index data, fully considering the interaction and synergy effects among the indicators, and making up for the deficiencies of the existing single-perspective evaluation. In addition, the present invention introduces the group decision-making theory on the basis of the traditional G1 weighting method, and combines the influence of the operation time on the health status of the transformer to dynamically adjust the weights, so that the evaluation process can accurately reflect the influence degree of each indicator on the health status of the transformer at different operation stages. This method helps the operation and maintenance personnel to timely master the operation status of the equipment, and thus reasonably arrange the operation and maintenance decisions, improving the safety and reliability of the traction power supply system. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for evaluating the health status of dry-type transformers based on the improved fuzzy analytic hierarchy process and mutation series method, so as to solve at least one of the technical problems existing in the above-mentioned background technology.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions:

[0007] In the first aspect, the present invention provides a method for evaluating the health status of dry-type transformers based on the improved fuzzy analytic hierarchy process and mutation series method, including:

[0008] S1 Refer to the structural characteristics, operating principles, and common fault modes of dry-type transformers, select evaluation indicators that can accurately reflect their health status, and thus construct a classified and hierarchical health status evaluation index system for dry-type transformers.

[0009] S2 Construct a health status comment set for the transformer according to the degree of deterioration of the transformer's health status.

[0010] S3 Perform normalization processing on each parameter data according to the data nature and normalization standard.

[0011] S4 Use the G1 weighting method combined with the group decision-making theory, consider the influence of the operating time on the health status of the transformer, and dynamically adjust the weights to determine the weights of each layer of indicators.

[0012] S5 Introduce the health index theory to improve the membership function, calculate the membership degrees of each indicator belonging to different health states, and combine them with the weight matrix to obtain the total fuzzy evaluation matrix B.

[0013] S6 According to the requirements of the mutation series method, determine the applicable mutation model for each group in the indicator layer. Derive the normalization calculation formula based on the type of mutation system to obtain the mutation membership function values of each indicator. Take the mean according to the "complementary" principle and recursively calculate from the indicator layer upwards to obtain the overall mutation membership function value S.

[0014] S7 Use the weighted average method to combine the total fuzzy evaluation matrix B and the total system mutation membership function value S to obtain the comprehensive evaluation value C. According to the principle of maximum membership degree, determine the health status level of the dry-type transformer to which the multi-factor state evaluation result belongs.

[0015] Furthermore, the classified and hierarchical health status evaluation index system for dry-type transformers in S1 is divided into three layers: the target layer, the factor layer, and the indicator layer. Among them, the factor layer includes regular test data X1, real-time monitoring data X2, and daily operation and maintenance data X3. The indicators corresponding to the regular test data X1 in the indicator layer include direct current resistance X 11 , insulation resistance X 12 and alternating current withstand voltage X 13The real-time monitoring data X2 corresponds to the winding hot spot temperature X 21 and vibration level X 22 , the indicator corresponding to the daily operation and maintenance data X3 is the family defect X 31 and troubleshooting situation X 32 , a total of 7 indicators.

[0016] Furthermore, in S2, the health status of the transformer is divided into four different levels: normal, caution, abnormal, and severe, and a comment set O is established.

[0017] Furthermore, in S3, the data is divided into quantitative indicators and qualitative indicators according to its nature. For quantitative indicators, a linear normalization method is used to project the normalized results into the interval [0,1]. For qualitative indicators, technicians normalize the state values ​​according to the normalization standard based on actual conditions. For AC withstand voltage, the 0-1 method is used for normalization. If the test passes, its normalized state value is 1, otherwise, its normalized state value is 0.

[0018] Furthermore, group decision-making theory is introduced in S4, and experts rank the importance of each layer of indicators based on their own experience and professional knowledge, and give their importance ratios. The arithmetic mean method is used to gather expert judgments to obtain a comprehensive importance ratio of adjacent indicators, and the G1 method is used to calculate the initial weight of each indicator. Considering the impact of operating time on the health status of the transformer, the initial weight is dynamically adjusted to determine the final weight of each layer of indicators.

[0019] Furthermore, the method for improving the traditional membership function in S5 is specifically as follows: by analyzing the changing characteristics of the health index, taking it as a reference variable and incorporating it into the original membership distribution function with fixed parameters, the membership function is improved and a membership matrix is ​​constructed so that its distribution conforms to the aging failure law of the transformer. The membership matrix is ​​combined with the indicator weights determined by the improved G1 method to obtain a fuzzy evaluation matrix of the indicator layer. Combined with the weight matrix of the factor layer, the total fuzzy evaluation matrix B of the health status of the dry-type transformer is obtained.

[0020] Furthermore, in S6, the mutation system type of each indicator group is determined based on the indicator system. For example, the three indicators in the periodic test data X1 are applicable to the dovetail mutation model, while the indicators of the real-time monitoring data X2 and the daily operation and maintenance data X3 are applicable to the cusp mutation model. According to the mutation system type, the potential function is differentiated, and the simultaneous equations are used to obtain the bifurcation point set equation, and then the normalized calculation formula is derived. The normalized indicator data is substituted into the calculation formula, and the mutation membership function value of each indicator is calculated. The average is taken according to the "complementary" principle, and the total mutation membership function value S of the system is obtained by upward recursive calculation.

[0021] Furthermore, in S7, the weighted average method is adopted to combine the total fuzzy evaluation matrix B and the total mutation membership function value S of the system, and the comprehensive evaluation value C is obtained through the formula. According to the principle of maximum membership degree, the element O of the evaluation set corresponding to the maximum evaluation index is taken as the result of the final evaluation.

[0022] In a second aspect, the present invention provides a dry-type transformer health state evaluation system based on an improved fuzzy analytic hierarchy process and mutation series method, including:

[0023] A selection module, configured to select evaluation indexes reflecting its health state with reference to the structural characteristics, operating principle, and common fault modes of the dry-type transformer, and construct a classified and hierarchical dry-type transformer health state evaluation index system;

[0024] A construction module, configured to construct a health state comment set of the transformer according to the degree of deterioration of the health state of the transformer;

[0025] A normalization processing module, configured to perform normalization processing on each parameter data according to the data nature and normalization standard;

[0026] A calculation module, configured to use the G1 weighting method combined with the group decision-making theory, consider the influence of the operating time on the health state of the transformer, dynamically adjust the weights, and thus determine the weights of each layer of indexes; introduce the health index theory to improve the membership function, calculate the membership degrees of each index belonging to different health states, and combine them with the weight matrix to obtain the total fuzzy evaluation matrix; according to the requirements of the mutation series method, determine the applicable mutation model for each group of the index layer; derive the normalization calculation formula according to the mutation system type to obtain the mutation membership function values of each index; take the mean according to the "complementary" principle and recursively calculate from the index layer upward to obtain the overall mutation membership function value;

[0027] A comprehensive evaluation module, configured to use the weighted average method to combine the total fuzzy evaluation matrix B and the total mutation membership function value S of the system to obtain the comprehensive evaluation value C; according to the principle of maximum membership degree, determine the health state level of the dry-type transformer to which the multi-factor state evaluation result belongs.

[0028] In a third aspect, the present invention provides a non-transitory computer-readable storage medium, which is used to store computer instructions. When the computer instructions are executed by a processor, the dry-type transformer health state evaluation method based on the improved fuzzy analytic hierarchy process and mutation series method as described in the first aspect is implemented.

[0029] Fourthly, the present invention provides a computer device, including a memory and a processor, where the processor and the memory communicate with each other, the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the dry-type transformer health state evaluation method based on the improved fuzzy analytic hierarchy process and mutation series method as described in the first aspect.

[0030] Fifthly, the present invention provides an electronic device, including: a processor, a memory, and a computer program; wherein, the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device runs, the processor executes the computer program stored in the memory, so that the electronic device executes the instructions for implementing the dry-type transformer health state evaluation method based on the improved fuzzy analytic hierarchy process and mutation series method as described in the first aspect.

[0031] Advantages of the present invention: It can follow the actual operation aging and failure law of the transformer. By organically combining the fuzzy analytic hierarchy process and the mutation series method, it comprehensively evaluates the health state of the transformer from different perspectives, fully considering the interaction and synergy effect between indicators, making up for the deficiency of the existing single-angle evaluation. At the same time, it uses the group decision-making theory to improve the G1 weight assignment method, and considers the influence of operation time to dynamically adjust the weight, accurately reflecting the influence degree of each indicator on the health state of the transformer at different operation stages, so as to realize the accurate and comprehensive evaluation of the health state of the dry-type transformer, provide a scientific basis for operation and maintenance decisions, and improve the safety and reliability of the traction power supply system.

[0032] The advantages of the additional aspects of the present invention will be more clearly given in the following description part, or understood through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0034] Figure 1 It is a flowchart of the dry-type transformer health state evaluation method based on the improved fuzzy analytic hierarchy process and mutation series method described in the embodiments of the present invention.

[0035] Figure 2 It is a schematic diagram of the dry-type transformer health state evaluation index system described in the embodiments of the present invention.

[0036] Figure 3 It is a distribution diagram of the improved semi-mountain-shaped and semi-trapezoidal membership function described in the embodiments of the present invention.

[0037] Figure 4 It is the distribution diagram of the improved semi-triangular and semi-trapezoidal membership function described in the embodiments of the present invention. Specific Embodiments

[0038] The following details the embodiments of the present invention. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention and should not be construed as a limitation to the present invention.

[0039] Those skilled in the art of this technology can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used here have the same meaning as the general understanding of those of ordinary skill in the art to which the present invention belongs.

[0040] It should also be understood that terms such as those defined in a general dictionary should be understood to have a meaning consistent with the meaning in the context of the prior art, and will not be interpreted in an idealized or overly formal sense unless defined as here.

[0041] Those skilled in the art of this technology can understand that, unless specifically stated, the singular forms "a", "an", "the" and "said" used here may also include the plural forms. It should be further understood that the term "comprising" used in the description of the present invention means the presence of the described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements and / or their groups.

[0042] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. Without conflict, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0043] For ease of understanding of the present invention, the following further explains the present invention with specific embodiments in conjunction with the accompanying drawings, and the specific embodiments do not constitute a limitation to the embodiments of the present invention.

[0044] Those skilled in the art should understand that the drawings are only schematic diagrams of the embodiments, and the components in the drawings are not necessarily essential for implementing the present invention.

[0045] Embodiment 1

[0046] In Embodiment 1, first, a health state assessment system for dry-type transformers based on the improved fuzzy analytic hierarchy process and the mutation series method is provided. The system includes:

[0047] A selection module, which is used to select evaluation indicators reflecting its health state with reference to the structural characteristics, operating principles, and common fault modes of dry-type transformers, and construct a classified and hierarchical health state evaluation index system for dry-type transformers;

[0048] A construction module, which is used to construct a health state comment set for the transformer according to the degree of deterioration of the transformer's health state;

[0049] A normalization processing module, which is used to perform normalization processing on each parameter data according to the data nature and normalization standard;

[0050] A calculation module, which is used to dynamically adjust the weights by using the G1 weighting method combined with the group decision-making theory, considering the influence of the operating time on the health state of the transformer, so as to determine the weights of each layer of indicators; introduce the health index theory to improve the membership function, calculate the membership degrees of each indicator belonging to different health states, and combine them with the weight matrix to obtain the total fuzzy evaluation matrix; determine the applicable mutation model for each group of the indicator layer according to the requirements of the mutation series method; derive the normalization calculation formula according to the type of mutation system to obtain the mutation membership function values of each indicator; take the mean according to the "complementary" principle and recursively calculate from the indicator layer upwards to obtain the overall mutation membership function value;

[0051] A comprehensive evaluation module, which is used to combine the total fuzzy evaluation matrix B and the total mutation membership function value S of the system by using the weighted average method to obtain the comprehensive evaluation value C; determine the health state level of the dry-type transformer to which the multi-factor state evaluation result belongs according to the maximum membership degree principle.

[0052] In this embodiment, the above system is used to implement a health state assessment method for dry-type transformers based on the improved fuzzy analytic hierarchy process and the mutation series method. The technical solution includes the following steps:

[0053] S1 Select evaluation indicators that can accurately reflect its health state with reference to the structural characteristics, operating principles, and common fault modes of dry-type transformers, so as to construct a classified and hierarchical health state evaluation index system for dry-type transformers.

[0054] S2 Construct a health state comment set for the transformer according to the degree of deterioration of the transformer's health state.

[0055] S3 Perform normalization processing on each parameter data according to the data nature and normalization standard.

[0056] S4 uses the G1 weighting method combined with the group decision-making theory, considers the impact of operation time on the health state of the transformer, dynamically adjusts the weights, and thus determines the weights of each layer of indicators.

[0057] S5 introduces the health index theory to improve the membership function, calculates the membership degrees of each indicator belonging to different health states, and combines them with the weight matrix to obtain the total fuzzy evaluation matrix B.

[0058] S6 determines the mutation models applicable to each group of the indicator layer according to the requirements of the mutation series method. Derives the normalization calculation formula based on the type of mutation system to obtain the mutation membership function values of each indicator. Take the mean according to the "complementary" principle and recursively calculate from the indicator layer upwards to obtain the overall mutation membership function value S.

[0059] S7 uses the weighted average method to combine the total fuzzy evaluation matrix B and the total mutation membership function value S of the system to obtain the comprehensive evaluation value C. According to the principle of maximum membership degree, determine the health state level of the dry-type transformer to which the multi-factor state evaluation result belongs.

[0060] Among them, the classified and hierarchical health state evaluation index system of the dry-type transformer in S1 is divided into three layers: the target layer, the factor layer, and the indicator layer. The factor layer includes regular test data X1, real-time monitoring data X2, and daily operation and maintenance data X3. The indicators corresponding to the regular test data X1 in the indicator layer include direct current resistance X 11 , insulation resistance X 12 , and alternating current withstand voltage X 13 . The indicators corresponding to the real-time monitoring data X2 are winding hot spot temperature X 21 and vibration degree X 22 . The indicators corresponding to the daily operation and maintenance data X3 are familial defects X 31 and fault repair situation X 32 , totaling 7 indicators.

[0061] In S2, the health state of the transformer is divided into four different levels: normal, attention, abnormal, and severe, and a comment set O is established.

[0062] In S3, according to the nature of the data, it is divided into quantitative indicators and qualitative indicators. For quantitative indicators, the linear normalization method is used to project the normalization result into the interval [0,1]; for qualitative indicators, technical personnel normalize the state value according to the normalization standard in combination with the actual situation. For alternating current withstand voltage, the 0-1 method is used for normalization. If the test passes, the normalization state value is taken as 1, and if the test fails, the normalization state value is taken as 0.

[0063] The group decision-making theory is introduced in S4. Experts rank the importance of each layer of indicators based on their own experience and professional knowledge, and give their importance ratios. The arithmetic mean method is used to gather expert judgments to obtain a comprehensive importance ratio of adjacent indicators, and the G1 method is used to calculate the initial weight of each indicator. Considering the impact of operating time on the health status of the transformer, the initial weight is dynamically adjusted to determine the final weight of each layer of indicators.

[0064] The method for improving the traditional membership function in S5 is specifically as follows: by analyzing the changing characteristics of the health index, taking it as a reference variable and incorporating it into the original membership distribution function with fixed parameters, the membership function is improved and a membership matrix is ​​constructed so that its distribution conforms to the aging failure law of the transformer. The membership matrix is ​​combined with the indicator weights determined by the improved G1 method to obtain a fuzzy evaluation matrix of the indicator layer. Combined with the weight matrix of the factor layer, the total fuzzy evaluation matrix B of the health status of the dry-type transformer is obtained.

[0065] In S6, the mutation system type of each indicator group is determined based on the indicator system. For example, the three indicators in the periodic test data X1 are applicable to the dovetail mutation model, while the indicators of the real-time monitoring data X2 and the daily operation and maintenance data X3 are applicable to the cusp mutation model. The potential function is differentiated according to the mutation system type, and the bifurcation point set equation is obtained by the simultaneous equations, and then the normalized calculation formula is derived. The normalized indicator data is substituted into the calculation formula, and the mutation membership function value of each indicator is calculated. The average is taken according to the "complementary" principle, and the total mutation membership function value S of the system is obtained by upward recursive calculation.

[0066] In S7, the weighted average method is used to combine the total fuzzy evaluation matrix B and the system total mutation membership function value S, and the comprehensive evaluation value C is obtained through the formula. According to the maximum membership principle, the evaluation set element O corresponding to the maximum evaluation index is taken as the final evaluation result.

[0067] Example 2

[0068] like Figure 1 As shown, in this embodiment 2, a dry-type transformer health status assessment method based on improved fuzzy analytic hierarchy process and catastrophe series method is proposed, which specifically includes the following steps:

[0069] Step 1: Starting from the structure of dry-type transformers, the common fault types and causes of dry-type transformers are analyzed, and the relevant state quantities that can reflect the health status of dry-type transformers are analyzed and sorted out, and evaluation indicators are selected to establish a classified and layered dry-type transformer health status evaluation indicator system. Figure 2As shown in the figure, the health status evaluation index system of dry-type transformers can be divided into three layers: the target layer, the factor layer, and the index layer. Among them, the factor layer includes regular test data X1, real-time monitoring data X2, and daily operation and maintenance data X3. The indexes corresponding to the regular test data X1 in the index layer include direct current resistance X 11 , insulation resistance X 12 , and alternating current withstand voltage X 13 . The indexes corresponding to the real-time monitoring data X2 are winding hot spot temperature X 21 and vibration level X 22 . The indexes corresponding to the daily operation and maintenance data X3 are familial defects X 31 and fault repair situation X 32 , totaling 7 indexes.

[0070] Step 2: According to the degree of deterioration of the health status of the dry-type transformer, construct the health status comment set O of the transformer = {normal, attention, abnormal, serious}, where the state descriptions of the transformer corresponding to each level are shown in Table 1.

[0071] Table 1 State descriptions of the transformer corresponding to each level

[0072]

[0073] Step 3: According to the different natures of the data, it can be divided into quantitative indexes and qualitative indexes.

[0074] Quantitative indexes include real-time monitoring data and regular test data except for alternating current withstand voltage. According to their natures, they can be further divided into indexes of the better the larger and indexes of the better the smaller. Using the linear normalization method, project the normalization result into the interval [0,1], and the formula is as follows:

[0075]

[0076] In the formula, l i is the value after the i-th linear normalization, x i is the measured value of this index, x good is the good value; x alarm is the attention value.

[0077] In the regular test data, use the three-phase direct current resistance unbalance rate B as the evaluation standard for the direct current resistance, and its calculation formula is:

[0078]

[0079] For the insulation resistance, compare the current test value R with the previous test value R L to judge the insulation state of the transformer.

[0080] In real-time monitoring data, the relative temperature difference of the actual measured temperature of the winding hot spot is used as the evaluation criterion to determine whether there are thermal defects or faults in the transformer. The relative temperature difference calculation formula is as follows:

[0081]

[0082] In the formula, δ t is the relative temperature difference, τ1 is the temperature rise of the normal part of the transformer, and τ2 is the temperature rise of the overheated part of the transformer.

[0083] The vibration level uses energy entropy as the evaluation criterion, which can effectively identify whether the transformer is in a normal working state.

[0084] Perform empirical mode decomposition on the vibration signal, and select the first six imf components for energy entropy feature extraction. The steps are as follows:

[0085] Merge the energies of different imf components into the feature vector of a certain sample. According to the energy definition, assume that a certain sample sequence x(t) has a length of N, then the energy of this sample is

[0086]

[0087] Then the energy of the p-th imf p component of the sample is E p , then there is

[0088]

[0089] In order to observe the changes of different components, normalize the feature vector and define the probability feature vector:

[0090]

[0091] Among them, p k is the proportion of each energy occupying the total normalized energy.

[0092] Then, according to the definition of information entropy, the energy entropy is obtained as:

[0093]

[0094] According to regulations such as "DL / T 596-2021 Regulations for Preventive Tests of Power Equipment", the good reference values and attention reference values of each quantitative index are summarized and sorted out, as shown in Table 2.

[0095] Table 2 Critical Values of Quantitative Indicators

[0096]

[0097] The qualitative indicators include AC withstand voltage and daily operation and maintenance data.

[0098] For AC withstand voltage, the 0-1 method is used for normalization. If the test passes, the normalized status value is taken as 1; otherwise, if the test fails, the normalized status value is taken as 0.

[0099] For daily operation and maintenance data, technicians obtain the normalized status value according to the normalization standard in combination with the actual situation, as shown in Table 3.

[0100] Table 3 Normalization Standard for Qualitative Indicators

[0101]

[0102] Step 4: Considering the subjectivity of the weight assignment method, the group decision-making theory is used to improve the G1 weight assignment method. A questionnaire is designed, and multiple personnel participating in relevant research conduct independent rankings. After knowing the above-mentioned transformer health assessment system structure and weight assignment method, the questionnaire content is designed as shown in Table 4:

[0103] Table 4 Questionnaire Content for Transformer Health Assessment Weight Allocation

[0104]

[0105]

[0106] Statistical questionnaire information, synthesize the ranking results of multiple experts, and use the weighted average method to obtain the group ranking score. The calculation formula is as follows:

[0107]

[0108] Among them, S j is the group ranking score of the jth indicator, r ij is the ranking of the jth indicator by the ith expert, and e i is the weight of the ith expert.

[0109] According to the group ranking score, calculate the weight of each layer of indicators according to the G1 weight assignment method to obtain the weight allocation of each person for this indicator. Finally, take the average value of each indicator in the multi-person evaluation as the initial weight of each indicator. The calculation formula is as follows:

[0110]

[0111] Among them, w k0 is the initial weight of the kth indicator, r i is the importance ratio of the ith indicator to the i+1th indicator, and n is the total number of indicators.

[0112] Considering the influence of operation time on the importance of each index, the dynamic weight of each index is calculated using the following formula:

[0113]

[0114] where w k is the dynamic weight of the k-th index, α is the influence coefficient of operation time on the weight, and its value range is between 0.01 and 0.1. t is the operation time of the transformer, and T is the expected total service life of the transformer, both in years.

[0115] At the same time, to ensure the rationality and additivity of the final weight, the above dynamic weight is normalized, and its formula is:

[0116]

[0117] After the above processing, the final weight of each index is obtained.

[0118] Step Five: Use the improved semi-trapezoidal semi-hill-shaped and semi-trapezoidal semi-triangular membership functions to calculate the membership degrees of quantitative and qualitative indexes respectively. The specific improvement method is as follows:

[0119] Utilize the designed service life T des of the transformer and the already-operated time ΔT to obtain the health index HI, and based on this, obtain the intersection point x' of the improved membership function and the x-axis. The relational expression is as follows:

[0120]

[0121] Take the health index HI as the reference variable and incorporate it into the original membership distribution function with fixed parameters to improve the membership function. Its function distribution is as Figure 3 、 Figure 4 shown.

[0122] For qualitative indexes, select the semi-triangular semi-trapezoidal membership function, and for quantitative indexes, select the semi-hill-shaped semi-trapezoidal membership function.

[0123] The improved semi-triangular semi-trapezoidal membership function is as follows:

[0124]

[0125] The improved semi-hill-shaped semi-trapezoidal membership function is as follows:

[0126]

[0127]

[0128] The membership matrix R X1 、RX2 and R X3 and the weight w of each state quantity indicator i Combined, the fuzzy evaluation matrix b of the indicator layer is obtained respectively. Xi The elements are as follows:

[0129]

[0130] Combined with the fuzzy evaluation matrix b of the indicator layer Xi and the weight matrix w of the factor layer YS , the total fuzzy evaluation matrix B of the health status of the dry-type transformer is obtained as follows:

[0131]

[0132] Step 6: Based on the indicator system constructed by the fuzzy hierarchical analysis method, analyze the relationship between the indicators and determine the mutation system type. For example, the three indicators in the periodic test data X1 are applicable to the dovetail mutation model, while the indicators of the real-time monitoring data X2 and the daily operation and maintenance data X3 are applicable to the cusp mutation model. Then, according to the mutation system type, the potential function is differentiated, and the state variables are eliminated by the simultaneous equations to obtain the bifurcation point set equation, and then the normalized calculation formula is derived. The functions, variables, bifurcation equations and commonly used normalized formulas corresponding to each mutation type are shown in Table 5, where x is the state variable of the system, f(x) is the potential function of the system state variable, and the control variables of the state variable are represented by the coefficients a, b, and c of x.

[0133] Table 5 Mutation type, potential function, variables, bifurcation equation and normalization formula

[0134]

[0135] According to the corresponding mutation type, the evaluation matrix corresponding to each evaluation index is converted into a comprehensive evaluation matrix under the mutation membership function using the normalized calculation formula. On this basis, the mean is taken in accordance with the "complementary" principle, that is, the arithmetic mean of the mutation membership function values ​​corresponding to each control variable is taken to obtain the mutation membership function value of the previous level evaluation index. Similarly, the total mutation membership function value S of the system is obtained by recursively calculating from the bottom of the inverted tree structure to the top.

[0136] Step 7: In S7, the weighted average method is used to combine the total fuzzy evaluation matrix B and the total mutation membership function value S of the system, and the comprehensive evaluation value C is obtained by the formula C = βB + (1-β) S (β is the weight coefficient). According to the maximum membership principle, the evaluation set element O corresponding to the maximum evaluation index is taken as the final evaluation result, that is: c max =max{c j}j=1,2,3,4.

[0137] Example 3

[0138] This Example 3 provides a non - transitory computer - readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the above - mentioned dry - type transformer health state evaluation method based on the improved fuzzy analytic hierarchy process and the mutation series method is implemented. This method includes:

[0139] Referring to the structural characteristics, operating principles, and common fault modes of dry - type transformers, select evaluation indicators that can accurately reflect their health states, so as to construct a classified and hierarchical health state evaluation index system for dry - type transformers;

[0140] Construct a health state comment set for the transformer according to the degree of deterioration of the transformer's health state;

[0141] Perform normalization processing on each parameter data according to the nature of the data and the normalization standard;

[0142] Use the G1 weighting method combined with the group decision - making theory, consider the influence of the operating time on the health state of the transformer, and dynamically adjust the weights to determine the weights of each layer of indicators;

[0143] Introduce the health index theory to improve the membership function, calculate the membership degrees of each indicator belonging to different health states, and combine them with the weight matrix to obtain the total fuzzy evaluation matrix B;

[0144] According to the requirements of the mutation series method, determine the applicable mutation models for each group of the indicator layer. Derive the normalization calculation formula according to the type of mutation system to obtain the mutation membership function values of each indicator. Take the mean according to the "complementary" principle and recursively calculate from the indicator layer upwards to obtain the overall mutation membership function value S;

[0145] Adopt the weighted average method to combine the total fuzzy evaluation matrix B and the total mutation membership function value S of the system to obtain the comprehensive evaluation value C. According to the maximum membership degree principle, determine the health state level of the dry - type transformer to which the multi - factor state evaluation result belongs.

[0146] Example 4

[0147] This Example 4 provides a computer device, including a memory and a processor. The processor and the memory communicate with each other. The memory stores program instructions executable by the processor. The processor calls the program instructions to execute the above - mentioned dry - type transformer health state evaluation method based on the improved fuzzy analytic hierarchy process and the mutation series method. This method includes:

[0148] Referring to the structural characteristics, operating principles, and common fault modes of dry - type transformers, select evaluation indicators that can accurately reflect their health states, so as to construct a classified and hierarchical health state evaluation index system for dry - type transformers;

[0149] Construct a health state evaluation set for the transformer according to the degree of deterioration of the transformer's health state;

[0150] Normalize the data of each parameter according to the data nature and the normalization standard;

[0151] Using the G1 weighting method combined with the group decision-making theory, considering the influence of the operation time on the transformer's health state, dynamically adjust the weights to determine the weights of each layer of indicators;

[0152] Introduce the health index theory to improve the membership function, calculate the membership degrees of each indicator belonging to different health states, and combine them with the weight matrix to obtain the total fuzzy evaluation matrix B;

[0153] According to the requirements of the mutation series method, determine the mutation model applicable to each group of the indicator layer. Derive the normalization calculation formula according to the type of mutation system to obtain the mutation membership function values of each indicator. Take the average value according to the "complementary" principle and calculate recursively from the indicator layer upward to obtain the overall mutation membership function value S;

[0154] Adopt the weighted average method to combine the total fuzzy evaluation matrix B and the total mutation membership function value S of the system to obtain the comprehensive evaluation value C. According to the principle of maximum membership degree, determine the health state level of the dry-type transformer to which the multi-factor state evaluation result belongs.

[0155] Example 5

[0156] This Example 5 provides an electronic device, including: a processor, a memory, and a computer program; wherein, the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device runs, the processor executes the computer program stored in the memory so that the electronic device executes the instructions for implementing the above-mentioned dry-type transformer health state evaluation method based on the improved fuzzy analytic hierarchy process and mutation series method, and this method includes:

[0157] Referring to the structural characteristics, operating principle and common fault modes of the dry-type transformer, select evaluation indicators that can accurately reflect its health state, so as to construct a classification and hierarchical health state evaluation index system for the dry-type transformer;

[0158] Construct a health state evaluation set for the transformer according to the degree of deterioration of the transformer's health state;

[0159] Normalize the data of each parameter according to the data nature and the normalization standard;

[0160] Using the G1 weighting method combined with the group decision-making theory, considering the influence of the operation time on the transformer's health state, dynamically adjust the weights to determine the weights of each layer of indicators;

[0161] Introduce the health index theory to improve the membership function, calculate the membership degrees of each index belonging to different health states, and combine them with the weight matrix to obtain the total fuzzy evaluation matrix B;

[0162] According to the requirements of the mutation series method, determine the applicable mutation model for each group in the index layer. Derive the normalization calculation formula based on the type of mutation system to obtain the mutation membership function values of each index. Take the mean according to the "complementary" principle and recursively calculate upward from the index layer to obtain the overall mutation membership function value S;

[0163] Adopt the weighted average method to combine the total fuzzy evaluation matrix B and the total mutation membership function value S of the system to obtain the comprehensive evaluation value C. According to the principle of maximum membership degree, determine the health state level of the dry-type transformer to which the multi-factor state evaluation result belongs.

[0164] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0165] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0166] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including instruction means, and the instruction means implement the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0167] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to perform a series of operational steps on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one process or a plurality of processes and / or blocks Figure 1 one process or a plurality of processes and / or blocks Figure 1 steps for implementing the functions specified in one block or a plurality of blocks.

[0168] Although the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, they are not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that, based on the technical solutions disclosed in the present invention, various modifications or variations that can be made by those skilled in the art without creative efforts should be covered by the scope of protection of the present invention.

Claims

1. A dry-type transformer health status assessment method based on improved fuzzy analytic hierarchy process and catastrophe series method, characterized in that: include: Referring to the structural characteristics, operating principles and common failure modes of dry-type transformers, we selected evaluation indicators that reflect their health status and constructed a classified and hierarchical dry-type transformer health status evaluation indicator system. According to the degree of transformer health degradation, a transformer health status evaluation set is constructed; According to the data properties and normalization standards, normalize the data of each parameter; The G1 weighting method is combined with group decision-making theory, the influence of operating time on the health status of the transformer is considered, and the weight is dynamically adjusted to determine the weight of each layer of indicators; The health index theory is introduced to improve the membership function, calculate the membership of each index to different health states, and combine it with the weight matrix to obtain the total fuzzy evaluation matrix B; According to the requirements of the mutation progression method, determine the mutation model applicable to each group of the indicator layer; derive the normalized calculation formula according to the mutation system type to obtain the mutation membership function value of each indicator; take the average according to the "complementary" principle, recursively calculate from the indicator layer upwards, and obtain the overall mutation membership function value S; The weighted average method is used to combine the total fuzzy evaluation matrix B and the system total mutation membership function value S to obtain the comprehensive evaluation value C; based on the maximum membership principle, the health status level of the dry-type transformer to which the multi-factor status evaluation result belongs is determined.

2. The dry-type transformer health status assessment method based on improved fuzzy analytic hierarchy process and catastrophe series method according to claim 1 is characterized in that: The classified and layered dry-type transformer health status assessment index system is divided into target layer, factor layer and indicator layer; among them, the factor layer includes regular test data, real-time monitoring data and daily operation and maintenance data; the indicators corresponding to the regular test data in the indicator layer include DC resistance, insulation resistance and AC withstand voltage, and the indicators corresponding to the real-time monitoring data are winding hot spot temperature and vibration level; the indicators corresponding to the daily operation and maintenance data are family defects and fault repair conditions.

3. The dry-type transformer health status assessment method based on improved fuzzy analytic hierarchy process and catastrophe series method according to claim 1 is characterized in that: The health status of the transformer is divided into four different levels: normal, caution, abnormal, and severe, and a comment set is established.

4. The dry-type transformer health status assessment method based on improved fuzzy analytic hierarchy process and catastrophe series method according to claim 1 is characterized in that: According to the nature of the data, it is divided into quantitative indicators and qualitative indicators. For quantitative indicators, the linear normalization method is used to project the normalized results into the [0,1] interval; For qualitative indicators, the state values ​​are normalized according to the normalization standard. For the experimental data of AC withstand voltage, the 0-1 method is used for normalization. If the test passes, its normalized state value is 1, otherwise the test fails, its normalized state value is 0.

5. The dry-type transformer health status assessment method based on improved fuzzy analytic hierarchy process and catastrophe series method according to claim 1 is characterized in that: Introducing group decision-making theory, experts ranked the importance of each layer of indicators based on their own experience and professional knowledge, and gave their importance ratios. Using the arithmetic mean method and gathering expert judgments, a comprehensive importance ratio of adjacent indicators was obtained, and the initial weight of each indicator was calculated using the G1 method. Considering the impact of operating time on the health status of the transformer, the initial weight was dynamically adjusted to determine the final weight of each layer of indicators.

6. The dry-type transformer health status assessment method based on improved fuzzy analytic hierarchy process and catastrophe series method according to claim 1 is characterized in that: The improved membership function is: by analyzing the changing characteristics of the health index, taking it as a reference variable and combining it into the original membership distribution function with fixed parameters, the membership function is improved and the membership matrix is ​​constructed so that its distribution conforms to the aging failure law of the transformer. The membership matrix is ​​combined with the indicator weights determined by the improved G1 method to obtain the fuzzy evaluation matrix of the indicator layer. Combined with the weight matrix of the factor layer, the total fuzzy evaluation matrix of the health status of the dry-type transformer is obtained.

7. The dry-type transformer health status assessment method based on improved fuzzy analytic hierarchy process and catastrophe series method according to claim 1 is characterized in that: According to the indicator system, determine the mutation system type of each indicator group. For example, the three indicators in the periodic test data are applicable to the dovetail mutation model, while the indicators of the real-time monitoring data and daily operation and maintenance data are applicable to the cusp mutation model. According to the mutation system type, the potential function is differentiated, and the simultaneous equations are used to obtain the bifurcation point set equation, and then the normalized calculation formula is derived. The normalized indicator data is substituted into the calculation formula to calculate the mutation membership function value of each indicator, and the average is taken according to the "complementary" principle. The total mutation membership function value of the system is obtained by upward recursive calculation.

8. The dry-type transformer health status assessment method based on improved fuzzy analytic hierarchy process and catastrophe series method according to claim 1 is characterized in that: The weighted average method is used to combine the total fuzzy evaluation matrix and the total mutation membership function value of the system, and the comprehensive evaluation value is calculated by the formula. According to the maximum membership principle, the element of the evaluation set corresponding to the maximum evaluation index is taken as the final evaluation result.

9. A dry-type transformer health status assessment system based on improved fuzzy analytic hierarchy process and catastrophe series method, characterized in that: include: The selection module is used to refer to the structural characteristics, operating principles and common failure modes of dry-type transformers, select evaluation indicators that reflect their health status, and build a classified and layered dry-type transformer health status evaluation indicator system; A construction module, used for constructing a transformer health status evaluation set according to the degree of transformer health status degradation; The normalization processing module is used to normalize the data of each parameter according to the data properties and normalization standards; The calculation module is used to use the G1 weighting method combined with group decision-making theory, consider the impact of operating time on the health status of the transformer, and dynamically adjust the weights to determine the weights of indicators at each level; introduce the health index theory to improve the membership function, calculate the membership of each indicator to different health states, and combine it with the weight matrix to obtain the total fuzzy evaluation matrix; determine the applicable mutation model for each group of the indicator layer according to the requirements of the mutation series method; derive the normalized calculation formula according to the mutation system type to obtain the mutation membership function value of each indicator; take the average according to the "complementary" principle, recursively calculate from the indicator layer upward, and obtain the overall mutation membership function value; The comprehensive evaluation module is used to combine the total fuzzy evaluation matrix B and the system total mutation membership function value S by using the weighted average method to obtain a comprehensive evaluation value C; According to the maximum membership principle, the health status level of the dry-type transformer to which the multi-factor status assessment results belong is determined.

10. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes instructions for implementing the dry-type transformer health status assessment method based on the improved fuzzy analytic hierarchy process and the catastrophe series method as described in any one of claims 1 to 8.

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