Health state evaluation method and system for dry-type transformer based on improved fuzzy analytic hierarchy process and catastrophe progression method

Abstract

The application provides a dry-type transformer health state evaluation method and system based on an improved fuzzy analytic hierarchy process and a mutation series method, belongs to the technical field of transformer state evaluation, establishes a health state evaluation index system, constructs a health state comment set according to the health state degradation degree of the transformer, dynamically adjusts the weight, determines the index weight of each layer, calculates the index membership degree, combines the weight matrix to obtain a total fuzzy evaluation matrix, calculates the total mutation membership function value of the system, adopts a weighted average method to comprehensively evaluate the results of the two, determines the health state grade of the dry-type transformer according to the maximum membership degree principle. The application considers the interaction and synergistic effect between indexes, makes up for the deficiency of the existing single-angle evaluation, considers the influence of the operation time, dynamically adjusts the weight, accurately reflects the influence degree of each index on the health state of the transformer in different operation stages, and thus accurately and comprehensively evaluates the health state of the dry-type transformer.

Description

Technical Field

[0001] This invention relates to the field of dry-type transformer condition assessment technology, specifically to a method and system for assessing the health status of dry-type transformers based on an improved fuzzy hierarchical analysis method and a catastrophe series method. Background Technology

[0002] As a key component of the rail transit traction power supply system, the health of dry-type transformers is crucial to ensuring the safe and stable operation of the entire system. However, in actual operation, dry-type transformers are frequently subjected to various voltage and current fluctuations, impact loads, and environmental contaminants such as dust and dirt, inevitably leading to aging and even breakdown of the insulation of corresponding components. Furthermore, as the service life of the dry-type transformer increases, its failure rate rises significantly as it approaches its design lifespan, resulting in frequent breakdowns and consequently affecting the safety and reliability of the traction power supply system.

[0003] To ensure the normal operation of dry-type transformers, most current methods employ condition-based maintenance, using various assessment models to monitor and evaluate their health status. However, existing assessment methods have significant limitations. First, some current methods, when analyzing transformer health status, are often limited to a single perspective, failing to comprehensively consider the interactions and synergistic effects between multiple indicators, resulting in incomplete and inaccurate assessment results. Second, existing assessment models often use static weight allocation to determine indicator weights. As transformer operating time increases, its internal structure and performance change, altering the degree of influence of different indicators on health status. Static weights cannot adapt to these changes, making it difficult for assessment results to accurately reflect the actual operating status of the equipment.

[0004] Therefore, this invention provides a method and system for assessing the health status of dry-type transformers based on an improved fuzzy hierarchical analysis (AHP) and catastrophe series method. This invention organically combines AHP and catastrophe series method to comprehensively assess the health status of transformers from different perspectives. Fuzzy hierarchical analysis is used to construct a comprehensive assessment index system, while catastrophe series method performs in-depth mining and comprehensive calculation of index data, fully considering the interaction and synergistic effects between indicators, thus overcoming the shortcomings of existing single-perspective assessments. Furthermore, this invention introduces group decision-making theory based on the traditional G1 weighting method and dynamically adjusts the weights by considering the impact of operating time on the transformer's health status, enabling the assessment process to accurately reflect the degree of influence of each indicator on the transformer's health status at different operating stages. This method helps maintenance personnel to promptly grasp the operating status of equipment, thereby rationally arranging maintenance decisions and improving the safety and reliability of the traction power supply system. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for assessing the health status of dry-type transformers based on an improved fuzzy hierarchical analysis method and a catastrophe series method, so as to solve at least one of the technical problems existing in the background art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a method for assessing the health status of dry-type transformers based on an improved fuzzy hierarchical analysis method and a catastrophe series method, comprising:

[0008] Based on the structural characteristics, operating principles, and common fault modes of dry-type transformers, S1 selects evaluation indicators that can accurately reflect their health status, thereby constructing a classified and hierarchical health status assessment indicator system for dry-type transformers.

[0009] S2 constructs a set of health status comments for the transformer based on the degree of health status deterioration.

[0010] S3 normalizes the data of each parameter according to the data properties and normalization standards.

[0011] S4 uses the G1 weighting method combined with group decision-making theory to consider the impact of operating time on the health status of the transformer and dynamically adjusts the weights to determine the weights of each level of indicators.

[0012] S5 introduces the health index theory to improve the membership function, calculates the membership degree of each indicator to different health states, and combines it with the weight matrix to obtain the total fuzzy evaluation matrix B.

[0013] Based on the requirements of the mutation series method, S6 determines the applicable mutation model for each group of indicators. A normalized calculation formula is derived according to the mutation system type to obtain the mutation membership function value for each indicator. Following the "complementarity" principle, the mean value is taken, and the calculation is recursively performed upwards from the indicator layer to obtain the overall mutation membership function value S.

[0014] S7 uses a 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. Based on the principle of maximum membership, the health status level of the dry-type transformer to which the multi-factor state evaluation results belong is determined.

[0015] Furthermore, the classification and stratification of the dry-type transformer health status assessment index system in S1 is divided into three layers: target layer, factor layer, and index layer. The factor layer includes periodic test data X1, real-time monitoring data X2, and daily operation and maintenance data X3. The index layer includes indicators corresponding to the periodic test data X1, such as DC resistance X. 11 Insulation resistance X 12 Withstand voltage X 13The real-time monitoring data X2 corresponds to the winding hot spot temperature X. 21 And vibration level X 22 The daily operation and maintenance data X3 corresponds to the indicator of family-related defects X. 31 and troubleshooting status X 32 There are a total of 7 indicators.

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

[0017] Furthermore, in S3, the data is divided into quantitative and qualitative indicators based on its nature. For quantitative indicators, the linear normalization method is used to project the normalization result into the [0,1] interval. For qualitative indicators, technicians combine the actual situation with the normalization standard to normalize the state value. For AC withstand voltage, the 0-1 method is used for normalization. If the test passes, the normalized state value is 1; otherwise, if the test fails, the normalized state value is 0.

[0018] Furthermore, S4 incorporates group decision-making theory, where experts rank the importance of each level of indicators based on their experience and expertise, and provide their importance ratios. Using the arithmetic mean method, expert judgments are aggregated to obtain a comprehensive importance ratio between adjacent indicators. The G1 method is then used to calculate the initial weights of each indicator. Considering the impact of operating time on the transformer's health status, the initial weights are dynamically adjusted to determine the final weights of each level of indicators.

[0019] Furthermore, the improvement method for the traditional membership function in S5 is as follows: by analyzing the changing characteristics of the health index and using it as a reference variable, it is incorporated into the original membership distribution function with fixed parameters. This improves the membership function, constructing a membership matrix whose distribution conforms to the aging and failure patterns of transformers. The membership matrix is ​​then combined with the index weights determined by the improved G1 method to obtain the fuzzy evaluation matrix for the index layer. Finally, combined with the weight matrix for the factor layer, the overall fuzzy evaluation matrix B of the dry-type transformer's health status is derived.

[0020] Furthermore, in step 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 suitable for the swallowtail mutation model, while the indicators in the real-time monitoring data X2 and the daily operation and maintenance data X3 are suitable for the cusp mutation model. Differentiating the potential function according to the mutation system type, the simultaneous equations are used to obtain the bifurcation point set equation, and then the normalized calculation formula is derived. Substituting the normalized indicator data into the calculation formula, the mutation membership function value of each indicator is calculated. The mean value is taken according to the "complementary" principle, and the total mutation membership function value S of the system is obtained by recursively calculating upwards.

[0021] Furthermore, in step S7, a 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 a comprehensive evaluation value C is obtained through a formula. Based on the principle of maximum membership, the evaluation set element O corresponding to the maximum evaluation index is taken as the final evaluation result.

[0022] Secondly, this invention provides a health status assessment system for dry-type transformers based on an improved fuzzy hierarchical analysis method and a catastrophe series method, comprising:

[0023] The module is selected to refer to the structural characteristics, operating principles and common fault modes of dry-type transformers, select evaluation indicators that reflect their health status, and construct a classified and hierarchical health status assessment index system for dry-type transformers.

[0024] A module is built to construct a set of health status comments for the transformer based on the degree of health status deterioration.

[0025] The normalization module is used to normalize the data of each parameter according to the data properties and normalization standards.

[0026] The calculation module utilizes the G1 weighting method combined with group decision-making theory, considering the impact of operating time on the transformer's health status, to dynamically adjust the weights and thus determine the weights of each level of indicators. It introduces health index theory to improve the membership function, calculates the membership degree of each indicator to different health states, and combines this with the weight matrix to obtain the overall fuzzy evaluation matrix. Based on the requirements of the catastrophe series method, it determines the applicable catastrophe model for each group of indicators. It derives a normalized calculation formula based on the catastrophe system type to obtain the catastrophe membership function value of each indicator. Following the "complementary" principle, it takes the mean and recursively calculates upwards from the indicator layer to obtain the overall catastrophe membership function value.

[0027] The comprehensive evaluation module is used to combine the total fuzzy evaluation matrix B and the total mutation membership function value S of the system using the weighted average method to obtain the comprehensive evaluation value C; and to determine the health status level of the dry-type transformer to which the multi-factor state evaluation results belong based on the principle of maximum membership degree.

[0028] Thirdly, the present invention provides a non-transitory computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the dry-type transformer health status assessment method based on the improved fuzzy hierarchical analysis method and the catastrophe series method as described in the first aspect.

[0029] Fourthly, the present invention provides a computer device including a memory and a processor, wherein the processor and the memory communicate with each other, the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the dry-type transformer health status assessment method based on the improved fuzzy hierarchical analysis method and the catastrophe series method as described in the first aspect.

[0030] Fifthly, the present invention provides an electronic device, comprising: 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 to cause the electronic device to execute instructions for implementing the dry-type transformer health status assessment method based on the improved fuzzy hierarchical analysis method and the catastrophe series method as described in the first aspect.

[0031] The beneficial effects of this invention are as follows: It can follow the actual aging and failure laws of transformers during operation, and by organically combining fuzzy hierarchical analysis with catastrophe series method, it can comprehensively evaluate the health status of transformers from different perspectives, fully consider the interaction and synergistic effect between indicators, and make up for the shortcomings of existing single-angle evaluation. At the same time, it uses group decision theory to improve the G1 weighting method, and considers the impact of operating time to dynamically adjust the weights, accurately reflecting the degree of influence of each indicator on the health status of transformers at different operating stages. Thus, it can achieve accurate and comprehensive evaluation of the health status of dry-type transformers, provide a scientific basis for operation and maintenance decisions, and improve the safety and reliability of traction power supply system.

[0032] The advantages of additional aspects of the invention will be set forth more clearly in the following description or will be learned by practice of the invention. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a flowchart of the dry-type transformer health status assessment method based on the improved fuzzy hierarchical analysis method and the catastrophe series method as described in the embodiments of the present invention.

[0035] Figure 2 This is a schematic diagram of the health status assessment index system for dry-type transformers according to an embodiment of the present invention.

[0036] Figure 3 This is the improved semi-ridge semi-trapezoidal membership function distribution diagram described in the embodiment of the present invention.

[0037] Figure 4 This is the improved semi-triangular semi-trapezoidal membership function distribution diagram described in the embodiment of the present invention. Detailed Implementation

[0038] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having 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 limiting the present invention.

[0039] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

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

[0041] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated 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 groups thereof.

[0042] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0043] To facilitate understanding of the present invention, the present invention will be further explained and described below with reference to the accompanying drawings and specific embodiments. However, the specific embodiments do not constitute a limitation on the embodiments of the present invention.

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

[0045] Example 1

[0046] In this embodiment 1, a health status assessment system for dry-type transformers based on an improved fuzzy hierarchical analysis method and a catastrophe series method is first provided. The system includes:

[0047] The module is selected to refer to the structural characteristics, operating principles and common fault modes of dry-type transformers, select evaluation indicators that reflect their health status, and construct a classified and hierarchical health status assessment index system for dry-type transformers.

[0048] A module is built to construct a set of health status comments for the transformer based on the degree of health status deterioration.

[0049] The normalization module is used to normalize the data of each parameter according to the data properties and normalization standards.

[0050] The calculation module utilizes the G1 weighting method combined with group decision-making theory, considering the impact of operating time on the transformer's health status, to dynamically adjust the weights and thus determine the weights of each level of indicators. It introduces health index theory to improve the membership function, calculates the membership degree of each indicator to different health states, and combines this with the weight matrix to obtain the overall fuzzy evaluation matrix. Based on the requirements of the catastrophe series method, it determines the applicable catastrophe model for each group of indicators. It derives a normalized calculation formula based on the catastrophe system type to obtain the catastrophe membership function value of each indicator. Following the "complementary" principle, it takes the mean and recursively calculates upwards from the indicator layer to obtain the overall catastrophe membership function value.

[0051] The comprehensive evaluation module is used to combine the total fuzzy evaluation matrix B and the total mutation membership function value S of the system using the weighted average method to obtain the comprehensive evaluation value C; and to determine the health status level of the dry-type transformer to which the multi-factor state evaluation results belong based on the principle of maximum membership degree.

[0052] In this embodiment, the above-described system is used to implement a method for assessing the health status of dry-type transformers based on the improved fuzzy hierarchical analysis method and the catastrophe series method. The technical solution includes the following steps:

[0053] Based on the structural characteristics, operating principles, and common fault modes of dry-type transformers, S1 selects evaluation indicators that can accurately reflect their health status, thereby constructing a classified and hierarchical health status assessment indicator system for dry-type transformers.

[0054] S2 constructs a set of health status comments for the transformer based on the degree of health status deterioration.

[0055] S3 normalizes the data of each parameter according to the data properties and normalization standards.

[0056] S4 uses the G1 weighting method combined with group decision-making theory to consider the impact of operating time on the health status of the transformer and dynamically adjusts the weights to determine the weights of each level of indicators.

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

[0058] Based on the requirements of the mutation series method, S6 determines the applicable mutation model for each group of indicators. A normalized calculation formula is derived according to the mutation system type to obtain the mutation membership function value for each indicator. Following the "complementarity" principle, the mean value is taken, and the calculation is recursively performed upwards from the indicator layer to obtain the overall mutation membership function value S.

[0059] S7 uses a 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. Based on the principle of maximum membership, the health status level of the dry-type transformer to which the multi-factor state evaluation results belong is determined.

[0060] The classification and stratification of the dry-type transformer health status assessment index system in S1 is divided into three layers: target layer, factor layer, and index layer. The factor layer includes periodic test data X1, real-time monitoring data X2, and daily operation and maintenance data X3. The index layer includes indicators corresponding to the periodic test data X1, such as DC resistance X. 11 Insulation resistance X 12 Withstand voltage X 13 The real-time monitoring data X2 corresponds to the winding hot spot temperature X. 21 And vibration level X 22 The daily operation and maintenance data X3 corresponds to the indicator of family-related defects X. 31 and troubleshooting status X 32 There are a total of 7 indicators.

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

[0062] In S3, the data is divided into quantitative and qualitative indicators based on its nature. For quantitative indicators, the linear normalization method is used to project the normalization result into the [0,1] interval. For qualitative indicators, technicians combine the actual situation with the normalization standard to normalize the state value. For AC withstand voltage, the 0-1 method is used for normalization. If the test passes, the normalized state value is 1; otherwise, if the test fails, the normalized state value is 0.

[0063] In step S4, group decision-making theory is introduced. Experts rank the importance of each level of indicators based on their experience and expertise, and provide their importance ratios. The arithmetic mean method is used to gather expert judgments and obtain a comprehensive importance ratio of adjacent indicators. The G1 method is then used to calculate the initial weights of each indicator. Considering the impact of operating time on the transformer's health status, the initial weights are dynamically adjusted to determine the final weights of each level of indicators.

[0064] The improvement method for the traditional membership function in S5 is as follows: By analyzing the changing characteristics of the health index, it is used as a reference variable and incorporated into the original membership distribution function with fixed parameters. This improves the membership function, constructing a membership matrix whose distribution conforms to the aging and failure patterns of transformers. The membership matrix is ​​then combined with the index weights determined by the improved G1 method to obtain the fuzzy evaluation matrix for the index layer. Finally, combined with the weight matrix for the factor layer, the overall fuzzy evaluation matrix B of the dry-type transformer's health status is derived.

[0065] In step 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 suitable for the swallowtail mutation model, while the indicators in the real-time monitoring data X2 and the daily operation and maintenance data X3 are suitable for the cusp mutation model. Differentiating the potential function according to the mutation system type, the simultaneous equations are used to obtain the bifurcation point set equation, and then the normalized calculation formula is derived. Substituting the normalized indicator data into the calculation formula, the mutation membership function value of each indicator is calculated. The mean value is taken according to the "complementary" principle, and the total mutation membership function value S of the system is obtained by recursively calculating upwards.

[0066] In step S7, a 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 obtain the comprehensive evaluation value C through a formula. Based on the principle of maximum membership, 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 Example 2, a method for assessing the health status of dry-type transformers based on an improved fuzzy hierarchical analysis method and a catastrophe series method is proposed, which specifically includes the following steps:

[0069] Step 1: Starting with the structure of dry-type transformers, this step analyzes common fault types and causes, and organizes relevant state variables that reflect the health status of dry-type transformers. Evaluation indicators are then selected to establish a categorized and hierarchical health status assessment indicator system for dry-type transformers. For example... Figure 2As shown, the health status assessment index system for dry-type transformers can be divided into three layers: the target layer, the factor layer, and the indicator layer. The factor layer includes periodic test data X1, real-time monitoring data X2, and daily operation and maintenance data X3. The indicator layer includes indicators corresponding to the periodic test data X1, such as DC resistance X. 11 Insulation resistance X 12 Withstand voltage X 13 The real-time monitoring data X2 corresponds to the winding hot spot temperature X. 21 And vibration level X 22 The daily operation and maintenance data X3 corresponds to the indicator of family-related defects X. 31 and troubleshooting status X 32 There are a total of 7 indicators.

[0070] Step 2: Based on the degree of health status deterioration of the dry-type transformer, construct a health status evaluation set O = {Normal, Caution, Abnormal, Severe}, where the transformer status descriptions corresponding to each level are shown in Table 1.

[0071] Table 1. Transformer status descriptions for each level.

[0072]

[0073] Step 3: Based on the different nature of the data, it can be divided into quantitative indicators and qualitative indicators.

[0074] Quantitative indicators include real-time monitoring data and periodic test data other than AC withstand voltage. Based on their nature, they can be further divided into higher-than-better and lower-than-better indicators. Using a linear normalization method, the normalized result is projected onto the [0,1] interval, as shown in the following formula:

[0075]

[0076] In the formula, l i Let x be the i-th linearly normalized value. i x is the measured value of this indicator. good Good value; x alarm This is a value to be noted.

[0077] In periodic test data, the three-phase DC resistance imbalance rate B is used as the evaluation standard for DC resistance, and its calculation formula is as follows:

[0078]

[0079] For insulation resistance, the current test value R is compared with the previous test value R. L In comparison, this is used to determine the insulation status of the transformer.

[0080] In real-time monitoring data, the relative temperature difference between the actual measured temperature and the winding hot spot temperature is used as the evaluation standard to determine whether the transformer has thermal defects or faults. The formula for calculating the relative temperature difference is as follows:

[0081]

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

[0083] The vibration level is evaluated using energy entropy, which can effectively identify whether the transformer is in normal working condition.

[0084] The vibration signal is subjected to empirical mode decomposition, and the first six imf components are selected for energy entropy feature extraction. The steps are as follows:

[0085] The energies of different IMF components are combined into a feature vector of a sample. According to the definition of energy, assuming a sample sequence x(t) has a length of N, then the energy of this sample is:

[0086]

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

[0088]

[0089] To observe the changes in different components, the eigenvectors are normalized, and a probabilistic eigenvector is defined:

[0090]

[0091] Where, p k The proportion of the total normalized energy occupied by each energy.

[0092] Based on the definition of information entropy, the energy entropy is then calculated as follows:

[0093]

[0094] Based on the provisions of DL / T 596-2021 Preventive Testing Procedures for Power Equipment, the good reference values ​​and precautionary reference values ​​for each quantitative indicator are summarized and compiled, as shown in Table 2.

[0095] Table 2 Critical Values ​​of Quantitative Indicators

[0096]

[0097] 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 state value is set to 1; otherwise, if the test fails, the normalized state value is set to 0.

[0099] For routine operation and maintenance data, technicians obtain normalized status values ​​based on the normalization standard according to the actual situation, as shown in Table 3.

[0100] Table 3 Normalization Standards for Qualitative Indicators

[0101]

[0102] Step 4: Considering the subjectivity of the weighting method, the G1 weighting method is improved using group decision-making theory. A questionnaire is designed, and multiple participants in the relevant research independently rank the participants. Given the above-mentioned transformer health assessment system structure and weighting method, the questionnaire content is designed as shown in Table 4:

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

[0104]

[0105]

[0106] The questionnaire data was analyzed, and the ranking results from multiple experts were combined. A weighted average method was used to obtain the group's ranking score. The calculation formula is as follows:

[0107]

[0108] Where, S j It is the group ranking score of the j-th indicator, r ij It is the ranking of the j-th indicator by the i-th expert, e i It is the weight of the i-th expert.

[0109] Based on the group ranking score, the weights of each indicator are calculated using the G1 weighting method to obtain the weight allocation for each person for that indicator. Finally, the average value of each indicator in the multi-person evaluation is used as the initial weight of each indicator. The calculation formula is as follows:

[0110]

[0111] Among them, w k0 The initial weight of the k-th indicator is r. i It is the importance ratio of the i-th indicator to the (i+1)-th indicator, where n is the total number of indicators.

[0112] Considering the impact of runtime on the importance of each indicator, the dynamic weight of each indicator is calculated using the following formula:

[0113]

[0114] Among them, w k α is the dynamic weight of the k-th indicator, and α is the influence coefficient of operating time on the weight, ranging from 0.01 to 0.1. t is the operating time of the transformer, and T is the expected total service life of the transformer, both in years.

[0115] To ensure the rationality and additivity of the final weights, the dynamic weights are normalized using the following formula:

[0116]

[0117] After the above processing, the final weights of each indicator are obtained.

[0118] Step 5: Calculate the membership degrees of quantitative and qualitative indicators using improved semi-trapezoidal / semi-ridge and semi-trapezoidal / semi-triangle membership functions, respectively. The specific improvement method is as follows:

[0119] Utilizing the transformer's design lifespan T des The health index HI is obtained by calculating the operational time ΔT, and the intersection point x' of the improved membership function and the x-axis is obtained from this. The relationship expression is as follows:

[0120]

[0121] By using the health index HI as a reference variable and incorporating it into the original membership distribution function with fixed parameters, the membership function is improved, and its distribution is as follows: Figure 3 , Figure 4 As shown.

[0122] For qualitative indicators, a semi-triangular semi-trapezoidal membership function is chosen, while for quantitative indicators, a semi-ridge semi-trapezoidal membership function is chosen.

[0123] The improved membership function for the semi-triangle and semi-trapezoidal shape is shown below:

[0124]

[0125] The improved membership function for the semi-ridge and semi-trapezoidal shape is shown below:

[0126]

[0127]

[0128] The membership matrix R of the index layer X1 RX2 and R X3 Weights w of each state variable index i By combining these methods, we obtain the fuzzy evaluation matrix b for the indicator layer. 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 Six: Based on the indicator system constructed using fuzzy hierarchical analysis, analyze the relationships between the indicators to determine the catastrophe system type. For example, the three indicators in the periodic experimental data X1 are suitable for the swallowtail catastrophe model, while the indicators in the real-time monitoring data X2 and the daily operation and maintenance data X3 are suitable for the cusp catastrophe model. Then, according to the catastrophe system type, differentiate the potential function, solve the simultaneous equations to eliminate the state variables to obtain the bifurcation point set equation, and then derive the normalized calculation formula. The functions, variables, bifurcation equations, and commonly used normalization formulas corresponding to each catastrophe type are shown in Table 5, where x is the system state variable, 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] Based on the corresponding mutation type, the evaluation matrix corresponding to each evaluation index is transformed into a comprehensive evaluation matrix under the mutation membership function using a normalized calculation formula. On this basis, following the "complementarity" principle, the mean is taken, 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 next-level evaluation index. This process is repeated recursively from the bottom of the inverted tree structure to the top to obtain the total mutation membership function value S of the system.

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

[0137] Example 3

[0138] This embodiment 3 provides a non-transitory computer-readable storage medium for storing computer instructions. When executed by a processor, the computer instructions implement the dry-type transformer health status assessment method based on the improved fuzzy hierarchical analysis method and the catastrophe series method as described above. The method includes:

[0139] Based on the structural characteristics, operating principles, and common fault modes of dry-type transformers, evaluation indicators that can accurately reflect their health status are selected, thereby constructing a classified and hierarchical health status assessment indicator system for dry-type transformers.

[0140] Based on the degree of health deterioration of the transformer, construct a set of health status evaluation criteria for the transformer;

[0141] Based on the data properties and normalization standards, the data of each parameter are normalized.

[0142] By using the G1 weighting method combined with group decision-making theory, and considering the impact of operating time on the health status of transformers, the weights are dynamically adjusted to determine the weights of each level of indicators.

[0143] The membership function is improved by introducing the health index theory, calculating the membership degree of each indicator to different health states, and combining it with the weight matrix to obtain the total fuzzy evaluation matrix B.

[0144] Based on the requirements of the mutation series method, the applicable mutation model for each group of indicators is determined. A normalized calculation formula is derived according to the mutation system type to obtain the mutation membership function value of each indicator. Following the "complementarity" principle, the mean value is taken, and the calculation is recursively performed upwards from the indicator layer to obtain the overall mutation membership function value S.

[0145] A weighted average method is used 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. Based on the principle of maximum membership degree, the health status level of the dry-type transformer to which the multi-factor state evaluation results belong is determined.

[0146] Example 4

[0147] This embodiment 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 that can be executed by the processor. The processor calls the program instructions to execute the dry-type transformer health status assessment method based on the improved fuzzy hierarchical analysis method and the catastrophe series method as described above. The method includes:

[0148] Based on the structural characteristics, operating principles, and common fault modes of dry-type transformers, evaluation indicators that can accurately reflect their health status are selected, thereby constructing a classified and hierarchical health status assessment indicator system for dry-type transformers.

[0149] Based on the degree of health deterioration of the transformer, construct a set of health status evaluation criteria for the transformer;

[0150] Based on the data properties and normalization standards, the data of each parameter are normalized.

[0151] By using the G1 weighting method combined with group decision-making theory, and considering the impact of operating time on the health status of transformers, the weights are dynamically adjusted to determine the weights of each level of indicators.

[0152] The membership function is improved by introducing the health index theory, calculating the membership degree of each indicator to different health states, and combining it with the weight matrix to obtain the total fuzzy evaluation matrix B.

[0153] Based on the requirements of the mutation series method, the applicable mutation model for each group of indicators is determined. A normalized calculation formula is derived according to the mutation system type to obtain the mutation membership function value of each indicator. Following the "complementarity" principle, the mean value is taken, and the calculation is recursively performed upwards from the indicator layer to obtain the overall mutation membership function value S.

[0154] A weighted average method is used 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. Based on the principle of maximum membership degree, the health status level of the dry-type transformer to which the multi-factor state evaluation results belong is determined.

[0155] Example 5

[0156] This embodiment 5 provides an electronic device, including: a processor, a memory, and a computer program; wherein, the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to cause the electronic device to execute instructions to implement the dry-type transformer health status assessment method based on the improved fuzzy hierarchical analysis method and the catastrophe series method as described above. The method includes:

[0157] Based on the structural characteristics, operating principles, and common fault modes of dry-type transformers, evaluation indicators that can accurately reflect their health status are selected, thereby constructing a classified and hierarchical health status assessment indicator system for dry-type transformers.

[0158] Based on the degree of health deterioration of the transformer, construct a set of health status evaluation criteria for the transformer;

[0159] Based on the data properties and normalization standards, the data of each parameter are normalized.

[0160] By using the G1 weighting method combined with group decision-making theory, and considering the impact of operating time on the health status of transformers, the weights are dynamically adjusted to determine the weights of each level of indicators.

[0161] The membership function is improved by introducing the health index theory, calculating the membership degree of each indicator to different health states, and combining it with the weight matrix to obtain the total fuzzy evaluation matrix B.

[0162] Based on the requirements of the mutation series method, the applicable mutation model for each group of indicators is determined. A normalized calculation formula is derived according to the mutation system type to obtain the mutation membership function value of each indicator. Following the "complementarity" principle, the mean value is taken, and the calculation is recursively performed upwards from the indicator layer to obtain the overall mutation membership function value S.

[0163] A weighted average method is used 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. Based on the principle of maximum membership degree, the health status level of the dry-type transformer to which the multi-factor state evaluation results belong is determined.

[0164] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0166] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0167] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment, whereby a series of operational steps are performed to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0168] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is 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 effort should be included within the scope of protection of the present invention.

Claims

1. A method for assessing the health status of dry-type transformers based on an improved fuzzy hierarchical analysis method and a catastrophe series method, characterized in that, include: Based on the structural characteristics, operating principles, and common fault modes of dry-type transformers, evaluation indicators reflecting their health status are selected, and a classified and hierarchical health status assessment indicator system for dry-type transformers is constructed. Based on the degree of health deterioration of the transformer, construct a set of health status evaluation criteria for the transformer; Based on the data properties and normalization standards, the data of each parameter are normalized. By employing the G1 weighting method combined with group decision-making theory and considering the impact of operating time on the transformer's health status, the weights are dynamically adjusted to determine the weights of each level of indicators. Taking into account the influence of operating time on the importance of each indicator, the dynamic weights of each indicator are calculated using the following formula: ； in, It is the dynamic weight of the k-th indicator. This is the coefficient representing the impact of runtime on the weights, with a value ranging from 0.01 to 0.

1. For the transformer's operating time, Both refer to the expected total service life of the transformer, expressed in years. It is the initial weight of the k-th indicator; The membership function is improved by introducing the health index theory, calculating the membership degree of each indicator to different health states, and combining it with the weight matrix to obtain the total fuzzy evaluation matrix B. Based on the requirements of the mutation series method, the applicable mutation model for each group of indicators is determined; the normalized calculation formula is derived according to the mutation system type to obtain the mutation membership function value of each indicator; the mean value is taken according to the "complementary" principle, and the calculation is recursively performed from the indicator layer upwards to obtain the overall mutation membership function value S. 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 to obtain the comprehensive evaluation value C; based on the principle of maximum membership degree, the health status level of the dry-type transformer to which the multi-factor status evaluation results belong is determined.

2. The method for assessing the health status of dry-type transformers based on the improved fuzzy hierarchical analysis method and the catastrophe series method according to claim 1, characterized in that, The classification and stratification of the health status assessment index system for dry-type transformers is divided into target layer, factor layer, and index layer. Among them, the factor layer includes periodic test data, real-time monitoring data, and daily operation and maintenance data. In the index layer, the indicators corresponding to the periodic test data include DC resistance, insulation resistance, and AC withstand voltage, while 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-related defects and fault repair status.

3. The method for assessing the health status of dry-type transformers based on the improved fuzzy hierarchical analysis method and the catastrophe series method according to claim 1, characterized in that, The health status of transformers is divided into four different levels: normal, attention, abnormal, and serious, and a set of comments is established.

4. The method for assessing the health status of dry-type transformers based on the improved fuzzy hierarchical analysis method and the catastrophe series method according to claim 1, characterized in that, Based on 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 [0,1] interval. For qualitative indicators, the normalized state values ​​are determined according to the normalization standard. For AC withstand voltage test data, the 0-1 method is used for normalization. If the test passes, the normalized state value is set to 1; otherwise, if the test fails, the normalized state value is set to 0.

5. The method for assessing the health status of dry-type transformers based on the improved fuzzy hierarchical analysis method and the catastrophe series method according to claim 1, characterized in that, By introducing group decision-making theory, experts rank the importance of indicators at each level based on their own experience and professional knowledge, and give their importance ratios. The arithmetic mean method was used, and expert judgment was gathered to obtain a comprehensive ratio of the importance of adjacent indicators. The initial weights of each indicator were calculated using the G1 weighting method. The impact of operating time on the health status of the transformer was considered, and the initial weights were dynamically adjusted to determine the final weights of each level of indicators.

6. The method for assessing the health status of dry-type transformers based on the improved fuzzy hierarchical analysis method and the catastrophe series method according to claim 1, characterized in that, The improved membership function is as follows: by analyzing the changing characteristics of the health index, it is used as a reference variable and combined with the original membership distribution function with fixed parameters. This improves the membership function and constructs a membership matrix so that its distribution conforms to the aging failure law of transformers. By combining the membership matrix with the index weights determined by the improved G1 weighting method, a fuzzy evaluation matrix for the index layer is obtained; then, by combining it with the weight matrix for the factor layer, the overall fuzzy evaluation matrix for the health status of the dry-type transformer is obtained.

7. The method for assessing the health status of dry-type transformers based on the improved fuzzy hierarchical analysis method and the catastrophe series method according to claim 1, characterized in that, Based on the indicator system, the mutation system type of each indicator group is determined; the swallowtail mutation model is applicable to the three indicators in the periodic test data, while the cusp mutation model is applicable to the indicators in the real-time monitoring data and daily operation and maintenance data; the potential function is differentiated according to the mutation system type, and the bifurcation point set equation is obtained by solving the simultaneous equations, 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. The mean value is taken according to the "complementary" principle, and the total mutation membership function value of the system is obtained by recursively calculating upwards.

8. The method for assessing the health status of dry-type transformers based on the improved fuzzy hierarchical analysis method and the catastrophe series method according to claim 1, 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 formula. Based on the principle of maximum membership, the evaluation set element corresponding to the maximum evaluation index is taken as the final evaluation result.

9. A health status assessment system for dry-type transformers based on improved fuzzy hierarchical analysis and catastrophe series method, characterized in that, include: The module is selected to refer to the structural characteristics, operating principles and common fault modes of dry-type transformers, select evaluation indicators that reflect their health status, and construct a classified and hierarchical health status assessment index system for dry-type transformers. A module is built to construct a set of health status comments for the transformer based on the degree of health status deterioration. The normalization module is used to normalize the data of each parameter according to the data properties and normalization standards. The calculation module utilizes the G1 weighting method combined with group decision-making theory, considering the impact of operating time on the transformer's health status, to dynamically adjust the weights, thereby determining the weights of each level of indicators. Taking into account the impact of operating time on the importance of each indicator, the dynamic weights of each indicator are calculated using the following formula: ； in, It is the dynamic weight of the k-th indicator. This is the coefficient representing the impact of runtime on the weights, with a value ranging from 0.01 to 0.

1. For the transformer's operating time, Both refer to the expected total service life of the transformer, expressed in years. The initial weight of the k-th indicator is given. The membership function is improved by introducing the health index theory. The membership degree of each indicator to different health states is calculated and combined with the weight matrix to obtain the total fuzzy evaluation matrix. According to the requirements of the mutation series method, the applicable mutation model for each group of indicators is determined. The normalized calculation formula is derived according to the mutation system type to obtain the mutation membership function value of each indicator. The mean value is taken according to the "complementary" principle, and the calculation is recursively performed from the indicator layer upwards to obtain the overall mutation membership function value. The comprehensive evaluation module is used to combine the total fuzzy evaluation matrix B and the total mutation membership function value S of the system using the weighted average method to obtain the comprehensive evaluation value C; and to determine the health status level of the dry-type transformer to which the multi-factor state evaluation results belong based on the principle of maximum membership degree.

10. An electronic device, characterized in that, include: The electronic device includes 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 to cause the electronic device to execute instructions that implement the dry-type transformer health status assessment method based on the improved fuzzy hierarchical analysis method and the catastrophe series method as described in any one of claims 1-8.

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