Ground wire state evaluation method and system based on improved network hierarchy method
By improving the network hierarchy method and combining fuzzy set theory and PCA, a conductor and ground wire state assessment model is constructed, which solves the problems of complex structure and neglect of expert preference fuzziness in traditional models, and achieves efficient and accurate assessment of conductor and ground wire state.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-18
- Publication Date
- 2026-03-31
AI Technical Summary
Traditional network hierarchy model has a complex structure in conductor and ground wire state assessment, ignores the ambiguity of expert decision preferences, and results in the assessment system lacking consideration of external influences.
An improved network hierarchy method is adopted, combined with fuzzy set theory and principal component analysis (PCA) to construct an evaluation index for conductor and ground wire units. An ANP method is improved by intuitionistic fuzzy numbers to establish a conductor and ground wire state assessment model, taking into account the fuzziness and uncertainty of expert decision-making.
It enables accurate assessment of the conductor and ground wire status, improves assessment efficiency and accuracy, and better reflects the actual operating status of the conductor and ground wire.
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Figure CN121765544A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of conductor condition assessment technology, specifically relating to a conductor and ground wire condition assessment method and system based on an improved network hierarchy method. Background Technology
[0002] As one of the most important components of power transmission lines, conductors and ground wires not only transmit current and electrical energy but also shield the conductors, reducing lightning overvoltage. Some ground wires can even be used for communication. As a critical component of the power system, the performance and reliability of conductors and ground wires directly affect the safety and efficiency of power transmission. Therefore, classifying typical conductor and ground wire faults and establishing conductor and ground wire state assessment models is beneficial for quickly locating faults and conducting effective assessments, thereby maintaining the safe operation of the power grid. The Analytic Network Process (ANP) is an extension of the Analytic Hierarchy Process (AHP) used to handle complex multi-criteria decision problems. Its flexibility and adaptability to dynamic environments make it perform well in various fields, and it is often used to build models in the field of state assessment. However, traditional ANP models have complex structures and neglect the weighting coefficients of expert decision preference ambiguity, resulting in a lack of consideration for external influences on the network layer in the assessment system.
[0003] Therefore, it is necessary to design a conductor and ground wire state assessment method and system based on the improved network hierarchy method to solve the above problems. Summary of the Invention
[0004] The technical problem to be solved by this invention is to provide a method and system for evaluating the state of conductors and ground wires based on an improved network hierarchy method. Considering fuzzy set theory, the traditional ANP method is improved, and an evaluation index for conductor and ground wire units after dimensionality reduction by principal component analysis (PCA) is constructed. On this basis, a state evaluation model is constructed to achieve accurate evaluation of the state of conductors and ground wires.
[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method and system for assessing the state of conductors and ground wires based on an improved network hierarchy method includes the following steps: S1, Construct the original parameter system for the conductor and ground wire: Based on industry standard guidelines, several key primary indicators were selected. These indicators cover conductor and ground wire strand breakage, proportion of damaged cross-sectional area, wire breakage, sag deviation, heat generation, wind deflection, loose strands, corrosion, foreign object suspension, drain wire damage, OPGW optical cable wear, detached excess cable, loose downleader wire, and various situations at important OPGW optical cable joints, thereby constructing a primary parameter system for conductors and ground wires. S2, Construct a graded system for conductor and ground wire defect strength: Based on industry standard guidelines, the condition of conductors and ground wires is subdivided into four levels: normal, attention, abnormal, and severe, and corresponding maintenance strategies are formulated for each level. At the same time, a conductor and ground wire defect intensity grading system is formulated in combination with these four levels to assess the defect intensity when a defect occurs in the conductor and ground wire, specifically including four levels: no risk, low risk, medium risk, and high risk. S3, Construct evaluation metrics for conductor-ground wire units after PCA dimensionality reduction; S4. Construct a conductor / ground wire state assessment model based on the improved network hierarchy analysis method.
[0006] Preferably, step S3 uses association rule confidence to construct a confidence matrix of the original parameters of the conductor and ground wire, extracts key parameters from the original parameters, and finally obtains the evaluation index of the conductor and ground wire unit after principal component analysis, specifically including: Based on the fundamental principles of PCA dimensionality reduction, a... The multidimensional matrix X of the state variables of the conductor-to-ground wire element is used to represent the data of the state variables of each element in the conductor-to-ground wire: ; Where m represents the health evaluation of the conductor and ground wire, and n represents the defect status of the conductor and ground wire evaluation index; Based on the obtained multidimensional matrix X, calculate the standardized matrix Y and the correlation coefficient matrix R of X; the standardized matrix Y For multidimensional matrices X The matrix obtained after standardization is centered and scaled to ensure that each variable has a mean of 0 and a variance of 1, thus eliminating the influence of different dimensions between variables. The specific calculation expression is shown below: ; The correlation coefficient matrix R is used to measure the linear correlation between different variables, providing a basis for subsequent principal component analysis. The specific calculation expression is shown below: ; In the formula, Y is the transpose of the normalized matrix X.
[0007] Preferably, singular value decomposition is performed on the correlation coefficient matrix R, including the calculation of the eigenvalues and eigenvectors of matrix R: Let the eigenvalues of the coefficient matrix R be... Arranged in order of size, we can obtain The corresponding feature vector is ; Based on the obtained features and eigenvectors, the number of principal components and their comprehensive scores are now determined: Cumulative contribution rate This is the sum of the variance contribution rates of the first k principal components, used to measure the total explanatory power of the first k principal components for the original data. From characteristic roots The calculation yields the following specific expression: ; When contribution rate When the calculation results are within the 85%-95% confidence interval, we define m principal components to represent n original index information. Let the principal components be... The specific expression is as follows: ; In the formula, The factor loading matrix represents the projected weights of the original indices in the principal component space. for Corresponding feature vectors; The m principal components are weighted, and their combined scores are calculated. The importance of each original indicator, H, is used to rank the original indicators. The specific calculation expression is as follows: ; In the formula, This indicates the weight of the principal components in the overall score; The comprehensive score weight H of the original indicators is calculated using the following formula: ; In the formula, This indicates the relationship between the original indicators and the overall score. The weights; Finally, the original indicators are normalized according to their weight values H. The larger the weight value, the stronger the correlation. In this way, the key indicators of the indicator system are extracted, and the indicators with a weight value greater than or equal to 0.5 are used as the evaluation indicators for assessing the state of the conductor and ground wire.
[0008] Preferably, in step S4, constructing a conductor / ground wire state assessment model based on the improved network hierarchical analysis method includes the following steps: Using the network hierarchical analysis method improved by intuitionistic fuzzy numbers, the importance of each indicator in the conductor and ground line status assessment is evaluated, and the subjective weights corresponding to each indicator are calculated accordingly, thus constructing a conductor and ground line status assessment model. Let X be a nonempty set, and let A be an intuitionistic fuzzy set on X as follows: ; In the formula, It represents the membership degree of x in the set X to the intuitionistic fuzzy set A on X, and is used to indicate the degree of association of fuzzy sets. Let represent the degree of non-membership of x in set X to an intuitionistic fuzzy set A on X, and let represent the degree of non-associative fuzzy sets. The two have the following relationship: ; ; In the formula This represents the degree of hesitation in a set X regarding whether x belongs to an intuitive fuzzy set A on X, and is used to indicate the degree of uncertainty among experts about whether x belongs to the intuitive fuzzy set A. For any two distinct intuitive fuzzy numbers and The distance is represented as follows: ; In the formula, the range of distance is .
[0009] Preferably, the ANP network hierarchy of the conductor / ground wire adopts a two-layer structure: a control layer and a network layer; the analysis steps of the ANP are expressed as follows: Constructing the ANP network model: Based on the index system for evaluating the state of conductors and ground wires, construct the corresponding ANP network structure model; Collect expert preference data: Compile experts' preference judgments on the relative importance of each indicator to form a preference matrix; Generating the hypermatrix: First, an unweighted initial hypermatrix is constructed, and then it is weighted based on expert preference data to form a weighted hypermatrix; Determine the limit weights: By performing limit operations on the weighted supermatrix, the limit supermatrix is obtained, and the limit ranking of each index and its corresponding weight value are determined accordingly. Actual conductor and ground wire environmental factor weighting: Considering the special environmental areas of the conductor and ground wire, a variable weighting formula is introduced for differentiated adjustment.
[0010] Preferably, the elements of the control layer in the ANP are defined as follows: Network layer elements are defined as , Child element is defined as Using control layer elements as the primary criterion and network layer elements as the secondary criterion, The influence of sub-elements in the matrix is compared to construct a judgment matrix; and the weight vector of the judgment matrix is obtained using the eigenvalue method, which is then transformed into a ranking vector. The final result is the local weight vector matrix. The specific expression is as follows: ; in Column vector representation Sub - element set pair Sorting of the influence degree of sub - element sets; If The sub - element set is not affected by the sub - element set, then the weight vector matrix is 0; For Re - construct the local weight vector matrix Finally, obtain the elements of the control layer The unweighted super - matrix W under is expressed as: ; Establish m unweighted super - matrices, and normalize these super - matrices, that is Denote the weighted super - matrix as specific calculation formula is as follows: ; The weighted super - matrix Reflects the further superiority degree of element i to element j, and is an element of the limit super - matrix Perform stability processing on the weighted super - matrix to determine its limit sorting and weight, and obtain the limit super - matrix formula as follows: ; In the formula, when T approaches infinity, exists, and at this time is denoted as the limit super - matrix; The j - th column of is the limit relative sorting vector of each element in the lower network layer to element j.
[0011] [[ID=
[49] ]Preferably, perform the ANP step analysis improved based on intuitionistic fuzzy numbers. The algorithm process is as follows: First, establish evaluation indicators according to the actual problem and construct the evaluation network hierarchy structure diagram of the ground wire state; Express the relative importance between indicators using intuitionistic fuzzy numbers, construct an intuitionistic fuzzy preference matrix, and establish a table to realize the conversion and fusion of the expert evaluation system and intuitionistic fuzzy numbers; Construct a consistent intuitionistic fuzzy preference relationship to obtain a consistent judgment matrix; The calculation formula of When k > i + 1, let where there are: ; When k = i + 1, let ; When k < i, let .
[0012] Preferably, the consistency of the intuitionistic fuzzy preference judgment matrix is verified: Let R be the intuitionistic fuzzy preference judgment matrix. If R satisfies If R is considered to be an acceptable multiplicative consistent intuitionistic fuzzy preference judgment matrix, then the intuitionistic fuzzy preference relation has acceptable consistency; where, This is the consistency threshold; Given an intuitionistic fuzzy preference relation R, the distance measure to its corresponding perfectly product consistent intuitionistic fuzzy preference relation; The calculation expression is as follows: ; Based on the information entropy formula, the intuitionistic fuzzy numbers and intuitionistic fuzzy entropy corresponding to each element in the consistency judgment matrix are calculated. From equation (14) ANP unweighted hypermatrix, the formula for the information entropy of the intuitionistic fuzzy numbers is obtained as follows: ; The unweighted supermatrix in the above formula is weighted to construct the ANP weighted matrix; finally, the weighted supermatrix is stabilized to obtain the limiting supermatrix; in the limiting supermatrix, the data in each column are equal, which are the subjective weights of each indicator.
[0013] Preferably, the weight parameters of the indicators are adjusted differently to take into account the special environmental conditions of the special sections, and the main special sections of the conductor and ground wire are divided into important crossing areas, areas with frequent lightning strikes, areas with frequent bird damage, areas susceptible to external damage, and heavily polluted areas.
[0014] Preferably, the system analyzes and identifies vulnerable key parameters in each special section, determines the impact coefficient based on the severity of their defects, and uses a variable weighting algorithm to dynamically and differentially adjust the weights of the key parameters. Specifically, the coefficients for general defects, major defects, and emergency defects are as follows: , , The variable weight formula is: ; In the formula, The weight is the adjusted value for the i-th key parameter; The original importance weights of each parameter before adjustment were obtained using the analytic hierarchy process (AHP). These are the adjustment coefficients for each parameter.
[0015] The beneficial effects of this invention are as follows: 1. Construct a basic parameter system for conductors and ground wires, covering various conditions such as broken strands, damage, overheating, wind deflection, loose strands, corrosion, foreign object suspension, and fiber optic cable wear. This system can comprehensively reflect the operating status of conductors and ground wires, providing a rich data foundation for subsequent assessments.
[0016] 2. Construct a fault intensity grading system for conductors and ground wires, clearly classifying the condition of conductors and ground wires into four levels: normal, attention, abnormal, and severe, making the assessment results clearer and more explicit.
[0017] 3. Construct evaluation indexes for conductor and ground wire units after PCA dimensionality reduction. The PCA dimensionality reduction method can effectively remove redundant information in the original parameters, reduce the number of indexes, reduce the complexity of the evaluation model, improve evaluation efficiency, and at the same time retain the main information of the data, ensuring the accuracy of the evaluation results.
[0018] 4. Construct a conductor and ground wire state assessment model based on the improved network hierarchical analysis method. Introductory fuzzy numbers are introduced to improve the traditional ANP method. This model takes into account the fuzziness and uncertainty of experts in the decision-making process and combines the objective information of the data, making the assessment model closer to reality and able to more accurately reflect the conductor and ground wire state. Attached Figure Description
[0019] Figure 1 This is a flowchart illustrating the construction of the conductor / ground wire state assessment model in an embodiment of the present invention. Figure 2 This is a diagram of the ANP network hierarchy of the ground wire in an embodiment of the present invention; Figure 3 This is a flowchart of the ANP algorithm improved based on intuitionistic fuzzy numbers in an embodiment of the present invention. Detailed Implementation
[0020] like Figure 1 As shown, a method and system for assessing the state of conductors and ground wires based on an improved network hierarchy method includes the following steps: Example 1: Constructing the original parameter system for conductors and ground wires; Example 2: Constructing a graded system for conductor and ground wire defect strength; Example 3: Constructing evaluation indices for conductor and ground wire elements after PCA dimensionality reduction; Example 4: Constructing a conductor and ground wire state assessment model based on an improved network hierarchy analysis method.
[0021] Example 5: Case Analysis In Embodiment 1, the construction of the original parameter system for the conductor and ground wire includes the following steps: The establishment of the original parameter system for conductors and ground wires is of great significance for their evaluation. To ensure the comprehensiveness of the evaluation, the original parameter system for conductors and ground wires was selected in accordance with industry standard guidelines DL / T 1249-2013 and Q / GDW 1173-2014. Before performing dimensionality reduction using principal component analysis, 14 original primary indicators were constructed, as shown in Table 1.
[0022] Table 1: Original parameter system for conductors and ground wires;
[0023] In Embodiment 2, the construction of a conductor / ground wire defect strength grading system includes the following steps: Example 2.1: Based on industry standard guidelines DL / T 1249-2013 and Q / GDW 1173-2014, the status of conductors and ground wires is divided into the following four categories of indicators: (1) Normal state: When the conductor and ground wire are in the normal state, all kinds of indicators are within the normal deviation range. At this time, the maintenance strategy for the conductor and ground wire in the normal state is mainly to use power outage testing and maintenance or live working methods for testing, maintenance, replacement, etc. (2) Caution Status: When the conductor / ground wire assessment result is "Caution Status," it means that some indicators may be approaching the critical value of the deviation range. In this case, the line needs to adopt corresponding maintenance strategies to restore it to normal. If the line can be restored to normal through live-line maintenance, or by performing potential checks, tests, and maintenance without power interruption, these maintenance methods can be given priority; otherwise, if the above methods cannot achieve the expected results, power outage testing and maintenance must be adopted. It is particularly important to note that if individual indicators in the primary indicators are assessed as "Caution Status," power outage maintenance should be arranged in advance; while when the overall indicator deduction assessment result is "Caution Status," maintenance work can be carried out according to the benchmark cycle.
[0024] (3) Abnormal state: When in an abnormal state, some indicators in the conductor and ground wire have exceeded the allowable deviation range of the specification, but have not yet reached the critical value. The line is in an abnormal working condition. The maintenance type should be determined according to the evaluation results, and maintenance should be carried out in a timely manner.
[0025] (4) Critical condition: When the line is in a critical condition, some indicators are already at the critical value outside the allowable deviation range. At this time, the conductor and ground wire are in a critical condition. The maintenance personnel need to determine the maintenance type based on the evaluation results and carry out maintenance as soon as possible.
[0026] Example 2.2: Based on industry standard guidelines DL / T 1249-2013 and Q / GDW 1173-2014, and considering the four state indicators mentioned in step 2.1, a set of conductor and ground wire defect strength grading system is proposed to evaluate the defect strength when a defect occurs in the conductor and ground wire. The specific indicators are shown in Table 2 below: Table 2: Defect Intensity Grading System for Conductors and Ground Wires;
[0027] In Embodiment 3, the construction of the evaluation index for the conductor-ground element after PCA dimensionality reduction includes the following steps: From Examples 1 and 2, we can obtain the original parameters of conductors and ground wires and the classification system for conductor and ground wire defect intensity in the conductor and ground wire condition assessment system. Considering that each index in the original parameters has a specific impact, and given the special characteristics of conductor and ground wire defects in real-world power grids, some original parameter indices have negligible impact, it is necessary to remove some redundant indices. To ensure the accuracy and rationality of the evaluation system, we now use association rule confidence to construct a confidence matrix for the original parameters of conductors and ground wires, extract key parameters from the original parameters, and finally obtain the conductor and ground wire unit evaluation index after applying the principal component analysis method.
[0028] Example 3.1: Association analysis is one of the core technologies of data mining. Its purpose is to discover interesting associations or relationships between itemsets from large amounts of data. An itemset is a set of several items, such as {beer, diapers}, which constitutes a binary itemset. Association rules help us extract relationships between items in a dataset, including the correlation between items and their interrelationships. Association rules contain three important concepts: support, confidence, and lift. Support represents the proportion of a single event occurring out of the total number of events, often used to identify popular events and filter low-frequency itemsets. Confidence reflects the connection between data, representing the probability of event B occurring given that event A has occurred, often used for high-confidence system recommendations and low-confidence anomaly detection. Lift reflects the correlation between itemsets, representing the degree to which the occurrence of event A increases the probability of event B occurring, often used to remove false associations and for targeted marketing. Association mining rules often apply all three together: support is used to filter high-frequency itemsets, confidence is used to evaluate rule strength, and lift is used to eliminate random associations.
[0029] Now we define itemsets A and B (where A contains itemsets B, B, and B). , , The total number of events is M, and the support is obtained. Represented as A The proportion of itemsets of B to the total number of events M reflects the frequency of the union of A and B in M, as expressed by the following formula: ; in M represents the number of events that simultaneously contain itemsets A and B, and M represents the total number of events.
[0030] Now define confidence level Represented as A The ratio of the number of events in itemset B that appear in the total number of events M to the number of events in itemset A reflects the reliability of itemset A. The formula is as follows: ; Example 3.2: The PCA dimensionality reduction method is a classic unsupervised dimensionality reduction method. It projects high-dimensional data into a low-dimensional space through linear transformation while尽可能保留数据的原始信息. Its core idea is to find the directions (principal components) with the largest data variance and represent the data using these directions, thereby achieving dimensionality reduction. Simply put, it is to reform a set of index sets with fewer indices and independent between different indices (m < n) to replace the original index set containing n indices .
[0031] Example 3.3: First, based on the basic principle of the PCA dimensionality reduction method, construct a -order multi-dimensional matrix X of the geodesic unit state quantity to represent the data of the state quantities of each unit of the ground wire line: ; where m represents the health evaluation of the ground wire state, and n represents the defect state of the ground wire evaluation index
[0032] Example 3.4: According to the obtained multi-dimensional matrix X, calculate the standardized matrix Y and the correlation coefficient matrix R of X. The standardized matrix Y is the matrix obtained after standardizing the multi-dimensional matrix X . Through centering and scaling, the mean of each variable is 0 and the variance is 1, eliminating the influence of the dimension between different variables and making the analysis more accurate. The specific calculation expression is as follows: ; The correlation coefficient matrix R is used to measure the linear correlation between different variables and provide a basis for subsequent principal component analysis. The specific calculation expression is as follows: ; In the formula, is the transpose matrix of the standardized matrix Y of X
[0033] Example 3.5: Perform singular value decomposition on the correlation coefficient matrix R, including the calculation of the eigenvalues and eigenvectors of the matrix R. The eigenvalues are the solutions of the characteristic equation of the correlation coefficient matrix R, representing the variance contribution of each principal component. The eigenvectors are the vectors corresponding to each eigenvalue, representing the direction of each principal component. Both are used to determine the number of principal components and the contribution degree of each principal component, as well as calculate the projection of each variable in the principal component space. Denote the eigenvalues of the coefficient matrix R as , and arranging them in descending order gives , and the corresponding eigenvectors are .
[0034] Example 3.6: Based on the obtained eigenvalues and eigenvectors, determine the number of principal components and their comprehensive scores. The cumulative contribution rate It refers to the sum of the variance contribution rates of the first k principal components, used to measure the overall explanatory power of the first k principal components for the original data. From characteristic roots The calculation yields the following specific expression: ; When contribution rate When the calculation results are within the 85%-95% confidence interval, we define m principal components to represent n original index information. Let the principal components be... The specific expression is as follows: ; In the formula, The factor loading matrix represents the projected weights of the original indices in the principal component space. for Corresponding feature vectors.
[0035] The m principal components are weighted, and their combined scores are calculated. The importance of each original indicator, H, is used to rank the original indicators. The specific calculation expression is as follows: ; In the formula, This indicates the weight of the principal components in the overall score.
[0036] The comprehensive score weight H of the original indicators is calculated using the following formula: ; In the formula, This indicates the relationship between the original indicators and the overall score. The weight.
[0037] Finally, the original indicators are normalized according to their weight values H. The larger the weight value, the stronger the correlation. In this way, the key indicators of the indicator system are extracted, and the indicators with a weight value greater than or equal to 0.5 are used as the evaluation indicators for assessing the state of the conductor and ground wire.
[0038] In Embodiment 4, the conductor / ground wire state assessment model based on the improved network hierarchical analysis method is constructed, including the following steps: The Analytic Hierarchy Process (ANP) is a multi-criteria decision-making method that considers the mutual influence and feedback relationships between decision elements, making it suitable for decision-making problems in complex systems. In ANP, decision-makers need to compare the relative importance of each indicator pairwise, construct a judgment matrix, and then determine the weight of each indicator through a consistency test. Traditional ANP uses a nine-scale method to construct the judgment matrix, requiring experts to provide precise scores (on a scale of 1 to 9) for the relative importance of indicators. However, in actual decision-making processes, experts often struggle to provide precise preference information because the decision-making process typically involves uncertainty and fuzziness. This invention introduces the concept of Intuitionistic Fuzzy Number (IFN) to determine the weight of indicators by calculating the standard deviation and correlation coefficient between each indicator, establishing a conductor-ground wire state evaluation model based on an improved Analytic Hierarchy Process.
[0039] Example 4.1: Using the network hierarchical analysis method improved with intuitionistic fuzzy numbers, the importance of each indicator in the conductor and ground wire condition assessment is evaluated, and the subjective weights corresponding to each indicator are calculated accordingly, thus constructing a conductor and ground wire condition assessment model; the specific flowchart is as follows. Figure 1 As shown.
[0040] Example 4.2: Let X be a non-empty set, and the intuitionistic fuzzy set A on X can be represented as: ; In the formula, It represents the membership degree of x in the set X to the intuitionistic fuzzy set A on X, and is used to indicate the degree of association of fuzzy sets. This represents the degree of non-membership of x in a set X to an intuitionistic fuzzy set A on X, and is used to represent the degree of non-associative fuzzy sets; the two have the following relationship: ; ; In the formula This represents the degree of hesitation in a set X regarding whether x belongs to an intuitive fuzzy set A on X. It is used to express the degree of uncertainty among experts about whether x belongs to an intuitive fuzzy set A.
[0041] For any two distinct intuitive fuzzy numbers and The distance can be represented as follows: ; In the formula, the range of distance is .
[0042] Example 4.3: In network hierarchy analysis, the system typically adopts a two-layer structure: a control layer and a network layer. The control layer contains the overall objective and decision criteria, where the grounding criteria are independent and governed only by the overall objective. However, in practical applications, not all objectives require decision criteria; therefore, the control layer may contain only the objective without decision criteria. The network layer consists of all elements governed by the control layer, and there are interrelationships between its internal elements. A specific structural diagram is shown below. Figure 2 As shown.
[0043] The analysis steps for ANP are as follows: (1) Constructing the ANP network model: Based on the index system for evaluating the state of conductors and ground wires, construct the corresponding ANP network structure model; (2) Collect expert preference data: Compile experts' preference judgments on the relative importance of each indicator to form a preference matrix; (3) Generating the supermatrix: First, an unweighted initial supermatrix is constructed, and then it is weighted based on expert preference data to form a weighted supermatrix; (4) Determine the limit weights: By performing limit operations on the weighted supermatrix, the limit supermatrix is obtained, and the limit ranking of each index and its corresponding weight value are determined accordingly; (5) Weighting of actual conductor and ground wire environmental factors: Considering the special environmental areas of the conductor and ground wire, a variable weighting formula is introduced for differentiated adjustment.
[0044] Example 4.4: Define the elements of the control layer in ANP as follows Network layer elements are defined as , Child element is defined as Using control layer elements as the primary criterion and network layer elements as the secondary criterion, The influence of sub-elements is compared to construct a judgment matrix. The weight vector of the judgment matrix is then calculated using the eigenvalue method, transforming it into a ranking vector. The final result is the local weight vector matrix. The specific expression is as follows: ; in column vector representation Sub-element pairs The order of influence of sub-element sets. If Subsets are not affected If the sub-element set has an influence, then the weight vector matrix is 0. For Reconstruct the local weight vector matrix Finally, the control layer elements are obtained. The unweighted hypermatrix W is represented as: ; Example 4.5: Establish m unweighted hypermatrices, and normalize these hypermatrices, i.e. Let the weighted hypermatrix be . The specific calculation formula is as follows: ; Step 4.6: Weighted Hypermatrix Reflecting the further advantage of element i over element j, it is the limiting supermatrix. By stabilizing the elements of the weighted hypermatrix and determining its limit order and weights, the formula for the limit hypermatrix is obtained as follows: ; In the formula, when T approaches infinity, It exists, at this time it will It is denoted as the limit hypermatrix.
[0045] The j-th column is In most cases, the relative ordering vector of each element in the lower network layer with respect to element j is directly used as the weight of each element in the network structure. If the elements in each group are independent, it needs to be divided by the number of elements in the group before it can be used as the weight of each element in the network structure.
[0046] Example 4.7: Now, we will perform a step-by-step analysis of ANP based on intuitionistic fuzzy numbers. The algorithm flowchart is as follows. Figure 3 As shown. (1) First, establish evaluation indicators based on actual problems and construct a hierarchical structure diagram of the conductor and ground wire status evaluation network; (2) The relative importance of the indicators is represented by intuitionistic fuzzy numbers, and an intuitionistic fuzzy preference matrix is constructed. The intuitionistic fuzzy preference relationships among the indicators are as follows: ,in, Table 3 is established to realize the conversion and integration of expert evaluation system and intuitive fuzzy numbers.
[0047] Table 3: Conversion and integration of expert evaluation system and intuitionistic fuzzy numbers;
[0048] (3) Construct a consistent intuitionistic fuzzy preference relation to obtain a consistent judgment matrix.
[0049] The calculation formula is as follows: When k>i+1, let Among them are, ; When k=i+1, let ; When k < i, let .
[0050] (4) Verify the consistency of the intuitionistic fuzzy preference judgment matrix. Let R be the intuitionistic fuzzy preference judgment matrix. If R satisfies , then R is considered an acceptable multiplicative consistency intuitionistic fuzzy preference judgment matrix, that is, the intuitionistic fuzzy preference relation has acceptable consistency. Among them, is the consistency threshold; is the distance measure from the given intuitionistic fuzzy preference relation R to its corresponding perfect multiplicative consistency intuitionistic fuzzy preference relation. The calculation expression of is as follows: (5) According to the information entropy formula, find the intuitionistic fuzzy entropy of each corresponding intuitionistic fuzzy number in the consistency judgment matrix. From the ANP unweighted supermatrix in formula (14), the intuitionistic fuzzy number information entropy formula is as follows ; Perform weighted calculation on the unweighted supermatrix to construct the ANP weighted matrix. Finally, perform stability processing on the weighted supermatrix to obtain the limit supermatrix. In the limit supermatrix, the data in each column are equal, which is the subjective weight of each index.
[0051] Example 4.8: If considering the special section environment, it is necessary to make differential adjustments to the index weight parameters. The main special sections of the overhead conductors and ground wires are divided into: important cross - over areas, frequent lightning strike areas, multiple bird - damage areas, vulnerable to external force damage areas, and heavy pollution areas, etc.; Through systematic analysis, identify the key parameters vulnerable to influence in each special section, determine the influence coefficient according to the severity level of their defects, and use the variable - weight algorithm to dynamically and differentially correct the weights of the key parameters. That is, the coefficients corresponding to general defects, major defects, and emergency defects are , , respectively. The variable - weight formula is: ; In the formula, is the adjusted weight value of the i - th key parameter (a total of n key parameters); is the original importance weight value obtained by the analytic hierarchy process for each parameter before adjustment; is the adjustment coefficient of each parameter.
[0052] In the said Example 5, the case analysis includes the following steps: Taking a power transmission line section in a central city as the evaluation object, this line passes through plain farmland, mountainous forest areas, and industrial areas near the city, and includes special sections such as areas with frequent lightning strikes, heavily polluted areas, and important crossings. Based on industry standard guidelines DL / T 1249-2013 and Q / GDW 1173-2014, 14 primary indicators were selected, and data were collected from 30 evaluation units of this line section. Some core indicator data are shown in the table below: Table 4 Original parameter system of conductor and ground wire
[0053] Using 30 evaluation units as a sample (m=30) and 14 original indicators as variables (n=14), a 30×14 multidimensional matrix X was constructed. The standardized matrix Y was calculated using a formula to eliminate the influence of dimensions. For example, after standardization, the standardized value of the sag deviation index for unit 20 was 1.82 (original value 5.2%), and the standardized value for unit 1 was -0.53 (original value 2.1%).
[0054] The correlation coefficient matrix R is calculated using the formula. Singular value decomposition is then performed on R to obtain 14 eigenvalues and their corresponding eigenvectors. The results, sorted from largest to smallest eigenvalue, are as follows (first 5 eigenvalues): Table 5 Characteristic parameters of conductor and ground wire
[0055] When the cumulative contribution rate reaches 85%-95%, the number of principal components is determined. Here, the cumulative contribution rate of the first 4 eigenvalues is 91.49%, which meets the requirement, so the first 4 principal components (m=4) are selected. The comprehensive score of the principal components and the importance H of the original indicators are calculated using the formula. After normalization, indicators with a weight value ≥0.5 are selected as the final evaluation indicators. The results are as follows: Table 6. Normalized index weights
[0056] Based on the improved network analytic hierarchy process (AHP) proposed in this paper, the conductor and ground wire status assessment model is used. Using the actual (standardized) measured values of five core indicators and the adjusted indicator weights, a weighted summation method is employed to calculate the comprehensive assessment score of 30 assessment units (score range 0-100, with lower scores indicating worse status). Some unit scores are shown below: Table 7 Calculation of Partial Unit Evaluation Scores
Claims
1. A method for evaluating the state of a ground wire based on an improved network hierarchy method, characterized by, The method comprises the following steps: S1, constructing an original parameter system of the ground wire: S2, constructing a defect intensity grading system of the ground wire: S3, constructing a ground wire unit evaluation index after PCA dimension reduction; S4, constructing a ground wire state evaluation model based on an improved network analytic hierarchy process.
2. The method according to claim 1, wherein, In step S1, a plurality of key original primary indexes are selected according to the industry standard guide, and the indexes cover 14 core indexes including ground wire strand breakage, damaged cross-sectional area ratio, broken wire, sag deviation, heating, wind deflection, loose strand, corrosion, foreign matter suspension, drain wire damage, OPGW cable wear, excess cable separation, loose downlead and various situations of OPGW cable joints of important crossings to construct the original parameter system of the ground wire.
3. The method of claim 1, wherein, Step S2 includes: according to the industry standard guide, the state of the ground wire is subdivided into normal, attention, abnormal and serious four grades, and the corresponding maintenance strategy is formulated for each grade; a ground wire defect intensity grading system is formulated in combination with the four grades, which includes four levels of no risk, low risk, medium risk and high risk, and is used to evaluate the defect intensity of the ground wire when a defect occurs.
4. The method of claim 1, wherein, In step S3, the confidence degree matrix of the original parameters of the ground wire is constructed by using the confidence degree of the association rule, the key parameters in the original parameters are extracted, and finally the ground wire unit evaluation index after the principal component analysis method is obtained, which specifically includes: According to the basic principle of PCA dimension reduction method, a multi-dimensional matrix X of the state quantity of the Nth-order ground wire unit is constructed to represent the data of the state quantity of each unit of the ground wire line: ； Wherein m represents the health degree evaluation of the ground wire state, and n represents the defect state of the ground wire evaluation index; According to the obtained multi-dimensional matrix X, a standardized matrix Y of X and a correlation coefficient matrix R are calculated; the standardized matrix Y Y is a matrix obtained after standardization processing of the multi-dimensional matrix X X, through centralization and scaling, each variable has a mean of 0 and a variance of 1, and the dimensional influence between different variables is eliminated, and the specific calculation expression is as follows: ； The correlation coefficient matrix R is used to measure the linear correlation between different variables, and provides a basis for subsequent principal component analysis, and the specific calculation expression is as follows: ； wherein is the transpose of the standardized matrix Y for X.
5. The method of claim 2, wherein the method is characterized by, The singular value decomposition of the correlation coefficient matrix R includes the calculation of the characteristic root and the characteristic vector of the matrix R: The characteristic root of the coefficient matrix R is , and the size order can be obtained , and the corresponding characteristic vector is ; Based on the obtained characteristic vector and characteristic vector, the number of principal components and the comprehensive score are determined: cumulative contribution rate The sum of the variance contribution rates of the first k principal components, used to measure the total explanatory ability of the first k principal components to the original data, The eigenvalue The specific calculation expression is as follows: ； When the calculation result of the contribution rate is in the confidence interval of 85%-95%, define the expression of n original index information (X1, X2,..., Xn) with m principal components (U1, U2,..., Um) , and the principal components are denoted as (U1, U2,..., Um) . The specific expression is as follows: ； In the formula, denotes the factor loading matrix, embodying the projection weight of the original index in the principal component space, is corresponds to the characteristic vector; The m principal components are weighted, and their combined scores are calculated. The importance of each original indicator, H, is used to rank the original indicators. The specific calculation expression is as follows: ； wherein denotes the weight of the main component on the overall score; The comprehensive score weight H of the original index is calculated, and the specific calculation expression is as follows: ； wherein represents the weight of the original indicator on the composite score . Finally, the original index is normalized according to the weight value H, and the greater the weight value, the stronger the correlation, so as to extract the key index of the index system, and the index with a weight value greater than or equal to 0.5 is used as the evaluation index of the ground wire state.
6. The method of claim 1, wherein, In step S4, the ground wire state evaluation model based on the improved network analytic hierarchy process is constructed, which comprises the following steps: The improved network analytic hierarchy process based on intuitionistic fuzzy number is used to evaluate the importance of each index in the ground wire route state evaluation, and the subjective weight corresponding to each index is calculated according to the evaluation, and the ground wire state evaluation model is constructed; Let X be a non-empty set, and the intuitionistic fuzzy set A on X can be expressed as: ； In the formula, denotes the membership degree of x belonging to the intuitionistic fuzzy set A on X in the set X, which is used to represent the degree of associated fuzzy set; denotes the non-membership degree of x belonging to the intuitionistic fuzzy set A on X in the set X, which is used to represent the degree of non-associated fuzzy set; wherein the two have the following relationship: ； ； In the formula represents the hesitation degree of x belonging to the intuitionistic fuzzy set A on X in the set X, which is used to represent the uncertainty degree of the expert on whether x belongs to the intuitionistic fuzzy set A. The distance between any two different intuitionistic fuzzy numbers and is expressed as follows: ； where the range of distances is .
7. The method of claim 4, wherein the method is characterized by, The ANP network structure of the ground wire adopts a double-layer structure: control layer and network layer; the analysis steps of ANP are as follows: Constructing an ANP network model: according to the index system of the ground wire state evaluation, the corresponding ANP network structure model is built; Collecting expert preference data: collecting the preference judgments of experts on the relative importance of each index to form a preference matrix; Generating a supermatrix: first, an unweighted initial supermatrix is constructed, and then it is weighted based on the expert preference data to form a weighted supermatrix; Limit weight determination: the limit supermatrix is obtained by limit operation on the weighted supermatrix, and the limit ranking of each index and its corresponding weight value are determined accordingly; Actual ground wire environment factor weighting: considering the actual special environment area of the ground wire, a variable weight formula is introduced for differential adjustment.
8. The method of claim 5, wherein, The elements of the control layer in the ANP are defined as , the network layer elements are defined as , sub-elements are defined as ; Using control layer elements as the primary criterion and network layer elements as the secondary criterion, The influence of sub-elements in the matrix is compared to construct a judgment matrix; and the weight vector of the judgment matrix is obtained using the eigenvalue method, which is then transformed into a ranking vector. The final result is the local weight vector matrix. The specific expression is as follows: ； wherein a column vector representation of a set of sub-elements pair a ranking of the influence degree of the set of sub-elements; If the child element set does not affect the weight vector matrix is 0; for , the local weight vector matrix is reconstructed , and finally the control layer element the unweighted hypermatrix W below is represented as: ； m unweighted hypermatrices are established, and these hypermatrices are normalized, that is , and the weighted hypermatrix is denoted as , and the specific calculation formula is shown as follows: ； Weighted hypermatrix The further advantage degree of element i to element j is the element of limit hypermatrix The limit ordering and weight of the weighted hypermatrix are determined by stabilizing the weighted hypermatrix, and the limit hypermatrix is shown as follows: ； where T tends to infinity, there exists, such that denoted the limit hypermatrix; the jth column of the limit relative ordering vector of the elements of the lower network layer with respect to element j.
9. The method of claim 6, wherein, The improved ANP step analysis based on intuitionistic fuzzy numbers is as follows: First, establish the evaluation index according to the actual problem, and construct the ground wire state evaluation network structure diagram; Express the relative importance between indexes with intuitionistic fuzzy numbers, construct the intuitionistic fuzzy preference matrix, and build a table to realize the conversion and fusion of expert evaluation system and intuitionistic fuzzy numbers; Construct a consistent intuitionistic fuzzy preference relationship to obtain a consistent judgment matrix; The calculation formula is as follows: when k > i + 1, let wherein there are: ； When k = i + 1, let ; When k < i, let .
10. The method of claim 7, wherein, Verify the consistency of the intuitionistic fuzzy preference judgment matrix: Let R be an intuitionistic fuzzy preference relation, R is considered to be an acceptable additive consistent intuitionistic fuzzy preference relation, i.e. the intuitionistic fuzzy preference relation has acceptable consistency, if R satisfies , where is a consistency threshold value; is a distance measure from a given intuitionistic fuzzy preference relation R to its corresponding perfect additive consistent intuitionistic fuzzy preference relation; The calculation expression of is shown as follows: ； According to the information entropy formula, the intuitionistic fuzzy entropy of each corresponding intuitionistic fuzzy number in the consistent judgment matrix is calculated, and the intuitionistic fuzzy number information entropy formula is obtained according to formula (14) ANP unweighted supermatrix ； According to the above formula, the weighted supermatrix is calculated, and the ANP weighted matrix is constructed; finally, the limit supermatrix is obtained by stabilizing the weighted supermatrix; in the limit supermatrix, the data in each column are equal, which is the subjective weight of each index.
11. The method of claim 8, wherein, Considering the special section environment, the index weight parameter is adjusted differently, and the main special sections of the ground wire are divided into important cross-over sections, frequent lightning sections, bird damage sections, easy-to-damage sections, and heavy pollution sections.
12. The method of claim 9, wherein, The vulnerable key parameters in each special section are identified by system analysis, the influence coefficients are determined according to the defect severity grading, and the variable weight algorithm is used to dynamically and differentially correct the weights of the key parameters, that is, the coefficients corresponding to general defects, major defects and emergency defects are , , , and the variable weight formula is: ； In the formula, is the adjusted weight value of the ith key parameter; is the original importance weight value of each parameter obtained by the analytic hierarchy process before adjustment; is the adjustment coefficient of each parameter.
13. A ground wire condition assessment system based on an improved network hierarchy method, characterized by, The specific steps of the system based on intuitionistic fuzzy number improved ANP include: first, establish the evaluation index according to the actual demand of ground wire state evaluation, and construct the ground wire state evaluation network structure diagram; second, express the relative importance between indexes with intuitionistic fuzzy numbers, construct the intuitionistic fuzzy preference matrix, and build a table to realize the conversion and fusion of expert evaluation system and intuitionistic fuzzy numbers; third, construct a consistent intuitionistic fuzzy preference relationship to obtain a consistent judgment matrix; fourth, verify the consistency of the intuitionistic fuzzy preference judgment matrix, if it meets the acceptable consistency requirement, then calculate the intuitionistic fuzzy entropy of each intuitionistic fuzzy number; finally, based on the intuitionistic fuzzy entropy, the ANP weighted supermatrix is constructed, the limit supermatrix is obtained by stabilizing the weighted supermatrix, and the subjective weight of each index is obtained.