Cable insulation condition detection method based on charged harmonic anomalies

Abstract

This application relates to a method for detecting the insulation condition of cables based on charged harmonic anomalies. The method includes: acquiring measurement data of multiple attributes of the experimental cable; determining the fuzzy evaluation of the insulation condition based on the attribute measurement data; determining the initial weight of each attribute using the fuzzy entropy weighting method; constructing an original data matrix based on the initial weight of each attribute, and calculating the correlation between attributes to form a correlation coefficient matrix; determining the correction coefficient of each attribute based on the correlation coefficient matrix, and correcting the initial weight of each attribute based on the correction coefficient to obtain the corrected weight of each attribute; for each attribute, obtaining the fuzzy weighted calculation results and synthesizing them into a fuzzy comprehensive evaluation value for the experimental cable; and determining the insulation condition of the experimental cable based on the fuzzy comprehensive evaluation value. This method can improve the accuracy of cable insulation condition assessment and judgment.

Description

Technical Field

[0001] This application relates to the field of power safety monitoring technology, and in particular to a method for detecting the insulation status of cables based on live harmonic anomalies. Background Technology

[0002] With the continuous development of modern power systems, cables, as a crucial component of power transmission, are directly related to the overall operational efficiency and reliability of the power system in terms of safety and stability. Cable insulation degradation has always been a significant factor affecting the stable operation of power systems. How to accurately and effectively assess the insulation condition of cables and predict their future degradation trends has become a critical issue that urgently needs to be addressed in power system operation and maintenance.

[0003] In comprehensive cable condition assessment, cable attributes (such as insulation resistance, harmonic characteristics, and operating environment) are often not independent but rather correlated and interact. However, many existing methods, such as traditional Fuzzy Multiple Attribute Decision Modeling (Fuzzy MADM) and Analytic Hierarchy Process (AHP), typically assume that attributes are independent during model construction. This assumption may lead to the neglect of complex relationships between attributes, thus affecting the accuracy of the assessment results. Ignoring the correlation between attributes may result in the underestimation or overestimation of certain key risk factors, especially when there are significant interactions between attributes. This deficiency may weaken the scientific rigor and reliability of the assessment, affecting the accurate judgment of cable insulation condition.

[0004] Therefore, improving the accuracy of cable insulation condition assessment and judgment remains a problem that needs to be solved. Summary of the Invention

[0005] Therefore, it is necessary to provide a cable insulation condition detection method based on live harmonic anomalies that can improve the accuracy of cable insulation condition assessment and judgment, addressing the aforementioned technical problems.

[0006] In a first aspect, this application provides a method for detecting the insulation condition of cables based on live harmonic anomalies, including:

[0007] Acquire measurement data of multiple attributes of the experimental cable, wherein the multiple attributes include any combination of conductor current, typical local defects, current-carrying aging time, partial discharge level, insulation resistance, dielectric loss factor, service life, laying method and load type;

[0008] For each attribute, a fuzzy evaluation of the attribute's influence on the insulation state is determined based on the attribute's measurement data;

[0009] The initial weight of each attribute is determined using the fuzzy entropy weight method;

[0010] An original data matrix is ​​constructed based on the initial weight of each attribute, and the correlation between each attribute is calculated. A correlation coefficient matrix between each attribute is formed based on the correlation and the original data matrix.

[0011] The correction coefficients of each attribute are determined based on the correlation coefficient matrix between the attributes, and the initial weights of each attribute are corrected based on the correction coefficients of each attribute to obtain the corrected weights of each attribute.

[0012] For each attribute, a fuzzy weighted calculation is performed based on the fuzzy evaluation and weight corresponding to the attribute to obtain the fuzzy weighted calculation result of the attribute;

[0013] The fuzzy weighted calculation results of the multiple attributes are combined using fuzzy operators to form a fuzzy comprehensive evaluation value for the experimental cable.

[0014] The insulation state of the experimental cable is determined based on the fuzzy comprehensive evaluation value.

[0015] In one embodiment, determining the initial weight of each attribute using the fuzzy entropy weighting method includes:

[0016] The evaluation values ​​of each decision scheme under different attributes are represented as fuzzy numbers, and the fuzzy number of each attribute is obtained.

[0017] The fuzzy number of each attribute is standardized to make the fuzzy number of each attribute at the same scale, wherein the standardization process includes at least one of maximum normalization, minimum normalization, and vector normalization;

[0018] The information entropy of each attribute is determined based on the fuzzy number of each attribute after standardization.

[0019] For each attribute, the initial weight is determined based on the information entropy.

[0020] In one embodiment, determining the information entropy of each attribute based on the fuzzy number of each attribute after standardization includes:

[0021] Obtain the normalized value of the attribute on the scheme, and obtain the logarithm of the normalized value;

[0022] The integral value between the normalized value and the logarithmic value is determined based on the number of properties possessed by the experimental cable.

[0023] The information entropy of the attribute is determined based on the integral value.

[0024] In one embodiment, determining the initial weights based on information entropy includes:

[0025] The initial weights are determined based on the information entropy of the attributes and the number of attributes of the experimental cable.

[0026] In one embodiment, calculating the correlation between the attributes and forming a correlation coefficient matrix between the attributes based on the correlation and the original data matrix includes:

[0027] The correlation between each attribute is calculated using the Pearson correlation coefficient statistical algorithm or the Spearman rank correlation coefficient statistical algorithm, and a correlation coefficient matrix between each attribute is formed based on the correlation and the original data matrix.

[0028] In one embodiment, determining the correction coefficient based on the correlation coefficient between the attributes includes:

[0029] Obtain the absolute value of the correlation coefficient between the first attribute and the second attribute;

[0030] The integral value of the absolute value is determined based on the number of experimental cable properties.

[0031] The correction factor is determined based on the number and integral value of the experimental cable properties.

[0032] Secondly, this application also provides a cable insulation condition detection device based on charged harmonic anomalies, comprising:

[0033] The acquisition module is used to acquire measurement data of multiple attributes of the experimental cable, wherein the multiple attributes include any combination of conductor current, typical local defects, current-carrying aging time, partial discharge level, insulation resistance, dielectric loss factor, service life, laying method and load type.

[0034] The processing module is used to determine the fuzzy evaluation of the insulation state of each attribute based on the attribute's measurement data.

[0035] The acquisition module is also used to determine the initial weight of each attribute using the fuzzy entropy weight method;

[0036] The processing module is further configured to construct an original data matrix based on the initial weight of each attribute, calculate the correlation between each attribute, and form a correlation coefficient matrix between each attribute based on the correlation and the original data matrix.

[0037] The processing module is further configured to determine the correction coefficient of each attribute based on the correlation coefficient matrix between each attribute, and to correct the initial weight of each attribute based on the correction coefficient of each attribute, so as to obtain the corrected weight of each attribute.

[0038] The processing module is also used to use fuzzy operators to synthesize the fuzzy weighted calculation results of the multiple attributes into a fuzzy comprehensive evaluation value for the experimental cable.

[0039] The processing module is also used to determine the insulation state of the experimental cable based on the fuzzy comprehensive evaluation value.

[0040] Thirdly, this application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0041] Acquire measurement data of multiple attributes of the experimental cable, wherein the multiple attributes include any combination of conductor current, typical local defects, current-carrying aging time, partial discharge level, insulation resistance, dielectric loss factor, service life, laying method and load type;

[0042] For each attribute, a fuzzy evaluation of the attribute's influence on the insulation state is determined based on the attribute's measurement data;

[0043] The initial weight of each attribute is determined using the fuzzy entropy weight method;

[0044] An original data matrix is ​​constructed based on the initial weight of each attribute, and the correlation between each attribute is calculated. A correlation coefficient matrix between each attribute is formed based on the correlation and the original data matrix.

[0045] The correction coefficients of each attribute are determined based on the correlation coefficient matrix between the attributes, and the initial weights of each attribute are corrected based on the correction coefficients of each attribute to obtain the corrected weights of each attribute.

[0046] For each attribute, a fuzzy weighted calculation is performed based on the fuzzy evaluation and weight corresponding to the attribute to obtain the fuzzy weighted calculation result of the attribute;

[0047] The fuzzy weighted calculation results of the multiple attributes are combined using fuzzy operators to form a fuzzy comprehensive evaluation value for the experimental cable.

[0048] The insulation state of the experimental cable is determined based on the fuzzy comprehensive evaluation value.

[0049] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0050] Acquire measurement data of multiple attributes of the experimental cable, wherein the multiple attributes include any combination of conductor current, typical local defects, current-carrying aging time, partial discharge level, insulation resistance, dielectric loss factor, service life, laying method and load type;

[0051] For each attribute, a fuzzy evaluation of the attribute's influence on the insulation state is determined based on the attribute's measurement data;

[0052] The initial weight of each attribute is determined using the fuzzy entropy weight method;

[0053] An original data matrix is ​​constructed based on the initial weight of each attribute, and the correlation between each attribute is calculated. A correlation coefficient matrix between each attribute is formed based on the correlation and the original data matrix.

[0054] The correction coefficients of each attribute are determined based on the correlation coefficient matrix between the attributes, and the initial weights of each attribute are corrected based on the correction coefficients of each attribute to obtain the corrected weights of each attribute.

[0055] For each attribute, a fuzzy weighted calculation is performed based on the fuzzy evaluation and weight corresponding to the attribute to obtain the fuzzy weighted calculation result of the attribute;

[0056] The fuzzy weighted calculation results of the multiple attributes are combined using fuzzy operators to form a fuzzy comprehensive evaluation value for the experimental cable.

[0057] The insulation state of the experimental cable is determined based on the fuzzy comprehensive evaluation value.

[0058] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0059] Acquire measurement data of multiple attributes of the experimental cable, wherein the multiple attributes include any combination of conductor current, typical local defects, current-carrying aging time, partial discharge level, insulation resistance, dielectric loss factor, service life, laying method and load type;

[0060] For each attribute, a fuzzy evaluation of the attribute's influence on the insulation state is determined based on the attribute's measurement data;

[0061] The initial weight of each attribute is determined using the fuzzy entropy weight method;

[0062] An original data matrix is ​​constructed based on the initial weight of each attribute, and the correlation between each attribute is calculated. A correlation coefficient matrix between each attribute is formed based on the correlation and the original data matrix.

[0063] The correction coefficients of each attribute are determined based on the correlation coefficient matrix between the attributes, and the initial weights of each attribute are corrected based on the correction coefficients of each attribute to obtain the corrected weights of each attribute.

[0064] For each attribute, a fuzzy weighted calculation is performed based on the fuzzy evaluation and weight corresponding to the attribute to obtain the fuzzy weighted calculation result of the attribute;

[0065] The fuzzy weighted calculation results of the multiple attributes are combined using fuzzy operators to form a fuzzy comprehensive evaluation value for the experimental cable.

[0066] The insulation state of the experimental cable is determined based on the fuzzy comprehensive evaluation value.

[0067] The aforementioned cable insulation condition detection method based on live harmonic anomalies integrates insulation performance data such as induced current harmonic characteristics, insulation resistance status, and power frequency dielectric loss, as well as inherent conditions such as cable length, voltage level, laying conditions, and operating environment. It employs a fuzzy attribute overlap decision method to systematically evaluate and predict the comprehensive insulation condition of the cable. When cable attributes overlap, traditional fuzzy entropy weighting methods may not effectively handle the interdependence and overlapping effects between attributes because they typically assume that each attribute is independent. In cases of attribute overlap, the calculation of information entropy may lose some validity, leading to inaccurate weight allocation. In traditional fuzzy multi-attribute decision methods, each attribute is usually considered independent, while attribute overlap methods (methods that establish correlation coefficient matrices) consider the mutual influence between attributes. The method provided in this embodiment is particularly suitable for determining the cable insulation condition when there is a certain degree of correlation or overlap between multiple attributes. This embodiment introduces correlation coefficients between attributes to represent the interaction relationships between attributes, thereby improving the accuracy of cable insulation condition assessment and judgment. Attached Figure Description

[0068] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0069] Figure 1 This is an application environment diagram of a cable insulation condition detection method based on charged harmonic anomalies in one embodiment;

[0070] Figure 2 This is a flowchart illustrating a cable insulation condition detection method based on charged harmonic anomalies in one embodiment.

[0071] Figure 3 This is a schematic diagram of the experimental cable in one embodiment;

[0072] Figure 4 This is a flowchart illustrating one step of a cable insulation condition detection method based on charged harmonic anomalies in one embodiment.

[0073] Figure 5 This is a structural block diagram of a cable insulation condition detection device based on charged harmonic anomalies in one embodiment;

[0074] Figure 6This is a diagram of the internal structure of an electronic device in one embodiment. Detailed Implementation

[0075] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0076] The cable insulation condition detection method based on charged harmonic anomalies provided in this application can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104, or it can be located in the cloud or on other network servers. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. Server 104 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing cloud computing services.

[0077] In one exemplary embodiment, such as Figure 2 As shown, a method for detecting the insulation condition of cables based on live harmonic anomalies is provided, and this method is applied to... Figure 1 Taking the server in the example, the explanation includes the following steps 201 to 206. Wherein:

[0078] S201, acquire measurement data of multiple attributes of the test cable, including any number of conductor current, typical local defects, current aging time, partial discharge level, insulation resistance, dielectric loss factor, service life, laying method and load type.

[0079] First, an experimental platform for harmonic detection of distribution network cables was constructed to detect harmonic signals within the metallic shielding layer of cables in insulation defect samples. Statistical feature analysis of the collected harmonic signal data was then performed to further extract the variation patterns of harmonic signal characteristics under different conditions.

[0080] Various defective cable samples were fabricated, and multiple sets of equipment, including through-core transformers, series resonant step-up devices, and harmonic signal detection devices, were installed. A simulated operation and harmonic detection experimental platform for distribution network cables, considering typical defects and insulation degradation conditions, was established. Figure 3 As shown.

[0081] Four types of 10kV distribution network cable samples were prepared, including those with water droplet defects, semi-conductive suspension defects, metallic suspension defects, and a defect-free sample as a control. The defect locations are shown in the figure. Figure 3 As shown. Each cable sample is 15 meters long and has a defect size of 1×3 cm. The water droplet defect is achieved by applying a water film within the grooves of the insulation layer. The experimental cable provided in this embodiment is one of four 10kV distribution network cable samples.

[0082] from Figure 3 As can be seen, the conductor sections of the defective cable sample, the defect-free cable sample, and the tie cable are first connected in series end to end. The ends of the metal shields of the two sample cables are then connected to form loops. A feedthrough transformer is then used to apply the rated current to the cable loops. Finally, a harmonic signal detection device simultaneously measures and collects the harmonic current data in the metal shield loops of the two sample cables. Furthermore, during the harmonic data measurement interval, the conductor temperature of all cable samples is maintained at 90°C by applying current to simulate the current-carrying aging process of the cable insulation.

[0083] By simulating the current-carrying aging process of cable insulation, three factors affecting the characteristics of harmonic anomalies were identified: conductor current, local typical defects, and current-carrying aging time.

[0084] In addition, there are six external environmental and historical data factors that affect the insulation characteristics of cables: partial discharge level, insulation resistance, dielectric loss factor, service life, laying method, and load type.

[0085] Therefore, these multiple attributes include any number of conductor current, typical local defects, current-carrying aging time, partial discharge level, insulation resistance, dielectric loss factor, service life, laying method, and load type.

[0086] S202, for each attribute, determine the fuzzy evaluation of the attribute's relationship to the insulation state based on the attribute's measurement data.

[0087] The measurement data for the attributes can come from on-site testing, historical records, or expert evaluation.

[0088] The data for each attribute is converted into a fuzzy number (such as a triangular fuzzy number or a trapezoidal fuzzy number) to represent the fuzzy evaluation of the attribute's relationship to the insulation state. For example:

[0089] The conductor current level is represented by fuzzy numbers [10,50,100] for "low current" state, [200,250,300] for "normal" state, and [400,500,600] for "deteriorated" state.

[0090] The typical local defects are represented by fuzzy numbers [100,104,109] to indicate the "generally damp" state, [107,115,124] to indicate the "minor discharge defect" state, and [106,109,129] to indicate the "severe discharge defect" state.

[0091] The partial discharge level is represented by fuzzy numbers [0, 0.1, 0.2] for "good" state, [100, 200, 300] for "normal" state, and [1000, 2000, 3000] for "deteriorated" state.

[0092] The dielectric loss factor [0.001, 0.002, 0.003] indicates "low loss", [0.004, 0.005, 0.006] indicates "high loss", and [0.007, 0.008, 0.009] indicates "high loss".

[0093] Insulation resistance [10k, 30k, 50k] indicates "low resistance", [1M, 2M, 5M] indicates "medium resistance", and [100M, 200M, 500M] indicates "high resistance".

[0094] S203, the initial weight of each attribute is determined by the fuzzy entropy weight method.

[0095] The fuzzy entropy weighting method is used to determine the initial weights of each attribute. Fuzzy entropy weighting is a method combining fuzzy logic and information entropy theory, primarily used to dynamically calculate the weights of each attribute in multi-attribute decision-making problems. Compared to traditional weight determination methods, fuzzy entropy weighting effectively reduces subjective bias and provides a more objective, data-driven weight allocation method.

[0096] S204. Construct an original data matrix based on the initial weight of each attribute, calculate the correlation between each attribute, and form a correlation coefficient matrix between each attribute based on the correlation and the original data matrix.

[0097] Statistical algorithms such as Pearson correlation coefficient or Spearman rank correlation coefficient can be used to calculate the correlation or correlation coefficient between attributes. Based on this correlation and the original data matrix, a correlation coefficient matrix R is formed. The elements in the correlation coefficient matrix R represent the degree of overlap between two attributes. The degree of overlap can be determined through expert evaluation, historical data analysis, or correlation calculation.

[0098] When attributes are positively correlated, there is a positive association between them; the closer the correlation coefficient is to 1, the stronger the association.

[0099] When attributes are negatively correlated, there is a negative association between them. The closer the correlation coefficient is to -1, the stronger the association.

[0100] When there is no correlation between attributes, the correlation coefficient is close to 0, indicating that the attributes are independent.

[0101] S205. Determine the correction coefficient of each attribute based on the correlation coefficient matrix between the attributes, and correct the initial weight of each attribute based on the correction coefficient of each attribute to obtain the corrected weight of each attribute.

[0102] Specifically, for each attribute, the correction coefficient is multiplied by the initial weight of the attribute to obtain the weight of the attribute.

[0103] For example, according to the formula Determine the weight of each attribute, where ω j The initial weight of the attribute, r j This represents the correction factor.

[0104] In one embodiment, the weights of all attributes are normalized: Ensure that the sum of all weights is 1.

[0105] By considering the correlation between attributes and reducing the impact of redundant attributes, the evaluation results are made more representative. The revised weights more accurately reflect the contribution of each attribute to the overall evaluation, improving the accuracy and reliability of the evaluation model.

[0106] S206. For each attribute, perform fuzzy weighted calculation based on the fuzzy evaluation and weight corresponding to the attribute to obtain the fuzzy weighted calculation result of the attribute.

[0107] Calculate the redundancy of each attribute based on the correlation coefficient matrix R. Attributes with high redundancy may have their information partially contained in other attributes, and their weights should be appropriately reduced.

[0108] In one embodiment, the absolute value of the correlation coefficient between the first attribute and the second attribute is obtained. The integral value of this absolute value is determined based on the number of experimental cable attributes. A correction coefficient is determined based on the number of experimental cable attributes and the integral value. For example, according to the formula... Determine the correction factor, where r jk The coefficient represents the correlation between attribute j and attribute k, and n represents the total number of attributes of the experimental cable.

[0109] S207. Use fuzzy operators to synthesize the fuzzy weighted calculation results of the multiple attributes into a fuzzy comprehensive evaluation value for the experimental cable.

[0110] First, the fuzzy weighted calculation results are defuzzified, converting the fuzzy comprehensive evaluation values ​​into explicit numerical values ​​for comparison and ranking. Commonly used defuzzification methods include the centroid method and the maximum membership method.

[0111] S208. The insulation status of the experimental cable is determined based on the fuzzy comprehensive evaluation value.

[0112] Based on the defuzzified values, the insulation condition of the cables can be classified (e.g., "good", "average", "poor"). Alternatively, different cables can be sorted by insulation condition to determine priority maintenance targets.

[0113] In one embodiment, the obtained insulation state can also be analyzed to interpret the insulation state of each cable. Influence diagrams of each attribute can be drawn to help identify key influencing factors.

[0114] In one embodiment, the cable's insulation condition supports operational and maintenance decisions. For cables in poor condition, more detailed inspection or maintenance plans can be developed.

[0115] In one embodiment, the fuzzy evaluation model and weight allocation can be adjusted in a timely manner based on actual operation and maintenance feedback and evaluation results to improve the accuracy of the evaluation.

[0116] In one embodiment, the evaluation model can be continuously optimized by incorporating new data or introducing new attributes to improve the accuracy and reliability of the comprehensive evaluation of cable insulation status.

[0117] The aforementioned cable insulation condition detection method based on live harmonic anomalies integrates insulation performance data such as induced current harmonic characteristics, insulation resistance status, and power frequency dielectric loss, as well as inherent conditions such as cable length, voltage level, laying conditions, and operating environment. A fuzzy attribute overlap decision method is then used to systematically evaluate and predict the comprehensive insulation condition of the cable. When cable attributes overlap, traditional fuzzy entropy weighting methods may not effectively handle the interdependence and overlapping effects between attributes because they typically assume that each attribute is independent. In cases of attribute overlap, the calculation of information entropy may lose some validity, leading to inaccurate weight allocation. In traditional fuzzy multi-attribute decision methods, each attribute is usually considered independent, while attribute overlap methods (methods that establish correlation coefficient matrices) consider the mutual influence between attributes. The method provided in this embodiment is particularly suitable for determining the cable insulation condition when multiple attributes have a certain degree of correlation or overlap. This embodiment introduces correlation coefficients between attributes to represent the interaction relationships between attributes, thereby improving the accuracy of cable insulation condition assessment and judgment.

[0118] In one exemplary embodiment, such as Figure 3 As shown, step S203 includes steps S401 to 304.

[0119] in:

[0120] S401, represent the evaluation values ​​of each decision scheme under different attributes as fuzzy numbers, and obtain the fuzzy number of each attribute.

[0121] The first step in the fuzzy entropy weight method is to construct a fuzzy decision matrix. Specifically, the evaluation values ​​of each decision scheme under different attributes are represented as fuzzy numbers, forming a fuzzy decision matrix. This matrix reflects the performance of each scheme under various attributes.

[0122] S402, standardize the fuzzy number of each attribute so that the fuzzy number of each attribute is on the same scale, wherein the standardization process includes at least one of maximum normalization, minimum normalization, and vector normalization.

[0123] The second step in the fuzzy entropy weighting method is the fuzzy number standardization process. Specifically, in order to eliminate the dimensional differences between different attributes, the fuzzy numbers need to be standardized. Max-min normalization or vector normalization methods are typically used to transform the fuzzy numbers of each attribute to the same scale.

[0124] Step 403: Determine the information entropy of each attribute based on the fuzzy number of each attribute after standardization.

[0125] The third step of the fuzzy entropy weight method is to calculate the information entropy of each attribute.

[0126] Specifically, for the standardized fuzzy decision matrix, the information entropy of each attribute is calculated.

[0127] In one embodiment, a normalized value of the attribute over the scheme is obtained, and the logarithm of the normalized value is obtained. An integral value between the normalized value and the logarithm is determined based on the number of attributes possessed by the experimental cable. The information entropy of the attribute is determined based on the integral value.

[0128] For example, according to the formula Determine the information entropy of each attribute.

[0129] Among them, P ij It is the normalized value of attribute j in scheme i. k is a constant, usually k is 1 / ln(n).

[0130] Step 404: For each attribute, determine the initial weight based on the information entropy.

[0131] The fourth step of the fuzzy entropy weight method is to calculate the entropy weight of each attribute. Specifically, the initial weights are determined based on the information entropy of the attributes and the number of attributes of the experimental cable.

[0132] Based on the calculated information entropy, the weight of each attribute is calculated. The formula for calculating entropy weight is:

[0133]

[0134] Where, ω j H represents the initial weight of attribute j. j The information entropy represents the attribute, and m represents the number of attributes in the experimental cable.

[0135] In this way, the smaller the information entropy of an attribute, the greater its ability to distinguish decisions, and the higher its initial weight.

[0136] In one example, a group of experimental cables (containing multiple experimental cables) is to be evaluated. The selected attributes are harmonic characteristics, historical partial discharge level, insulation resistance, and dielectric loss factor. The weights are determined using fuzzy logic: ω1 = 0.4, ω2 = 0.35, and ω3 = 0.25. After the fuzzy comprehensive evaluation value is calculated using fuzzy operators, it is defuzzified to obtain the evaluation score for each cable. Finally, the cable status is classified and ranked according to the scores.

[0137] The cable insulation condition detection method based on live harmonic anomalies described in any of the above embodiments can systematically and comprehensively assess the insulation condition of cables, helping decision-makers make more scientific operation and maintenance decisions.

[0138] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0139] Based on the same inventive concept, this application also provides a cable insulation condition detection device based on charged harmonic anomalies for implementing the cable insulation condition detection method based on charged harmonic anomalies described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the cable insulation condition detection device based on charged harmonic anomalies provided below can be found in the limitations of the cable insulation condition detection method based on charged harmonic anomalies described above, and will not be repeated here.

[0140] In one exemplary embodiment, such as Figure 5 As shown, a cable insulation condition detection device 50 based on charged harmonic anomalies is provided, comprising:

[0141] The acquisition module 51 is used to acquire measurement data of multiple attributes of the experimental cable, including any number of conductor current, typical local defects, current aging time, partial discharge level, insulation resistance, dielectric loss factor, service life, laying method and load type.

[0142] Processing module 52 is used to determine the fuzzy evaluation of the insulation state of each attribute based on the measurement data of the attribute.

[0143] The acquisition module 51 is also used to determine the initial weight of each attribute using the fuzzy entropy weight method.

[0144] The processing module 52 is also used to construct an original data matrix based on the initial weight of each attribute, calculate the correlation between each attribute, and form a correlation coefficient matrix between each attribute based on the correlation and the original data matrix.

[0145] The processing module 52 is also used to determine the correction coefficient of each attribute based on the correlation coefficient matrix between each attribute, and to correct the initial weight of each attribute based on the correction coefficient of each attribute, so as to obtain the corrected weight of each attribute.

[0146] The processing module 52 is also used to perform fuzzy weighted calculation for each attribute based on the fuzzy evaluation and weight corresponding to the attribute, so as to obtain the fuzzy weighted calculation result of the attribute.

[0147] The processing module 52 is also used to synthesize the fuzzy weighted calculation results of the multiple attributes into a fuzzy comprehensive evaluation value for the experimental cable using fuzzy operators.

[0148] The processing module 52 is also used to determine the insulation status of the experimental cable based on the fuzzy comprehensive evaluation value.

[0149] In one embodiment, the acquisition module 51 is used to represent the evaluation values ​​of each decision scheme under different attributes as fuzzy numbers, and acquire the fuzzy number of each attribute; to perform standardization processing on the fuzzy number of each attribute so that the fuzzy number of each attribute is at the same scale, wherein the standardization processing includes at least one of maximum normalization, minimum normalization, and vector normalization; to determine the information entropy of each attribute based on the fuzzy number of each attribute after standardization processing; and to determine the initial weight for each attribute based on the information entropy.

[0150] In one embodiment, the acquisition module 51 is used to acquire the normalized value of the attribute on the scheme, and to acquire the logarithmic value of the normalized value; to determine the integral value between the normalized value and the logarithmic value based on the number of attributes of the experimental cable; and to determine the information entropy of the attribute based on the integral value.

[0151] In one embodiment, the acquisition module 51 is used to determine the initial weight based on the information entropy of the attribute and the number of experimental cable attributes.

[0152] In one embodiment, the processing module 52 is used to calculate the correlation between attributes using the Pearson correlation coefficient statistical algorithm or the Spearman rank correlation coefficient statistical algorithm, and to form a correlation coefficient matrix between attributes based on the correlation and the original data matrix.

[0153] In one embodiment, the processing module 52 is used to obtain the absolute value of the correlation coefficient between the first attribute and the second attribute; determine the integral value of the absolute value based on the number of experimental cable attributes; and determine the correction coefficient based on the number of experimental cable attributes and the integral value.

[0154] Each module in the aforementioned cable insulation condition detection device 50 based on live harmonic fluctuations can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the electronic device in hardware form or independently of it, or stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of each module.

[0155] In one exemplary embodiment, an electronic device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6 As shown, the electronic device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a method for detecting the insulation status of cables based on charged harmonic anomalies.

[0156] Those skilled in the art will understand that Figure 6The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0157] In one exemplary embodiment, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0158] Acquire measurement data for multiple attributes of the experimental cable, including any number of conductor current, typical local defects, current aging time, partial discharge level, insulation resistance, dielectric loss factor, service life, laying method, and load type.

[0159] For each attribute, a fuzzy evaluation of the attribute's influence on the insulation state is determined based on the attribute's measurement data;

[0160] The initial weight of each attribute is determined using the fuzzy entropy weight method;

[0161] Construct an original data matrix based on the initial weight of each attribute, calculate the correlation between each attribute, and form a correlation coefficient matrix between each attribute based on the correlation and the original data matrix.

[0162] The correction coefficient of each attribute is determined based on the correlation coefficient matrix between the attributes, and the initial weight of each attribute is corrected based on the correction coefficient of each attribute to obtain the corrected weight of each attribute.

[0163] For each attribute, a fuzzy weighted calculation is performed based on the fuzzy evaluation and weight corresponding to the attribute to obtain the fuzzy weighted calculation result of the attribute;

[0164] The fuzzy weighted calculation results of the multiple attributes are combined using fuzzy operators to form a fuzzy comprehensive evaluation value for the experimental cable.

[0165] The insulation state of the experimental cable is determined based on the fuzzy comprehensive evaluation value.

[0166] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0167] The evaluation values ​​of each decision scheme under different attributes are represented as fuzzy numbers, and the fuzzy number of each attribute is obtained.

[0168] The fuzzy numbers of each attribute are standardized to ensure that the fuzzy numbers of each attribute are at the same scale. The standardization process includes at least one of maximum normalization, minimum normalization, and vector normalization.

[0169] The information entropy of each attribute is determined based on the fuzzy number of each attribute after standardization.

[0170] For each attribute, the initial weight is determined based on the information entropy.

[0171] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0172] Get the normalized value of the attribute in the scheme, and get the logarithm of that normalized value;

[0173] Determine the integral value between the normalized value and the logarithmic value based on the number of properties possessed by the experimental cable.

[0174] The information entropy of the attribute is determined based on this integral value.

[0175] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0176] The initial weights are determined based on the information entropy of the attributes and the number of attributes of the experimental cable.

[0177] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0178] The correlation between each attribute is calculated using the Pearson correlation coefficient statistical algorithm or the Spearman rank correlation coefficient statistical algorithm. Based on the correlation and the original data matrix, a correlation coefficient matrix between each attribute is formed.

[0179] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0180] Obtain the absolute value of the correlation coefficient between the first attribute and the second attribute;

[0181] The integral value of this absolute value is determined based on the number of experimental cable properties.

[0182] The correction factor is determined based on the number and integral value of the experimental cable properties.

[0183] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0184] Acquire measurement data for multiple attributes of the experimental cable, including any number of conductor current, typical local defects, current aging time, partial discharge level, insulation resistance, dielectric loss factor, service life, laying method, and load type.

[0185] For each attribute, a fuzzy evaluation of the attribute's influence on the insulation state is determined based on the attribute's measurement data;

[0186] The initial weight of each attribute is determined using the fuzzy entropy weight method;

[0187] Construct an original data matrix based on the initial weight of each attribute, calculate the correlation between each attribute, and form a correlation coefficient matrix between each attribute based on the correlation and the original data matrix.

[0188] The correction coefficient of each attribute is determined based on the correlation coefficient matrix between the attributes, and the initial weight of each attribute is corrected based on the correction coefficient of each attribute to obtain the corrected weight of each attribute.

[0189] For each attribute, a fuzzy weighted calculation is performed based on the fuzzy evaluation and weight corresponding to the attribute to obtain the fuzzy weighted calculation result of the attribute;

[0190] The fuzzy weighted calculation results of the multiple attributes are combined using fuzzy operators to form a fuzzy comprehensive evaluation value for the experimental cable.

[0191] The insulation state of the experimental cable is determined based on the fuzzy comprehensive evaluation value.

[0192] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0193] The evaluation values ​​of each decision scheme under different attributes are represented as fuzzy numbers, and the fuzzy number of each attribute is obtained;

[0194] The fuzzy numbers of each attribute are standardized to ensure that the fuzzy numbers of each attribute are at the same scale. The standardization process includes at least one of maximum normalization, minimum normalization, and vector normalization.

[0195] The information entropy of each attribute is determined based on the fuzzy number of each attribute after standardization.

[0196] For each attribute, the initial weight is determined based on the information entropy.

[0197] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0198] Get the normalized value of the attribute in the scheme, and get the logarithm of that normalized value;

[0199] Determine the integral value between the normalized value and the logarithmic value based on the number of properties possessed by the experimental cable.

[0200] The information entropy of the attribute is determined based on this integral value.

[0201] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0202] The initial weights are determined based on the information entropy of the attributes and the number of attributes of the experimental cable.

[0203] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0204] The correlation between each attribute is calculated using the Pearson correlation coefficient statistical algorithm or the Spearman rank correlation coefficient statistical algorithm. Based on the correlation and the original data matrix, a correlation coefficient matrix between each attribute is formed.

[0205] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0206] Obtain the absolute value of the correlation coefficient between the first attribute and the second attribute;

[0207] The integral value of this absolute value is determined based on the number of experimental cable properties.

[0208] The correction factor is determined based on the number and integral value of the experimental cable properties.

[0209] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0210] Acquire measurement data for multiple attributes of the experimental cable, including any number of conductor current, typical local defects, current aging time, partial discharge level, insulation resistance, dielectric loss factor, service life, laying method, and load type.

[0211] For each attribute, a fuzzy evaluation of the attribute's influence on the insulation state is determined based on the attribute's measurement data;

[0212] The initial weight of each attribute is determined using the fuzzy entropy weight method;

[0213] Construct an original data matrix based on the initial weight of each attribute, calculate the correlation between each attribute, and form a correlation coefficient matrix between each attribute based on the correlation and the original data matrix.

[0214] The correction coefficient of each attribute is determined based on the correlation coefficient matrix between the attributes, and the initial weight of each attribute is corrected based on the correction coefficient of each attribute to obtain the corrected weight of each attribute.

[0215] For each attribute, a fuzzy weighted calculation is performed based on the fuzzy evaluation and weight corresponding to the attribute to obtain the fuzzy weighted calculation result of the attribute;

[0216] The fuzzy weighted calculation results of the multiple attributes are combined using fuzzy operators to form a fuzzy comprehensive evaluation value for the experimental cable.

[0217] The insulation state of the experimental cable is determined based on the fuzzy comprehensive evaluation value.

[0218] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0219] The evaluation values ​​of each decision scheme under different attributes are represented as fuzzy numbers, and the fuzzy number of each attribute is obtained.

[0220] The fuzzy numbers of each attribute are standardized to ensure that the fuzzy numbers of each attribute are at the same scale. The standardization process includes at least one of maximum normalization, minimum normalization, and vector normalization.

[0221] The information entropy of each attribute is determined based on the fuzzy number of each attribute after standardization.

[0222] For each attribute, the initial weight is determined based on the information entropy.

[0223] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0224] Get the normalized value of the attribute in the scheme, and get the logarithm of that normalized value;

[0225] Determine the integral value between the normalized value and the logarithmic value based on the number of properties possessed by the experimental cable.

[0226] The information entropy of the attribute is determined based on this integral value.

[0227] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0228] The initial weights are determined based on the information entropy of the attributes and the number of attributes of the experimental cable.

[0229] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0230] The correlation between each attribute is calculated using the Pearson correlation coefficient statistical algorithm or the Spearman rank correlation coefficient statistical algorithm. Based on the correlation and the original data matrix, a correlation coefficient matrix between each attribute is formed.

[0231] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0232] Obtain the absolute value of the correlation coefficient between the first attribute and the second attribute;

[0233] The integral value of this absolute value is determined based on the number of experimental cable properties.

[0234] The correction factor is determined based on the number and integral value of the experimental cable properties.

[0235] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0236] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0237] The above embodiments merely illustrate several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for detecting the insulation condition of cables based on charged harmonic anomalies, characterized in that, The method includes: Acquire measurement data of multiple attributes of the experimental cable, wherein the multiple attributes include any combination of conductor current, typical local defects, current-carrying aging time, partial discharge level, insulation resistance, dielectric loss factor, service life, laying method and load type; For each attribute, a fuzzy evaluation of the attribute's influence on the insulation state is determined based on the attribute's measurement data; The initial weight of each attribute is determined using the fuzzy entropy weight method; An original data matrix is ​​constructed based on the initial weight of each attribute, and the correlation between each attribute is calculated. A correlation coefficient matrix between each attribute is formed based on the correlation and the original data matrix. The correction coefficients for each attribute are determined based on the correlation coefficient matrix between the attributes, including obtaining the absolute value of the correlation coefficient between the first attribute and the second attribute, determining the integral value of the absolute value based on the number of attributes of the experimental cable, determining the correction coefficient based on the number of attributes of the experimental cable and the integral value, and correcting the initial weights of each attribute based on the correction coefficients of each attribute to obtain the corrected weights of each attribute. For each attribute, a fuzzy weighted calculation is performed based on the fuzzy evaluation and weight corresponding to the attribute to obtain the fuzzy weighted calculation result of the attribute; The fuzzy weighted calculation results of the multiple attributes are combined using fuzzy operators to form a fuzzy comprehensive evaluation value for the experimental cable. The insulation state of the experimental cable is determined based on the fuzzy comprehensive evaluation value.

2. The method according to claim 1, characterized in that, The method of determining the initial weight of each attribute using fuzzy entropy weighting includes: The evaluation values ​​of each decision scheme under different attributes are represented as fuzzy numbers, and the fuzzy number of each attribute is obtained; The fuzzy number of each attribute is standardized to make the fuzzy number of each attribute at the same scale, wherein the standardization process includes at least one of maximum normalization, minimum normalization, and vector normalization; The information entropy of each attribute is determined based on the fuzzy number of each attribute after standardization. For each attribute, the initial weight is determined based on the information entropy.

3. The method according to claim 2, characterized in that, The step of determining the information entropy of each attribute based on the fuzzy number of each attribute after standardization includes: Obtain the normalized value of the attribute on the scheme, and obtain the logarithm of the normalized value; The integral value between the normalized value and the logarithmic value is determined based on the number of properties possessed by the experimental cable. The information entropy of the attribute is determined based on the integral value.

4. The method according to claim 2, characterized in that, The step of determining the initial weights based on information entropy includes: The initial weights are determined based on the information entropy of the attributes and the number of attributes of the experimental cable.

5. The method according to claim 1, characterized in that, The step of calculating the correlation between attributes and forming a correlation coefficient matrix between attributes based on the correlation and the original data matrix includes: The correlation between each attribute is calculated using the Pearson correlation coefficient statistical algorithm or the Spearman rank correlation coefficient statistical algorithm, and a correlation coefficient matrix between each attribute is formed based on the correlation and the original data matrix.

6. A cable insulation condition detection device based on charged harmonic anomalies, characterized in that, The device includes: The acquisition module is used to acquire measurement data of multiple attributes of the experimental cable, wherein the multiple attributes include any combination of conductor current, typical local defects, current-carrying aging time, partial discharge level, insulation resistance, dielectric loss factor, service life, laying method and load type. The processing module is used to determine the fuzzy evaluation of the insulation state of each attribute based on the attribute's measurement data. The acquisition module is also used to determine the initial weight of each attribute using the fuzzy entropy weight method; The processing module is further configured to construct an original data matrix based on the initial weight of each attribute, calculate the correlation between each attribute, and form a correlation coefficient matrix between each attribute based on the correlation and the original data matrix. The processing module is further configured to determine the correction coefficient of each attribute based on the correlation coefficient matrix between the attributes, including obtaining the absolute value of the correlation coefficient between the first attribute and the second attribute, determining the integral value of the absolute value based on the number of experimental cable attributes, determining the correction coefficient based on the number of experimental cable attributes and the integral value, and correcting the initial weight of each attribute based on the correction coefficient of each attribute to obtain the corrected weight of each attribute. The processing module is also used to perform fuzzy weighted calculation for each attribute based on the fuzzy evaluation and weight corresponding to the attribute, so as to obtain the fuzzy weighted calculation result of the attribute. The processing module is also used to use fuzzy operators to synthesize the fuzzy weighted calculation results of the multiple attributes into a fuzzy comprehensive evaluation value for the experimental cable. The processing module is also used to determine the insulation state of the experimental cable based on the fuzzy comprehensive evaluation value.

7. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

Images