Cable insulation state detection method based on charged harmonic wave transaction

Abstract

The invention relates to a cable insulation state detection method based on charged harmonic wave transaction. The method comprises the following steps: acquiring measurement data of a plurality of attributes of an experimental cable; determining fuzzy evaluation of the attributes on the insulation state according to the measurement data of the attributes; determining the initial weight of each attribute by adopting a fuzzy entropy weight method; constructing an original data matrix according to the initial weight of each attribute, and calculating the correlation between the attributes to form a correlation coefficient matrix between the attributes; determining a correction coefficient of each attribute according to the correlation coefficient matrix among the attributes, and correcting the initial weight of each attribute according to the correction coefficient of each attribute to obtain the corrected weight of each attribute; for each attribute, obtaining a fuzzy weighting calculation result of the attribute, and synthesizing the fuzzy weighting calculation result into a fuzzy comprehensive evaluation value for the experimental cable; and determining the insulation state of the experimental cable according to the fuzzy comprehensive evaluation value. By adopting the method, the accuracy of evaluating and judging the insulation state of the cable can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of power safety monitoring, and in particular to a method for detecting the insulation status of a cable based on live harmonic fluctuations. Background Art

[0002] With the continuous development of modern power systems, cables, as an important part of power transmission, are directly related to the overall operating efficiency and reliability of the power system. The insulation degradation of cables has always been one of the important factors affecting the stable operation of power systems. How to accurately and effectively evaluate the insulation status of cables and predict their future degradation trends has become a key issue that needs to be solved in the operation and maintenance of power systems.

[0003] In the comprehensive condition evaluation of cables, the various attributes of cables (such as insulation resistance, harmonic characteristics, operating environment, etc.) are often not independent of each other, but have certain correlations and interactions. However, many existing methods, such as the traditional fuzzy multiple attribute decision method (Fuzzy MADM) and analytic hierarchy process (AHP), usually assume that the attributes are independent when building the model. This assumption may lead to the neglect of the complex relationship between attributes, thereby affecting the accuracy of the evaluation results. Ignoring the correlation between attributes may lead to the underestimation or overestimation of certain key risk factors, especially when there are significant interactions between the attributes. This deficiency may weaken the scientificity and reliability of the evaluation and affect the accurate judgment of the cable insulation status.

[0004] Therefore, how to improve the accuracy of cable insulation status assessment and judgment still needs to be solved. Summary of the invention

[0005] Based on this, it is necessary to provide a cable insulation status detection method based on live harmonic fluctuations, which can improve the accuracy of cable insulation status evaluation and judgment, in order to address the above technical problems.

[0006] In a first aspect, the present application provides a method for detecting the insulation status of a cable based on live harmonic fluctuations, comprising:

[0007] Obtaining measurement data of multiple attributes of the experimental cable, wherein the multiple attributes include any multiple of conductor current, local typical defects, current 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 on the insulation state is determined according to the measured data of the attribute;

[0009] The fuzzy entropy weight method is used to determine the initial weight of each attribute;

[0010] Constructing an original data matrix according to the initial weight of each attribute, and calculating the correlation between the attributes, and forming a correlation coefficient matrix between the attributes according to the correlation and the original data matrix;

[0011] Determine the correction coefficient of each attribute according to the correlation coefficient matrix between the attributes, and correct the initial weight of each attribute according to the correction coefficient of each attribute to obtain the corrected weight of each attribute;

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

[0013] Using a fuzzy operator to synthesize the fuzzy weighted calculation results of the multiple attributes into a fuzzy comprehensive evaluation value for the experimental cable;

[0014] The insulation state of the test cable is determined according to the fuzzy comprehensive evaluation value.

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

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

[0017] Performing normalization processing on the fuzzy number of each attribute so that the fuzzy number of each attribute is at the same scale, wherein the normalization processing includes at least one of maximum normalization, minimum normalization, and vector normalization;

[0018] According to the fuzzy number of each attribute after standardization, the information entropy of each attribute is determined;

[0019] For each attribute, the initial weight is determined according to the information entropy.

[0020] In one embodiment, determining the information entropy of each attribute according to the fuzzy number of each attribute after the normalization process comprises:

[0021] Obtaining a normalized value of an attribute on a scheme, and obtaining a logarithmic value of the normalized value;

[0022] Determining an integral value between the normalized value and the logarithmic value according to the number of properties possessed by the experimental cable;

[0023] The information entropy of the attribute is determined according to the integral value.

[0024] In one embodiment, determining the initial weight according to the information entropy includes:

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

[0026] In one embodiment, the calculating the correlation between the attributes and forming a correlation coefficient matrix between the attributes according to the correlation and the original data matrix comprises:

[0027] The Pearson correlation coefficient statistical algorithm or the Spearman rank correlation coefficient statistical algorithm is used to calculate the correlation between the attributes, and a correlation coefficient matrix between the attributes is formed according to the correlation and the original data matrix.

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

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

[0030] determining an integral value of the absolute value based on a quantity of the experimental cable property;

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

[0032] In a second aspect, the present application also provides a cable insulation state detection device based on live harmonic fluctuations, comprising:

[0033] An acquisition module is used to acquire measurement data of multiple attributes of the experimental cable, wherein the multiple attributes include any multiple of conductor current, local typical defects, current aging time, partial discharge level, insulation resistance, dielectric loss factor, operating years, laying method and load type;

[0034] A processing module, for determining, for each attribute, a fuzzy evaluation of the attribute on the insulation state according to the measured data of the attribute;

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

[0036] The processing module is further used to construct an original data matrix according to the initial weight of each attribute, and calculate the correlation between the attributes, and form a correlation coefficient matrix between the attributes according to the correlation and the original data matrix;

[0037] The processing module is further used to determine the correction coefficient of each attribute according to the correlation coefficient matrix between the attributes, and to correct the initial weight of each attribute according to the correction coefficient of each attribute to obtain the corrected weight of each attribute;

[0038] The processing module is further used to use a fuzzy operator 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 test cable according to the fuzzy comprehensive evaluation value.

[0040] In a third aspect, the present application further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0041] Obtaining measurement data of multiple attributes of the experimental cable, wherein the multiple attributes include any multiple of conductor current, local typical defects, current 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 on the insulation state is determined according to the measured data of the attribute;

[0043] The fuzzy entropy weight method is used to determine the initial weight of each attribute;

[0044] Constructing an original data matrix according to the initial weight of each attribute, and calculating the correlation between the attributes, and forming a correlation coefficient matrix between the attributes according to the correlation and the original data matrix;

[0045] Determine the correction coefficient of each attribute according to the correlation coefficient matrix between the attributes, and correct the initial weight of each attribute according to the correction coefficient of each attribute to obtain the corrected weight of each attribute;

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

[0047] Using a fuzzy operator to synthesize the fuzzy weighted calculation results of the multiple attributes into a fuzzy comprehensive evaluation value for the experimental cable;

[0048] The insulation state of the test cable is determined according to the fuzzy comprehensive evaluation value.

[0049] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0050] Obtaining measurement data of multiple attributes of the experimental cable, wherein the multiple attributes include any multiple of conductor current, local typical defects, current 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 on the insulation state is determined according to the measured data of the attribute;

[0052] The fuzzy entropy weight method is used to determine the initial weight of each attribute;

[0053] Constructing an original data matrix according to the initial weight of each attribute, and calculating the correlation between the attributes, and forming a correlation coefficient matrix between the attributes according to the correlation and the original data matrix;

[0054] Determine the correction coefficient of each attribute according to the correlation coefficient matrix between the attributes, and correct the initial weight of each attribute according to the correction coefficient of each attribute to obtain the corrected weight of each attribute;

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

[0056] Using a fuzzy operator to synthesize the fuzzy weighted calculation results of the multiple attributes into a fuzzy comprehensive evaluation value for the experimental cable;

[0057] The insulation state of the test cable is determined according to the fuzzy comprehensive evaluation value.

[0058] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0059] Obtaining measurement data of multiple attributes of the experimental cable, wherein the multiple attributes include any multiple of conductor current, local typical defects, current 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 on the insulation state is determined according to the measured data of the attribute;

[0061] The fuzzy entropy weight method is used to determine the initial weight of each attribute;

[0062] Constructing an original data matrix according to the initial weight of each attribute, and calculating the correlation between the attributes, and forming a correlation coefficient matrix between the attributes according to the correlation and the original data matrix;

[0063] Determine the correction coefficient of each attribute according to the correlation coefficient matrix between the attributes, and correct the initial weight of each attribute according to the correction coefficient of each attribute to obtain the corrected weight of each attribute;

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

[0065] Using a fuzzy operator to synthesize the fuzzy weighted calculation results of the multiple attributes into a fuzzy comprehensive evaluation value for the experimental cable;

[0066] The insulation state of the test cable is determined according to the fuzzy comprehensive evaluation value.

[0067] The above-mentioned cable insulation state detection method based on live harmonic anomaly integrates insulation performance data such as induced current harmonic characteristics, insulation resistance state, power frequency dielectric loss, and inherent conditions such as cable length, voltage level, laying conditions and operating environment, and adopts fuzzy attribute overlapping decision method to systematically evaluate and predict the comprehensive insulation state of the cable. In the case of overlapping cable attributes, the traditional fuzzy entropy weight method may not be able to effectively deal with the interdependence and overlapping effects between attributes, because it usually assumes that each attribute is independent of each other. In the case of attribute overlap, the calculation of information entropy may lose some effectiveness, resulting in inaccurate weight distribution. In the traditional fuzzy multiple attribute decision method, each attribute is usually regarded as independent, while the attribute overlap method (the method of establishing a correlation coefficient matrix) considers the mutual influence between attributes. The method provided in this embodiment is particularly suitable for determining the insulation state of the cable when there is a certain degree of correlation or overlap between multiple attributes. This embodiment introduces the correlation coefficient between attributes to represent the interactive relationship between attributes, thereby improving the accuracy of evaluating and judging the insulation state of the cable. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. 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 creative work.

[0069] FIG1 is a diagram showing an application environment of a cable insulation state detection method based on live harmonic fluctuations in one embodiment;

[0070] FIG2 is a schematic flow chart of a cable insulation status detection method based on live harmonic fluctuations in one embodiment;

[0071] FIG3 is a schematic diagram of an experimental cable in one embodiment;

[0072] FIG4 is a flow chart of one step of a method for detecting cable insulation status based on live harmonic fluctuations in one embodiment;

[0073] FIG5 is a structural block diagram of a cable insulation state detection device based on live harmonic fluctuations in one embodiment;

[0074] FIG. 6 is a diagram showing the internal structure of an electronic device in one embodiment. DETAILED DESCRIPTION

[0075] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0076] The cable insulation status detection method based on live harmonic fluctuations provided in an embodiment of the present application can be applied in an application environment as shown in Figure 1. Among them, the terminal 102 communicates with the server 104 through a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The server 104 can be an independent physical server, or it can be a server cluster or distributed system composed of multiple physical servers, or it can be a cloud server that provides cloud computing services.

[0077] In an exemplary embodiment, as shown in FIG2 , a cable insulation state detection method based on live harmonic fluctuation is provided, and the method is applied to the server in FIG1 as an example for description, including the following steps 201 to 206. Among them:

[0078] S201, obtaining measurement data of multiple properties of the experimental cable, wherein the multiple properties include any multiple of conductor current, local typical defects, current aging time, local discharge level, insulation resistance, dielectric loss factor, operating years, laying method and load type.

[0079] Firstly, a distribution network cable harmonic detection experimental platform was built to detect the harmonic signal in the cable metal shielding layer of the insulation defect sample. By performing statistical feature analysis on the collected harmonic signal data, the variation law of harmonic signal characteristics under different conditions was further extracted.

[0080] We have successively carried out the production of various defective cable samples and the installation of multiple sets of equipment such as core-through transformers, series resonant boost devices and harmonic signal detection devices, and built a distribution network cable simulation operation and harmonic detection experimental platform taking into account typical defects and insulation degradation conditions, as shown in Figure 3.

[0081] Four 10kV distribution network cable samples were prepared, including water drop defect, semi-conductive suspension defect, metal suspension defect and a non-defective sample as a control, and the defect positions are shown in Figure 3. The length of each cable sample is 15 meters, and the defect size is 1×3 cm, wherein the water drop defect is achieved by applying a water film in the groove of the insulation layer. The experimental cable provided in this embodiment is one of the four 10KV distribution network cable samples.

[0082] As shown in Figure 3, the conductor parts of the defective cable sample, the defect-free cable sample and the tie-line cable are first connected in series, and the two ends of the metal shielding wires of the two sample cables are connected into a ring. Then, the rated current is applied to the cable loop using a through-core transformer. Finally, the harmonic current data in the metal shielding wire loops of the two sample cables are simultaneously measured and collected by the harmonic signal detection device. In addition, during the harmonic data measurement interval, the conductor temperature of all cable samples is maintained at 90°C by applying current to simulate the cable insulation current aging process.

[0083] By simulating the cable insulation aging process, it is determined that there are three factors that affect the harmonic abnormal characteristics, namely conductor current, local typical defects and aging time.

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

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

[0086] S202: for each attribute, determine a fuzzy evaluation of the attribute on the insulation state according to the measurement data of the attribute.

[0087] Measurement data for properties can come from field inspections, historical records, or expert assessments.

[0088] The data of 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 on the insulation status. For example:

[0089] The conductor current level uses fuzzy numbers [10, 50, 100] to represent the “low current” state, [200, 250, 300] to represent the “normal” state, and [400, 500, 600] to represent the “degraded” state.

[0090] The local typical defects are represented by fuzzy numbers [100, 104, 109] to represent the “general moisture” state, [107, 115, 124] to represent the “minor discharge defect” state, and [106, 109, 129] to represent the “serious discharge defect” state.

[0091] The partial discharge level is represented by a fuzzy number [0, 0.1, 0.2] representing a “good” state, [100, 200, 300] representing a “normal” state, and [1000, 2000, 3000] representing a “degraded” 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 "very high loss".

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

[0094] S203, using fuzzy entropy weight method to determine the initial weight of each attribute.

[0095] The fuzzy entropy weight method is used to determine the initial weight of each attribute. The fuzzy entropy weight method is a method that combines fuzzy logic and information entropy theory. It is mainly used to dynamically calculate the weight of each attribute in multi-attribute decision-making problems. Compared with traditional weight determination methods, the fuzzy entropy weight method can effectively reduce subjective bias and provide a more objective and data-driven weight allocation method.

[0096] S204, constructing an original data matrix according to the initial weight of each attribute, and calculating the correlation between the attributes, and forming a correlation coefficient matrix between the attributes according to the correlation and the original data matrix.

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

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

[0099] When there is a negative correlation between attributes, there is a negative association between the attributes, and 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, determining a correction coefficient of each attribute according to the correlation coefficient matrix between the attributes, and correcting the initial weight of each attribute according to the correction coefficient of each attribute to obtain a 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 represents the initial weight of the attribute, Represents the correction factor.

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

[0105] By considering the correlation between attributes, the influence of redundant attributes is reduced, making the evaluation results more representative. The revised weights can more truly reflect the contribution of each attribute to the comprehensive evaluation, and improve the accuracy and reliability of the evaluation model.

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

[0107] The redundancy of each attribute is calculated based on the correlation coefficient matrix R. The information of attributes with high redundancy may be partially included by 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 the absolute value is determined according to the number of experimental cable attributes. The correction coefficient is determined according to the number and integral value of the experimental cable attributes. For example, according to the formula Determine the correction factor. Where, Representative attributes With attributes The correlation coefficient between Representative experimental cables have attributes.

[0109] S207, using a fuzzy operator 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, and the fuzzy comprehensive evaluation value is converted into a clear numerical value through defuzzification technology for comparison and sorting. Commonly used defuzzification methods include the centroid method and the maximum membership method.

[0111] S208, determining the insulation status of the test cable according to the fuzzy comprehensive evaluation value.

[0112] According to the defuzzified values, the insulation status of the cable can be classified (such as "good", "average", "poor", etc.). Different cables can also be sorted by insulation status to determine the priority maintenance objects.

[0113] In one embodiment, the insulation status obtained can also be analyzed to explain the insulation status of each cable. An influence diagram of each attribute can be drawn to help identify key influencing factors.

[0114] In one embodiment, support is provided for cable operation and maintenance decisions based on the insulation status of the cable. For cables in poor condition, a more detailed inspection or maintenance plan can be formulated.

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

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

[0117] In the above-mentioned cable insulation state detection method based on live harmonic anomaly, by integrating insulation performance data such as induced current harmonic characteristics, insulation resistance state, power frequency dielectric loss, and inherent conditions such as cable length, voltage level, laying conditions and operating environment, a fuzzy attribute overlapping decision method is used to systematically evaluate and predict the comprehensive insulation state of the cable. In the case of overlapping cable attributes, the traditional fuzzy entropy weight method may not be able to effectively handle the interdependence and overlapping effects between attributes, because it usually assumes that each attribute is independent of each other. In the case of attribute overlap, the calculation of information entropy may lose some of its effectiveness, resulting in inaccurate weight distribution. In the traditional fuzzy multiple attribute decision method, each attribute is usually regarded as independent, while the attribute overlap method (the method of establishing a correlation coefficient matrix) considers the mutual influence between attributes. The method provided in this embodiment is particularly suitable for determining the insulation state of the cable when there is a certain degree of correlation or overlap between multiple attributes. This embodiment introduces the correlation coefficient between attributes to represent the interactive relationship between attributes, thereby improving the accuracy of evaluating and judging the insulation state of the cable.

[0118] In an exemplary embodiment, as shown in FIG3 , step S203 includes step S401 to step 304. Among them:

[0119] S401, expressing the evaluation values ​​of each decision plan under different attributes as fuzzy numbers, and obtaining the fuzzy number of each attribute.

[0120] The first step of the fuzzy entropy weight method is to construct a fuzzy decision matrix. Specifically, the evaluation values ​​of each decision plan under different attributes are expressed as fuzzy numbers to form a fuzzy decision matrix. This matrix reflects the performance of each plan under each attribute.

[0121] S402, performing normalization processing on the fuzzy number of each attribute so that the fuzzy number of each attribute is at the same scale, wherein the normalization processing includes at least one of maximum normalization, minimum normalization, and vector normalization.

[0122] The second step of the fuzzy entropy weight method is the fuzzy number standardization. Specifically, in order to eliminate the dimensional differences between different attributes, the fuzzy numbers need to be standardized. Usually, the maximum and minimum normalization or vector normalization method is used to convert the fuzzy numbers of each attribute to the same scale.

[0123] Step 403: determine the information entropy of each attribute according to the fuzzy number of each attribute after the normalization process.

[0124] The third step of the fuzzy entropy weight method is to calculate the information entropy of each attribute.

[0125] Specifically, for the standardized fuzzy decision matrix, the information entropy of each attribute is calculated.

[0126] In one embodiment, a normalized value of the attribute on the scheme is obtained, and a logarithmic value of the normalized value is obtained. An integral value between the normalized value and the logarithmic value is determined according to the number of attributes possessed by the experimental cable. The information entropy of the attribute is determined according to the integral value.

[0127] For example, according to the formula , determine the information entropy of each attribute.

[0128] Among them, P ij is the normalized value of attribute j on solution i. k is a constant, usually 1 / ln(n).

[0129] Step 404: for each attribute, determine an initial weight according to information entropy.

[0130] The fourth step of the fuzzy entropy weight method is to calculate the entropy weight of each attribute. Specifically, the initial weight is determined according to the information entropy of the attribute and the number of experimental cable attributes.

[0131] According to the calculated information entropy, the weight of each attribute is calculated. The calculation formula of entropy weight is:

[0132]

[0133] in, represents the initial weight of attribute j, The information entropy representing the attribute, Representative experimental cables have attributes.

[0134] In this way, the smaller the information entropy of an attribute, the greater its discrimination for decision making and the higher its initial weight.

[0135] In one example, a group of experimental cables (including multiple experimental cables) is to be evaluated. The attributes are selected as harmonic characteristics, historical partial discharge levels, insulation resistance and dielectric loss factor, and the weights are determined by fuzzy: ω1=0.4, ω2=0.35, ω3=0.25. The fuzzy comprehensive evaluation value is calculated by the fuzzy operator, and then defuzzified to obtain the evaluation score of each cable. Finally, the cable status is classified and sorted according to the score.

[0136] The cable insulation status detection method based on live harmonic fluctuations described in any of the above embodiments can systematically and comprehensively evaluate the insulation status of the cable, helping decision makers make more scientific operation and maintenance decisions.

[0137] It should be understood that, although the steps in the flowcharts of the embodiments described above are shown in sequence according to the instructions of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of the steps or stages in other steps.

[0138] Based on the same inventive concept, the embodiment of the present application also provides a cable insulation state detection device based on live harmonic fluctuations for realizing the cable insulation state detection method based on live harmonic fluctuations involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the cable insulation state detection device based on live harmonic fluctuations provided below can refer to the limitations of the cable insulation state detection method based on live harmonic fluctuations above, and will not be repeated here.

[0139] In an exemplary embodiment, as shown in FIG5 , a cable insulation state detection device 50 based on live harmonic fluctuation is provided, comprising:

[0140] The acquisition module 51 is used to obtain measurement data of multiple properties of the experimental cable, wherein the multiple properties include any multiple of conductor current, local typical defects, current aging time, local discharge level, insulation resistance, dielectric loss factor, operating years, laying method and load type.

[0141] The processing module 52 is used to determine, for each attribute, a fuzzy evaluation of the attribute on the insulation state according to the measured data of the attribute.

[0142] The acquisition module 51 is also used to determine the initial weight of each attribute by using a fuzzy entropy weight method.

[0143] The processing module 52 is further used to construct an original data matrix according to the initial weight of each attribute, and calculate the correlation between the attributes, and form a correlation coefficient matrix between the attributes according to the correlation and the original data matrix.

[0144] The processing module 52 is further used to determine the correction coefficient of each attribute according to the correlation coefficient matrix between the attributes, and to correct the initial weight of each attribute according to the correction coefficient of each attribute to obtain the corrected weight of each attribute.

[0145] The processing module 52 is further used to perform fuzzy weighted calculation on each attribute according to the fuzzy evaluation and weight corresponding to the attribute, so as to obtain the fuzzy weighted calculation result of the attribute.

[0146] The processing module 52 is further used to use a fuzzy operator to synthesize the fuzzy weighted calculation results of the multiple attributes into a fuzzy comprehensive evaluation value for the experimental cable.

[0147] The processing module 52 is also used to determine the insulation state of the test cable according to the fuzzy comprehensive evaluation value.

[0148] In one embodiment, the acquisition module 51 is used to represent the evaluation values ​​of each decision plan under different attributes as fuzzy numbers, and obtain the fuzzy number of each attribute; standardize the fuzzy number of each attribute so that the fuzzy number of each attribute is on the same scale, wherein the standardization includes at least one of maximum normalization, minimum normalization, and vector normalization; determine the information entropy of each attribute based on the fuzzy number of each attribute after the standardization; and determine the initial weight for each attribute based on the information entropy.

[0149] In one embodiment, the acquisition module 51 is used to obtain a normalized value of the attribute on the scheme, and to obtain a logarithmic value of the normalized value; determine an integral value between the normalized value and the logarithmic value based on the number of attributes possessed by the experimental cable; and determine the information entropy of the attribute based on the integral value.

[0150] In one embodiment, the acquisition module 51 is used to determine the initial weight according to the information entropy of the attribute and the number of experimental cable attributes.

[0151] In one embodiment, the processing module 52 is used to calculate the correlation between the attributes using a Pearson correlation coefficient statistical algorithm or a Spearman rank correlation coefficient statistical algorithm, and form a correlation coefficient matrix between the attributes according to the correlation and the original data matrix.

[0152] 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 according to the number of experimental cable attributes; and determine the correction coefficient according to the number and integral value of the experimental cable attributes.

[0153] Each module in the above-mentioned cable insulation state detection device 50 based on live harmonic fluctuation can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in an electronic device in the form of hardware, or can be stored in a memory in the electronic device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0154] In an exemplary embodiment, an electronic device is provided, which may be a terminal, and its internal structure diagram may be shown in FIG6. The electronic device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, the memory, and the input / output interface are connected via a system bus, and the communication interface, the display unit, and the input device are connected to the system bus via the input / output interface. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the electronic device is used to exchange information between the processor and an external device. The communication interface of the electronic device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC) or other technologies. When the computer program is executed by the processor, a cable insulation state detection method based on live harmonic fluctuations is implemented.

[0155] Those skilled in the art will understand that the structure shown in FIG. 6 is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the electronic device to which the scheme of 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 a different arrangement of components.

[0156] In an exemplary embodiment, an electronic device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0157] Obtaining measurement data of multiple attributes of the test cable, wherein the multiple attributes include any number of conductor current, local typical defects, current aging time, partial discharge level, insulation resistance, dielectric loss factor, service life, laying method and load type;

[0158] For each attribute, a fuzzy evaluation of the attribute on the insulation state is determined according to the measured data of the attribute;

[0159] The fuzzy entropy weight method is used to determine the initial weight of each attribute;

[0160] The original data matrix is ​​constructed according to the initial weight of each attribute, and the correlation between the attributes is calculated, and the correlation coefficient matrix between the attributes is formed according to the correlation and the original data matrix;

[0161] Determine the correction coefficient of each attribute according to the correlation coefficient matrix between the attributes, and correct the initial weight of each attribute according to the correction coefficient of each attribute to obtain the corrected weight of each attribute;

[0162] For each attribute, fuzzy weighted calculation is performed according to the fuzzy evaluation and weight corresponding to the attribute to obtain the fuzzy weighted calculation result of the attribute;

[0163] The fuzzy weighted calculation results of the multiple attributes are synthesized into a fuzzy comprehensive evaluation value for the experimental cable by using a fuzzy operator;

[0164] The insulation state of the test cable is determined according to the fuzzy comprehensive evaluation value.

[0165] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0166] The evaluation values ​​of each decision-making scheme under different attributes are expressed as fuzzy numbers, and the fuzzy number of each attribute is obtained;

[0167] Performing normalization processing on the fuzzy number of each attribute so that the fuzzy number of each attribute is at the same scale, wherein the normalization processing includes at least one of maximum normalization, minimum normalization, and vector normalization;

[0168] According to the fuzzy number of each attribute after standardization, the information entropy of each attribute is determined;

[0169] For each attribute, the initial weight is determined according to the information entropy.

[0170] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0171] Obtain a normalized value of the attribute on the scheme, and obtain a logarithm of the normalized value;

[0172] Determining an integral value between the normalized value and the logarithmic value according to the number of properties possessed by the experimental cable;

[0173] The information entropy of the attribute is determined based on the integral value.

[0174] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0175] The initial weights are determined based on the information entropy of the attributes and the number of experimental cable attributes.

[0176] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0177] The Pearson correlation coefficient statistical algorithm or the Spearman rank correlation coefficient statistical algorithm is used to calculate the correlation between the attributes, and a correlation coefficient matrix between the attributes is formed according to the correlation and the original data matrix.

[0178] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0179] Obtaining the absolute value of the correlation coefficient between the first attribute and the second attribute;

[0180] Determine the integral value of the absolute value based on the number of experimental cable properties;

[0181] The correction factor is determined based on the quantity and integral value of the experimental cable properties.

[0182] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0183] Obtaining measurement data of multiple attributes of the test cable, wherein the multiple attributes include any number of conductor current, local typical defects, current aging time, partial discharge level, insulation resistance, dielectric loss factor, service life, laying method and load type;

[0184] For each attribute, a fuzzy evaluation of the attribute on the insulation state is determined according to the measured data of the attribute;

[0185] The fuzzy entropy weight method is used to determine the initial weight of each attribute;

[0186] The original data matrix is ​​constructed according to the initial weight of each attribute, and the correlation between the attributes is calculated, and the correlation coefficient matrix between the attributes is formed according to the correlation and the original data matrix;

[0187] Determine the correction coefficient of each attribute according to the correlation coefficient matrix between the attributes, and correct the initial weight of each attribute according to the correction coefficient of each attribute to obtain the corrected weight of each attribute;

[0188] For each attribute, fuzzy weighted calculation is performed according to the fuzzy evaluation and weight corresponding to the attribute to obtain the fuzzy weighted calculation result of the attribute;

[0189] The fuzzy weighted calculation results of the multiple attributes are synthesized into a fuzzy comprehensive evaluation value for the experimental cable by using a fuzzy operator;

[0190] The insulation state of the test cable is determined according to the fuzzy comprehensive evaluation value.

[0191] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0192] The evaluation values ​​of each decision-making scheme under different attributes are expressed as fuzzy numbers, and the fuzzy number of each attribute is obtained;

[0193] Performing normalization processing on the fuzzy number of each attribute so that the fuzzy number of each attribute is at the same scale, wherein the normalization processing includes at least one of maximum normalization, minimum normalization, and vector normalization;

[0194] According to the fuzzy number of each attribute after standardization, the information entropy of each attribute is determined;

[0195] For each attribute, the initial weight is determined according to the information entropy.

[0196] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0197] Obtain a normalized value of the attribute on the scheme, and obtain a logarithm of the normalized value;

[0198] Determining an integral value between the normalized value and the logarithmic value according to the number of properties possessed by the experimental cable;

[0199] The information entropy of the attribute is determined based on the integral value.

[0200] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0201] The initial weights are determined based on the information entropy of the attributes and the number of experimental cable attributes.

[0202] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0203] The Pearson correlation coefficient statistical algorithm or the Spearman rank correlation coefficient statistical algorithm is used to calculate the correlation between the attributes, and a correlation coefficient matrix between the attributes is formed according to the correlation and the original data matrix.

[0204] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0205] Obtaining the absolute value of the correlation coefficient between the first attribute and the second attribute;

[0206] Determine the integral value of the absolute value based on the number of experimental cable properties;

[0207] The correction factor is determined based on the quantity and integral value of the experimental cable properties.

[0208] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0209] Obtaining measurement data of multiple attributes of the test cable, wherein the multiple attributes include any number of conductor current, local typical defects, current aging time, partial discharge level, insulation resistance, dielectric loss factor, service life, laying method and load type;

[0210] For each attribute, a fuzzy evaluation of the attribute on the insulation state is determined according to the measured data of the attribute;

[0211] The fuzzy entropy weight method is used to determine the initial weight of each attribute;

[0212] The original data matrix is ​​constructed according to the initial weight of each attribute, and the correlation between the attributes is calculated, and the correlation coefficient matrix between the attributes is formed according to the correlation and the original data matrix;

[0213] Determine the correction coefficient of each attribute according to the correlation coefficient matrix between the attributes, and correct the initial weight of each attribute according to the correction coefficient of each attribute to obtain the corrected weight of each attribute;

[0214] For each attribute, fuzzy weighted calculation is performed according to the fuzzy evaluation and weight corresponding to the attribute to obtain the fuzzy weighted calculation result of the attribute;

[0215] The fuzzy weighted calculation results of the multiple attributes are synthesized into a fuzzy comprehensive evaluation value for the experimental cable by using a fuzzy operator;

[0216] The insulation state of the test cable is determined according to the fuzzy comprehensive evaluation value.

[0217] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0218] The evaluation values ​​of each decision-making scheme under different attributes are expressed as fuzzy numbers, and the fuzzy number of each attribute is obtained;

[0219] Performing normalization processing on the fuzzy number of each attribute so that the fuzzy number of each attribute is at the same scale, wherein the normalization processing includes at least one of maximum normalization, minimum normalization, and vector normalization;

[0220] According to the fuzzy number of each attribute after standardization, the information entropy of each attribute is determined;

[0221] For each attribute, the initial weight is determined according to the information entropy.

[0222] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0223] Obtain a normalized value of the attribute on the scheme, and obtain a logarithm of the normalized value;

[0224] Determining an integral value between the normalized value and the logarithmic value according to the number of properties possessed by the experimental cable;

[0225] The information entropy of the attribute is determined based on the integral value.

[0226] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0227] The initial weights are determined based on the information entropy of the attributes and the number of experimental cable attributes.

[0228] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0229] The Pearson correlation coefficient statistical algorithm or the Spearman rank correlation coefficient statistical algorithm is used to calculate the correlation between the attributes, and a correlation coefficient matrix between the attributes is formed according to the correlation and the original data matrix.

[0230] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0231] Obtaining the absolute value of the correlation coefficient between the first attribute and the second attribute;

[0232] Determine the integral value of the absolute value based on the number of experimental cable properties;

[0233] The correction factor is determined based on the quantity and integral value of the experimental cable properties.

[0234] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium 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. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0235] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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.

[0236] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A cable insulation status detection method based on live harmonic fluctuations, characterized in that: The method comprises: Obtaining measurement data of multiple attributes of the experimental cable, wherein the multiple attributes include any multiple of conductor current, local typical defects, current 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 on the insulation state is determined according to the measured data of the attribute; The fuzzy entropy weight method is used to determine the initial weight of each attribute; Constructing an original data matrix according to the initial weight of each attribute, and calculating the correlation between the attributes, and forming a correlation coefficient matrix between the attributes according to the correlation and the original data matrix; Determine the correction coefficient of each attribute according to the correlation coefficient matrix between the attributes, and correct the initial weight of each attribute according to the correction coefficient of each attribute to obtain the corrected weight of each attribute; For each attribute, fuzzy weighted calculation is performed according to the fuzzy evaluation and weight corresponding to the attribute to obtain the fuzzy weighted calculation result of the attribute; Using a fuzzy operator to synthesize the fuzzy weighted calculation results of the multiple attributes into a fuzzy comprehensive evaluation value for the experimental cable; The insulation state of the test cable is determined according to the fuzzy comprehensive evaluation value.

2. The method according to claim 1, characterized in that: The method of using the fuzzy entropy weight method to determine the initial weight of each attribute includes: The evaluation values ​​of each decision-making scheme under different attributes are expressed as fuzzy numbers, and the fuzzy number of each attribute is obtained; Performing normalization processing on the fuzzy number of each attribute so that the fuzzy number of each attribute is at the same scale, wherein the normalization processing includes at least one of maximum normalization, minimum normalization, and vector normalization; According to the fuzzy number of each attribute after normalization, the information entropy of each attribute is determined; For each attribute, the initial weight is determined according to the information entropy.

3. The method according to claim 2, characterized in that Determining the information entropy of each attribute according to the fuzzy number of each attribute after the standardization process includes: Obtaining a normalized value of an attribute on a scheme, and obtaining a logarithmic value of the normalized value; Determining an integral value between the normalized value and the logarithmic value according to the number of properties possessed by the experimental cable; The information entropy of the attribute is determined according to the integral value.

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

5. The method according to claim 1, characterized in that The calculating the correlation between the attributes and forming the correlation coefficient matrix between the attributes according to the correlation and the original data matrix comprises: The Pearson correlation coefficient statistical algorithm or the Spearman rank correlation coefficient statistical algorithm is used to calculate the correlation between the attributes, and a correlation coefficient matrix between the attributes is formed according to the correlation and the original data matrix.

6. The method according to claim 1, characterized in that Determining the correction coefficient according to the correlation coefficient between the attributes includes: Obtaining the absolute value of the correlation coefficient between the first attribute and the second attribute; determining an integral value of the absolute value based on a quantity of the experimental cable property; The correction factor is determined based on the quantity and integral value of the experimental cable property.

7. A cable insulation status detection device based on live harmonic fluctuations, characterized in that: The device comprises: An acquisition module is used to acquire measurement data of multiple attributes of the experimental cable, wherein the multiple attributes include any multiple of conductor current, local typical defects, current aging time, partial discharge level, insulation resistance, dielectric loss factor, operating years, laying method and load type; A processing module, for determining, for each attribute, a fuzzy evaluation of the attribute on the insulation state according to the measured data of the attribute; The acquisition module is also used to determine the initial weight of each attribute using a fuzzy entropy weight method; The processing module is further used to construct an original data matrix according to the initial weight of each attribute, and calculate the correlation between the attributes, and form a correlation coefficient matrix between the attributes according to the correlation and the original data matrix; The processing module is further used to determine the correction coefficient of each attribute according to the correlation coefficient matrix between the attributes, and to correct the initial weight of each attribute according to the correction coefficient of each attribute to obtain the corrected weight of each attribute; The processing module is further used to perform fuzzy weighted calculation on each attribute according to 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 further used to use a fuzzy operator 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 test cable according to the fuzzy comprehensive evaluation value.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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