A method, system, device and medium for evaluating insulation status of cable intermediate joint

Abstract

The present invention relates to the technical field of power systems and discloses a method, system, device and medium for evaluating the insulation state of a cable intermediate joint. The method collects partial discharge signals from the cable intermediate joint and extracts multiple partial discharge feature quantities that better characterize the insulation state of the cable intermediate joint. A ternary interval data set is constructed to characterize sample data of each partial discharge feature quantity under the insulation state of the cable intermediate joint at multiple different continuous acquisition intervals, thereby better handling information uncertainty and improving the objectivity and credibility of evaluation results. The method also determines a feature weight for each partial discharge feature quantity and, based on a ternary interval gray target decision, uses the ternary interval data set and feature weight of each partial discharge feature quantity to determine a relative bullseye of the cable intermediate joint. The insulation state of the cable intermediate joint is determined based on the relative bullseye, thereby improving the evaluation accuracy of the insulation state of the cable intermediate joint.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method, system, equipment and medium for evaluating the insulation status of a cable intermediate joint. Background Art

[0002] Cable joints, primarily used to connect cables, are critical components for the safe and reliable operation of cable lines. With the widespread use of large numbers of cables in urban power transmission lines, the number of cable joints is also increasing. However, due to factors such as manufacturing quality, insulation materials, and the external environment, cable joints are highly susceptible to insulation breakdown failures and have become the primary weak link in cable lines. Long-term operational experience shows that the gradual deterioration of insulation in cable joints, leading to breakdown, is often accompanied by significant partial discharge.

[0003] Currently, cable joint insulation condition assessments primarily rely on discharge level and insulation resistance testing. These data collection intervals are long, making it difficult to assess the cable joint condition during these intervals. Consequently, it's difficult to fully reflect the true insulation condition of cable joints. In recent years, the continuous development of online monitoring technology has provided a valuable data source for real-time status analysis of power equipment. However, online monitoring data is subject to measurement errors, signal interference, and transmission losses, and the collected status data may be inaccurate, leading to errors in the overall assessment.

[0004] A Chinese patent application with publication number CN110286303A proposes a method for evaluating the insulation aging status of coaxial cables based on a BP neural network. First, a BP neural network model is constructed, and the BP neural network model is trained according to the characteristic parameters and status level of each coaxial cable sample to obtain a trained BP neural network model; secondly, the characteristic parameters of the coaxial cable to be evaluated are obtained, and then the status level of the coaxial cable to be evaluated is evaluated by the trained BP neural network model based on the characteristic parameters of the coaxial cable to be evaluated.

[0005] A Chinese patent application with publication number CN110308375A proposes a cable insulation condition assessment method based on neural network integration. First, different cable insulation condition assessment reference features are integrated to construct a neural network cable insulation condition assessment model based on bagging. Secondly, the characteristic parameters of the cable to be evaluated are measured, and the measured parameters are input into the cable insulation condition assessment model. Finally, the cable insulation condition grade is obtained to complete the assessment of the cable insulation condition.

[0006] Chinese patent application publication number CN114252748A proposes a method, system and device for evaluating the insulation status of cable intermediate joints. The method extracts characteristic vectors from the partial discharge signals of the cable intermediate joints and uses them as evaluation indicators. A hierarchical analysis method is then used to determine the constant weights of the evaluation indicators. The constant weights are then modified according to the degradation degree of the evaluation indicators. Finally, a fuzzy comprehensive evaluation method is used to obtain the insulation status evaluation results of the cable intermediate joints based on the modified weights and the subsequently established membership function.

[0007] Chinese patent application publication number CN104407270A proposes an online fault monitoring device for cable joints in 10-35 kV distribution networks and a method for assessing system status. The device uses laser-induced pressure waves to detect cable joints. The device first collects the amplitude of the ripple in and around the cable joint. A model for assessing internal defects and insulation levels of the cable joint is constructed using a support vector machine (SVM) and the particle swarm algorithm (PSO) is used to optimize the model parameters. Finally, a suitable kernel function is selected to construct the model, enabling accurate assessment of internal defects and insulation levels of the cable joint.

[0008] Through research, it was found that in the method proposed in the above-mentioned Chinese patent application, the proposed method for evaluating the insulation aging status of coaxial cables based on BP neural network is difficult to evaluate the changes in cable status during the interval due to the long data collection interval, and can only reflect the operating status of the cable by detecting whether the data exceeds the limit.

[0009] The proposed cable insulation condition assessment method based on neural network integration has certain reference value for cable insulation condition assessment. However, the neural network model must first be trained with sample data. Therefore, the assessment results are extremely dependent on the accuracy and completeness of the sample data. In addition, the invention requires a large amount of condition monitoring data to improve the neural network to ensure its generalization ability and fault tolerance.

[0010] The core of the proposed fuzzy comprehensive evaluation method, system, and device for assessing the insulation condition of cable intermediate joints lies in the rational selection and allocation of membership and weights for the evaluation factors of the measured object. However, the proposed method employs the Analytic Hierarchy Process (AHP) method, which primarily determines indicator weights based on expert experience and is highly subjective. Furthermore, the variable weight correction theory fails to achieve the desired results when multiple indicators are in extreme deterioration. Furthermore, if the membership function chosen is inappropriate for the cable joint insulation degradation scenario, the accuracy of the insulation condition assessment will be affected.

[0011] The proposed device for online fault monitoring of cable joints in 10-35 kV distribution networks and its method for assessing system status utilize a non-electrical method for partial discharge detection, which has low accuracy. Furthermore, the laser-induced pressure wave method can potentially damage live cable joints. Furthermore, the invention uses a single evaluation reference feature, making it difficult to objectively and comprehensively characterize the joint insulation degradation process. Summary of the Invention

[0012] In view of this, in order to solve the above technical problems, the present invention provides a method, system, device and medium for evaluating the insulation status of a cable intermediate joint.

[0013] A first aspect of the present invention provides a method for evaluating the insulation status of a cable intermediate joint, comprising:

[0014] Collecting a partial discharge signal of a cable intermediate joint, and extracting a plurality of partial discharge characteristic quantities representing the insulation state of the cable intermediate joint from the partial discharge signal;

[0015] Constructing a ternary interval data set of each partial discharge characteristic value of the cable intermediate joint according to sample data of each partial discharge characteristic value under the insulation state of the cable intermediate joint at a plurality of different continuous sampling intervals;

[0016] Determining a feature weight for each of the partial discharge feature quantities;

[0017] Based on the three-element interval grey target decision, the relative bull's-eye degree of the cable intermediate joint is determined using the three-element interval data set of each partial discharge characteristic quantity and the characteristic weight;

[0018] The insulation state of the cable intermediate joint is determined by using the relative bull's-eye degree of the cable intermediate joint.

[0019] Preferably, the partial discharge characteristic quantities include discharge repetition rate, total discharge energy, average discharge amount and maximum discharge amount.

[0020] Preferably, constructing a ternary interval data set of each partial discharge characteristic quantity of the cable intermediate joint according to the sample data of each partial discharge characteristic quantity under the insulation state of the cable intermediate joint at a plurality of different continuous acquisition intervals comprises:

[0021] Acquire sample data of each of the partial discharge characteristic quantities under the insulation state of the intermediate cable joint at a plurality of different continuous acquisition intervals, and describe the sample data of each of the partial discharge characteristic quantities under the insulation state of the intermediate cable joint at each continuous acquisition interval using a ternary interval number;

[0022] By using the number of ternary intervals of the sample data of each partial discharge characteristic quantity under the insulation state of the cable intermediate joint at each continuous sampling interval, a decision matrix for each partial discharge characteristic quantity under the insulation state of the cable intermediate joint at multiple different continuous sampling intervals is constructed, and the decision matrix is ​​used as the ternary interval data set; wherein the ternary interval data set is:

[0023]

[0024] Where, is the number of ternary intervals under PD characteristic value j in continuous acquisition interval i, i = 1, 2, ..., n; j = 1, 2, ..., m; n is the number of continuous acquisition intervals; m is the number of PD characteristic values; is the upper limit of the sample data of the partial discharge characteristic quantity, is the lower limit of the sample data of the partial discharge characteristic quantity, It is the number with the greatest probability in the sample data of the partial discharge characteristic quantity, that is, the preference value.

[0025] Preferably, before the step of determining the relative bull's center of the ternary interval data sets corresponding to the plurality of partial discharge feature quantities using the feature weights corresponding to the plurality of partial discharge feature quantities based on the ternary interval number grey target evaluation method, the method further comprises:

[0026] The ternary interval data set is normalized.

[0027] Preferably, determining a feature weight for each of the partial discharge feature quantities includes:

[0028] determining an objective weight of each of the partial discharge feature quantities based on a degree of deviation of sample data corresponding to each of the partial discharge feature quantities;

[0029] constructing a penalty state variable weight function of the partial discharge feature quantity according to the expected value of the interval data of each partial discharge feature quantity;

[0030] The objective weight is balanced and corrected by using the penalty state variable weight function to obtain the variable weight of each partial discharge feature quantity as the feature weight of the partial discharge feature quantity.

[0031] Preferably, the grey target decision-making based on the ternary interval number, using the ternary interval data set of each of the partial discharge characteristic quantities and the characteristic weight, to determine the relative bull's-eye degree of the cable intermediate joint, includes:

[0032] Determining the optimal ternary interval number and the worst ternary interval number based on the ternary interval data set of each partial discharge characteristic value of the cable intermediate joint;

[0033] The optimal ternary interval number and the worst ternary interval number are respectively used as the expected bull's-eye and the marginal bull's-eye of the grey target decision;

[0034] Determining the expected bull's-eye degree and the edge bull's-eye degree of each of the partial discharge characteristic quantities using the expected bull's-eye and edge bull's-eye determined by the grey target;

[0035] Determining the total expected bull's-eye degree and the total edge bull's-eye degree of the cable intermediate joint at continuous acquisition intervals according to the expected bull's-eye degree and the edge bull's-eye degree of each partial discharge characteristic quantity and the characteristic weight of each partial discharge characteristic quantity;

[0036] The relative bull's-eye degree of the cable intermediate joint at consecutive sampling intervals is determined according to the total expected bull's-eye degree and the total marginal bull's-eye degree of the cable intermediate joint.

[0037] Preferably, the determining the insulation state of the cable intermediate joint by using the relative bull's-eye degree of the cable intermediate joint comprises:

[0038] Comparing the relative bull's-eye degree of the cable intermediate joint with preset bull's-eye degree range thresholds under a plurality of different insulation status levels, and determining the preset bull's-eye degree range threshold within which the relative bull's-eye degree of the cable intermediate joint falls;

[0039] The insulation status level of the cable intermediate joint is determined according to the preset bull's-eye range threshold into which the relative bull's-eye degree of the cable intermediate joint falls.

[0040] In a second aspect, the present invention provides a cable intermediate joint insulation status assessment system, comprising:

[0041] A partial discharge data acquisition module is used to collect partial discharge signals of the cable intermediate joint and extract a plurality of partial discharge characteristic quantities representing the insulation state of the cable intermediate joint from the partial discharge signals;

[0042] A ternary interval construction module is used to construct a ternary interval data set of each partial discharge characteristic quantity of the cable intermediate joint according to the sample data of each partial discharge characteristic quantity under the insulation state of the cable intermediate joint at a plurality of different continuous collection intervals;

[0043] A weight determination module, configured to determine a feature weight for each of the partial discharge feature quantities;

[0044] A relative bull's-eye determination module is configured to determine the relative bull's-eye degree of the cable intermediate joint based on a three-element interval gray target decision and utilizing a three-element interval data set of each partial discharge characteristic quantity and the characteristic weight;

[0045] The insulation evaluation module is used to determine the insulation status of the cable intermediate joint by using the relative bull's-eye degree of the cable intermediate joint.

[0046] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the cable intermediate joint insulation status assessment method as described in the first aspect.

[0047] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the steps of the method for evaluating the insulation status of an intermediate cable joint as described in the first aspect.

[0048] It can be seen from the above technical solution that the present invention collects the partial discharge signal of the cable intermediate joint and extracts multiple partial discharge feature quantities that better characterize the insulation state of the cable intermediate joint. By constructing a ternary interval data set to characterize the sample data of each partial discharge feature quantity under the insulation state of the cable intermediate joint under multiple different continuous collection intervals, the uncertainty of information is better handled and the objectivity and credibility of the evaluation results are improved. The feature weight of each partial discharge feature quantity is determined, and based on the ternary interval gray target decision, the ternary interval data set and feature weight of each partial discharge feature quantity are used to determine the relative bull's-eye degree of the cable intermediate joint, and the insulation state of the cable intermediate joint is determined by the relative bull's-eye degree, thereby improving the evaluation accuracy of the insulation state of the cable intermediate joint. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 An application environment for a cable intermediate joint insulation status assessment method provided by an embodiment of the present invention;

[0051] Figure 2 A flowchart of a method for evaluating the insulation status of a cable intermediate joint provided by an embodiment of the present invention;

[0052] Figure 3 A schematic structural diagram of a cable intermediate joint insulation status assessment system provided by an embodiment of the present invention;

[0053] Figure 4 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0055] The cable intermediate joint insulation status evaluation method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. The cable intermediate joint is connected to the terminal 102. The data storage system can store the data that the terminal 102 needs to process. The data storage system can be integrated on the terminal 102, or placed on the cloud or other network servers. The terminal 102 collects the partial discharge signal of the cable intermediate joint and extracts multiple partial discharge feature quantities that characterize the insulation state of the cable intermediate joint from the partial discharge signal; based on the sample data of each partial discharge feature quantity under the insulation state of the cable intermediate joint at multiple different continuous acquisition intervals, a ternary interval data set of each partial discharge feature quantity of the cable intermediate joint is constructed; a feature weight is determined for each partial discharge feature quantity; based on the ternary interval gray target decision, the ternary interval data set and feature weight of each partial discharge feature quantity are used to determine the relative bull's-eye degree of the cable intermediate joint; the insulation state of the cable intermediate joint is determined using the relative bull's-eye degree of the cable intermediate joint. The terminal 102 can be a mobile terminal or a PC terminal, etc.

[0056] like Figure 2 As shown, the embodiment of the present application provides a method for evaluating the insulation status of a cable intermediate joint, which is applied to Figure 1 The terminal 102 in the example is used as an example to illustrate the process, including the following steps S1 to S5.

[0057] Step S1: collecting a partial discharge signal of a cable intermediate joint, and extracting a plurality of partial discharge characteristic quantities representing the insulation state of the cable intermediate joint from the partial discharge signal.

[0058] To monitor the operating status of cable joints in real time, it is necessary to select appropriate partial discharge (PD) characteristics to characterize the insulation state of cable intermediate joints, thereby establishing a scientific condition assessment system. Relevant theoretical analysis indicates that during the aging process of cable intermediate joint insulation, one or more state parameters will exhibit significant changes. By studying the changes in this state information, the insulation condition of the cable joint can be effectively determined. Among various state information, the amount of partial discharge (PD) is the primary factor in determining the degree of insulation degradation in cable joints and can effectively characterize the insulation state of cable intermediate joints. The research and application of online PD monitoring methods are relatively mature and currently a reliable method for monitoring the insulation state of cross-linked polyethylene (XLPE) cable joints. Therefore, by statistically analyzing the characteristic parameters of PD in cable joints, real-time changes in the insulation state of the joints can be intuitively and effectively determined.

[0059] The embodiment of the present application collects the high-frequency pulse signal generated when partial discharge occurs at the cable joint based on the high-frequency current detection method as the partial discharge (PD) signal of the cable intermediate joint, selects the partial discharge characteristic quantity that characterizes the insulation state of the cable intermediate joint, and processes the interval data.

[0060] In practical applications, a charge collector with a circumference equal to that of a 10kV cable joint is fabricated from copper foil and wrapped around the outer insulation layer of the cable joint. Based on partial discharge signals acquired by an online partial discharge monitoring system, selected partial discharge characteristics include discharge repetition rate, total discharge energy, average discharge amount, and maximum discharge amount.

[0061] The following is a detailed explanation of the principle and calculation method of the selected partial discharge characteristic quantity:

[0062] Discharge repetition rate: refers to the number of discharges detected per unit time. The higher the discharge repetition rate, the more frequent the discharge phenomenon, and the more serious the insulation degradation of the cable joint. The calculation formula of the discharge repetition rate is shown in formula (1).

[0063] (1)

[0064] In formula (1), R is the discharge repetition rate, M is the total number of detection power frequency cycles; N s is the number of discharges in the sth detection power frequency cycle.

[0065] Total discharge energy: This refers to the total energy consumed by partial discharge during the detection cycle. Analysis of the mechanism of partial discharge reveals that during the discharge process, electrical energy is released in the form of photons, heat, and mechanical energy, damaging the insulation material of the cable joint. Therefore, the more energy released by partial discharge, the more severe the cable joint insulation aging. This value reflects the progression of cable joint insulation aging, and its calculation formula is shown in Equation (2).

[0066] (2)

[0067] In formula (2), is the total discharge energy, For the The energy value of the discharge pulse is The apparent discharge capacity of the discharge is , the initial discharge voltage is , then the calculation model of discharge energy is shown in formula (3).

[0068] (3)

[0069] Average discharge capacity: This refers to the arithmetic value of the total apparent discharge capacity divided by the total number of discharges during the test cycle. This value reflects the level of partial discharge development. The more severe the insulation degradation of the cable joint, the faster the average discharge capacity increases, indicating a higher discharge capacity per single partial discharge pulse. Its calculation formula is shown in Equation (4).

[0070] (4)

[0071] In formula (4), Q avg is the average discharge capacity, is the apparent discharge capacity of the i-th discharge; is the number of partial discharges within the detection period.

[0072] Maximum discharge capacity: refers to the maximum apparent discharge capacity value among all discharge pulses within the detection cycle. As the insulation degradation of the cable intermediate joint increases, the partial discharge capacity often increases significantly. Especially before the insulation breakdown, the partial discharge capacity will far exceed the specified threshold. Therefore, this value reflects the maximum degree of insulation degradation of the cable intermediate joint. The calculation formula of the maximum discharge capacity is shown in formula (5).

[0073] (5)

[0074] In formula (5), Q m is the maximum discharge capacity, is the apparent discharge capacity of the ith discharge.

[0075] Step S2: constructing a ternary interval data set of each partial discharge characteristic quantity of the cable intermediate joint according to the sample data of each partial discharge characteristic quantity of the cable intermediate joint under the insulation state at multiple different continuous sampling intervals.

[0076] Among them, the embodiment of the present application aims at the multi-feature evaluation problem of the cable intermediate joint, and constructs a ternary interval data set of each partial discharge feature quantity of the cable intermediate joint to determine the data trend of each partial discharge feature quantity.

[0077] In some embodiments, step S2 constructs a ternary interval data set of each partial discharge characteristic value of the cable intermediate joint according to the sample data of each partial discharge characteristic value of the cable intermediate joint under the insulation state at multiple different continuous acquisition intervals, including:

[0078] Step S201: Obtain sample data of each partial discharge characteristic quantity of the cable intermediate joint under the insulation state at multiple different continuous acquisition intervals, and use ternary interval numbers to describe the sample data of each partial discharge characteristic quantity of the cable intermediate joint under the insulation state at each continuous acquisition interval.

[0079] Among them, the status of the cable intermediate joint is evaluated based on the online monitoring data. The monitoring data is collected every 15 minutes. The continuous collection interval refers to the continuous data collection cycle of the cable intermediate joint during operation. The data collection cycle is a specific continuous time sequence. Suppose the insulation status of the cable intermediate joint at each continuous collection interval is a finite set of evaluation objects: , the m partial discharge characteristic quantities of each evaluation object can be composed of a set , the corresponding indicator weight is ,satisfy and An evaluation object In the indicator The number of available ternary intervals for the sample data values ​​under , (i=1, 2, ..., n; j=1, 2, ..., m) represents.

[0080] Step S202: Using the number of ternary intervals of sample data of each partial discharge characteristic quantity under the insulation state of the cable intermediate joint at each continuous sampling interval, a decision matrix for each partial discharge characteristic quantity under the insulation state of the cable intermediate joint at multiple different continuous sampling intervals is constructed, and the decision matrix is ​​used as a ternary interval data set; wherein the ternary interval data set is:

[0081]

[0082] Where, is the number of ternary intervals under PD characteristic value j in continuous acquisition interval i, i = 1, 2, ..., n; j = 1, 2, ..., m; n is the number of continuous acquisition intervals; m is the number of PD characteristic values; is the upper limit of the sample data of the partial discharge characteristic quantity, is the lower limit of the sample data of the partial discharge characteristic quantity, It is the number with the greatest probability in the sample data of the partial discharge characteristic quantity, that is, the preference value.

[0083] Among them, the embodiment of the present application takes into account the dynamic and random nature of cable joint status information, and adopts ternary interval numbers to represent the cable joint status information, which better handles the uncertainty of information and improves the objectivity and credibility of the evaluation results.

[0084] In some embodiments, in order to eliminate the incommensurability between the indicators and unify the trend requirements of the indicators, the ternary interval data set is normalized, that is, the ternary interval data set is normalized. Standardize the process so that As a benefit indicator, is a cost-type indicator. Normalizing the ternary interval number, we get:

[0085] (6)

[0086] (7)

[0087] Where, are respectively the upper limit of the sample data of the normalized partial discharge characteristic quantity, the lower limit of the sample data of the normalized partial discharge characteristic quantity, and the number with the greatest probability of taking the value in the sample data of the normalized partial discharge characteristic quantity.

[0088] The normalized ternary interval data set is:

[0089] (8)

[0090] Where R is the normalized ternary interval dataset.

[0091] Step S3: Determine a feature weight for each partial discharge feature.

[0092] Specifically, determining the feature weight for each partial discharge feature in step S3 includes:

[0093] Step S301: Determine the objective weight of each partial discharge feature quantity based on the degree of dispersion of sample data corresponding to each of the plurality of partial discharge feature quantities.

[0094] The objective weight is a quantitative assignment of the PD characteristic quantity based on its numerical value, using certain standards or logical rules. Considering data volatility, the greater the volatility of a PD characteristic quantity, the richer its information content and the more significant its impact on the overall assessment, and thus the higher the weight should be considered. To this end, the present invention uses a deviation method to measure the degree of fluctuation in the interval index data of each PD characteristic quantity, thereby determining the objective weight of the PD characteristic quantity.

[0095] Specifically, step S301 determines the objective weight of each partial discharge feature quantity based on the degree of dispersion of the sample data corresponding to each of the plurality of partial discharge feature quantities, including:

[0096] Step S3011: Determine the partial discharge characteristic quantity The membership value of The mean of is:

[0097] (9)

[0098] Where, is the characteristic quantity of partial discharge The mean of They are The mean of .

[0099] Step S3012: Based on the partial discharge characteristic quantity The membership value of determines the partial discharge characteristic quantity The mean square error is:

[0100] (10)

[0101] Where, is the characteristic quantity of partial discharge The mean square error, They are The mean square error of .

[0102] Step S3013: Based on the partial discharge characteristic quantity Determine the partial discharge characteristic quantity The interval weights are:

[0103] (11)

[0104] Where, is the characteristic quantity of partial discharge The interval weight of They are The interval weight of .

[0105] Step S3014: convert the interval weight into a certain value as the objective weight of the partial discharge feature value.

[0106] Among them, the interval weight is presented in the form of interval number. If the interval weight is directly used for status evaluation, the evaluation result may diverge, resulting in the inability to accurately judge the status level of the cable joint. Therefore, the embodiment of the present application adopts the possibility formula in the interval number theory to convert the interval weight into a definite value to ensure the uniqueness of the evaluation result. The possibility of an interval number refers to the probability that one interval number is greater than or equal to another interval number under uncertainty conditions. Its value is generally between 0 and 1, and is used to measure the size relationship between the two interval numbers. The specific conversion process is as follows:

[0107] Let the number of ternary intervals be 、 、 、 , where 、 is the upper limit of the partial discharge characteristic data, 、 is the lower limit of the PD characteristic data, 、 is the number with the greatest probability of taking the value among the PD characteristic quantities, which is used to calculate the elements of the possibility degree complementary matrix. The possibility degree formula is:

[0108] (12)

[0109] In formula (12), It represents the probability that the ternary interval number A is greater than or equal to B.

[0110] The possibility degree complementary matrix of the interval weight of the partial discharge characteristic quantity is calculated using formula (12): ,in , and calculate the correction vector of the interval weight according to formula (13) for:

[0111] (13)

[0112] Among them, the correction vector As the only objective weight of the partial discharge characteristic quantity.

[0113] Step S302: construct a punitive state variable weight function of the partial discharge feature quantity according to the expected value of the interval data of each partial discharge feature quantity.

[0114] It is understandable that objective weights, as constant weights, are primarily used to measure the relative importance of indicators, but they cannot reflect the data variation characteristics of the evaluation object. To this end, the embodiments of this application introduce variable weight theory to dynamically adjust the objective weights, so that the feature weights can better reflect the actual impact of state quantity changes on the evaluation results.

[0115] Let the characteristic state vector :

[0116] Definition 1: For a set of m-dimensional variable weights, there are n mappings:

[0117] ,satisfy:

[0118] 1) Normalization:

[0119] 2) Continuity: About each variable continuous.

[0120] 3) Monotonicity: About each variable Monotonically increasing (incentive) or monotonically decreasing (punitive).

[0121] Assume that the weight vector , then the following definition:

[0122] Definition 2: For an n-dimensional penalized state variable weight vector, it refers to the mapping:

[0123] satisfy:

[0124] Monotonicity: If ,but .

[0125] Continuity: (j=1, 2, ..., n) for each variable continuous.

[0126] Arbitrary constant weight vector When the normalization, continuity, and monotonicity in Definition 1 are satisfied, then:

[0127] (14)

[0128] In formula (14), S(V) represents the state function vector; W(V) represents the variable weight vector.

[0129] Definition 2 shows that when the indicator value is greater than a given value, a corresponding penalized variable weight function is required to assign it a greater weight. Currently, most technologies use a balanced function to construct a corresponding variable weight model, but this model does not conform to the changing characteristics of cable joint partial discharge indicator data. To this end, the embodiments of the present application analyze the partial discharge development patterns of typical defects in cable intermediate joints to construct a penalized state variable weight function for the partial discharge characteristic quantity of the cable intermediate joint.

[0130] Based on relevant experimental research, the development process of partial discharge (PD) in typical defects of cable intermediate joints can be divided into three stages: initiation, development, and severity. In the initial stage of PD, the statistical characteristics of PD show significant fluctuations at the beginning of discharge, but quickly stabilize and maintain relative stability. In the development stage of PD, the cable joint insulation ages faster, and the statistical characteristics of PD increase slowly and show a nearly linear growth trend. In the severe stage of PD, the cable joint insulation is on the verge of failure, and the amplitude of the statistical characteristics of PD changes dramatically and shows a nonlinear trend.

[0131] Therefore, the embodiment of the present application calculates the expected value of the interval data of the partial discharge characteristic quantity as the standard for measuring the degree of indicator degradation, thereby constructing a punitive state-variable weight function, thereby reasonably correcting the weights of the partial discharge indicators at different stages. Specifically, in step S302, the punitive state-variable weight function of the partial discharge characteristic quantity is constructed based on the expected value of the interval data of each partial discharge characteristic quantity, including:

[0132] Step S3021: Collect online monitoring data of partial discharge of cable joints within a certain time range, express the index data value in the form of ternary interval numbers, and perform normalization processing according to formulas (6) to (7).

[0133] Step S3022: Calculate the expected value of the interval data of the partial discharge characteristic quantity, as shown in formula (15).

[0134] (15)

[0135] Where, is the expected value of the interval data of the partial discharge characteristic quantity.

[0136] Step S3023: Based on the development law of partial discharge of typical defects of cable joints and considering the expected value of the interval of partial discharge characteristic quantity, the following three-stage state variable weight function of partial discharge characteristic quantity is constructed, as shown in formula (16).

[0137] (16)

[0138] In formula (16), It is a sample No. The state variable weight function of the partial discharge characteristic quantity; It is a sample No. The normalized value of the interval expectation of each partial discharge characteristic quantity; 、 To evaluate the strategy; 、 are the normal and severe level thresholds of the partial discharge characteristic quantity; is the penalty factor and is greater than 0. The larger the value, the lower the tolerance to serious deviations of certain partial discharge characteristic quantities. Combined with the equipment characteristics of the cable intermediate joint, the parameter values ​​of the partial discharge state weight function can be taken as 、 、 、 .

[0139] Step S303: Use the penalty state variable weight function to perform balanced correction on the objective weight to obtain the variable weight of each partial discharge feature quantity as the feature weight of the partial discharge feature quantity.

[0140] Among them, after determining the penalty state variable weight function value through the three-stage state variable weight function, the penalty state variable weight function value is substituted into formula (14), and the variable weight of each partial discharge feature quantity is obtained according to formula (14) as the feature weight of the partial discharge feature quantity.

[0141] It can be understood that the embodiment of the present application uses the expected normalized value of the index interval data as a standard for measuring the degree of degradation of the partial discharge index, and based on the partial discharge development law of typical defects of cable joints, constructs a punitive state-variable weight function to correct the constant weight of the index, solving the problem that the traditional variable weight model with the equilibrium function as the state-variable weight function does not conform to the law of change of the partial discharge index.

[0142] Step S4: Based on the three-element interval grey target decision, the relative bull's-eye degree of the cable intermediate joint is determined using the three-element interval data set and feature weight of each partial discharge feature quantity.

[0143] Specifically, the grey target decision based on the ternary interval number in step S4 uses the ternary interval data set and feature weight of each partial discharge feature quantity to determine the relative bull's-eye degree of the cable intermediate joint, including:

[0144] Step S401: determining the optimal number of ternary intervals and the worst number of ternary intervals based on the ternary interval data set of each partial discharge characteristic value of the cable intermediate joint;

[0145] Determine the optimal number of ternary intervals in a standard post-processed ternary interval dataset during the operation phase of a cable intermediate joint. ,in, Similarly, determine the number of worst ternary intervals in the ternary interval data set after standard post-processing ,in, .

[0146] Step S402: The optimal number of ternary intervals and the worst number of ternary intervals are used as the expected bull's-eye and marginal bull's-eye of the grey target decision, respectively.

[0147] Among them, the optimal number of three-element intervals is the expected bull's-eye of the grey target decision, and the worst number of three-element intervals is the marginal bull's-eye of the grey target decision.

[0148] Step S403: Determine the expected bull's-eye degree and edge bull's-eye degree of each partial discharge feature using the expected bull's-eye and edge bull's-eye determined by the grey target.

[0149] Among them, the operating status of the cable intermediate joint is related to the partial discharge characteristic quantity The expected bull's-eye degree is:

[0150] (17)

[0151] In formula (17), ; is the characteristic quantity of partial discharge The expected bull's-eye degree.

[0152] The operating status of the cable intermediate joint is related to the partial discharge characteristic quantity The edge bull's-eye degree is:

[0153] (18)

[0154] In formula (18), ; For the partial discharge characteristic The edge of the bull's eye degree.

[0155] Step S404: Determine the total expected bull's-eye degree and total edge bull's-eye degree of the cable intermediate joint in continuous acquisition intervals based on the expected bull's-eye degree and edge bull's-eye degree of each partial discharge characteristic quantity and the characteristic weight of each partial discharge characteristic quantity.

[0156] Among them, combining the feature weight of each partial discharge feature quantity and the expected bull's-eye degree of each partial discharge feature quantity, the total expected bull's-eye degree of the cable intermediate joint in continuous acquisition intervals is determined as:

[0157] (19)

[0158] Where, is the total expected bull's-eye degree, is the feature weight of the PD feature.

[0159] Among them, combining the feature weight of each partial discharge feature quantity and the edge bull's-eye degree of each partial discharge feature quantity, the total edge bull's-eye degree of the cable intermediate joint in continuous acquisition intervals is determined as:

[0160] (20)

[0161] Where, is the total edge bull's-eye degree.

[0162] Step S405: Determine the relative bull's-eye degree of the cable intermediate joint at consecutive acquisition intervals based on the total expected bull's-eye degree and the total edge bull's-eye degree of the cable intermediate joint.

[0163] The relative bull's-eye degree of the cable intermediate joint at continuous sampling intervals is calculated as:

[0164] (twenty one)

[0165] Where, It is the relative bull's eye degree.

[0166] Step S5: Determine the insulation state of the cable intermediate joint using the relative bull's-eye degree of the cable intermediate joint.

[0167] In this embodiment of the present application, relative bull's-eye degree is used as a metric to determine the insulation status level of the cable intermediate joint. Specifically, the use of relative bull's-eye degree of the cable intermediate joint in step S5 to determine the insulation status of the cable intermediate joint includes:

[0168] Step S501: Compare the relative bull's-eye degree of the cable intermediate joint with preset bull's-eye degree range thresholds under multiple different insulation status levels, and determine the preset bull's-eye degree range threshold within which the relative bull's-eye degree of the cable intermediate joint falls.

[0169] Step S502: Determine the insulation status level of the cable intermediate joint according to the preset bull's-eye degree range threshold into which the relative bull's-eye degree of the cable intermediate joint falls.

[0170] For example, according to relevant guidelines, the status of 10kV cable intermediate joints is divided into four levels: normal (I), caution (II), abnormal (III), and severe (IV). Based on actual field conditions and test results of joint insulation degradation, the bullseye values ​​representing different cable joint conditions are shown in Table 1.

[0171] Table 1 Classification of cable connector status

[0172]

[0173] It should be noted that the embodiment of the present application collects the partial discharge signal of the cable intermediate joint and extracts multiple partial discharge feature quantities that better characterize the insulation state of the cable intermediate joint. By constructing a ternary interval data set to characterize the sample data of each partial discharge feature quantity under the insulation state of the cable intermediate joint under multiple different continuous collection intervals, the uncertainty of information is better handled, and the objectivity and credibility of the evaluation results are improved. By determining the feature weight of each partial discharge feature quantity, based on the ternary interval gray target decision, the ternary interval data set and feature weight of each partial discharge feature quantity are used to determine the relative bull's-eye degree of the cable intermediate joint, and the insulation state of the cable intermediate joint is determined by the relative bull's-eye degree, thereby improving the evaluation accuracy of the insulation state of the cable intermediate joint.

[0174] Based on the same inventive concept, an embodiment of the present application further provides a cable intermediate joint insulation state assessment system for implementing the above-mentioned cable intermediate joint insulation state assessment method.

[0175] The implementation solution provided by the system to solve the problem is similar to the implementation solution described in the above method. Therefore, the specific limitations in one or more cable intermediate joint insulation status assessment system embodiments provided below can refer to the limitations of the cable intermediate joint insulation status assessment method above and will not be repeated here.

[0176] like Figure 3 As shown, an embodiment of the present application provides a cable intermediate joint insulation status assessment system, comprising:

[0177] The partial discharge data acquisition module 100 is used to collect partial discharge signals of the cable intermediate joint and extract multiple partial discharge characteristic quantities representing the insulation state of the cable intermediate joint from the partial discharge signals;

[0178] The ternary interval construction module 200 is used to construct a ternary interval data set of each partial discharge characteristic quantity of the cable intermediate joint based on the sample data of each partial discharge characteristic quantity of the cable intermediate joint under the insulation state at multiple different continuous collection intervals;

[0179] A weight determination module 300 is used to determine a feature weight for each partial discharge feature;

[0180] The relative bull's-eye determination module 400 is used to determine the relative bull's-eye degree of the cable intermediate joint based on the three-element interval gray target decision and the three-element interval data set and feature weight of each partial discharge feature;

[0181] The insulation evaluation module 500 is used to determine the insulation status of the cable intermediate joint by using the relative bull's-eye degree of the cable intermediate joint.

[0182] In some embodiments, the partial discharge characteristic quantities include discharge repetition rate, total discharge energy, average discharge amount, and maximum discharge amount.

[0183] In some embodiments, the ternary interval construction module 200 is used to obtain sample data of each partial discharge characteristic quantity under the insulation state of the cable intermediate joint at multiple different continuous acquisition intervals, and use the ternary interval number to describe the sample data of each partial discharge characteristic quantity under the insulation state of the cable intermediate joint at each continuous acquisition interval;

[0184] The number of ternary intervals of sample data of each partial discharge characteristic quantity under the insulation state of the cable intermediate joint at each continuous sampling interval is used to construct a decision matrix for each partial discharge characteristic quantity under the insulation state of the cable intermediate joint at multiple different continuous sampling intervals, and the decision matrix is ​​used as a ternary interval data set; wherein the ternary interval data set is:

[0185]

[0186] Where, is the number of ternary intervals under PD characteristic value j in continuous acquisition interval i, i = 1, 2, ..., n; j = 1, 2, ..., m; n is the number of continuous acquisition intervals; m is the number of PD characteristic values; is the upper limit of the sample data of the partial discharge characteristic quantity, is the lower limit of the sample data of the partial discharge characteristic quantity, It is the number with the greatest probability in the sample data of the partial discharge characteristic quantity, that is, the preference value.

[0187] In some embodiments, the system further comprises:

[0188] Normalization module, used to normalize the ternary interval dataset.

[0189] In some embodiments, the weight determination module 300 is configured to determine an objective weight of each partial discharge feature quantity based on the degree of dispersion of sample data corresponding to each of the plurality of partial discharge feature quantities;

[0190] According to the expected value of the interval data of each partial discharge characteristic quantity, a penalty state variable weight function of the partial discharge characteristic quantity is constructed;

[0191] The objective weights are balanced and corrected using a penalty state variable weight function, and the variable weight of each partial discharge characteristic quantity is obtained as the characteristic weight of the partial discharge characteristic quantity.

[0192] In some embodiments, the relative bull's-eye determination module 400 is used to determine the optimal ternary interval number and the worst ternary interval number based on the ternary interval data set of each partial discharge characteristic value of the cable intermediate joint;

[0193] The optimal number of three-element intervals and the worst number of three-element intervals are used as the expected bull's-eye and marginal bull's-eye of grey target decision respectively;

[0194] The expected bull's-eye and edge bull's-eye of the grey target decision are used to determine the expected bull's-eye degree and edge bull's-eye degree of each partial discharge characteristic quantity.

[0195] Determining the total expected bull's-eye degree and total edge bull's-eye degree of the cable intermediate joint at continuous acquisition intervals based on the expected bull's-eye degree and edge bull's-eye degree of each partial discharge characteristic quantity and the characteristic weight of each partial discharge characteristic quantity;

[0196] The relative bull's-eye degree of the cable intermediate joint at consecutive sampling intervals is determined based on the total expected bull's-eye degree and the total edge bull's-eye degree of the cable intermediate joint.

[0197] In some embodiments, the insulation assessment module 500 is configured to compare the relative bull's-eye degree of the cable intermediate joint with preset bull's-eye degree range thresholds at a plurality of different insulation status levels, and determine the preset bull's-eye degree range threshold within which the relative bull's-eye degree of the cable intermediate joint falls;

[0198] The insulation state level of the cable intermediate joint is determined according to the preset bull's-eye degree range threshold into which the relative bull's-eye degree of the cable intermediate joint falls.

[0199] like Figure 4 As shown, an embodiment of the present application provides an electronic device, the electronic device 10 includes a memory 20 and a processor 30, the memory 20 stores a computer program, and when the computer program is executed by the processor 30, the processor 30 performs the steps of the cable intermediate joint insulation status assessment method as in the above embodiment.

[0200] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed, the steps of the method for evaluating the insulation status of an intermediate cable joint as described in the above embodiment are implemented.

[0201] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, electronic devices, and computer storage media can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0202] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products or apparatuses.

[0203] In several embodiments provided by the present invention, it is understood that each box in the flow chart or block diagram can represent a module, program segment or part of the code, and the module, program segment or part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved.

[0204] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, electronic devices, computer storage media and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0205] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0206] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0207] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the method described in each embodiment of the present invention via a computer device (which can be a personal computer, server, or network device, etc.). The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0208] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for evaluating the insulation status of a cable intermediate joint, characterized in that: include: Collecting a partial discharge signal of a cable intermediate joint, and extracting a plurality of partial discharge characteristic quantities representing the insulation state of the cable intermediate joint from the partial discharge signal; Constructing a ternary interval data set of each partial discharge characteristic value of the cable intermediate joint according to sample data of each partial discharge characteristic value under the insulation state of the cable intermediate joint at a plurality of different continuous sampling intervals; Determining a feature weight for each of the partial discharge feature quantities; Based on the three-element interval grey target decision, the relative bull's-eye degree of the cable intermediate joint is determined by utilizing the three-element interval data set of each partial discharge characteristic quantity and the characteristic weight, including: Determining the optimal ternary interval number and the worst ternary interval number based on the ternary interval data set of each partial discharge characteristic value of the cable intermediate joint; The optimal ternary interval number and the worst ternary interval number are respectively used as the expected bull's-eye and the marginal bull's-eye of the grey target decision; Determining the expected bull's-eye degree and the edge bull's-eye degree of each of the partial discharge characteristic quantities using the expected bull's-eye and edge bull's-eye determined by the grey target; Determining the total expected bull's-eye degree and the total edge bull's-eye degree of the cable intermediate joint at continuous acquisition intervals according to the expected bull's-eye degree and the edge bull's-eye degree of each partial discharge characteristic quantity and the characteristic weight of each partial discharge characteristic quantity; Determining a relative bull's-eye degree of the cable intermediate joint at consecutive sampling intervals based on a total expected bull's-eye degree and a total edge bull's-eye degree of the cable intermediate joint; Determining the insulation state of the cable intermediate joint by using the relative bull's-eye degree of the cable intermediate joint includes: Comparing the relative bull's-eye degree of the cable intermediate joint with preset bull's-eye degree range thresholds under a plurality of different insulation status levels, and determining the preset bull's-eye degree range threshold within which the relative bull's-eye degree of the cable intermediate joint falls; The insulation status level of the cable intermediate joint is determined according to the preset bull's-eye range threshold into which the relative bull's-eye degree of the cable intermediate joint falls.

2. The method for evaluating the insulation status of a cable intermediate joint according to claim 1, characterized in that: The partial discharge characteristic quantities include discharge repetition rate, total discharge energy, average discharge amount and maximum discharge amount.

3. The method for evaluating the insulation status of a cable intermediate joint according to claim 1, wherein: The constructing of a ternary interval data set of each partial discharge characteristic value of the cable intermediate joint according to the sample data of each partial discharge characteristic value of the cable intermediate joint under the insulation state at a plurality of different continuous sampling intervals comprises: Acquire sample data of each of the partial discharge characteristic quantities under the insulation state of the intermediate cable joint at a plurality of different continuous acquisition intervals, and describe the sample data of each of the partial discharge characteristic quantities under the insulation state of the intermediate cable joint at each continuous acquisition interval using a ternary interval number; By using the number of ternary intervals of the sample data of each partial discharge characteristic quantity under the insulation state of the cable intermediate joint at each continuous sampling interval, a decision matrix for each partial discharge characteristic quantity under the insulation state of the cable intermediate joint at multiple different continuous sampling intervals is constructed, and the decision matrix is ​​used as the ternary interval data set; wherein the ternary interval data set is: ； Where, is the number of ternary intervals under the partial discharge characteristic value j in the continuous acquisition interval i, i=1, 2, ..., n; j=1, 2, ..., m; n is the number of continuous acquisition intervals; m is the number of partial discharge characteristic values; is the upper limit of the sample data of the partial discharge characteristic quantity, is the lower limit of the sample data of the partial discharge characteristic quantity, It is the number with the greatest probability in the sample data of the partial discharge characteristic quantity, that is, the preference value.

4. The method for evaluating the insulation status of a cable intermediate joint according to claim 1, wherein: Before the step of determining the relative bull's center of the ternary interval data sets corresponding to the plurality of partial discharge feature quantities using the feature weights corresponding to the plurality of partial discharge feature quantities based on the ternary interval number grey target evaluation method, the method further includes: The ternary interval data set is normalized.

5. The method for evaluating the insulation status of a cable intermediate joint according to claim 1, wherein: Determining a feature weight for each of the partial discharge feature quantities includes: determining an objective weight of each of the partial discharge feature quantities based on a degree of deviation of sample data corresponding to each of the partial discharge feature quantities; constructing a penalty state variable weight function of the partial discharge feature quantity according to the expected value of the interval data of each partial discharge feature quantity; The objective weight is balanced and corrected by using the penalty state variable weight function to obtain the variable weight of each partial discharge feature quantity as the feature weight of the partial discharge feature quantity.

6. A cable intermediate joint insulation status assessment system, characterized in that: include: A partial discharge data acquisition module is used to collect partial discharge signals of the cable intermediate joint and extract a plurality of partial discharge characteristic quantities representing the insulation state of the cable intermediate joint from the partial discharge signals; A ternary interval construction module is used to construct a ternary interval data set of each partial discharge characteristic quantity of the cable intermediate joint according to the sample data of each partial discharge characteristic quantity under the insulation state of the cable intermediate joint at a plurality of different continuous collection intervals; A weight determination module, configured to determine a feature weight for each of the partial discharge feature quantities; A relative bull's-eye determination module is configured to determine the relative bull's-eye degree of the cable intermediate joint based on a three-element interval gray target decision and utilizing a three-element interval data set of each partial discharge characteristic quantity and the characteristic weight; Based on the three-element interval grey target decision, the relative bull's-eye degree of the cable intermediate joint is determined by utilizing the three-element interval data set of each partial discharge characteristic quantity and the characteristic weight, including: Determining the optimal ternary interval number and the worst ternary interval number based on the ternary interval data set of each partial discharge characteristic value of the cable intermediate joint; The optimal ternary interval number and the worst ternary interval number are respectively used as the expected bull's-eye and the marginal bull's-eye of the grey target decision; Determining the expected bull's-eye degree and the edge bull's-eye degree of each of the partial discharge characteristic quantities using the expected bull's-eye and edge bull's-eye determined by the grey target; Determining the total expected bull's-eye degree and the total edge bull's-eye degree of the cable intermediate joint at continuous acquisition intervals according to the expected bull's-eye degree and the edge bull's-eye degree of each partial discharge characteristic quantity and the characteristic weight of each partial discharge characteristic quantity; Determining a relative bull's-eye degree of the cable intermediate joint at consecutive sampling intervals based on a total expected bull's-eye degree and a total edge bull's-eye degree of the cable intermediate joint; an insulation evaluation module, configured to determine the insulation status of the cable intermediate joint using the relative bull's-eye degree of the cable intermediate joint; Determining the insulation state of the cable intermediate joint by using the relative bull's-eye degree of the cable intermediate joint includes: Comparing the relative bull's-eye degree of the cable intermediate joint with preset bull's-eye degree range thresholds under a plurality of different insulation status levels, and determining the preset bull's-eye degree range threshold within which the relative bull's-eye degree of the cable intermediate joint falls; The insulation status level of the cable intermediate joint is determined according to the preset bull's-eye range threshold into which the relative bull's-eye degree of the cable intermediate joint falls.

7. An electronic device, characterized in that: The electronic device includes a memory and a processor, wherein a computer program is stored in the memory. When the computer program is executed by the processor, the processor executes the steps of the cable intermediate joint insulation status evaluation 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, the steps of the method for evaluating the insulation status of a cable intermediate joint according to any one of claims 1 to 5 are implemented.

Images