Cable intermediate joint insulation state evaluation method, system, equipment and medium

Abstract

The invention relates to the technical field of power systems, and discloses a cable intermediate joint insulation state evaluation method, system, equipment and medium, and the method comprises the steps: collecting a partial discharge signal of a cable intermediate joint, and extracting a plurality of partial discharge characteristic quantities which better represent the insulation state of the cable intermediate joint; sample data of each partial discharge characteristic quantity of the cable intermediate joint in an insulation state under a plurality of different continuous acquisition intervals is represented by constructing a ternary interval data set, so that the uncertainty of information is better processed, the objectivity and credibility of an evaluation result are improved, and by determining the characteristic weight of each partial discharge characteristic quantity, the reliability of the evaluation result is improved. And based on a ternary interval number grey target decision, determining the relative target degree of the cable intermediate joint by using the ternary interval data set and the feature weight of each partial discharge feature quantity, and determining the insulation state of the cable intermediate joint through the relative target degree, 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 intermediate joints are mainly used to connect the cable body and are key components to ensure the safe and reliable operation of cable lines. With the widespread use of a large number of cables in urban power transmission lines, the number of cable joints is also increasing. However, cable intermediate joints are easily affected by factors such as manufacturing level, insulation materials and external environment, and are prone to insulation breakdown failures. At present, they have become the biggest weak link in cable lines. Long-term operating experience shows that the gradual deterioration of the insulation of cable intermediate joints until breakdown is often accompanied by obvious partial discharge.

[0003] At present, the insulation status assessment of cable intermediate joints is mainly based on items such as discharge level and insulation resistance detection. The data collection interval is long, and it is difficult to evaluate the cable joint status during the interval. Therefore, it is difficult to fully reflect the true status of the cable intermediate joint insulation. In recent years, the continuous development of online monitoring technology has provided a good data source for real-time status analysis of power equipment. However, online monitoring data is affected by measurement errors, signal interference, and transmission losses. The collected status data may not be accurate, resulting in certain errors in the overall assessment.

[0004] A Chinese patent application with publication number CN110286303A proposes a method for evaluating the insulation aging status of a coaxial cable 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 levels 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 through the trained BP neural network model according to the characteristic parameters of the coaxial cable to be evaluated.

[0005] A Chinese patent application with publication number CN110308375A proposes a cable insulation status assessment method based on neural network integration. First, different cable insulation status assessment reference features are fused, and a neural network cable insulation status assessment model is constructed 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 status assessment model. Finally, the cable insulation status grade is obtained to complete the assessment of the cable insulation status.

[0006] The Chinese patent application with publication number CN114252748A proposes a method, system and device for evaluating the insulation status of a cable intermediate joint. The local discharge signal of the cable intermediate joint is selected to extract a feature vector and used as an evaluation index. Then, the hierarchical analysis method is used to determine the constant weight of each evaluation index, and the constant weight is corrected according to the degradation degree of the evaluation index. Then, based on the corrected weight and the membership function established subsequently, a fuzzy comprehensive evaluation method is used to obtain the insulation status evaluation result of the cable intermediate joint.

[0007] The Chinese patent application with publication number CN104407270A proposes an online fault monitoring device for cable joints in a 10~35KV distribution network and a method for evaluating the system status thereof, which uses laser-induced pressure waves to detect cable joints. First, the amplitude of the ripples in the cable joint and its surroundings is collected, and then a cable joint internal defect and insulation evaluation model is constructed based on a support vector machine, and the model parameters are optimized using a particle swarm algorithm; finally, a suitable kernel function is selected to construct a cable joint internal defect and insulation evaluation model, thereby achieving accurate evaluation of the cable joint internal defects and insulation.

[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 a BP neural network is difficult to evaluate the changes in the 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 status assessment method based on neural network integration has certain reference value for cable insulation status assessment. However, the neural network model needs to be trained with sample data first, so the assessment result is extremely dependent on the accuracy and completeness of the sample data. In addition, the invention also requires a large amount of status monitoring data to improve the neural network to ensure its generalization ability and fault tolerance.

[0010] The core of the proposed fuzzy comprehensive evaluation of the insulation status evaluation method, system and device of the cable intermediate joint lies in how to reasonably select and allocate the membership and weight of the evaluation factors of the measured object. The hierarchical analysis method adopted in this scheme mainly determines the indicator weights based on expert experience, which is highly subjective. In addition, the variable weight correction theory cannot achieve the expected effect when the state quantities of multiple indicators are in extreme deterioration. At the same time, if the selection of the membership function does not conform to the insulation degradation of the cable joint, it will also affect the evaluation accuracy of the insulation status of the cable joint.

[0011] The partial discharge detection in the proposed online fault monitoring device for cable joints in a 10~35KV distribution network and the method for evaluating the system status is a non-electrical measurement method with low detection accuracy, and the laser-induced pressure wave method may cause damage to the online cable joints. In addition, the evaluation reference features selected by the invention are single, 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 quantity of the cable intermediate joint according to 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;

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

[0017] Based on the three-element interval number grey target decision, the relative bull's-eye degree of the cable intermediate joint is determined by using the three-element interval data set of each of the partial discharge characteristic quantities 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, the constructing of 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] Obtaining sample data of each of the partial discharge characteristic quantities of the cable intermediate joint under the insulation state at a plurality of different continuous acquisition intervals, and describing the sample data of each of the partial discharge characteristic quantities of the cable intermediate joint under the insulation state at each continuous acquisition interval by using a ternary interval number;

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

[0023]

[0024] In the formula, is the number of ternary intervals of continuous acquisition interval i under partial discharge characteristic quantity j, i=1, 2, ..., n; j=1, 2, ..., m; n is the number of continuous acquisition intervals; m is the number of partial discharge characteristic quantities; is the upper limit of the sample data of the PD characteristic quantity, is the lower limit of the sample data of the PD characteristic quantity, It is the number with the highest 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 multiple partial discharge feature quantities using the feature weights corresponding to the multiple partial discharge feature quantities based on the ternary interval number grey target evaluation method, the step further includes:

[0026] The ternary interval data set is normalized.

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

[0028] Determining the objective weight of each of the partial discharge feature quantities based on the degree of deviation of the sample data respectively corresponding to the plurality of partial discharge feature quantities;

[0029] Constructing 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;

[0030] The objective weight is balanced and corrected by using the punitive 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 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, comprises:

[0032] Determine the optimal ternary interval number and the worst ternary interval number according to the ternary interval data set of each partial discharge characteristic quantity of the cable intermediate joint;

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

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

[0035] Determine 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 of the partial discharge characteristic quantities and the characteristic weight of each of the partial discharge characteristic quantities;

[0036] The relative bull's-eye degree of the cable intermediate joint at continuous acquisition intervals is determined according to the total expected bull's-eye degree and the total edge 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 state 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 state level of the cable intermediate joint is determined according to the preset bull's-eye range threshold into which the relative bull's-eye 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 acquisition intervals;

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

[0044] A relative bull's-eye determination module is used to determine the relative bull's-eye degree of the cable intermediate joint based on a three-element interval digital grey target decision, using a three-element interval data set of each of the partial discharge characteristic quantities and the characteristic weight;

[0045] The insulation evaluation module is used to determine the insulation state 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 the memory stores a computer program, 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, wherein the computer program, 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 scheme 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, and constructs 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 acquisition intervals, thereby better handling the uncertainty of information and improving the objectivity and credibility of the evaluation result, and by determining the feature weight of each partial discharge feature quantity, based on the ternary interval digital gray target decision, using the ternary interval data set and feature weight of each partial discharge feature quantity, the relative bull's-eye degree of the cable intermediate joint is determined, 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 drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 creative labor.

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

[0051] Figure 2 A flow chart 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 diagram of the structure of a cable intermediate joint insulation status assessment system provided by an embodiment of the present invention;

[0053] Figure 4 A schematic diagram of the structure 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 scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in 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 creative work are 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. Among them, 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 it can be 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 characterizing the insulation state of the cable intermediate joint from the partial discharge signal; according to the sample data of each partial discharge feature quantity of the cable intermediate joint under the insulation state of 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, and the method is applied to Figure 1 The terminal 102 in the example is used as an example to illustrate, including the following steps S1 to S5. Among them:

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

[0058] In order to monitor the operating status of cable joints in real time, it is necessary to select reasonable partial discharge characteristic quantities to characterize the insulation status of cable intermediate joints, so as to build a scientific status assessment system. According to relevant theoretical analysis, during the aging process of cable intermediate joint insulation, one or more state parameters will show obvious change characteristics. By studying the changes in these state information, the insulation condition of the cable joint can be effectively determined. Among the many state information, the partial discharge amount is the main factor in judging the degree of insulation degradation of the cable joint, which can effectively characterize the insulation status of the cable intermediate joint. The research and application of partial discharge online monitoring methods are relatively mature, and it is currently a reliable method for monitoring the insulation status of cross-linked polyethylene cable joints. Therefore, by statistically analyzing the characteristic parameters of partial discharge of cable joints, the real-time changes in the insulation status 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 as the partial discharge (PD) signal of the cable intermediate joint based on the high-frequency current detection method, selects the partial discharge characteristic quantity characterizing the insulation state of the cable intermediate joint, and processes the interval data.

[0060] In practical applications, copper foil is used to make a charge collector with a circumference equal to that of the 10kV cable intermediate joint, and it is wrapped on the outer insulation layer of the cable intermediate joint. According to the partial discharge signal obtained by the partial discharge online monitoring system, the selected partial discharge characteristic quantities include discharge repetition rate, total discharge energy, average discharge amount and maximum discharge amount.

[0061] The principle and calculation method of the selected partial discharge characteristic quantity are described in detail below:

[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 detected power frequency cycles; N s is the number of discharges in the sth detection power frequency cycle.

[0065] Total discharge energy: refers to the total energy consumed by partial discharge during the detection cycle. According to the analysis of the mechanism of partial discharge, during the discharge process, electrical energy will be released in the form of photons, heat energy and mechanical energy, causing damage to the insulation material of the cable joint. Therefore, the more energy released by partial discharge, the more serious the insulation aging of the cable joint. This value reflects the development of insulation aging of the cable joint, and its calculation formula is shown in formula (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 starting discharge voltage is , then the calculation model of discharge energy is shown in formula (3).

[0068] (3)

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

[0070] (4)

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

[0072] Maximum discharge capacity: refers to the maximum apparent discharge capacity value of all discharge pulses within the detection cycle. As the insulation degradation of the cable intermediate joint increases, the local discharge capacity often increases significantly, especially before the insulation is about to break down, the local 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: 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 of the cable intermediate joint under the insulation state at a plurality of different continuous acquisition 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 characteristic quantity of the cable intermediate joint to determine the data trend of each partial discharge characteristic quantity.

[0077] In some embodiments, in step S2, based on the sample data of each partial discharge characteristic quantity of the cable intermediate joint under the insulation state at a plurality of different continuous acquisition intervals, a ternary interval data set of each partial discharge characteristic quantity of the cable intermediate joint is constructed, including:

[0078] Step S201, obtaining sample data of each partial discharge characteristic quantity of the cable intermediate joint under the insulation state at multiple different continuous acquisition intervals, and using 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 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. The insulation state 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 form a set , the corresponding indicator weight is ,satisfy and . An evaluation object In the indicator The number of available ternary intervals for sample data values ​​under , (i=1, 2, ..., n; j=1, 2, ..., m) represents.

[0080] Step S202, using the ternary interval number of sample data of each partial discharge characteristic quantity of the cable intermediate joint under the insulation state at each continuous acquisition interval, construct a decision matrix of each partial discharge characteristic quantity of the cable intermediate joint under the insulation state at multiple different continuous acquisition intervals, and use the decision matrix as a ternary interval data set; wherein the ternary interval data set is:

[0081]

[0082] In the formula, is the number of ternary intervals of continuous acquisition interval i under partial discharge characteristic quantity j, i=1, 2, ..., n; j=1, 2, ..., m; n is the number of continuous acquisition intervals; m is the number of partial discharge characteristic quantities; is the upper limit of the sample data of the PD characteristic quantity, is the lower limit of the sample data of the PD characteristic quantity, It is the number with the highest 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 the cable joint status information, and adopts a ternary interval number to represent the cable joint status information, which better handles the uncertainty of the 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 three-element interval data set is normalized, that is, the three-element interval data set is normalized. To standardize the As a benefit indicator, is a cost-type indicator. After normalizing the ternary interval number, we get:

[0085] (6)

[0086] (7)

[0087] In the formula, They 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 values ​​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 data set.

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

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

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

[0094] Among them, the objective weight is based on the numerical value of the partial discharge characteristic quantity, and the partial discharge characteristic quantity is quantitatively assigned through certain standards or logical rules. From the perspective of data volatility, the greater the volatility of a certain partial discharge characteristic quantity data, the richer its information content, and the more significant the impact on the overall evaluation, and a higher weight should be considered. For this reason, the present invention adopts the deviation method to measure the degree of fluctuation of the interval index data of each partial discharge characteristic quantity, so as to determine the objective weight of the partial discharge characteristic quantity.

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

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

[0097] (9)

[0098] In the formula, is the PD characteristic 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] In the formula, is the PD characteristic The mean square error, They are The mean square error of .

[0102] Step S3013: Based on the partial discharge characteristic quantity The mean square error of the partial discharge characteristic quantity is determined The interval weights are:

[0103] (11)

[0104] In the formula, is the PD characteristic The interval weights of They are The interval weight of .

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

[0106] Among them, the interval weight is presented in the form of interval number. If the interval weight is used directly 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 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 in the PD characteristic quantity, which is used to calculate the elements of the possibility degree complementary matrix. The possibility degree formula is:

[0108] (12)

[0109] In formula (12), 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 weights of the partial discharge characteristic quantity is calculated using formula (12): ,in , and calculate the correction vector of 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 mainly used to measure the relative importance of indicators, but they cannot reflect the data change characteristics of the evaluation object. To this end, the embodiment of the present application introduces variable weight theory to dynamically adjust the objective weights so that the feature weights can better reflect the real 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: For each variable continuous.

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

[0121] Let the variable weight vector , then the following definition is given:

[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] It can be seen from Definition 2 that when the index value is greater than a given value, a corresponding punitive variable weight function needs to be used to give it a greater weight. At present, most technologies choose a balance function to construct a corresponding variable weight model, but this model does not conform to the changing characteristics of the cable joint partial discharge index data. To this end, the embodiment of the present application analyzes the partial discharge development law of typical defects of the cable intermediate joint, thereby constructing a punitive state variable weight function of the partial discharge characteristic quantity of the cable intermediate joint.

[0130] According to relevant experimental research, the development process of partial discharge of typical defects in cable intermediate joints can be divided into three stages: initiation, development and severity. In the initial stage of partial discharge, the statistical characteristic quantities of partial discharge show obvious fluctuations at the beginning of discharge, but soon tend to develop steadily and maintain relative stability; in the development stage of partial discharge, the aging speed of cable joint insulation accelerates, and the statistical characteristic quantities of partial discharge increase slowly and show a trend of approximately linear growth; in the severe stage of partial discharge, the insulation of cable joints is on the verge of failure, and the amplitude of the statistical characteristic quantities of partial discharge 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, so as to reasonably modify the weights of the partial discharge indicators at different stages. Specifically, the punitive state variable weight function of the partial discharge characteristic quantity is constructed according to the expected value of the interval data of each partial discharge characteristic quantity in step S302, including:

[0132] Step S3021: Collect online monitoring data of partial discharge of cable joints within a certain time range, and 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] In the formula, is the expected value of the interval data of the partial discharge characteristic quantity.

[0136] Step S3023: Based on the local discharge development law of typical defects of cable joints and considering the interval expected value of the local discharge characteristic quantity, the following three-stage state variable weight function for the local 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 the partial discharge characteristic quantity; , To evaluate strategies; , 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 for serious deviations of some partial discharge characteristic quantities. Combined with the equipment characteristics of the cable intermediate joint, the parameter values ​​of the partial discharge state variable weight function can be taken as , , , .

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

[0140] Among them, after the penalty state variable weight function value is determined by 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, thereby 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 ternary interval number grey target decision, the relative bull's-eye degree of the cable intermediate joint is determined by using the ternary 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 according to the ternary interval data set of each partial discharge characteristic quantity of the cable intermediate joint;

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

[0146] Step S402: taking the optimal ternary interval number and the worst ternary interval number as the expected bull's eye and the marginal bull's eye of the grey target decision, respectively.

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

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

[0149] Among them, the operating state of the cable intermediate joint is related to the partial discharge characteristic quantity The expected bullseye of is:

[0150] (17)

[0151] In formula (17), ; is the PD characteristic The expected bullseye.

[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), ; is the characteristic quantity of partial discharge The edge of the bull's eye.

[0155] Step S404: Determine the total expected bull's-eye degree and total edge bull's-eye degree of the cable intermediate joint at continuous acquisition intervals according to the expected bull's-eye degree and edge bull's-eye degree of each partial discharge feature quantity and the feature weight of each partial discharge feature quantity.

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

[0157] (19)

[0158] In the formula, 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 under continuous acquisition intervals is determined as:

[0160] (20)

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

[0162] Step S405: Determine the relative bull's-center degree of the cable intermediate joint at continuous acquisition intervals according to the total expected bull's-center degree and the total edge bull's-center degree of the cable intermediate joint.

[0163] Among them, the relative center of the cable intermediate joint under continuous sampling intervals is calculated as:

[0164] (twenty one)

[0165] In the formula, Relative bull's-eye degree.

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

[0167] In this embodiment of the present application, the relative bull's-eye degree is used as a metric to determine the insulation state level of the cable intermediate joint. Specifically, the step S5 of determining the insulation state of the cable intermediate joint using the relative bull's-eye degree 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 state level of the cable intermediate joint according to the preset bull's-eye 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 state (I), caution state (II), abnormal state (III) and severe state (IV). Combined with the actual situation on site and the test results of joint insulation degradation, the bull's-eye degree characterizing different states of cable joints is obtained as 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 partial discharge signals of the intermediate joint of the cable and extracts multiple partial discharge feature quantities that better characterize the insulation state of the intermediate joint of the cable, and constructs a ternary interval data set to characterize the sample data of each partial discharge feature quantity under the insulation state of the intermediate joint of the cable at multiple different continuous collection intervals, thereby better handling the uncertainty of information and improving the objectivity and credibility of the evaluation results. 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 intermediate joint of the cable, and the insulation state of the intermediate joint of the cable is determined by the relative bull's-eye degree, thereby improving the evaluation accuracy of the insulation state of the intermediate joint of the cable.

[0174] Based on the same inventive concept, an embodiment of the present application also 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 recorded 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 on the cable intermediate joint insulation status assessment method above, and will not be repeated here.

[0176] like Figure 3 As shown, the 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 the partial discharge signal 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 signal;

[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 according to the sample data of each partial discharge characteristic quantity of the cable intermediate joint under the insulation state at a plurality of different continuous acquisition intervals;

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

[0180] A 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 number grey target decision, using the three-element interval data set and feature weight of each partial discharge feature quantity;

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

[0185]

[0186] In the formula, is the number of ternary intervals of continuous acquisition interval i under partial discharge characteristic quantity j, i=1, 2, ..., n; j=1, 2, ..., m; n is the number of continuous acquisition intervals; m is the number of partial discharge characteristic quantities; is the upper limit of the sample data of the PD characteristic quantity, is the lower limit of the sample data of the PD characteristic quantity, It is the number with the highest 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] The normalization module is used to normalize the ternary interval data set.

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

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

[0191] The objective weight is balanced and corrected by using the punitive 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 quantity of the cable intermediate joint;

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

[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] Determine 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;

[0196] The relative bull's-eye degree of the cable intermediate joint at continuous 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 evaluation module 500 is used to 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;

[0198] The insulation state 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.

[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 executes the steps of the cable intermediate joint insulation status assessment method in the above embodiment.

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

[0201] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, electronic device and computer storage medium 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 inherent to these processes, methods, products or apparatus.

[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, a program segment or a part of a code, and a part of a module, a program segment or a code includes 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 from 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 only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, 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 an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

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

[0206] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0207] If the integrated unit is implemented in the form of 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 is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for executing all or part of the steps of the method described in each embodiment of the present invention through a computer device (which can be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk and other media that can store program codes.

[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 the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. 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 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 quantity of the cable intermediate joint according to 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; Determining a feature weight for each of the partial discharge feature quantities; Based on the three-element interval number grey target decision, the relative bull's-eye degree of the cable intermediate joint is determined by using the three-element interval data set of each of the partial discharge characteristic quantities and the characteristic weight; The insulation state of the cable intermediate joint is determined by using the relative bull's-eye degree of the cable intermediate joint.

2. The cable intermediate joint insulation status evaluation method 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, characterized in that: The constructing of 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 a plurality of different continuous acquisition intervals comprises: Obtaining sample data of each of the partial discharge characteristic quantities of the cable intermediate joint under the insulation state at a plurality of different continuous acquisition intervals, and describing the sample data of each of the partial discharge characteristic quantities of the cable intermediate joint under the insulation state at each continuous acquisition interval by using a ternary interval number; By using the ternary interval number of the sample data of each partial discharge characteristic quantity under the insulation state of the intermediate cable joint at each continuous acquisition interval, a decision matrix of each partial discharge characteristic quantity under the insulation state of the intermediate cable joint at a plurality of different continuous acquisition intervals is constructed, and the decision matrix is ​​used as the ternary interval data set; wherein the ternary interval data set is: In the formula, is the number of ternary intervals of continuous acquisition interval i under partial discharge characteristic quantity j, i=1, 2, ..., n; j=1, 2, ..., m; n is the number of continuous acquisition intervals; m is the number of partial discharge characteristic quantities; is the upper limit of the sample data of the PD characteristic quantity, is the lower limit of the sample data of the PD characteristic quantity, It is the number with the highest 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, characterized in that: Before the step of determining the relative center of the ternary interval data sets corresponding to the multiple partial discharge feature quantities using the feature weights corresponding to the multiple 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, characterized in that: Determining a feature weight for each of the partial discharge feature quantities comprises: Determining the objective weight of each of the partial discharge feature quantities based on the degree of deviation of the sample data respectively corresponding to the plurality of partial discharge feature quantities; Constructing 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; The objective weight is balanced and corrected by using the punitive 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. The method for evaluating the insulation status of a cable intermediate joint according to claim 1, characterized in that: The grey target decision based on the ternary interval number, using the ternary interval data set of each of the partial discharge feature quantities and the feature weight, to determine the relative bull's-eye degree of the cable intermediate joint, comprises: Determine the optimal ternary interval number and the worst ternary interval number according to the ternary interval data set of each partial discharge characteristic quantity of the cable intermediate joint; The optimal number of ternary intervals and the worst number of ternary intervals are used as the expected bull's-eye and the marginal bull's-eye of grey target decision respectively; Determine the expected bull's-eye degree and edge bull's-eye degree of each of the partial discharge characteristic quantities by using the expected bull's-eye and edge bull's-eye of the grey target decision; Determine 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 of the partial discharge characteristic quantities and the characteristic weight of each of the partial discharge characteristic quantities; The relative bull's-eye degree of the cable intermediate joint at continuous acquisition intervals is determined according to the total expected bull's-eye degree and the total edge bull's-eye degree of the cable intermediate joint.

7. The method for evaluating the insulation status of a cable intermediate joint according to claim 1, characterized in that: The method of determining the insulation state of the cable intermediate joint by using the relative bull's-eye degree of the cable intermediate joint comprises: 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 state 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 state level of the cable intermediate joint is determined according to the preset bull's-eye range threshold into which the relative bull's-eye of the cable intermediate joint falls.

8. 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 acquisition intervals; A weight determination module, used for determining a feature weight for each of the partial discharge feature quantities; A relative bull's-eye determination module is used to determine the relative bull's-eye degree of the cable intermediate joint based on a three-element interval digital grey target decision, using a three-element interval data set of each of the partial discharge characteristic quantities and the characteristic weight; The insulation evaluation module is used to determine the insulation state of the cable intermediate joint by using the relative bull's-eye degree of the cable intermediate joint.

9. An electronic device, characterized in that: The electronic device includes 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 method for evaluating the insulation status of a cable intermediate joint as described in any one of claims 1-7.

10. 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 as described in any one of claims 1 to 7 are implemented.

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