Power cable quality evaluation method and device based on cable performance

Abstract

The invention discloses a power cable quality evaluation method and device based on cable performance. The method comprises the steps that a power cable operation state three-layer index system is established, and the three-layer index system comprises a target layer, a performance layer and an index layer; determining the comprehensive weight of each power cable quality index of the index layer by adopting a fuzzy analytic hierarchy process, an entropy weight method and a grey correlation analysis method according to the power cable performance test data; calculating the power cable performance test data by using a cloud model, and determining a membership matrix of each power cable quality index corresponding to each cable operation state; calculating a performance evaluation result of each performance of the performance layer according to the membership matrix and the comprehensive weight; and calculating a cable operation quality evaluation result corresponding to the target layer of the power cable performance test data according to the performance evaluation result.

Description

Technical Field

[0001] The present invention relates to the technical field of cable evaluation, and more specifically, to a method and device for evaluating the quality of a power cable based on cable performance. Background Art

[0002] Power cables are key equipment in power systems, and accurate evaluation of their performance and product quality is an effective means to ensure the safe and stable operation of power systems. Currently, several researchers have proposed data-driven methods to evaluate the quality of power cables.

[0003] Patent CN202110441777.1 proposes a neural network model to evaluate the quality of power cables. The patent uses big data technology to collect production data of high-voltage power cable samples, takes the sample data as input, and establishes a high-voltage power cable quality evaluation neural network model based on a three-layer BP neural network. The input data of the production data neural network model is used to automatically obtain the power cable quality evaluation results. This method only considers objective weights, not the objective weights of experts, ignores professional knowledge, lacks consideration of actual scenarios, and also ignores the complexity and dynamics of actual problems, which may introduce bias.

[0004] Patent CN202310420845.5 proposes a method for predicting the quality of low-voltage power cable products. The patent uses multiple weighting methods to calculate the indicator weight set separately, and optimizes the linear combination weighting with the minimization of deviation as the optimization goal. This method takes into account the subjective and objective factors in the evaluation process, but lacks analysis of the randomness and uncertainty in the quality evaluation process.

[0005] Patent CN202211401245.6 proposes a low-voltage power cable quality evaluation method based on production process data. This method constructs a production process indicator weight distribution model based on production process data, and controls and evaluates the quality of low-voltage power cables based on production process data in the production process and quality inspection links through the production process quality control model and the low-voltage power cable targeting model. This method determines the indicator weights based on fuzzy comprehensive evaluation, only considering objective weights and ignoring subjective weights, resulting in incomplete evaluation information and bringing expert misjudgment into the results.

[0006] With the continuous advancement of smart grid construction, advanced detection and testing technologies are applied in the quality control of power cables. Power cables are the infrastructure for transmitting electric energy and connecting various power equipment. In the stable operation of the power grid, power transmission equipment occupies an important link and serves as the power transmission function. In the statistical data of domestic cable power quality evaluation, there is a lack of effective evaluation methods for operating reliability-related indicators such as component electrical performance and thermal performance, making it difficult to conduct a comprehensive evaluation of the quality of cables and accessories. The "Okai" incident fully exposed the problems in the quality management of power cable equipment.

[0007] The present invention is applicable to the quality evaluation of the operation stage of power cable products. At present, there are a large number of cable operation quality evaluation methods and systems on the market. However, all of these methods and systems evaluate the power cable as a whole, and only perform different optimization designs and improvements on the index system and parameter weights to obtain more reasonable evaluation efficiency and accuracy. The existing methods only consider objective weights, but do not consider the objective weights of experts. They ignore professional knowledge, lack consideration of actual scenarios, and also ignore the complexity and dynamics of actual problems, which may introduce bias. There are also considerations for subjective and objective factors in the evaluation process, but there is a lack of analysis of randomness and uncertainty in the quality evaluation process. At the same time, there are methods that only consider objective weights, ignoring subjective weights, resulting in incomplete evaluation information, and also bringing expert misjudgment into the results. Summary of the invention

[0008] In view of the deficiencies in the prior art, the present invention provides a method and device for evaluating the quality of a power cable based on cable performance.

[0009] According to one aspect of the present invention, there is provided a method for evaluating the quality of a power cable based on cable performance, comprising:

[0010] Establish a three-layer indicator system for the operation status of power cables, which includes: target layer, performance layer and indicator layer;

[0011] According to the power cable performance test data, the fuzzy hierarchical analysis method, entropy weight method and grey correlation analysis method are used to determine the comprehensive weight of the index layer;

[0012] Use the cloud model to calculate the power cable performance test data and determine the membership matrix of each power cable quality index corresponding to each cable operation status;

[0013] Calculate the performance evaluation results of each performance layer based on the membership matrix and comprehensive weights;

[0014] The cable operation quality evaluation results corresponding to the target layer of the power cable performance test data are calculated based on the performance evaluation results.

[0015] Optionally, according to the power cable performance test data, the fuzzy hierarchical analysis method, entropy weight method and grey correlation analysis method are used to determine the comprehensive weight of the indicator layer, including:

[0016] The first weight is calculated using fuzzy analytic hierarchy process;

[0017] The second weight is calculated by using the entropy weight method according to the power cable performance test data;

[0018] The third weight is calculated by using grey correlation analysis method according to the power cable performance test data;

[0019] The comprehensive weight is calculated according to the first weight, the second weight and the third weight.

[0020] Optionally, the first weight is calculated using a fuzzy analytic hierarchy process, including:

[0021] A fuzzy judgment matrix is ​​constructed based on the experts’ pairwise importance evaluation results;

[0022] Calculate the fuzzy consistency matrix of the fuzzy judgment matrix;

[0023] According to the fuzzy consistency matrix, a first weight is calculated.

[0024] Optionally, the second weight is calculated using an entropy weight method according to the power cable performance test data, including:

[0025] Calculate entropy value based on power cable performance test data;

[0026] Calculate the information utility value based on the entropy value;

[0027] The information utility value is normalized to determine the second weight.

[0028] Optionally, the third weight is calculated using a grey correlation analysis method according to the power cable performance test data, including:

[0029] The historical optimal value sequence is used as a reference sequence, and multiple detection sequences in the power cable performance test data are used as comparison sequences;

[0030] Calculate the absolute difference sequence between the comparison sequence and the reference sequence;

[0031] Calculate the grey correlation coefficient based on the absolute difference sequence;

[0032] Calculate the grey relational degree according to the grey relational coefficient;

[0033] The third weight is calculated according to the grey relational degree.

[0034] Optionally, the cloud model is used to calculate the power cable performance test data to determine the membership matrix of each power cable quality index corresponding to each cable operation state, including:

[0035] Step 1: Determine the standard cloud model parameters of the preset cable operation state;

[0036] Step 2: Generate a normally distributed random number of the preset cable operation status according to the standard cloud model parameters;

[0037] Step 3: Input the calculated normal distribution random number, expectation and variance of the power cable performance test data into the cloud model to determine the model parameter value of each power cable performance test data;

[0038] Step 4: Compare the model parameter value with the random number to determine the membership value of the power cable performance test data belonging to each cable operation state;

[0039] Step 5: Repeat steps 2-4 to obtain the membership values ​​of the preset number of tests, and average the membership values ​​of the preset number of tests to obtain the final membership value of the power cable performance test data;

[0040] Step 6: According to the final membership value, construct the membership of each power cable quality index corresponding to each cable operation status

[0041] Optionally, the calculation formula for the cable operation quality evaluation result is:

[0042] U=U e ×x1+U i ×x2+U m ×x3

[0043] in,

[0044] U e =w(x e )×Y(y e )

[0045] U i =w(x i )×Y(y i )

[0046] U m =w(x m )×Y(y m )

[0047] Where U e is the electrical property, w(x e ) is the weight of the electrical performance index, Y(y e ) is the electrical performance index membership matrix, U i is the insulation performance, w(x i ) is the insulation performance index weight, Y(y i ) is the insulation performance index membership matrix, U mis the mechanical property, w(x m ) is the weight of mechanical performance index, Y(y m ) is the mechanical performance index membership matrix, U is the power cable quality, x1, x2, x3 are the weights of electrical performance, insulation performance and mechanical performance respectively.

[0048] According to another aspect of the present invention, there is provided a power cable quality evaluation device based on cable performance, comprising:

[0049] Establish a module for establishing a three-layer indicator system for the operation status of power cables, wherein the three-layer indicator system includes: a target layer, a performance layer, and an indicator layer;

[0050] A determination module is used to determine the comprehensive weight of the index layer by using fuzzy hierarchical analysis method, entropy weight method and grey correlation analysis method according to the power cable performance test data;

[0051] The first calculation module is used to calculate the power cable performance test data using the cloud model to determine the membership matrix of each power cable quality index corresponding to each cable operation state;

[0052] The second calculation module is used to calculate the performance evaluation result of the performance layer according to the membership matrix and the comprehensive weight;

[0053] The third calculation module is used to calculate the cable operation quality evaluation result corresponding to the target layer of the power cable performance test data according to the performance evaluation result.

[0054] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and the computer program is used to execute the method described in any one of the above aspects of the present invention.

[0055] According to another aspect of the present invention, an electronic device is provided, comprising: a processor; a memory for storing instructions executable by the processor; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of the above aspects of the present invention.

[0056] Therefore, the present invention analyzes the performance and parameters of the power cable, designs a comprehensive weighting method of fuzzy hierarchical analysis method, entropy weight method and grey correlation analysis, and the index system corresponds to the overall operating status of the power cable, the performance of the power cable and the performance index. Due to the comprehensive consideration of the performance of the power cable, the evaluation method and system are more hierarchical and targeted, can evaluate the quality of the power cable more timely and accurately, and can give the performance status and quality score of specific components, which can realize the refined evaluation of the power cable. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0058] Figure 1 It is a flow chart of a method for evaluating power cable quality based on cable performance provided by an exemplary embodiment of the present invention;

[0059] Figure 2 is a probability distribution diagram of a cloud model based on cable performance provided by an exemplary embodiment of the present invention;

[0060] Figure 3 is another flow chart of a method for evaluating power cable quality based on cable performance provided by an exemplary embodiment of the present invention;

[0061] Figure 4 It is a structural schematic diagram of a power cable quality evaluation device based on cable performance provided by an exemplary embodiment of the present invention;

[0062] Figure 5 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0063] Below, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described here.

[0064] It should be noted that the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0065] Those skilled in the art can understand that the terms "first" and "second" in the embodiments of the present invention are only used to distinguish different steps, devices or modules, etc., and neither represent any specific technical meaning nor indicate the necessary logical order between them.

[0066] It should also be understood that, in the embodiments of the present invention, “plurality” may refer to two or more than two, and “at least one” may refer to one, two or more than two.

[0067] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more, unless explicitly limited or otherwise indicated in the context.

[0068] In addition, the term "and / or" in the present invention is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects before and after are in an "or" relationship.

[0069] It should also be understood that the description of the various embodiments of the present invention focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced to each other, and for the sake of brevity, they will not be described one by one.

[0070] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0071] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0072] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0073] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0074] Embodiments of the present invention can be applied to electronic devices such as terminal devices, computer systems, servers, etc., which can operate with many other general or special computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, servers, etc. include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, small computer systems, large computer systems, and distributed cloud computing technology environments including any of the above systems, etc.

[0075] Electronic devices such as terminal devices, computer systems, servers, etc. can be described in the general context of computer system executable instructions (such as program modules) executed by computer systems. Generally, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in a distributed cloud computing environment, where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media including storage devices.

[0076] Exemplary Methods

[0077] Figure 1 FIG. 1 is a flow chart of a method for evaluating the quality of a power cable based on cable performance provided by an exemplary embodiment of the present invention. This embodiment can be applied to electronic devices, such as Figure 1 As shown, the power cable quality evaluation method 100 based on cable performance includes the following steps:

[0078] Step 101, establishing a three-layer indicator system for the operation status of a power cable, wherein the three-layer indicator system includes: a target layer, a performance layer, and an indicator layer;

[0079] Step 102, according to the power cable performance test data, the fuzzy analytic hierarchy process, the entropy weight method and the grey correlation analysis method are used to determine the comprehensive weight of the index layer;

[0080] Step 103, using the cloud model to calculate the power cable performance test data, and determining the membership matrix of each power cable quality index corresponding to each cable operation state;

[0081] Step 104, calculating the performance evaluation results of each performance layer according to the membership matrix and the comprehensive weight;

[0082] Step 105, calculating the cable operation quality evaluation result corresponding to the target layer of the power cable performance test data according to the performance evaluation result.

[0083] Specifically, the present invention combines the advantages of fuzzy hierarchical analysis method, entropy weight method, grey correlation analysis, combined weighting method and cloud theory algorithm. The brief introduction of each algorithm is as follows:

[0084] 1) Fuzzy analytic hierarchy process

[0085] Compared with the traditional analytic hierarchy process, the AHP method based on triangular fuzzy numbers can solve the uncertainty of expert subjective consciousness and fuzzy environment. The difference from the traditional AHP is that the judgment matrix is ​​composed of triangular fuzzy numbers rather than a single real number, which leads to different weight vector solution methods. The experts' pairwise importance evaluation of indicators is collected according to the 0.1-0.9 scale table, and the meaning is shown in Table 1.

[0086] Table 1 Meaning of 0.1-0.9 scaling method

[0087]

[0088] (1) Construct a fuzzy judgment matrix. Expert k evaluates the pairwise importance of indicators i and j using the elements Indicates that is the lower limit of triangular fuzzy judgment, is the median value, Is the upper limit value. The fuzzy judgment matrix A is obtained by taking the average value of the fuzzy judgment of k-bit armor.

[0089]

[0090] (2) Calculate the fuzzy consistency matrix. ij Perform the following conversion operation to transform it into the fuzzy consistency matrix M1.

[0091]

[0092] According to the formula Determine the consistency of the fuzzy consistency matrix M1. If σ<0.1, the consistency is satisfied.

[0093] (3) Calculate the index weight. Based on the correlation theorem of triangular fuzzy numbers, calculate the comprehensive importance of the i-th evaluation criterion relative to all other criteria according to Formula 3. Select its minimum value as the initial index weight.

[0094]

[0095] ω′ i =minv(a i ≥a j ) (4)

[0096] In the formula, ω′ i After normalization, the weight set W1 is obtained.

[0097] 2) Entropy Weight Method

[0098] The entropy weight method comprehensively evaluates the importance and information content of each indicator, and determines the indicator weight more objectively than the fuzzy hierarchical analysis method. The concept and solution method of entropy are used to determine the objective weight value of each indicator in the power cable status evaluation, and the entropy value E of each indicator is calculated according to formula 5. i , where p i It represents the probability of occurrence of different values ​​of evaluation index i.

[0099]

[0100] The essence of information entropy is the expectation of the amount of information. The larger the information entropy, the lower the utility of the surface index value for distinguishing different evaluation objects, and the lower the weight. The information utility value is calculated according to Formula 6.

[0101] d i =1-E i (6)

[0102] The information utility value d of each indicator i Normalization processing obtains the weights of each indicator of the entropy weight method And weight set W2.

[0103] 3) Grey correlation analysis

[0104] In 1981, Professor Deng Julong, an expert in cybernetics, first proposed the definition of grey system and then established grey system theory. Grey correlation analysis is one of the main methods of grey system theory and a multi-factor analysis method. The steps of grey correlation analysis are as follows:

[0105] 1. The reference sequence is X0 = {{X0(1), X0(2), ..., X0(n)}, where X0(n) is the optimal value of each parameter; the comparison sequence is Y i = {Y i (1), Y i (2), ..., Y i (n)},Y i is the sample data. In the absence of a specified target value or known quality situation, the optimal value of each indicator is selected as the reference sequence.

[0106] 2. Using the reference sequence and comparison sequence determined in the previous step, calculate their differences one by one and form an absolute difference sequence in the form of absolute values, that is:

[0107] Δ oi (k)=|X0(k)-Y i (k)| (7)

[0108] Where i is the number of indicators i = 1, 2, 3, ..., n, k is the number of test data k = 1, 2, 3, ..., m; X0(k) is the reference sequence, Y i (k) is the kth detection value of the ith evaluation indicator.

[0109] 3. Calculate the grey correlation coefficient:

[0110]

[0111] ρ is the resolution coefficient, generally between 0 and 1, and is taken as 0.5.

[0112] 4. Calculate the grey relational degree

[0113]

[0114] Then the weight W of the jth indicator is 3j It can be calculated according to the following formula:

[0115]

[0116] 4) Combination weighting method

[0117] In view of the advantages and disadvantages of these three weighting methods, a combined weighting method combining fuzzy AHP, entropy weight method and grey comprehensive analysis method is used to determine the final indicator weight. The final comprehensive weight is determined according to formula 6:

[0118]

[0119] W 1i is the weight calculated by fuzzy AHP of index i, W 2i is the weight calculated by entropy weight method of index i, W 2i is the weight calculated by grey relational analysis, W i It is the final weight determined by the comprehensive weighting method of indicator i.

[0120] 5) Cloud Theory Algorithm

[0121] Li Deyi, an academician of the Chinese Academy of Engineering, proposed a new cognitive model cloud model to describe the uncertainty of concepts, especially randomness and fuzziness. In the literature, Li defined U as the domain and U as the normal cloud. is a concept in the domain U, if x∈U is a concept A random instantiation of , and satisfies x~N(Ex, En′ 2 ), En′~N(Ex,He 2 ), then x belongs to the concept The degree of certainty is y.

[0122]

[0123] Where Ex is the mathematical expectation of cloud droplets, entropy En is determined by the randomness and fuzziness of the concept, and super entropy He is the uncertainty of entropy En.

[0124] Cloud generators are generation algorithms for cloud models

[0125] The forward cloud generator can establish a correspondence between quantitative data and qualitative concepts. The principle is to use the digital characteristics (Ex, En, He) of the cloud model to generate cloud droplets and generate the corresponding cloud model probability distribution map, such as Figure 2 shown.

[0126] This type of cloud generator is suitable for situations where the actual data reserve of the evaluation elements is insufficient. It only needs to pre-set the digital characteristic values ​​of each standard cloud. Even if there is only one set of data, it can meet the subsequent data needs. However, this set of data cannot be compared and analyzed with other data, and the accuracy and effectiveness of the evaluation cannot be guaranteed. Therefore, when using a forward cloud generator, a certain amount of data reserve should be available to compare and verify the feasibility of the solution.

[0127] The implementation steps are as follows:

[0128] 1. Generate a value with En as the expected value and He as the 2 is a normal random number with variance E′ n ;

[0129] 2. Generate a value with Ex as the expected value and E′ n 2 is a normal random number with variance xi;

[0130] 3. Use formula 12 to calculate the membership degree yi of xi.

[0131] 4. Repeat steps 1 to 3 and stop when the number of cloud droplets meets the preset requirements.

[0132] According to the existing national standards for power cables, a three-tier indicator system for the operation status of power cables is established as shown in Table 2:

[0133] Table 2 Index system

[0134]

[0135] The present invention introduces a comprehensive weighting method that integrates entropy weight, fuzzy hierarchical analysis and grey correlation analysis to determine the comprehensive weight of power cable quality indicators, then non-dimensionalizes the original data sequence, and uses the cloud model to calculate the membership of indicators at all levels. The comprehensive weight and membership are combined to determine the performance and overall quality of the power cable according to the principle of maximum membership. This method can effectively consider the randomness and fuzziness in the evaluation process, and its process is shown in Figure 3 ,Specific steps of quality evaluation method:

[0136] Step 1: Calculate weight w1 using fuzzy analytic hierarchy process:

[0137] According to formula 1-4, triangular fuzzy numbers are used to record expert opinions and the weights of hierarchical quantitative indicators are determined.

[0138] Step 2: Calculate the weight w2 using the entropy weight method:

[0139] The test data of multiple power cables were collected, including the DC resistance of the conductor at 20°C, volume resistivity, insulation resistance at the highest temperature, voltage test, average insulation thickness, insulation thickness at the thinnest point, average outer sheath thickness, outer sheath thickness at the thinnest point, tensile strength before aging, elongation at break before aging, tensile strength after aging, elongation at break after aging, permanent elongation after cooling, and elongation under load. The entropy weight method weight of each indicator was calculated according to Formulas 5 and 6.

[0140] Step 3: Non-dimensionalize the original data sequence according to Formula 7:

[0141] Step 4: Calculate the grey correlation analysis weight w3 according to formula 8-10:

[0142] Step 5: Calculate the comprehensive weight w according to formula 11:

[0143] Step 6: Based on the HI index of the British EA company, the cable operation status is divided into 5 levels: excellent, normal, attention, abnormal, and fault. Assume that the domain [0,1] is divided into 5 levels, and the center point 0.500 of the domain [0,1] is taken as the intermediate evaluation level, and its model parameter Ex = 0.500 is taken. The super entropy He should be adjusted according to the randomness and fuzziness of the key indicators themselves. The present invention is based on the cloud droplet condensation degree of the standard cloud map, and takes it as 0.005 based on experience, that is, He = 0.005. Then its cloud model scale is shown in Table 2.

[0144] Table 2 Parameters of cloud model in each state

[0145]

[0146] Step 7: Generate a normally distributed random number En′ for each state cloud model i , the expectation is En and the variance is He 2 .

[0147] Step 8: En′ i ,Ex,x i Substitute (a certain actual detection data) into formula 12 to obtain the membership of the detection data of a certain indicator to a certain state.

[0148] Step 9: Repeat steps 7-8 to obtain the membership of k detection data, and take the average of these k memberships to obtain the final membership.

[0149]

[0150] Wherein, i is the indicator number i=1, 2, ..., n, j is the state number j=1, 2, 3, 4, 5, and k is the detection number k=1, 2, ..., k.

[0151] Step 10: Construct the membership matrix of each power cable quality index corresponding to each cable operation status:

[0152]

[0153] Step 11: Based on the final weight w and the membership matrix Y, calculate the performance evaluation results and the cable operation quality evaluation results.

[0154]

[0155] Among them U e is the electrical property, w(x e ) is the weight of the electrical performance index, Y(y e ) is the electrical performance index membership matrix, U i is the insulation performance, w(x i ) is the insulation performance index weight, Y(y i ) is the insulation performance index membership matrix, U m is the mechanical property, w(x m ) is the weight of mechanical performance index, Y(y m ) is the mechanical performance index membership matrix, U is the power cable quality, x1, x2, x3 are the weights of electrical performance, insulation performance and mechanical performance respectively.

[0156] The present invention has the following technical effects:

[0157] 1. Accurately evaluate the performance of power cables:

[0158] By adopting the comprehensive weighting method of fuzzy hierarchical analysis, entropy weight method and grey correlation analysis and cloud theory algorithm, the electrical performance, insulation performance and mechanical performance of power cables can be comprehensively and accurately evaluated, which makes up for the shortcomings of existing evaluation methods in comprehensively evaluating performance.

[0159] 2. Solve the problem of objective and subjective weight fusion:

[0160] Compared with the traditional method, the present invention adopts a comprehensive weighting method that integrates three methods, which comprehensively considers objective and subjective weights, avoids the bias that may be introduced by considering only objective weights and the lack of consideration of actual scenarios brought about by considering only subjective weights, and improves the accuracy of the evaluation.

[0161] 3. Consider the dynamics and uncertainty of the quality evaluation process:

[0162] The cloud model is introduced to consider the uncertainty of the operating status of the power cable by generating normally distributed random numbers, making the evaluation results more reliable and robust, and avoiding the errors that may be caused by considering only static factors.

[0163] 4. Highly interpretable:

[0164] By calculating the membership matrix, the present invention provides the membership of each evaluation index in different states, making the evaluation result more interpretable and helping users understand the formation process of the power cable performance evaluation result.

[0165] 5. Realize refined evaluation of power cables:

[0166] Through the three-layer hierarchical analysis index system, the present invention can give the performance status and quality score of the specific power cable, realize the refined evaluation of the power cable, and make the evaluation more comprehensive and detailed.

[0167] 6. Improve the efficiency of power cable quality control:

[0168] By introducing comprehensive weighting of multiple evaluation methods, the present invention can grasp the quality of power cables more comprehensively and globally, which helps to discover problems in advance and repair them in time, thereby improving the efficiency and accuracy of power cable quality control.

[0169] 7. Modular design of comprehensive evaluation system:

[0170] The modular design of the expert evaluation module, data analysis module and comprehensive quality evaluation module facilitates the management and updating of the system, and makes the invention easier to promote and apply.

[0171] In general, the present invention uses a variety of evaluation methods in combination, solves the problems of unreasonable subjective and objective weights and insufficient consideration in the existing power cable quality evaluation methods, realizes a comprehensive, accurate and dynamic evaluation of the power cable performance, improves the level of power cable quality control, and is expected to produce broad and far-reaching technical effects in the field of power cables.

[0172] Therefore, the present invention analyzes the performance and parameters of the power cable, designs a comprehensive weighting method of fuzzy hierarchical analysis method, entropy weight method and grey correlation analysis, and the index system corresponds to the overall operating status of the power cable, the performance of the power cable and the performance index. Due to the comprehensive consideration of the performance of the power cable, the evaluation method and system are more hierarchical and targeted, can evaluate the quality of the power cable more timely and accurately, and can give the performance status and quality score of specific components, which can realize the refined evaluation of the power cable.

[0173] The present invention adopts the comprehensive weighting method of fuzzy hierarchical analysis method, entropy weight method and grey correlation analysis to repeatedly consider the unreasonable problem of subjective and objective integration in the quality evaluation process, taking into account the influence of subjective and objective factors in the evaluation process. Using the cloud model to determine the membership of quality indicators can reduce the interference caused by quality randomness and uncertainty, making the evaluation results more accurate. At the same time, the membership combined with the weight can intuitively explain how the evaluation results are obtained, which is interpretable.

[0174] Exemplary Devices

[0175] Figure 4 FIG. 1 is a schematic diagram of a power cable quality evaluation device based on cable performance provided by an exemplary embodiment of the present invention. Figure 4 As shown, the device 400 includes:

[0176] Establishing module 410, for establishing a three-layer indicator system for the operation status of the power cable, wherein the three-layer indicator system includes: a target layer, a performance layer and an indicator layer;

[0177] A determination module 420 is used to determine the comprehensive weight of the index layer by using fuzzy analytic hierarchy process, entropy weight method and grey correlation analysis method according to the power cable performance test data;

[0178] The first calculation module 430 is used to calculate the power cable performance test data using the cloud model to determine the membership matrix of each power cable quality index corresponding to each cable operation state;

[0179] The second calculation module 440 is used to calculate the performance evaluation results of each performance of the performance layer according to the membership matrix and the comprehensive weight;

[0180] The third calculation module 450 is used to calculate the cable operation quality evaluation result corresponding to the target layer of the power cable performance test data according to the performance evaluation result.

[0181] Optionally, the determination module 420 includes:

[0182] A first calculation submodule, used for calculating a first weight by using a fuzzy analytic hierarchy process;

[0183] A second calculation submodule is used to calculate a second weight using an entropy weight method according to the power cable performance test data;

[0184] A third calculation submodule is used to calculate a third weight using a grey correlation analysis method according to the power cable performance test data;

[0185] The fourth calculation submodule is used to calculate the comprehensive weight according to the first weight, the second weight and the third weight.

[0186] Optionally, the first computing submodule includes:

[0187] A construction unit, used to construct a fuzzy judgment matrix according to the expert's pairwise importance evaluation results;

[0188] A first calculation unit, used for calculating a fuzzy consistency matrix of a fuzzy judgment matrix;

[0189] The second calculation unit is used to calculate the first weight according to the fuzzy consistency matrix.

[0190] Optionally, the second computing submodule includes:

[0191] A third calculation unit is used to calculate the entropy value according to the power cable performance test data;

[0192] A fourth calculation unit, used for calculating the information utility value according to the entropy value;

[0193] The determination unit is used to normalize the information utility value and determine the second weight.

[0194] Optionally, the third computing submodule includes:

[0195] As a unit, it is used to take the historical optimal value sequence as a reference sequence and take multiple detection sequences in the power cable performance test data as comparison sequences;

[0196] A fifth calculation unit, used for calculating an absolute difference sequence between the comparison sequence and the reference sequence;

[0197] The sixth calculation unit is used to calculate the grey correlation coefficient according to the absolute difference sequence;

[0198] A seventh calculation unit, used for calculating the grey relational degree according to the grey relational coefficient;

[0199] The eighth calculation unit is used to calculate the third weight according to the grey relational degree.

[0200] Optionally, the first computing module includes:

[0201] A standard cloud model parameter determination unit, used to determine standard cloud model parameters for a preset cable operation state;

[0202] A random number generation unit, used to generate a normally distributed random number of a preset cable operation state according to standard cloud model parameters;

[0203] A model parameter value determination unit, used to input the calculated normal distribution random number, expectation and variance of the power cable performance test data into the cloud model to determine the model parameter value of each power cable performance test data;

[0204] An initial membership value determination unit is used to compare the model parameter value with the random number to determine the membership value of the power cable performance test data belonging to each cable operation state;

[0205] A final membership value determination unit is used to repeat the random number generation unit to the initial membership value determination unit to obtain the membership values ​​detected for a preset number of times, and average the membership values ​​for the preset number of times to obtain the final membership value of the power cable performance test data;

[0206] The membership degree construction unit is used to construct the membership degree of each power cable quality indicator corresponding to each cable operation state according to the final membership degree value.

[0207] Optionally, the calculation formula for the cable operation quality evaluation result is:

[0208] U=U e ×x1+U i ×x2+U m ×x3

[0209] in,

[0210] U e =w(x e )×Y(y e )

[0211] U i =w(x i )×Y(y i )

[0212] U m =w(x m )×Y(y m )

[0213] Where U e is the electrical performance, W(x e ) is the weight of the electrical performance index, Y(y e ) is the electrical performance index membership matrix, U i is the insulation performance, w(x i ) is the insulation performance index weight, Y(y i ) is the insulation performance index membership matrix, U m is the mechanical property, w(x m ) is the weight of mechanical performance index, Y(y m ) is the mechanical performance index membership matrix, U is the power cable quality, x1, x2, x3 are the weights of electrical performance, insulation performance and mechanical performance respectively.

[0214] Exemplary Electronic Devices

[0215] Figure 5This is a structure of an electronic device provided by an exemplary embodiment of the present invention. Figure 5 As shown, the electronic device 50 includes one or more processors 51 and a memory 52 .

[0216] The processor 51 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0217] The memory 52 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 51 may run the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above and / or other desired functions. In one example, the electronic device may also include: an input device 53 and an output device 54, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0218] In addition, the input device 53 may also include, for example, a keyboard, a mouse, etc.

[0219] The output device 54 can output various information to the outside. The output device 54 can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto.

[0220] Of course, to simplify, Figure 5 Only some of the components related to the present invention in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application conditions.

[0221] Exemplary computer program products and computer-readable storage media

[0222] In addition to the above-mentioned methods and devices, an embodiment of the present invention may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above-mentioned "Exemplary Method" section of this specification.

[0223] The computer program product may be written in any combination of one or more programming languages ​​to write program code for performing the operations of the embodiments of the present invention, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0224] In addition, an embodiment of the present invention may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above “Exemplary Method” section of this specification.

[0225] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, system or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0226] The basic principle of the present invention is described above in conjunction with specific embodiments. However, it should be pointed out that the advantages, strengths, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. must be possessed by each embodiment of the present invention. In addition, the specific details disclosed above are only for the purpose of illustration and facilitation of understanding, rather than limitation, and the above details do not limit the present invention to being implemented by adopting the above specific details.

[0227] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0228] The block diagrams of the devices, systems, equipment, and systems involved in the present invention are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, systems, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The word "such as" used here refers to the phrase "such as but not limited to", and can be used interchangeably with it.

[0229] The method and system of the present invention may be implemented in many ways. For example, the method and system of the present invention may be implemented by software, hardware, firmware or any combination of software, hardware, firmware. The above order of steps for the method is only for illustration, and the steps of the method of the present invention are not limited to the order specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present invention may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.

[0230] It should also be noted that in the system, device and method of the present invention, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. The above description of the disclosed aspects is provided to enable any technician in the field to make or use the present invention. Various modifications to these aspects are very obvious to those skilled in the art, and the general principles defined here can be applied to other aspects without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the aspects shown here, but in accordance with the widest range consistent with the principles and novel features disclosed here.

[0231] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present invention to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A method for evaluating the quality of a power cable based on cable performance, characterized in that: include: Establishing a three-layer indicator system for the operation status of power cables, wherein the three-layer indicator system includes: a target layer, a performance layer, and an indicator layer; According to the power cable performance test data, the comprehensive weights of the power cable quality indicators in the indicator layer are determined by using fuzzy hierarchical analysis method, entropy weight method and grey correlation analysis method; Using the cloud model to calculate the power cable performance test data, determine the membership matrix of each power cable quality index corresponding to each cable operation state; Calculating the performance evaluation results of each performance of the performance layer according to the membership matrix and the comprehensive weight; The cable operation quality evaluation result corresponding to the target layer of the power cable performance test data is calculated according to the performance evaluation result.

2. The method according to claim 1, characterized in that: According to the power cable performance test data, the fuzzy hierarchical analysis method, entropy weight method and grey correlation analysis method are used to determine the comprehensive weights of the power cable quality indicators in the indicator layer, including: The fuzzy analytic hierarchy process is used to calculate the first weight of each power cable quality index; Calculate the second weight of each power cable quality index by using the entropy weight method according to the power cable performance test data; Calculating the third weight of each power cable quality index by using the grey correlation analysis method according to the power cable performance test data; The comprehensive weight is calculated according to the first weight, the second weight and the third weight.

3. The method according to claim 2, characterized in that The fuzzy analytic hierarchy process is used to calculate the first weight of each power cable quality index, including: A fuzzy judgment matrix is ​​constructed based on the expert evaluation results of the pairwise importance of each power cable quality index; Calculating a fuzzy consistency matrix of the fuzzy judgment matrix; The first weight is calculated according to the fuzzy consistency matrix.

4. The method according to claim 2, characterized in that: Calculating the second weight of each power cable quality index using the entropy weight method according to the power cable performance test data, including: Calculating the entropy value of each power cable quality index according to the power cable performance test data; Calculate the information utility value of each power cable quality index according to the entropy value; The information utility value is normalized to determine the second weight.

5. The method according to claim 2, characterized in that: The third weight of each power cable quality index is calculated using the grey correlation analysis method according to the power cable performance test data, including: Taking the historical optimal value sequence of each power cable quality index as a reference sequence, and taking the multiple detection sequences in the power cable performance test data as comparison sequences; Calculating a sequence of absolute differences between the comparison sequence and the reference sequence; Calculating the grey correlation coefficient of each power cable quality index according to the absolute difference sequence; Calculate the grey correlation degree of each power cable quality index according to the grey correlation coefficient; The third weight is calculated according to the grey relational degree.

6. The method according to claim 1, characterized in that The cloud model is used to calculate the power cable performance test data to determine the membership matrix of each power cable quality index corresponding to each cable operation state, including: Step 1: Determine the standard cloud model parameters of the preset cable operation state; Step 2: Generate a normally distributed random number of the preset cable operation state according to the standard cloud model parameters; Step 3: Input the calculated normal distribution random number, expectation and variance of the power cable performance test data into the cloud model to determine the model parameter value of each power cable performance test data; Step 4: Compare the model parameter value with the random number to determine the membership value of the power cable performance test data belonging to each cable operation state; Step 5: Repeat steps 2-4 to obtain the membership values ​​detected for a preset number of times, and average the membership values ​​for the preset number of times to obtain the final membership value of the power cable performance test data; Step 6: According to the final membership value, construct the membership of each power cable quality indicator corresponding to each cable operation state.

7. The method according to claim 1, characterized in that The calculation formula for the cable operation quality evaluation result is: U=U e ×x1+U i ×x2+U m ×x3 in, U e =w(x e )×Y(y e ) U i =w(x i )×Y(y i ) U m =w(x m )×Y(y m ) Where U e is the electrical property, w(x e ) is the weight of the electrical performance index, Y(y e ) is the electrical performance index membership matrix, U i is the insulation performance, w(x i ) is the insulation performance index weight, Y(y i ) is the insulation performance index membership matrix, U m is the mechanical property, w(x m ) is the weight of mechanical performance index, Y(y m ) is the mechanical performance index membership matrix, U is the power cable quality, x1, x2, x3 are the weights of electrical performance, insulation performance and mechanical performance respectively.

8. A power cable quality evaluation device based on cable performance, characterized in that: include: Establishing a module for establishing a three-layer indicator system for the operation status of a power cable, wherein the three-layer indicator system includes: a target layer, a performance layer, and an indicator layer; A determination module is used to determine the comprehensive weight of each power cable quality index of the index layer by using fuzzy hierarchical analysis method, entropy weight method and grey correlation analysis method according to the power cable performance test data; A first calculation module is used to calculate the power cable performance test data using a cloud model to determine a membership matrix of each power cable quality index corresponding to each cable operation state; A second calculation module, used for calculating the performance evaluation results of each performance of the performance layer according to the membership matrix and the comprehensive weight; The third calculation module is used to calculate the cable operation quality evaluation result corresponding to the target layer of the power cable performance test data according to the performance evaluation result.

9. The device according to claim 8, characterized in that Identify modules, including: A first calculation submodule, used for calculating a first weight by using a fuzzy analytic hierarchy process; A second calculation submodule, configured to calculate a second weight using an entropy weight method according to the power cable performance test data; A third calculation submodule, configured to calculate a third weight using a grey correlation analysis method according to the power cable performance test data; The fourth calculation submodule is used to calculate the comprehensive weight according to the first weight, the second weight and the third weight.

10. The device according to claim 9, characterized in that The first computing submodule includes: A construction unit, used for constructing a fuzzy judgment matrix according to the evaluation results of the pairwise importance of each power cable quality index by experts; A first calculation unit, used for calculating a fuzzy consistency matrix of the fuzzy judgment matrix; The second calculation unit is used to calculate the first weight according to the fuzzy consistency matrix.

11. The device according to claim 9, characterized in that The second computing submodule includes: A third calculation unit, used to calculate the entropy value of each power cable quality index according to the power cable performance test data; a fourth calculation unit, configured to calculate an information utility value according to the entropy value; A determination unit is used to normalize the information utility value to determine the second weight.

12. The device according to claim 9, characterized in that The third computing submodule includes: As a unit, used to take the historical optimal value sequence of each power cable quality index as a reference sequence, and take multiple detection sequences in the power cable performance test data as comparison sequences; a fifth calculation unit, configured to calculate an absolute difference sequence between the comparison sequence and the reference sequence; A sixth calculation unit, used for calculating a grey relational coefficient according to the absolute difference sequence; A seventh calculation unit, used for calculating a grey relational degree according to the grey relational coefficient; An eighth calculation unit is used to calculate the third weight according to the grey relational degree.

13. The device according to claim 8, characterized in that The first computing module includes: A standard cloud model parameter determination unit, used to determine standard cloud model parameters for a preset cable operation state; A random number generating unit, used for generating a normally distributed random number of the preset cable operation state according to the standard cloud model parameters; A model parameter value determination unit, used for inputting the calculated normal distribution random number, expectation and variance of the power cable performance test data into the cloud model to determine the model parameter value of each power cable performance test data; An initial membership value determination unit, used for comparing the model parameter value with the random number to determine the membership value of the power cable performance test data belonging to each cable operation state; A final membership value determination unit, used to repeat the random number generation unit to the initial membership value determination unit, obtain the membership values ​​detected for a preset number of times, and average the membership values ​​for the preset number of times to obtain the final membership value of the power cable performance test data; The membership degree construction unit is used to construct the membership degree of each power cable quality indicator corresponding to each cable operation state according to the final membership degree value.

14. The device according to claim 8, characterized in that The calculation formula for the cable operation quality evaluation result is: U=U e ×x1+U i ×x2+U m ×x3 in, U e =w(x e )×Y(y e ) U i =w(x i )×Y(y i ) U m =w(x m )×Y(y m ) Where U e is the electrical property, w(x e ) is the weight of the electrical performance index, Y(y e ) is the electrical performance index membership matrix, U i is the insulation performance, w(x i ) is the insulation performance index weight, Y(y i ) is the insulation performance index membership matrix, U m is the mechanical property, w(x m ) is the weight of mechanical performance index, Y(y m ) is the mechanical performance index membership matrix, U is the power cable quality, x1, x2, x3 are the weights of electrical performance, insulation performance and mechanical performance respectively.

15. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and the computer program is used to execute the method according to any one of claims 1 to 7.

16. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is used to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1 to 7.

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