Distribution network reliability assessment index weight formulation method, device and storage medium

Abstract

The present invention discloses a method, device and storage medium for formulating weights of distribution network reliability assessment indicators, and relates to the technical field of distribution network reliability assessment. The present invention includes collecting basic data obtained by distribution network measurement; substituting into the reliability assessment system, and calculating the evaluation index value; analyzing the correlation of reliability indicators based on grey correlation; formulating a reference model for the importance of reliability assessment indicators facing the user side and the system side; formulating the subjective weight of the assessment indicators based on the analytic hierarchy process; and obtaining the reliability index weight calculation result based on the combination of subjective and objective factors. The present invention calculates the reliability index weight result based on the combination of subjective and objective factors by analyzing the correlation between indicators, formulating the importance classification reference model, and calculating the reliability index weight result based on the combination of subjective and objective factors, thereby avoiding a single weight calculation standard to a certain extent, comprehensively reflecting the power supply reliability of the distribution network, and having great promotion and practical value.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network reliability assessment, and in particular relates to a method, device and storage medium for formulating weights of distribution network reliability assessment indicators. Background Art

[0002] The distribution network is an important part of the power grid. It is directly facing end users and is closely related to the production and life of the general public. It is an important infrastructure serving the people's livelihood. At present, with the construction and transformation of the "Energy Internet", the function and form of the distribution network will change significantly, and the requirements for power supply safety, reliability and adaptability will become higher and higher. Faced with new situations and new challenges, the power sector attaches great importance to the development of the distribution network, unifies planning, unifies standards, and strengthens standardization construction.

[0003] The traditional distribution network evaluation method is relatively simple, mainly relying on the average power outage time as an independent indicator to judge the reliability of power supply. However, with the continuous development of society, the distribution reliability needs of different users are quite different. According to the user type, there are basically four types of users: time type, frequency type, electricity type, and time + frequency type. The reliability needs of a substation are complex and diverse, and mainly vary according to factors such as the type of users and the number of users in the substation. The reliability needs of the substation are determined according to the proportion of user types (time type, frequency type, electricity type, time + frequency type) within the distribution range to the total number of users.

[0004] Among them, the publication number CN113112136A discloses a comprehensive evaluation method and system for distribution network reliability, and designs evaluation indicators and constructs an evaluation system for the reliability of the distribution network on the system side. However, the above patent proposes 13 evaluation indicators, and there are still certain problems in actual application: each indicator has a certain evaluation result, and it is difficult to choose between many results. In actual applications, users / stations will pay more attention to some indicators, and the focus of different users / stations will inevitably differ according to their own characteristics.

[0005] In view of the above problems, a method, equipment and storage medium for formulating weights of distribution network reliability assessment indicators are designed. Firstly, the relationship between indicators is analyzed based on grey correlation, and a reference model of the importance of indicators on the user side and the system side is given. Then, the comprehensive weights of distribution network reliability assessment indicators are formulated from a combination of subjective and objective perspectives. Summary of the invention

[0006] The purpose of the present invention is to provide a method, device and storage medium for formulating weights of distribution network reliability assessment indicators, and to formulate comprehensive weights of distribution network reliability assessment indicators from a subjective and objective perspective through a user-side and system-side indicator importance reference model.

[0007] In order to solve the above technical problems, the present invention is achieved through the following technical solutions:

[0008] The present invention is a method for formulating weights of distribution network reliability evaluation indicators, including subjective weights of evaluation indicators:

[0009] Construct a hierarchical model with the indicator system included in the user side and the system side;

[0010] Compare the indicators of the same level in different sides pairwise to construct a judgment matrix;

[0011] After the judgment of the judgment matrix is ​​completed, the consistency check is performed on the judgment matrix. After the judgment matrix meets the consistency requirements, the characteristic response corresponding to the maximum eigenvalue is used as the proportional vector of the weight, and the weight of each indicator is obtained after normalization.

[0012] Furthermore, it also includes obtaining the weight of each indicator to obtain the objective weight of the reliability assessment indicator, including:

[0013] Construct the original data matrix;

[0014] Data normalization, given m indicators, where X i ={X1,…,X n}, the standardized values ​​of each indicator data are Y1,…,Y k , after normalization;

[0015]

[0016] Information entropy calculation:

[0017]

[0018] in,

[0019]

[0020] Calculate the weight according to the entropy value of each indicator:

[0021]

[0022] is the mean value of information entropy.

[0023] Furthermore, it also includes obtaining the weight of the reliability index combining subjective and objective factors;

[0024] Construct the original matrix P,

[0025]

[0026] The matrix P is normalized,

[0027]

[0028] The weight vector w = [w1...w n ];

[0029] Calculate the index weighted evaluation matrix V,

[0030]

[0031] Combined with the reliability evaluation index values, the comprehensive evaluation score is calculated.

[0032] Furthermore, the indicators on the user side and the system side are used to include time-based users, frequency-based users, power-based users, and time+frequency-based users.

[0033] Furthermore, it is necessary to obtain basic data of the distribution network before evaluating the indicator weights.

[0034] Furthermore, the basic data is brought into the evaluation system to obtain evaluation indicators.

[0035] Furthermore, the reliability indexes are analyzed for correlation.

[0036] Further, the correlation coefficient was calculated;

[0037]

[0038] ρ∈(0,∞), called the resolution coefficient.

[0039] Calculate the correlation, the correlation formula ri is as follows:

[0040]

[0041] Relevance sorting: Sort by size.

[0042] An electronic device, characterized in that it includes a processor and a memory; a computer program is stored in the memory, and the processor is configured to run the computer program to execute the above-mentioned distribution network reliability assessment index weight formulation method, device and storage medium.

[0043] A storage medium, characterized in that the storage medium stores a computer program, and the computer program is configured to execute the above-mentioned distribution network reliability assessment index weight formulation method, device and storage medium when executed by a processor.

[0044] The present invention has the following beneficial effects:

[0045] 1. It can break through the relatively single evaluation method of traditional distribution networks and comprehensively utilize the diversified indicators in the distribution network reliability evaluation system to judge the power supply reliability.

[0046] 2. It can meet the reliability requirements of different user types, including time-based, frequency-based, power-based, and time+frequency-based users, and provide guidance for system scheduling based on actual reliability assessment results.

[0047] 3. The weights of distribution network reliability assessment indicators based on a combination of subjective and objective factors can be formulated through the hierarchical analysis method and entropy weight method to avoid errors and misjudgments caused by a single weight formulation method.

[0048] The invention proposes a method, device and storage medium for formulating weights of distribution network reliability assessment indicators. It is feasible to analyze the correlation characteristics between indicators with grey correlation degree and formulate comprehensive weights using hierarchical analysis method and entropy weight method. The method starts from the user side and the system side, analyzes and studies the correlation characteristics of terminal user power supply reliability indicators and the weight assignment method, designs a distribution network power supply reliability assessment method that can meet the needs of personalized and refined management, provides power supply companies with more guiding evaluation means, and guides power supply companies to maximize the conversion of distribution network power supply reliability work into customer satisfaction benefits, thereby enhancing their competitiveness in the power market.

[0049] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0051] Figure 1 is a flow chart of a method for formulating weights of distribution network reliability assessment indicators according to an embodiment of the present invention;

[0052] Figure 2 It is the weight and scoring diagram of indicators A, B, and C of the distribution network facing the user side in an embodiment of the present invention;

[0053] Figure 3 It is the weight and scoring diagram of indicators A, B, and C of the distribution network facing the system side in an embodiment of the present invention;

[0054] Figure 4 It is a curve chart comparing the results of different indicator weight calculation methods in the embodiments of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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.

[0056] The present invention aims at the problem that the traditional distribution network evaluation method is relatively single and even if the evaluation system has been established, it is impossible to obtain a comprehensive evaluation result. Based on the relationship between the indicators, the gray correlation analysis is used to give a reference model of the importance of the indicators on the user side and the system side. The comprehensive weight of the distribution network reliability evaluation indicator is formulated from the perspective of combining the subjective and objective, and the effectiveness of the evaluation indicator weight formulation method is verified by using examples and simulations. Through the distribution network reliability evaluation indicator weight formulation method, equipment and storage medium described in this patent, it is possible to break through the relatively single evaluation method of the traditional distribution network, and comprehensively utilize the diversified indicators in the distribution network reliability evaluation system to judge the power supply reliability; it can meet the reliability requirements of different user types, including four types of users such as time type, frequency type, electricity type, and time + frequency type, and guide the system scheduling according to the actual reliability evaluation situation; it can formulate the distribution network reliability evaluation indicator weight based on the combination of subjective and objective through the hierarchical analysis method and the entropy weight method, avoiding the errors and misjudgments caused by a single weight formulation method.

[0057] like Figure 1 As shown, a method, device and storage medium for formulating weights of distribution network reliability assessment indicators include the following steps:

[0058] S1. Collect basic data obtained from distribution network measurements;

[0059] S2. Substitute the reliability evaluation system and calculate the evaluation index value (actual value);

[0060] S3, correlation analysis based on grey correlation reliability index;

[0061] S4. Formulate a reference model for the importance classification of reliability assessment indicators for the user side and the system side;

[0062] S5. Formulation of subjective weights of evaluation indicators based on the analytic hierarchy process (S501 constructs the evaluation object hierarchy; S502 constructs the judgment matrix based on expert experience; S503 consistency test; S504 subjective weight vector);

[0063] S6. Formulate objective weights of evaluation indicators based on entropy weight method (wherein, S601 constructs original data matrix; S602 normalizes original data; S603 calculates information entropy; S604 objective weight vector);

[0064] S7. Obtain the calculation result of the reliability index weight based on the combination of subjective and objective factors.

[0065] Specifically, in S3, the relevance analysis of reliability indicators is based on grey relational degree. The Grey Relational Analysis (GRA) model ranks the pros and cons of each plan by calculating the degree of association between each plan and the optimal plan. The basic steps are as follows:

[0066] The first step: Determine the analysis sequences (reference sequence, comparison sequence)

[0067] The second step, dimensionless processing of variables (initial value processing, mean value processing)

[0068] The third step, calculate the correlation coefficient

[0069]

[0070] ρ ∈ (0, ∞), which is called the discrimination coefficient. The smaller ρ is, the greater the discrimination power. Generally, the value range of ρ is (0, 1), and the specific value can be determined according to the situation. When ρ ≤ 0.5463, the discrimination power is the best, and usually ρ = 0.5 is taken.

[0071] The fourth step, calculate the correlation degree, and the correlation degree is r i The formula is as follows:

[0072]

[0073] The fifth step, correlation degree ranking: Sort by size. If r1 < r2, then the reference sequence y is more similar to the comparison sequence x2.

[0074] Specifically, in S4, formulate the importance grading reference model for reliability evaluation indicators on the user side and system side.

[0075] From the perspective of the user side, based on the power supply demands of various terminal user types, users are divided into time-based users, frequency-based users, power-based users, and time + frequency-based users. The importance grading reference model for their reliability indicators is as follows: 1) For time-based users, the corresponding time-related indicators should have a higher priority and a greater weight, such as the user's power outage time, etc.; 2) For frequency-based users, the corresponding power-related indicators should have a higher priority and a greater weight, such as the user's power outage frequency, etc.; 3) For power-based users, the corresponding power-related indicators should have a higher priority and a greater weight, such as the user's power outage power shortage, etc.; 4) For time + frequency-based users, the importance priorities of the corresponding time and frequency indicators should be ranked among the top.

[0076] From the perspective of the system, since the type and number of users in the distribution area affect the reliability requirements of the area, the reference model for the importance classification of reliability indicators is as follows based on the proportion of user types in the area: 1) When the main power supply object in the distribution area is a time-type user, the corresponding time-type indicators should have a higher priority and a larger weight, such as the average power outage time of the system; 2) When the main power supply object in the distribution area is a frequency-type user, the corresponding power-type indicators should have a higher priority and a larger weight, such as the average power outage frequency of the system; 3) When the main power supply object in the distribution area is a power-type user, the corresponding power-type indicators should have a higher priority and a larger weight, such as the average power outage power shortage of users; 4) For time + frequency type users, the importance priority of the corresponding time and frequency indicators should be ranked first; 5. According to the proportion of user types (time type, frequency type, power type, time + frequency type) in the distribution range to the total number of users, determine the proportion of the importance of the corresponding indicators according to the proportion, and formulate the importance priority of the distribution reliability evaluation indicators on the relevant system side.

[0077] Specifically, in S5, the subjective weights of the evaluation indicators based on the hierarchical analysis method are formulated in the following steps:

[0078] S501. Based on the in-depth analysis of the system, a hierarchical model is constructed based on the indicator system;

[0079] S502. For indicators of the same level belonging to the same superior, a judgment matrix is ​​established by using a 1-9 scale pairwise comparison method, and the degree of comparison is determined by Table 1. Based on expert experience, a judgment matrix A is obtained;

[0080] Table 1 Scale and meaning of pairwise comparison of factors

[0081]

[0082] S503: Since expert scoring may be biased, in order to ensure the credibility of the results, the consistency of the judgment matrix must be checked. If the consistency check fails, the judgment matrix needs to be reconstructed. The consistency check steps are as follows:

[0083] ① Calculate the consistency test index CI, λ max represents the maximum eigenvalue, and n represents the matrix order;

[0084]

[0085] ② According to n, query the corresponding average random consistency index RI. Table 2 gives the RI values ​​of matrices of order 1 to 9;

[0086] Table 2 Average random consistency index RI value

[0087]

[0088] ③ Calculate the consistency ratio CR. If CR < 0.1, the judgment matrix meets the consistency. If CR > 0.1, the judgment matrix needs to be reconstructed.

[0089]

[0090] S504. When the consistency requirement is met, the maximum eigenvalue λ max The corresponding eigenvector is the weight ratio vector, and the weight of each indicator is obtained after normalization. The steps for calculating the weight vector are as follows:

[0091] ①Sum the columns of the matrix to get the vector

[0092] B=[b1...b n ] (5)

[0093] ② Normalized matrix C, where each element is

[0094]

[0095] ③Calculate the weight vector

[0096] w=[w1...w n ] (7)

[0097]

[0098] Specifically, in S6, the objective weights of the reliability evaluation indicators based on the entropy weight method are formulated in the following steps:

[0099] S601, constructing an original data matrix;

[0100] S602, data normalization, assuming that m indicators are given, where X i ={X1,…,X n The standardized values ​​of each indicator data are Y1,…,Y k , after normalization;

[0101]

[0102] S603, information entropy calculation:

[0103]

[0104] in,

[0105]

[0106] S604, calculate the weight according to the entropy value of each indicator,

[0107]

[0108] When E i When it approaches 1 infinitely, the numerator will become very small, so a small change in the entropy value will cause the weight to change exponentially, which is unreasonable. This paper optimizes the traditional entropy weight method and changes the weight calculation formula to ( is the mean value of information entropy)

[0109]

[0110] Specifically, in S7, based on the subjective and objective reliability index weight calculation results, the analytic hierarchy process-entropy weight method is used, and the steps are as follows:

[0111] ①Construct the original matrix P,

[0112]

[0113] ② Normalize the matrix P,

[0114]

[0115] ③Use the hierarchical analysis method-entropy weight method to obtain the weight vector w=[w1...w n ];

[0116] ④ Calculate the index weighted evaluation matrix V,

[0117]

[0118] ⑤ Combine the reliability evaluation index values ​​to calculate the comprehensive evaluation score.

[0119] The system-side distribution network reliability evaluation index system used in the embodiment is shown in Table 3; the user-side distribution network reliability evaluation index system used is shown in Table 4.

[0120] Table 3 System side distribution network reliability evaluation index system used in the embodiment

[0121]

[0122]

[0123] Table 4 User side distribution network reliability evaluation index system used in the embodiment

[0124]

[0125] In order to verify the effectiveness of the comprehensive reliability evaluation system for distribution networks proposed in this project, data from three different regional distribution networks A, B, and C are used for example analysis. Distribution network A is based on the tripping fault in Wuqing, Tianjin on May 16, 2021. The basic parameters of distribution networks A, B, and C are shown in Table 6.

[0126] Table 5: Tripping fault situation in Wuqing, Tianjin on May 16

[0127]

[0128] Table 6 Basic parameters of distribution networks A, B, and C

[0129]

[0130] In order to evaluate the reliability of distribution networks A, B, and C, the power outage data of the three distribution networks in the same year were selected, and some of the power outage data are shown in Table 7.

[0131] Table 7 Partial power outage data of distribution networks A, B and C in a certain year

[0132]

[0133] Using the above data, we calculated and obtained the weights and scoring results of indicators A, B, and C of the distribution network facing the user side in Table 8, and drew Figure 2 Weights and scoring diagram of indicators A, B, and C of the distribution network facing the user side.

[0134] Table 8 Weights and scoring results of indicators A, B, and C of the distribution network facing the user side

[0135]

[0136] Table 9 Comprehensive scoring results of distribution networks A, B, and C facing the user side

[0137]

[0138] Similarly, the calculation results of the weights of the distribution network A, B, and C indicators on the system side are shown in Table 10. Figure 3 shown.

[0139] Table 10 Weights and scoring results of indicators A, B, and C of the distribution network facing the system side

[0140]

[0141] Table 11 Comprehensive scoring results of distribution networks A, B, and C on the system side

[0142]

[0143] To verify the effectiveness of the proposed weight calculation method, the weights calculated by the average index (1 / 13 is about 0.769), the weights calculated based on the subjective analytic hierarchy process, and the weights calculated based on the objective improved entropy weight method are compared with Table 12. In order to more intuitively show the difference in data between the results of different weight calculation methods, Figure 4 Comparison curve of the results of different indicator weight calculation methods.

[0144] Table 12 Comparison of results of different indicator weight calculation methods

[0145]

[0146] It can be seen that the average weight coefficient cannot reflect the differentiated attention of each distribution network to different indicators; the weight results calculated based on the subjective analytic hierarchy process are highly discretized, and the subjective impression of expert scoring alone cannot objectively show the actual needs; the weight results calculated based on the objective improved entropy weight method are close to the average weight coefficient, indicating that it is objectively impossible to distinguish the differences between indicators and it is impossible to adjust and improve according to the main user types within the distribution network; in contrast, the reliability indicator weight results based on the combination of subjective and objective methods can fully reflect the differences between indicators and the focus of user needs within the distribution network. Compared with other methods, it is more reasonable and more applicable.

[0147] According to another aspect of the present invention, there is provided a device comprising:

[0148] A memory for storing program instructions;

[0149] The processor is used to call the program instructions stored in the memory and execute the distribution network reliability assessment index weight formulation method, device and storage medium in any of the above-mentioned embodiments according to the obtained program.

[0150] Based on the above embodiments and the same concept, an embodiment of the present invention also provides a computer storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the distribution network reliability assessment index weight formulation method, device and storage medium in any of the aforementioned embodiments.

[0151] It should be noted that the division of units in the embodiment of the present invention is schematic and is only a logical function division. There may be other division methods in actual implementation. Each functional unit in the embodiment of the present invention may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0152] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the process or function according to the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) methods to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integration. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk Solid State Disk (SSD)), etc.

[0153] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as a method, a system, or a computer program product. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0154] The embodiments of the present invention are described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0155] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0156] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0157] The present invention is not limited to the above-mentioned optimal implementation method. Anyone can derive other various forms of products under the inspiration of the present invention. However, no matter what changes are made in the shape or structure, all technical solutions that are the same or similar to those of the present application fall within the protection scope of the present invention.

Claims

1. Method for formulating weights of distribution network reliability assessment indicators, including subjective weights of assessment indicators: Construct a hierarchical model with the indicator system included in the user side and the system side; Compare the indicators of the same level in different sides pairwise to construct a judgment matrix; Complete the judgment of the judgment matrix and perform consistency check on the judgment matrix. After the judgment matrix meets the consistency requirements, the characteristic response corresponding to the maximum eigenvalue is used as the proportional vector of the weight, and the weight of each indicator is obtained after normalization; It also includes obtaining the weights of each indicator to obtain the objective weights of the reliability assessment indicators, including: Construct the original data matrix; Data normalization, given m indicators, where X i ={X1,…,X n }, the standardized values ​​of each indicator data are Y1,…,Y k , after normalization; Information entropy calculation: in, Calculate the weight according to the entropy value of each indicator: E is the mean value of information entropy; It also includes obtaining the weight of reliability indicators combining subjective and objective factors; Construct the original matrix P, The matrix P is normalized, The weight vector w = [w1...w n ]; Calculate the index weighted evaluation matrix V, Combined with the reliability evaluation index values, the comprehensive evaluation score is calculated.

2. The method for formulating weights of distribution network reliability assessment indicators according to claim 1, characterized in that: The indicators on both the user side and the system side include time-based users, frequency-based users, power-based users, and time+frequency-based users.

3. The method for formulating weights of distribution network reliability assessment indicators according to claim 2, characterized in that: Before evaluating the indicator weights, it is necessary to obtain basic data of the distribution network.

4. The method for formulating weights of distribution network reliability assessment indicators according to claim 3, characterized in that: Bring basic data into the evaluation system to obtain evaluation indicators.

5. The method for formulating weights of distribution network reliability assessment indicators according to claim 4, characterized in that: Conduct correlation analysis on reliability indicators.

6. The method, device and storage medium for formulating weights of distribution network reliability assessment indicators according to claim 5, characterized in that: Calculate the correlation coefficient; ρ∈(0,∞), called the resolution coefficient; Calculate the correlation, correlation r i The formula is as follows: Relevance sorting: Sort by size.

7. An electronic device, characterized in that: It includes a processor and a memory; the memory stores a computer program, and the processor is configured to run the computer program to execute the distribution network reliability assessment index weight formulation method, device and storage medium described in any one of claims 1-6.

8. A storage medium, characterized in that: The storage medium stores a computer program, and the computer program is configured to execute the distribution network reliability assessment index weight formulation method, device and storage medium described in any one of claims 1-6 when the processor runs the computer program.

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