A method for evaluating the emergency repair response capability of distribution networks

Abstract

The present invention discloses a method for evaluating the emergency repair response capability of a distribution network, comprising the following steps: Step 1: extracting fault features from the fault duration, resource configuration, and fault location in historical fault information; Step 2: converting the fault feature extraction results into corresponding capability indicators; Step 3: using an evaluation model to weight and combine the output capability indicators, ultimately outputting a resource configuration rationality evaluation result, and optimizing the resource allocation plan based on the configuration rationality evaluation result. The substantial effects of the present invention include: providing strong support for the quantitative evaluation of the distribution network resource allocation effect by evaluating the core indicators and selecting the optimal item as the weight value of the benchmark point, and adopting a method for converting the weight percentage and the empirical score, which can effectively help provide suggestions and solutions for the resource allocation effect of the power supply station.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power engineering, and in particular to a method for evaluating the emergency repair response capability of a distribution network. Background Art

[0002] In recent years, with the continued advancement of optimizing the business environment and the increasing demands of power customers for power supply services, the smart grid needs to extend to distribution network management, creating an urgent need to build a smart distribution network with automation and informationization features. At the same time, with the establishment of new power systems, the number of distributed energy access points in the distribution network has increased, the scale of the distribution network has increased, and the topology complexity has surged, leading to a higher probability of failure. However, the demand for power supply service reliability has increased rather than decreased, resulting in a significant increase in the timely perception capabilities required of the distribution network, necessitating an urgent shift from the traditional fault management model and passive service approach of the distribution network.

[0003] With the continuous improvement of people's living standards, the public's demand for emergency repair services after sudden power outages is also increasing, placing higher demands on power companies' distribution network power restoration capabilities and emergency repair response capabilities. Driven by this, research on the allocation and optimization of distribution network emergency repair resources in China, based on big data analysis, has received increasing attention. Currently, the integration of distribution network control and emergency repair command services is limited. Business operations still face challenges such as limited utilization of multi-faceted integrated data, delayed access to fault information by distribution network emergency repair resources, long average fault assessment time, ineffective means of handling large numbers of work orders under multiple distribution network faults, and a lack of scientific guidance based on distribution network emergency repair experience. These issues result in low distribution network fault handling efficiency and room for improvement in emergency repair resource allocation. Therefore, how to rationally evaluate the allocation of distribution network emergency repair resources using emergency repair response capability analysis is a pressing technical challenge. Summary of the Invention

[0004] In response to the problems that existing technologies cannot reasonably allocate emergency repair resources and have low fault handling efficiency, the present invention provides a distribution network emergency repair response capability evaluation method. By using an evaluation model, the rationality of resource allocation in the distribution network emergency repair response capability analysis is evaluated in a multi-dimensional and comprehensive manner to solve the problems of low fault handling efficiency and unreasonable allocation of emergency repair resources.

[0005] The following are technical solutions of the present invention:

[0006] A method for evaluating the emergency repair response capability of a distribution network comprises the following steps:

[0007] Step 1: Extract fault features based on fault duration, resource configuration, and fault location in historical fault information;

[0008] Step 2: Convert the fault feature extraction results into corresponding capability indicators;

[0009] Step 3: Use the evaluation model to assign weights to the output capability indicators, and finally output the resource allocation rationality evaluation results, and optimize the resource allocation plan based on the configuration rationality evaluation results.

[0010] Preferably, the step 1 comprises the following steps:

[0011] A1: Fill in gaps and process outliers in the fault work order acceptance time and power restoration time information in historical fault information. Then, match the processed results with the fault month to obtain analysis charts of the average work order acceptance time and the average power restoration time.

[0012] A2: Associate resource factors including power station personnel, emergency repair vehicles, and fault grids with the number of historical fault types;

[0013] A3: Match the faulty distribution transformer information with the corresponding distribution transformer latitude and longitude information, and use the fault bias rate algorithm to calculate the fault bias rate.

[0014] Preferably, the fault bias rate algorithm includes:

[0015] Cluster the longitude and latitude information of distribution transformers and perform location information averaging on the clustering results;

[0016] Calculate the distance between the typical fault location and the power supply station, distinguish the major and minor axes based on the distance, and obtain the fault bias rate.

[0017] Preferably, step 2 comprises the following steps:

[0018] B1: The work order acceptance index and fault repair index are calculated by the formula based on the work order acceptance time and fault repair time obtained by fault duration feature analysis. Among them, Tg i Represents the monthly work order acceptance time, R i Represents the weight value set according to the number of faults each month; fault repair index Among them, Th i Represents the power supply restoration time each month, R i Represents the weight value set based on the number of failures each month;

[0019] B2: Calculate resource allocation indicators Among them, M f Represents the total number of failures per month, M f ′=α1*M fk +α2*M ff +α3*M fp, α1 represents the inverse of the feeder fault ratio, α2 represents the inverse of the branch line fault ratio, α3 represents the inverse of the distribution transformer fault ratio, M fk Represents the number of feeder failures per month, M ff Represents the number of branch line failures per month, M fp Represents the number of distribution transformer failures each month, M wg Represents the number of faulty grids per month, M r Represents the number of faulty grids per month, M c represents the number of faulty grids per month;

[0020] B3: Calculate the fault bias index based on the fault bias rate algorithm. Among them, Pz i Represents the monthly fault bias rate, R i Represents the weight value set based on the number of failures each month.

[0021] Preferably, step 3 comprises the following steps:

[0022] C1: Build an evaluation model, including a three-layer evaluation indicator system consisting of the target layer, the criteria layer, and the solution layer. The resource coordination and coordination rational evaluation indicator system M includes the work order acceptance indicator M1, the fault repair indicator M2, the resource allocation indicator M3, and the fault bias indicator M4;

[0023] C2: Compare and score the importance of each indicator at the same level of the resource coordination and coordination rationality evaluation indicator system relative to the indicators at the previous level based on the proportional scale table to construct a judgment matrix; calculate the maximum eigenvalue λmax and eigenvector ξ of the judgment matrix A and perform a consistency test on the judgment matrix A; determine the weight vector w of each indicator in the hierarchical structure model by Aξ=λmaxw; and correlate the weight vectors of the criterion-level judgment matrix and the solution-level judgment matrix to obtain the weight value of each point;

[0024] C3: For the construction of judgment matrix, firstly, a ratio scale table is established. The ratio scale table consists of two parts: midpoint aij and aji. The value of aij is a cardinality of judgment, or the mean of random variables in the judgment scale. Secondly, the ratio comparison table is scored to form the judgment criterion number a. ij , when indicator i is more important than indicator j, a ij ≥1; when indicator j is more important than indicator i, a ji ≥1; the values ​​of the corresponding elements aij and aji of the judgment matrix A are as follows:

[0025]

[0026] Where i = 1, 2, ..., n; j = 1, 2, ..., n; n refers to the total number of single-layer indicators;

[0027] C4: Calculate the relative consistency coefficient CR of the judgment matrix A;

[0028]

[0029] Where n is the sum of the diagonal elements of matrix A. By querying the average random consistency index RI, the required RI value can be obtained for consistency verification. The consistency ratio coefficient CR is calculated:

[0030]

[0031] If the relative consistency coefficient CR of the judgment matrix A is less than 0.1, the judgment matrix A is considered feasible and has passed the consistency test. The smaller the CR value, the better. If the CR of the judgment matrix A is not less than 0.1 and has not passed the consistency test, the judgment process of the judgment matrix A is reversed and pairwise comparisons are repeated from the beginning to reconstruct a judgment matrix A that meets the requirements.

[0032] C5: The weight vector w0 obtained by the criterion layer judgment matrix and the weight vector w obtained by the solution layer judgment matrix i (i=1-m) Multiply the values ​​of the vector containing the same attribute value, and then add up the weight values ​​to get the weight value x of the specific point of the solution layer to the target layer. i ;

[0033]

[0034] Where n represents the dimension of the criterion layer judgment matrix; m represents the dimension of the solution layer judgment matrix;

[0035] x i =b1×c 1i +b2×c 2i +…+b n ×c ni ;

[0036] Among them, x i It is the weight value of the i-th indicator.

[0037] The substantial effects of the present invention include: by evaluating the core indicators and selecting the optimal items as the weight values ​​of the benchmark points, and adopting the method of converting the weight percentages and the empirical scores, it provides strong support for the quantitative evaluation of the resource allocation effect of the distribution network, and can effectively help provide suggestions and solutions for the resource allocation effect of the power supply station. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flow chart of an embodiment of the present invention;

[0039] Figure 2This is the fault bias rate algorithm flow of an embodiment of the present invention;

[0040] Figure 3 This is an analysis chart of the average acceptance time of fault work orders according to an embodiment of the present invention;

[0041] Figure 4 This is an analysis diagram of the average power restoration time according to an embodiment of the present invention;

[0042] Figure 5 This is a diagram illustrating a fault bias situation analysis according to an embodiment of the present invention;

[0043] Figure 6 This is a diagram of resource configuration rationality evaluation results in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] It should be understood that in various embodiments of the present invention, the size of the sequence number of each process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0046] It should be understood that in the present invention, "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or apparatuses.

[0047] It should be understood that in the present invention, "multiple" refers to two or more. "And / or" is only a description of the association relationship of associated objects, indicating that three relationships can exist. For example, and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "Contains A, B and C", "Contains A, B, C" means that A, B, and C are all included, "Contains A, B or C" means that one of A, B, and C is included, and "Contains A, B and / or C" means that any one, any two, or any three of A, B, and C are included.

[0048] The technical solution of the present invention is described in detail below with reference to specific embodiments. The embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0049] Example:

[0050] A method for evaluating the emergency repair response capability of a distribution network, comprising: Figure 1 The following steps are shown:

[0051] Step 1: Extract fault features from the fault duration, resource configuration, and fault location in historical fault information.

[0052] Step 1 specifically includes the following steps:

[0053] A1: Fill in gaps and process outliers in the fault work order acceptance time and power restoration time information in historical fault information. Then, match the processed results with the fault month to obtain analysis charts of the average work order acceptance time and the average power restoration time.

[0054] A2: Associate resource factors including power station personnel, emergency repair vehicles, and fault grids with the number of historical fault types;

[0055] A3: Match the faulty distribution transformer information with the corresponding distribution transformer latitude and longitude information, and use the fault bias rate algorithm to calculate the fault bias rate.

[0056] Among them, Figure 2 As shown, the fault bias rate algorithm includes:

[0057] Cluster the longitude and latitude information of distribution transformers and perform location information averaging on the clustering results;

[0058] Calculate the distance between the typical fault location and the power supply station, distinguish the major and minor axes based on the distance, and obtain the fault bias rate.

[0059] Step 2: Convert the fault feature extraction results into corresponding capability indicators.

[0060] like Figure 3 — Figure 5 The figure shows the work order acceptance time, fault repair time, and fault offset of a power supply station. Based on this, after completing the fault data information analysis and feature extraction, step 2 specifically includes the following steps:

[0061] B1: The work order acceptance index and fault repair index are calculated by the formula based on the work order acceptance time and fault repair time obtained by fault duration feature analysis. Among them, Tg i Represents the monthly work order acceptance time, R iRepresents the weight value set according to the number of faults each month; fault repair index Among them, Th i Represents the power supply restoration time each month, R i Represents the weight value set based on the number of failures each month;

[0062] B2: Calculate resource allocation indicators Among them, M f Represents the total number of failures per month, M f ′=α1*M fk +α2*M ff +α3*M fp , α1 represents the inverse of the feeder fault ratio, α2 represents the inverse of the branch line fault ratio, α3 represents the inverse of the distribution transformer fault ratio, M fk Represents the number of feeder failures per month, M ff Represents the number of branch line failures per month, M fp Represents the number of distribution transformer failures each month, M wg Represents the number of faulty grids per month, M r Represents the number of faulty grids per month, M c represents the number of faulty grids per month;

[0063] B3: Calculate the fault bias index based on the fault bias rate algorithm. Among them, Pz i Represents the monthly fault bias rate, R i Represents the weight value set based on the number of failures each month.

[0064] The work order acceptance index, fault repair index, resource allocation index and fault bias index of each power supply station are shown in Table 1.

[0065] Table 1

[0066] Power supply station name <![CDATA[W g ]]> <![CDATA[W X ]]> <![CDATA[W Z ]]> <![CDATA[W P ]]> A 0.751 1 0.934 1 B 0.836 0.668 1 0.782 C 0.782 0.824 0.886 0.925 D 1 0.776 0.792 0.888 E 0.957 0.902 0.751 0.857

[0067] Through step 2, the four indicators of the five power supply stations are evaluated respectively, and finally the evaluation model of step 3 is used to determine the weights to obtain the final score. Step 3: Use the evaluation model to combine the output capacity indicators with weights, and finally output the resource allocation rationality evaluation results, and optimize the resource allocation plan based on the configuration rationality evaluation results.

[0068] Step 3 specifically includes the following steps:

[0069] C1: Build an evaluation model, including a three-layer evaluation indicator system consisting of the target layer, the criteria layer, and the solution layer. The resource coordination and coordination rational evaluation indicator system M includes the work order acceptance indicator M1, the fault repair indicator M2, the resource allocation indicator M3, and the fault bias indicator M4;

[0070] C2: Compare and score the importance of each indicator at the same level of the resource coordination and coordination rationality evaluation indicator system relative to the indicators at the previous level based on the proportional scale table to construct a judgment matrix; calculate the maximum eigenvalue λmax and eigenvector ξ of the judgment matrix A and perform a consistency test on the judgment matrix A; determine the weight vector w of each indicator in the hierarchical structure model by Aξ=λmaxw; and correlate the weight vectors of the criterion-level judgment matrix and the solution-level judgment matrix to obtain the weight value of each point;

[0071] C3: For the construction of judgment matrix, firstly, a ratio scale table is established. The ratio scale table consists of two parts: midpoint aij and aji. The value of aij is a cardinality of judgment, or the mean of random variables in the judgment scale. Secondly, the ratio comparison table is scored to form the judgment criterion number a. ij , when indicator i is more important than indicator j, a ij ≥1; when indicator j is more important than indicator i, a ji ≥1; the values ​​of the corresponding elements aij and aji of the judgment matrix A are as follows:

[0072]

[0073] Where i = 1, 2, ..., n; j = 1, 2, ..., n; n refers to the total number of single-layer indicators;

[0074] C4: Calculate the relative consistency coefficient CR of the judgment matrix A;

[0075]

[0076] Where n is the sum of the diagonal elements of matrix A. By querying the average random consistency index RI, the required RI value can be obtained for consistency verification, as shown in Table 2.

[0077] Table 2

[0078] n 1 2 3 4 5 6 7 8 9 RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45

[0079] Calculate the consistency ratio coefficient CR:

[0080]

[0081] If the relative consistency coefficient CR of the judgment matrix A is less than 0.1, the judgment matrix A is considered feasible and has passed the consistency test. The smaller the CR value, the better. If the CR of the judgment matrix A is not less than 0.1 and has not passed the consistency test, the judgment process of the judgment matrix A is reversed and pairwise comparisons are repeated from the beginning to reconstruct a judgment matrix A that meets the requirements.

[0082] C5: The weight vector w0 obtained by the criterion layer judgment matrix and the weight vector w obtained by the solution layer judgment matrix i (i=1-m) Multiply the values ​​of the vector containing the same attribute value, and then add up the weight values ​​to get the weight value x of the specific point of the solution layer to the target layer. i ;

[0083]

[0084] x1=0.641×0.535+0.727×0.825+0.862×0.771+0.799×0.903;

[0085] x 2＝ 0.641×0.753+0.727×0.678+0.862×0.875+0.799×0.773;

[0086] x 3＝ 0.641×0.660+0.727×0.861+0.862×0.879+0.799×0.902;

[0087] x 4＝ 0.641×0.690+0.727×0.769+0.862×0.905+0.799×0.857;

[0088] The normalized weight values ​​are shown in Table 3.

[0089] Table 3

[0090]

[0091] Finally, the evaluation results of resource allocation rationality of each power supply station are obtained as follows: Figure 6 shown.

[0092] Through the description of the above implementation methods, technical personnel in the relevant field can understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional modules as needed, that is, the internal structure of the specific device can be divided into different functional modules to complete all or part of the functions described above.

[0093] In the embodiments provided in this application, it should be understood that the disclosed structures and methods can be implemented in other ways. For example, the embodiments of the structure described above are merely illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another structure, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, structure or unit, which can be electrical, mechanical or other forms.

[0094] The units described as separate components may or may not be physically separate, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple places. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0095] In addition, the functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware or software functional units.

[0096] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0097] The above content is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for evaluating the emergency repair response capability of a distribution network, characterized in that: The following steps are involved: Step 1: Extract fault features based on fault duration, resource configuration, and fault location in historical fault information; Step 2: Convert the fault feature extraction results into corresponding capability indicators; Step 3: Use the evaluation model to assign weights to the output capability indicators, and finally output the resource allocation rationality evaluation results. Then optimize the resource allocation plan based on the configuration rationality evaluation results. The step 1 comprises the following steps: A1: Fill in gaps and process outliers in the fault work order acceptance time and power restoration time information in historical fault information. Then, match the processed results with the fault month to obtain analysis charts of the average work order acceptance time and the average power restoration time. A2: Associate resource factors including power station personnel, emergency repair vehicles, and fault grids with the number of historical fault types; A3: Match the faulty distribution transformer information with the corresponding distribution transformer latitude and longitude information, and use the fault bias rate algorithm to calculate the fault bias rate. The step 2 comprises the following steps: B1: The work order acceptance index and fault repair index are calculated by the formula based on the work order acceptance time and fault repair time obtained by fault duration feature analysis; ,in, Represents the monthly work order acceptance time, Represents the weight value set according to the number of faults each month; fault repair index ,in, Represents the duration of power restoration each month, Represents the weight value set based on the number of failures each month; B2: Calculate resource allocation indicators ,in Represents the total number of failures per month, , Represents the inverse of the feeder fault ratio, Represents the inverse of the branch line fault ratio, Represents the inverse of the distribution transformer fault ratio, represents the number of feeder failures per month, Represents the number of branch line failures each month, Represents the number of distribution transformer failures each month, represents the number of faulty grids per month, represents the number of faulty grids per month, represents the number of faulty grids per month; B3: Calculate the fault bias index based on the fault bias rate algorithm. ,in, represents the monthly fault bias rate, Represents the weight value set based on the number of failures each month.

2. A distribution network emergency repair response capability evaluation method according to claim 1, characterized in that: The fault bias rate algorithm includes: Cluster the longitude and latitude information of distribution transformers and perform location information averaging on the clustering results; Calculate the distance between the typical fault location and the power supply station, distinguish the major and minor axes based on the distance, and obtain the fault bias rate.

3. A method for evaluating the emergency repair response capability of a distribution network according to claim 1, characterized in that: The step 3 comprises the following steps: C1: Build an evaluation model, including a three-layer evaluation indicator system consisting of the target layer, the criteria layer, and the solution layer. The resource coordination and coordination rational evaluation indicator system M includes the work order acceptance indicator M1, the fault repair indicator M2, the resource allocation indicator M3, and the fault bias indicator M4; C2: Compare and score the importance of each indicator at the same level of the resource coordination and coordination rationality evaluation index system relative to the indicators at the previous level based on the proportional scale table to construct a judgment matrix. Calculate the maximum eigenvalue λmax and eigenvector ξ of the judgment matrix A and perform a consistency test on the judgment matrix A. Determine the weight vector w of each indicator in the hierarchical structure model using Aξ=λmaxw. Correlate the weight vectors of the criterion-level judgment matrix and the solution-level judgment matrix to obtain the weight value of each point. C3: For the construction of judgment matrix, firstly establish a scale table, which consists of midpoint and It consists of two parts, The value of is a cardinality of judgment, or the mean of random variables within the judgment scale; secondly, the score is given according to the proportion comparison table to form the judgment criterion number , when indicator i is more important than indicator j, ; When indicator j is more important than indicator i, ; Obtain the corresponding elements of the judgment matrix A and The values ​​of are as follows: ； Where i=1,2,…,n; j=1,2,…,n; n refers to the total number of single-layer indicators; C4: Calculate the relative consistency coefficient CR of the judgment matrix A; ； Where n is the sum of the diagonal elements of matrix A. By querying the average random consistency index RI, the required RI value can be obtained for consistency verification. The consistency ratio coefficient CR is calculated: ； If the relative consistency coefficient CR of the judgment matrix A is less than 0.1, the judgment matrix A is considered feasible and has passed the consistency test. The smaller the CR value, the better. If the CR of the judgment matrix A is not less than 0.1 and has not passed the consistency test, the judgment process of the judgment matrix A is reversed and pairwise comparisons are repeated from the beginning to reconstruct a judgment matrix A that meets the requirements. C5: weight vector obtained by the criterion layer judgment matrix The weight vector obtained by the solution layer judgment matrix (i=1-m) Multiply the values ​​in the vector that contain the same attribute value, and then add up the weight values ​​to get the weight value of the specific point in the solution layer to the target layer. ; ； ； … ； Where n represents the dimension of the criterion layer judgment matrix; m represents the dimension of the solution layer judgment matrix; ； Among them, x i That is the weight value of the i-th indicator.

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