Low-voltage power distribution network fragile area identification method

Abstract

The invention relates to a low-voltage power distribution network fragile area identification method. The method comprises the steps of obtaining related power data of a low-voltage power distribution network; in combination with a complex network theory, constructing a node importance evaluation index model reflecting the structural vulnerability of the power distribution network, and performing calculation; constructing a state vulnerability evaluation index model reflecting the operation state of the power distribution network and calculating; performing weighting by using a weight analysis method, and performing weighting on the node importance evaluation index and the state vulnerability evaluation index to obtain subjective and objective weights of each index; coordinating subjective and objective weights according to Nash equilibrium of the game theory, and calculating a comprehensive weight; comprehensive vulnerability is calculated by using a TOPSIS algorithm improved by grey relational degree, and vulnerability grades are divided according to Euclidean distance; and constructing a routine and fault determination process, distinguishing routine vulnerability assessment and emergency vulnerability assessment, and outputting node, line and region vulnerability levels. The method can improve the accuracy of low-voltage power distribution network fragile area discrimination, thereby improving the reliability and safety of the power distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of low-voltage distribution network vulnerable area identification, and in particular to a low-voltage distribution network vulnerable area identification method. Background Art

[0002] The low-voltage distribution network is an important part of the power system, responsible for transmitting electricity from substations to end users. With the development of social economy and the acceleration of electrification, the load of low-voltage distribution networks has continued to increase. In particular, after a large number of distributed photovoltaic power generation and other renewable energy sources have been connected, the operation mode and structure of the distribution network have changed significantly. These changes have not only increased the complexity of distribution network operation, but also introduced more uncertainty and vulnerability.

[0003] Vulnerable areas usually refer to those areas where the load power is long-term overloaded, the high proportion of photovoltaic power generation is obvious, and the fault protection equipment is mainly applicable to the AC system. These areas often show problems such as voltage over-limit, high penetration of new energy, and failure of fault detection, which may lead to the failure of power supply lines and selective protection, thereby increasing the risk of permanent failure of the power supply system. Therefore, identifying and analyzing vulnerable areas in low-voltage distribution networks is of great significance to improving the reliability and safety of distribution networks.

[0004] Existing vulnerable area methods often target transmission networks, but the structural differences between transmission networks and distribution networks are large, making it difficult to establish an accurate model; and the operating status of distribution network nodes and lines has not yet been considered, and the effectiveness of vulnerable area determination needs to be further improved.

[0005] Therefore, a new method is needed to determine the vulnerable areas of the new low-voltage distribution network. Summary of the invention

[0006] The object of the present invention is to provide a method for identifying vulnerable areas of a low-voltage distribution network, which can improve the accuracy of the identification of vulnerable areas of the low-voltage distribution network, thereby improving the reliability and safety of the distribution network.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is: a method for identifying vulnerable areas of a low-voltage distribution network, comprising:

[0008] Obtain low-voltage distribution network node data, line data, transformer data, switch and circuit breaker data, load data, new energy access data, topology data, voltage data of each node of the low-voltage distribution network, current data of each line, and fault judgment data;

[0009] Combined with complex network theory, a node importance evaluation index model reflecting the structural vulnerability of the distribution network is constructed and calculated;

[0010] Construct and calculate the state vulnerability assessment index model that reflects the operating status of the distribution network;

[0011] The weight analysis method is used to assign weights to the node importance assessment index and the state vulnerability assessment index, and the subjective and objective weights of each index are obtained;

[0012] According to the Nash equilibrium of game theory, coordinate the subjective and objective weights and calculate the comprehensive weight;

[0013] The comprehensive vulnerability is calculated using the TOPSIS algorithm improved by grey correlation, and the vulnerability levels are divided according to the Euclidean distance.

[0014] Build routine and fault determination processes, distinguish routine vulnerability assessments from emergency vulnerability assessments, increase attention to vulnerable areas, and output node, line, and regional vulnerability levels.

[0015] Furthermore, the node importance evaluation index model reflecting the structural vulnerability of the distribution network is constructed and calculated by combining the complex network theory, including:

[0016] Complex network modeling of low voltage distribution networks:

[0017] Firstly, according to the topological data, the nodes, lines, interconnecting switches and transformers in the low-voltage distribution network are abstracted into a graph, and the nodes are divided into equivalent power supply nodes and equivalent load nodes, while the transmission lines and transformer branches are regarded as edges; then, the nodes and edges are labeled respectively, with the power supply node of the system as the initial node and the lines connected to the power supply nodes as the initial edges; considering that the resistance in the distribution network cannot be ignored compared with the reactance, the impedance modulus is set as the edge weight, and the shortest path between the equivalent power supply node and the equivalent load node is taken as the power supply path, and the distance between the nodes is the length corresponding to the shortest path between the two points; then, considering that the distribution network structure is sparse, if some nodes are removed, the network will be paralyzed, so the topology change is not considered. On this basis, a structural vulnerability index system is constructed;

[0018] The structural vulnerability index system includes improved node degree and centrality indexes;

[0019] The improved node degree is expressed as follows:

[0020]

[0021] In the formula, i represents the starting point of the line, j represents the end point of the line, and D Ni Denotes the improved node degree, D i represents the traditional node degree, represents the average degree of all nodes, V ad represents the set of all nodes connected to node i, D j represents the traditional node degree of node j;

[0022] The centrality is expressed as follows:

[0023]

[0024] In the formula, C i represents the centrality of node i; d ij Represents the total impedance of the shortest path between nodes i and j; N is the number of nodes in the set of all nodes.

[0025] Furthermore, a state vulnerability assessment index model reflecting the operating status of distribution network nodes and lines is constructed and calculated, including:

[0026] Input the voltage and current data of each node, as well as the transformer data;

[0027] Calculate the node voltage deviation, expressed as follows:

[0028]

[0029] Where U d,i is the voltage deviation of node i, is the voltage set of each phase at node i at time t;

[0030] Calculate the line overload rate, expressed as follows:

[0031]

[0032] Where, L PRi is the line overload rate, L Pi It is the ratio of actual active power of the line to the rated active power;

[0033] Calculate the transformer load factor, expressed as follows:

[0034]

[0035] Furthermore, the weight analysis method is used to assign weights to the node importance assessment index and the state vulnerability assessment index, and the subjective and objective weights of each index are obtained, including:

[0036] First, the subjective weight adopts the AHP method, and AHP uses the measurement scale 1, 3, 5, 7, and 9 to compare the indicators in pairs; among them, 1 means that A and B are equally important / prioritized, 3 means that A is slightly more important / prioritized than B, 5 means that A is generally more important / prioritized than B, 7 means that A is very important / prioritized than B, and 9 means that A is extremely important / prioritized than B; in addition to the values ​​of 1, 3, 5, 7, and 9, the intermediate value is also used to indicate the relative importance or preference between indicators, and the ratio between the two is formed in the AHP judgment matrix;

[0037] Next, a consistency test is performed, and the consistency index CI and consistency ratio CR are introduced. If CI < 0.1, the AHP judgment matrix passes the test; the consistency index CI and consistency ratio CR are expressed as follows:

[0038]

[0039] In the formula, λ max is the maximum eigenvalue; e is the order of the matrix, RI represents the random index of random matrix change;

[0040] Then, the objective weight calculation is performed; before that, the indicator data is preprocessed and normalized to the interval [0,1];

[0041] Positive indicators are processed as follows:

[0042]

[0043] In the formula, p = 1, 2, ..., z; q = 1, 2, ..., h; where p is the evaluation object, z is the number of nodes, q is the evaluation index, and h is the number of indicators; x pq represents the value of the p-th node on the q-th index in the original decision matrix, that is, the element in the original data matrix X; x p Indicates the value range of the pth indicator;

[0044] Negative indicators are handled as follows:

[0045]

[0046] The intermediate indicators are processed as follows:

[0047]

[0048] In the formula, H is the distance value, which represents the distance between the intermediate index and the optimal value x best The maximum deviation of x best is the best value close to the indicator; Yp represents the standardized indicator value;

[0049] Finally, using the standardized data, the standard deviation of each indicator is calculated, as follows:

[0050]

[0051] Where M pq is the element in the normalized decision matrix, that is, the standardized value of the p-th node on the q-th index; M p 、M q are the pth and qth columns of the matrix respectively; ζ jrepresents the standard deviation of the jth indicator, which is used to measure the volatility or dispersion of the indicator. The larger the standard deviation, the more information the indicator contains. pq represents the elements in the normalized matrix, represents the average value of the qth column in the matrix; z represents the number of rows in the matrix, that is, the number of nodes;

[0052] Calculate the amount of information contained in each indicator. The amount of information contained in the qth indicator is c q The calculation formula is as follows:

[0053]

[0054]

[0055] In the formula, E q is the calculated value of redundant information entropy; b pq is the proportion of the normalized index, which means the proportion of the standardized value of the p-th scheme on the q-th index to the total index; z represents the number of nodes; r pq is the correlation coefficient between different nodes;

[0056] Since the amount of information contained in each indicator is proportional to the weight of the indicator in the evaluation system, the amount of information is converted into weight, and the expression is as follows:

[0057]

[0058] Furthermore, according to the Nash equilibrium of game theory, the subjective and objective weights are coordinated to calculate the comprehensive weight, including:

[0059] Combined with the subjective weight w h1 , objective weight w h2 , and combine them into a weight set w h , perform linear optimization on it, and finally obtain the combined weight w j ; as shown below:

[0060] w h =(w h1 ,w h2 )

[0061]

[0062] In the formula, w a is the weight set; f w is the objective function, which indicates the value that needs to be minimized in the weight optimization process; α h is the linear combination coefficient, which is used to balance the influence of subjective weight and objective weight in comprehensive weighting.

[0063] Furthermore, the TOPSIS algorithm improved by grey correlation is used to calculate the comprehensive vulnerability, and the vulnerability levels are divided according to the Euclidean distance, including:

[0064] First, construct a weighted evaluation matrix and multiply the weights of the combined weights by the standardized matrix to obtain the standardized weighted evaluation matrix A hz , the expression is as follows:

[0065]

[0066]

[0067] In the formula, is the standardized evaluation matrix, which represents the standardized value of the p-th node on the q-th index; w j The combined weight obtained for calculation;

[0068] Compute the positive and negative ideal solutions as follows:

[0069]

[0070] In the formula, G + is a positive ideal solution, indicating the maximum value of each positive index; G - is a negative ideal, indicating the minimum value of each negative indicator; and denote the positive ideal solution and negative ideal solution of the qth index respectively;

[0071] Next, the grey relational degree is calculated as follows:

[0072]

[0073] In the formula, g pq Represents the elements in the weighted standardized matrix, that is, the weighted evaluation matrix A hz Elements of and Represent the grey correlation degree with positive ideal solution and negative ideal solution respectively; and are the corresponding values ​​in the positive and negative ideal solutions; ε is the resolution coefficient, which is used to adjust the sensitivity of the calculation;

[0074] Then, the grey relational degree-Euclidean distance measure is constructed as follows:

[0075]

[0076] In the formula, α1 and α2 are the preference coefficients for Euclidean distance and grey correlation degree, α1+α2=1; and Indicates the closeness to the positive and negative ideal solutions; and Represent the Euclidean distance from the positive and negative ideal solutions respectively; and Respectively represent the grey correlation degree with positive and negative ideal solutions;

[0077] Finally, calculate the closeness and distinguish the vulnerability levels according to the closeness. The closeness is expressed as follows:

[0078]

[0079] In the formula, is the closeness of the p-th node, with a value between 0 and 1. The closer the closeness is to 1, the better the solution is, that is, the closer it is to the ideal solution.

[0080] Furthermore, we build routine and fault determination processes, distinguish routine vulnerability assessment from emergency vulnerability assessment, increase the attention to vulnerable areas, and output node, line and regional vulnerability levels, including:

[0081] First, the corresponding data is obtained through the on-site detection terminal. When the fault detection unit detects a fault, the system immediately collects real-time data after the protection action, performs emergency vulnerability assessment, and quickly determines the system status. When the fault detection unit does not detect a fault, the system performs a regular vulnerability assessment at set intervals. If it is identified as a mildly vulnerable node or line at this time, the attention to it is increased, and it is identified again after a set time. If the node or line is still vulnerable, the vulnerability level is increased and observation continues, otherwise the vulnerability level is output.

[0082] Compared with the prior art, the present invention has the following beneficial effects: The present invention proposes a method for identifying vulnerable areas of low-voltage distribution networks. By combining complex network theory and operating status analysis, the method can not only determine the vulnerable areas of the distribution network from a topological perspective, but also determine the vulnerable areas based on the operating status. In terms of the weighting method, the analytic hierarchy process is combined to determine the subjective weights; the CRITIC method is used to determine the objective weights; and the subjective and objective weights are combined through game theory to ensure the rationality of the weights; the TOPSIS method is used to calculate the vulnerability of each node and line, and to divide the vulnerability level; the fault and non-fault discrimination modes are distinguished to realize the dynamic monitoring function. This method can not only dynamically monitor and evaluate the operating status of the distribution network, but also scientifically and rationally identify the vulnerable areas in the distribution network, thereby improving the reliability and safety of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 is a flow chart of the identification of vulnerable areas of a low voltage distribution network in an embodiment of the present invention;

[0084] Figure 2 is a flow chart of routine and fault determination in an embodiment of the present invention;

[0085] Figure 3 4 is a topological diagram of a distribution network according to an embodiment of the present invention. DETAILED DESCRIPTION

[0086] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0087] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0088] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0089] like Figure 1 As shown, this embodiment provides a method for identifying vulnerable areas of a low-voltage distribution network to identify vulnerable areas of the distribution network, including the following steps:

[0090] S1. Obtain low-voltage distribution network node data, line data, transformer data, switch and circuit breaker data, load data, new energy access data, topology data, voltage data of each node of the low-voltage distribution network, current data of each line, and fault judgment data.

[0091] S2. Combining complex network theory, a node importance evaluation index model that reflects the vulnerability of the distribution network structure is constructed and calculated. The specific implementation method is as follows.

[0092] Complex network modeling of low voltage distribution networks:

[0093] Firstly, according to the topological data, the nodes, lines, interconnecting switches and transformers in the low-voltage distribution network are abstracted into a graph, and the nodes are divided into equivalent power supply nodes and equivalent load nodes, while the transmission lines and transformer branches are regarded as edges. Then, the nodes and edges are labeled respectively, with the power supply node of the system as the initial node and the lines connected to the power supply nodes as the initial edges. Considering that the resistance in the distribution network cannot be ignored compared with the reactance, the impedance modulus is set as the edge weight, and the shortest path between the equivalent power supply node and the equivalent load node is taken as the power supply path, and the distance between the nodes is the length corresponding to the shortest path between the two points. Then, considering that the distribution network structure is sparse, if some nodes are removed, the network will be paralyzed, so the topology change is not considered. On this basis, a structural vulnerability index system is constructed.

[0094] The structural vulnerability index system includes improved node degree and centrality indicators.

[0095] The improved node degree is expressed as follows:

[0096]

[0097] In the formula, i represents the starting point of the line, j represents the end point of the line, and D Ni Denotes the improved node degree, D i represents the traditional node degree, represents the average degree of all nodes, V ad represents the set of all nodes connected to node i, D j represents the traditional node degree of node j.

[0098] The centrality is expressed as follows:

[0099]

[0100] In the formula, C i represents the centrality of node i; d ij Represents the total impedance of the shortest path between nodes i and j; N is the number of nodes in the set of all nodes.

[0101] S3. Construct and calculate the state vulnerability assessment index model reflecting the operation state of the distribution network. The specific implementation method is as follows.

[0102] Input the voltage and current data of each node, as well as the transformer data.

[0103] Calculate the node voltage deviation, expressed as follows:

[0104]

[0105] Where U d,i is the voltage deviation of node i, is the voltage set of each phase at node i at time t.

[0106] Calculate the line overload rate, expressed as follows:

[0107]

[0108] Where, L PRi is the line overload rate, L Pi It is the ratio of actual active power of the line to the rated active power;

[0109] Calculate the transformer load factor, expressed as follows:

[0110]

[0111] S4. Use the weight analysis method to assign weights to the node importance assessment index and the state vulnerability assessment index to obtain the subjective and objective weights of each index. The specific implementation method is as follows.

[0112] First, the subjective weights were calculated using the AHP method, which used measurement scales 1, 3, 5, 7, and 9 as shown in Table 1 to perform pairwise comparisons between indicators.

[0113] Table 1 AHP ​​measurement scale correspondence table

[0114]

[0115] In addition to the values ​​of 1, 3, 5, 7, and 9 shown in Table 1, intermediate values ​​are also used to represent the relative importance or preference between indicators, and the ratio between the two is formed in the AHP judgment matrix.

[0116] Next, a consistency test is performed, and the consistency index CI and consistency ratio CR are introduced. If CI < 0.1, the AHP judgment matrix passes the test; the consistency index CI and consistency ratio CR are expressed as follows:

[0117]

[0118] In the formula, λ max is the maximum eigenvalue; e is the order of the matrix, and RI represents the random index of random matrix change.

[0119] Then, the objective weight calculation is performed. Before that, the indicator data is preprocessed and normalized to the interval [0,1];

[0120] Positive indicators are processed as follows:

[0121]

[0122] In the formula, p = 1, 2, ..., z; q = 1, 2, ..., h; where p is the evaluation object, z is the number of nodes, q is the evaluation index, and h is the number of indicators; x pq represents the value of the p-th node on the q-th index in the original decision matrix, that is, the element in the original data matrix X; x p Indicates the value range of the pth indicator.

[0123] Negative indicators are handled as follows:

[0124]

[0125] The intermediate indicators are processed as follows:

[0126]

[0127] In the formula, H is the distance value, which represents the distance between the intermediate index and the optimal value x best The maximum deviation of x best is the optimal value that the indicator approaches; Yp represents the standardized indicator value.

[0128] Finally, using the standardized data, the standard deviation of each indicator is calculated, as follows:

[0129]

[0130] Where M pq is the element in the normalized decision matrix, that is, the standardized value of the p-th node on the q-th index; M p 、M q are the pth and qth columns of the matrix respectively; ζ j Represents the standard deviation of the jth indicator, which is used to measure the volatility or dispersion of the indicator. The larger the standard deviation, the more information the indicator contains. pq represents the elements in the normalized matrix, represents the average value of the qth column in the matrix; z represents the number of rows in the matrix, which is the number of nodes here.

[0131] Calculate the amount of information contained in each indicator. The amount of information contained in the qth indicator is c q The calculation formula is as follows:

[0132]

[0133] In the formula, E q is the calculated value of redundant information entropy; b pq is the proportion of the normalized index, which means the proportion of the standardized value of the p-th scheme on the q-th index to the total index; z represents the number of nodes; r pq is the correlation coefficient between different nodes.

[0134] Since the amount of information contained in each indicator is proportional to the weight of the indicator in the evaluation system, the amount of information is converted into weight, and the expression is as follows:

[0135]

[0136] S5. According to the Nash equilibrium of game theory, coordinate the subjective and objective weights and calculate the comprehensive weight. The specific implementation method is as follows.

[0137] Combined with the subjective weight w h1 , objective weight w h2 , and combine them into a weight set w h , perform linear optimization on it, and finally obtain the combined weight w j ; as shown below:

[0138] w h =(w h1 ,w h2 )

[0139]

[0140] In the formula, w a is the weight set; f w is the objective function, which indicates the value that needs to be minimized in the weight optimization process; α h is the linear combination coefficient, which is used to balance the influence of subjective weight and objective weight in comprehensive weighting.

[0141] S6. TOPSIS algorithm improved by grey correlation is used to calculate comprehensive vulnerability, and vulnerability levels are divided according to Euclidean distance. The specific implementation method is as follows.

[0142] First, construct a weighted evaluation matrix and multiply the weights of the combined weights by the standardized matrix to obtain the standardized weighted evaluation matrix A hz , the expression is as follows:

[0143]

[0144]

[0145] In the formula, is the standardized evaluation matrix, which represents the standardized value of the p-th node on the q-th index; w j The combined weight obtained for calculation.

[0146] Compute the positive and negative ideal solutions as follows:

[0147]

[0148] In the formula, G + is a positive ideal solution, indicating the maximum value of each positive index; G - is a negative ideal, indicating the minimum value of each negative indicator; and They represent the positive ideal solution and negative ideal solution of the qth index respectively.

[0149] Next, the grey relational degree is calculated as follows:

[0150]

[0151] In the formula, g pq Represents the elements in the weighted standardized matrix, that is, the weighted evaluation matrix A hz Elements of and Represent the grey correlation degree with positive ideal solution and negative ideal solution respectively; and are the corresponding values ​​in the positive and negative ideal solutions; ε is the resolution coefficient, which is generally taken as 0.5 and is used to adjust the sensitivity of the calculation.

[0152] Then, the grey relational degree-Euclidean distance measure is constructed as follows:

[0153]

[0154] In the formula, α1 and α2 are the preference coefficients for Euclidean distance and grey correlation degree, α1+α2=1; and Indicates the closeness to the positive and negative ideal solutions; and Represent the Euclidean distance from the positive and negative ideal solutions respectively; and Represent the grey correlation degree with positive and negative ideal solutions respectively.

[0155] Finally, calculate the closeness and distinguish the vulnerability levels according to the closeness. The closeness is expressed as follows:

[0156]

[0157] In the formula, is the closeness of the p-th node, with a value between 0 and 1. The closer the closeness is to 1, the better the solution is, that is, the closer it is to the ideal solution.

[0158] S7. Construct routine and fault determination processes, distinguish routine vulnerability assessment from emergency vulnerability assessment, increase the attention to vulnerable areas, and output node, line and regional vulnerability levels. The specific implementation method is as follows.

[0159] like Figure 2 As shown, first, the corresponding data is obtained through the on-site detection terminal. When the fault detection unit detects a fault, the system immediately collects real-time data after the protection action, performs emergency vulnerability assessment, and quickly determines the system status; when the fault detection unit does not detect a fault, the system performs a regular vulnerability assessment every hour. If it is identified as a slightly vulnerable node or line at this time, the attention to it will be increased, and it will be identified again after half an hour. If the node or line is still vulnerable, the vulnerability level will be increased and observation will continue, otherwise the vulnerability level will be output.

[0160] In this embodiment, the topology of the distribution network is as follows: Figure 3 As shown, it includes transformers, transmission lines, tie switches, and load units. The identification of vulnerable areas of the distribution network includes the following steps:

[0161] S1. Obtain low-voltage distribution network node data, line data, transformer data, switch and circuit breaker data, load data, new energy access data, topology data, voltage data of each node of the low-voltage distribution network, current data of each line, and fault judgment data. In this embodiment, the total active power of the load is 3490kW, the total reactive power is 1920kVar, the voltage level is 4.16kV, the reference power is 1MVA, and 150 nodes are connected to the upper power grid.

[0162] S2. Preprocess the distribution network topology and calculate its improved node degree, centrality, voltage peak, line overload rate and transformer load rate.

[0163] S3. Normalize the existing data, construct a judgment matrix, calculate the subjective weight of each indicator, calculate the indicator contrast, calculate the objective weight of each indicator, and use the Nash equilibrium theory of game theory to linearly optimize the subjective and objective weights to obtain the combined weight.

[0164] S4. Determine the positive and negative optimal solutions, calculate the grey correlation degree-Euclidean distance, and distinguish the vulnerability levels according to the degree of proximity.

[0165] S5. No fault is set, so conventional vulnerability judgment is performed to obtain the vulnerability level of each node and line.

[0166] Construct node importance assessment index model and operation status assessment index model for distribution network structure vulnerability.

[0167] A1. Complex network modeling of low-voltage distribution network and construction of index system:

[0168] A11. Based on the topological data, the nodes, lines, interconnecting switches and transformers in the low-voltage distribution network are abstracted into a graph, the nodes are divided into equivalent power nodes and equivalent load nodes, and the transmission lines and transformer branches are regarded as edges.

[0169] A12. Label the nodes and edges respectively, with the power supply node of the system as the initial node and the line connected to the power supply node as the initial edge. Considering that the resistance in the distribution network cannot be ignored compared with the reactance, the impedance modulus is set as the edge weight, and the shortest path between the equivalent power supply node and the equivalent load node is used as the power supply path. The distance between the nodes is the length corresponding to the shortest path between the two points.

[0170] A13. Considering the sparse structure of the distribution network, if some nodes are removed, the network will be paralyzed. Therefore, the topology change is not considered. On this basis, a structural vulnerability index system is constructed.

[0171] A14. Improved node degree is expressed as follows:

[0172]

[0173] In the formula, i represents the starting point of the line, j represents the end point of the line, and D Ni Denotes the improved node degree, D i represents the traditional node degree, represents the average degree of all nodes, V ad represents the set of all nodes connected to node i, D j represents the traditional node degree of node j.

[0174] The centrality is expressed as follows:

[0175]

[0176] Where, d ij Represents the total impedance of the shortest path between nodes i and j. i represents the centrality of node i; N is the set of all nodes.

[0177] A2. Construction of operating status evaluation indicator system:

[0178] A21, node voltage deviation is expressed as follows:

[0179]

[0180] In the formula, is the voltage set of each phase at node i at time t; U d,i Represents the voltage deviation of node i.

[0181] A22. Line overload rate is expressed as follows:

[0182]

[0183] Where, L Pi It is the ratio of the actual active power of the line to the rated active power. PRi is the line overload rate.

[0184] A23. Transformer load factor is expressed as follows:

[0185]

[0186] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0187] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes 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.

[0188] 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 operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including 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.

[0189] 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.

[0190] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any technician familiar with the profession may use the above disclosed technical content to change or modify it into an equivalent embodiment with equivalent changes. However, any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the technical solution of the present invention still belongs to the protection scope of the technical solution of the present invention.

Claims

1. A method for identifying vulnerable areas of a low voltage distribution network, characterized in that: include: Obtain low-voltage distribution network node data, line data, transformer data, switch and circuit breaker data, load data, new energy access data, topology data, voltage data of each node of the low-voltage distribution network, current data of each line, and fault judgment data; Combined with complex network theory, a node importance evaluation index model reflecting the structural vulnerability of the distribution network is constructed and calculated; Construct and calculate the state vulnerability assessment index model that reflects the operating status of the distribution network; The weight analysis method is used to assign weights to the node importance assessment index and the state vulnerability assessment index, and the subjective and objective weights of each index are obtained; According to the Nash equilibrium of game theory, coordinate the subjective and objective weights and calculate the comprehensive weight; The comprehensive vulnerability was calculated using the TOPSIS algorithm improved by grey correlation, and the vulnerability levels were divided according to the Euclidean distance. Build routine and fault determination processes, distinguish routine vulnerability assessments from emergency vulnerability assessments, increase attention to vulnerable areas, and output node, line, and regional vulnerability levels.

2. A method for identifying vulnerable areas of a low-voltage distribution network according to claim 1, characterized in that: The node importance evaluation index model reflecting the structural vulnerability of the distribution network is constructed and calculated by combining complex network theory, including: Complex network modeling of low voltage distribution networks: Firstly, according to the topological data, the nodes, lines, interconnecting switches and transformers in the low-voltage distribution network are abstracted into a graph, and the nodes are divided into equivalent power supply nodes and equivalent load nodes, while the transmission lines and transformer branches are regarded as edges; then, the nodes and edges are labeled respectively, with the power supply node of the system as the initial node and the lines connected to the power supply nodes as the initial edges; considering that the resistance in the distribution network cannot be ignored compared with the reactance, the impedance modulus is set as the edge weight, and the shortest path between the equivalent power supply node and the equivalent load node is taken as the power supply path, and the distance between the nodes is the length corresponding to the shortest path between the two points; then, considering that the distribution network structure is sparse, if some nodes are removed, the network will be paralyzed, so the topology change is not considered. On this basis, a structural vulnerability index system is constructed; The structural vulnerability index system includes improved node degree and centrality indexes; The improved node degree is expressed as follows: In the formula, i represents the starting point of the line, j represents the end point of the line, and D Ni Denotes the improved node degree, D i represents the traditional node degree, represents the average degree of all nodes, V ad represents the set of all nodes connected to node i, D j represents the traditional node degree of node j; The centrality is expressed as follows: In the formula, C i represents the centrality of node i; d ij Represents the total impedance of the shortest path between nodes i and j; N is the number of nodes in the set of all nodes.

3. A method for identifying vulnerable areas of a low-voltage distribution network according to claim 1, characterized in that: Construct and calculate the state vulnerability assessment index model that reflects the operating status of distribution network nodes and lines, including: Input the voltage and current data of each node, as well as the transformer data; Calculate the node voltage deviation, expressed as follows: Where U d,i is the voltage deviation of node i, is the voltage set of each phase at node i at time t; Calculate the line overload rate, expressed as follows: Where, L PRi is the line overload rate, L Pi It is the ratio of actual active power of the line to the rated active power; Calculate the transformer load factor, expressed as follows:

4. A method for identifying vulnerable areas of a low-voltage distribution network according to claim 1, characterized in that: The weight analysis method is used to assign weights to the node importance assessment index and the state vulnerability assessment index, and the subjective and objective weights of each index are obtained, including: First, the subjective weight adopts the AHP method, and AHP uses the measurement scale 1, 3, 5, 7, and 9 to compare the indicators in pairs; among them, 1 means that A and B are equally important / prioritized, 3 means that A is slightly more important / prioritized than B, 5 means that A is generally more important / prioritized than B, 7 means that A is very important / prioritized than B, and 9 means that A is extremely important / prioritized than B; in addition to the values ​​of 1, 3, 5, 7, and 9, the intermediate value is also used to indicate the relative importance or preference between indicators, and the ratio between the two is formed in the AHP judgment matrix; Next, a consistency test is performed, and the consistency index CI and consistency ratio CR are introduced. If CI < 0.1, the AHP judgment matrix passes the test; the consistency index CI and consistency ratio CR are expressed as follows: In the formula, λ max is the maximum eigenvalue; e is the order of the matrix, RI represents the random index of random matrix change; Then, the objective weight calculation is performed; before that, the indicator data is preprocessed and normalized to the interval [0,1]; Positive indicators are processed as follows: In the formula, p = 1, 2, ..., z; q = 1, 2, ..., h; where p is the evaluation object, z is the number of nodes, q is the evaluation index, and h is the number of indicators; x pq represents the value of the p-th node on the q-th index in the original decision matrix, that is, the element in the original data matrix X; x p Indicates the value range of the pth indicator; Negative indicators are handled as follows: The intermediate indicators are processed as follows: In the formula, H is the distance value, which represents the distance between the intermediate index and the optimal value x best The maximum deviation of x best is the best value close to the indicator; Yp represents the standardized indicator value; Finally, using the standardized data, the standard deviation of each indicator is calculated, as follows: Where M pq is the element in the normalized decision matrix, that is, the standardized value of the p-th node on the q-th index; M p 、M q are the pth and qth columns of the matrix respectively; ζ j represents the standard deviation of the jth indicator, which is used to measure the volatility or dispersion of the indicator. The larger the standard deviation, the more information the indicator contains. pq represents the elements in the normalized matrix, represents the average value of the qth column in the matrix; z represents the number of rows in the matrix, that is, the number of nodes; Calculate the amount of information contained in each indicator. The amount of information contained in the qth indicator is c q The calculation formula is as follows: In the formula, E q is the calculated value of redundant information entropy; b pq is the proportion of the normalized index, which means the proportion of the standardized value of the p-th scheme on the q-th index to the total index; z represents the number of nodes; r pq is the correlation coefficient between different nodes; Since the amount of information contained in each indicator is proportional to the weight of the indicator in the evaluation system, the amount of information is converted into weight, and the expression is as follows:

5. A method for identifying vulnerable areas of a low-voltage distribution network according to claim 1, characterized in that: According to the Nash equilibrium of game theory, the subjective and objective weights are coordinated and the comprehensive weight is calculated, including: Combined with the subjective weight w h1 , objective weight w h2 , and combine them into a weight set w h , perform linear optimization on it, and finally obtain the combined weight w j ; as shown below: In h =(in h1 ,In h2 ) In the formula, w a is the weight set; f w is the objective function, which indicates the value that needs to be minimized in the weight optimization process; α h is the linear combination coefficient, which is used to balance the influence of subjective weight and objective weight in comprehensive weighting.

6. A method for identifying vulnerable areas of a low voltage distribution network according to claim 1, characterized in that: The comprehensive vulnerability is calculated using the TOPSIS algorithm improved by grey correlation, and the vulnerability levels are divided according to the Euclidean distance, including: First, construct a weighted evaluation matrix, multiply the weights of the combined weights by the standardized matrix to obtain the standardized weighted evaluation matrix A hz , the expression is as follows: In the formula, is the standardized evaluation matrix, which represents the standardized value of the p-th node on the q-th index; w j The combined weight obtained for calculation; Compute the positive and negative ideal solutions as follows: In the formula, G + is a positive ideal solution, indicating the maximum value of each positive index; G - is a negative ideal, indicating the minimum value of each negative indicator; and denote the positive ideal solution and negative ideal solution of the qth index respectively; Next, the grey relational degree is calculated as follows: In the formula, g pq Represents the elements in the weighted standardized matrix, that is, the weighted evaluation matrix A hz Elements of and Represent the grey correlation degree with positive ideal solution and negative ideal solution respectively; and are the corresponding values ​​in the positive and negative ideal solutions; ε is the resolution coefficient, which is used to adjust the sensitivity of the calculation; Then, the grey relational degree-Euclidean distance measure is constructed as follows: In the formula, α1 and α2 are the preference coefficients for Euclidean distance and grey correlation degree, α1+α2=1; and Indicates the closeness to the positive and negative ideal solutions; and Represent the Euclidean distance from the positive and negative ideal solutions respectively; and Respectively represent the grey correlation degree with positive and negative ideal solutions; Finally, calculate the closeness and distinguish the vulnerability levels according to the closeness. The closeness is expressed as follows: In the formula, is the closeness of the p-th node, with a value between 0 and 1. The closer the closeness is to 1, the better the solution is, that is, the closer it is to the ideal solution.

7. A method for identifying vulnerable areas of a low-voltage distribution network according to claim 1, characterized in that: Build routine and fault determination processes, distinguish routine vulnerability assessment from emergency vulnerability assessment, increase attention to vulnerable areas, and output node, line and regional vulnerability levels, including: First, the corresponding data is obtained through the on-site detection terminal. When the fault detection unit detects a fault, the system immediately collects real-time data after the protection action, performs emergency vulnerability assessment, and quickly determines the system status. When the fault detection unit does not detect a fault, the system performs a regular vulnerability assessment at set intervals. If it is identified as a mildly vulnerable node or line at this time, the attention to it is increased, and it is identified again after a set time. If the node or line is still vulnerable, the vulnerability level is increased and observation continues, otherwise the vulnerability level is output.

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