Power distribution network weak node identification method and device based on short circuit parameters and medium

Abstract

The invention relates to the technical field of power systems and automation thereof, and discloses a power distribution network weak node identification method and device based on short-circuit parameters and a medium, and the method comprises the steps: building a multi-dimensional evaluation system, collecting electrical parameters, topological parameters and operation parameters of power distribution network nodes, carrying out the standardization processing of the collected original data, and obtaining a power distribution network weak node identification result; and dimension difference is eliminated. Based on the standardized data, calculating a short-circuit parameter related index, a voltage stability index and a network topology strength index of the node; and determining a weight coefficient of each evaluation index by adopting an objective weighting method, and establishing a weighted evaluation model. A dynamic response model based on frequency deviation is introduced, and the dynamic response characteristics of the nodes under the disturbance condition are evaluated. And according to the propagation characteristics of the fault in the network, the influence degree of the fault on adjacent nodes is analyzed, and a comprehensive evaluation model is generated. According to the method, accurate identification of weak nodes is realized by establishing a complete node strength evaluation index system and combining short-circuit parameter characteristics.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power systems and their automation, and in particular to a weak node identification method for distribution networks based on short-circuit parameters, a device and a medium. BACKGROUND

[0002] Weak node identification for distribution networks is an important part of power grid planning and operation. Traditional identification methods are mainly based on voltage stability analysis and power flow calculation, and do not fully consider the impact of short-circuit faults on the system. Existing short-circuit analysis methods focus on fault point characteristics, ignoring the influence of network topology structure and node interaction. In addition, the current evaluation index system is relatively single and difficult to fully reflect the weakness of the nodes. SUMMARY

[0003] In view of the above existing problems, the present application is proposed.

[0004] Therefore, the present application provides a weak node identification method for distribution networks based on short-circuit parameters, which can solve the problems of incomplete indicators, single calculation method and insufficient consideration of dynamic characteristics in weak node identification for distribution networks.

[0005] To solve the above technical problems, the present application provides the following technical solutions. A weak node identification method for distribution networks based on short-circuit parameters, comprising: establishing a multi-dimensional evaluation system for comprehensive evaluation of the strength characteristics of the nodes in the distribution network; collecting electrical parameters, topological parameters and operating parameters of the nodes in the distribution network, and standardizing the collected raw data to eliminate dimensional differences; based on the standardized data, calculating the short-circuit parameter related indicators, voltage stability indicators and network topology strength indicators of the nodes respectively; determining the weight coefficients of each evaluation indicator by using an objective weighting method, and establishing a weighted evaluation model; introducing a dynamic response model based on frequency deviation to evaluate the dynamic response characteristics of the nodes under disturbance conditions; according to the propagation characteristics of the fault in the network, analyzing the influence degree of the fault on the adjacent nodes, and generating a comprehensive evaluation model.

[0006] As a preferred scheme of the weak node identification method for distribution networks based on short-circuit parameters, the multi-dimensional evaluation system includes short-circuit parameter indicators reflecting the fault resistance of the nodes, operating parameter indicators reflecting the voltage stability, and topological feature indicators reflecting the importance of the network structure, thereby obtaining a multi-dimensional evaluation index system covering electrical characteristics, operating state and network structure.

[0007] As a preferred embodiment of the distribution network weak node identification method based on short-circuit parameters described in this invention, the step of determining the weight coefficients of each evaluation index using an objective weighting method includes calculating the information entropy of each index data and allocating weight coefficients according to the information entropy value to obtain a weight set that is inversely proportional to the index distinguishability.

[0008] As a preferred embodiment of the distribution network weak node identification method based on short-circuit parameters described in this invention, the establishment of the weighted evaluation model includes analyzing and quantifying the dynamic response characteristics of nodes and the impact of fault propagation to obtain an enhanced comprehensive evaluation index system that includes dynamic behavior and fault propagation effects.

[0009] As a preferred embodiment of the short-circuit parameter-based method for identifying weak nodes in a distribution network according to the present invention, the short-circuit parameter related indicators of the calculated node include: short-circuit capacity, which is a fundamental indicator for measuring node strength and directly reflects the node's ability to withstand fault conditions; considering the relationship between short-circuit capacity and node impedance, the following calculation model is established.

[0010]

[0011] In the formula, V n Z is the node rated voltage. ii Let be the self-impedance of node i; the smaller the self-impedance value, the larger the short-circuit capacity of the node and the stronger its fault resistance.

[0012] The voltage stability indicators include:

[0013]

[0014] In the formula, ΔV i V represents the node voltage deviation. n For the rated voltage, S i S represents the actual load of the node. n This is the system's baseline capacity;

[0015] The topology strength index includes:

[0016] TS i =α·BC i +β·CC i

[0017] In the formula, BC i CC represents the betweenness centrality of nodes. i Let α and β be the node compact centrality, and let α + β = 1.

[0018] As a preferred embodiment of the distribution network weak node identification method based on short-circuit parameters described in this invention, the dynamic response characteristics of the evaluated node under disturbance conditions include: establishing a normalized weakness assessment model based on the node strength index, and mapping the assessment results to the [0,1] interval through linear transformation.

[0019]

[0020] In the formula, WI i SI is the weakness index of node i. i SI is the node strength index. max SI min These are the maximum and minimum values ​​of the strength index for all nodes, respectively.

[0021] As a preferred embodiment of the distribution network weak node identification method based on short-circuit parameters described in this invention, the analysis of the impact of faults on adjacent nodes includes evaluating the dynamic response characteristics of nodes under disturbance conditions, introducing a dynamic response model based on frequency deviation, the model adopting a PD control structure, and considering both the instantaneous value and rate of change of the frequency deviation.

[0022]

[0023] In the formula, DR i (t) represents the dynamic response value of node i at time t, Δf i (t) represents the frequency deviation, indicating the difference between the actual frequency and the rated frequency; K p K is the proportional coefficient, representing the response strength of the control system to frequency deviation; d The differential coefficient is used to suppress frequency fluctuations, and is adjusted by K. p and K d The value of is used to optimize the dynamic response characteristics of nodes.

[0024] As a preferred embodiment of the distribution network weak node identification method based on short-circuit parameters described in this invention, the generation of the comprehensive evaluation model includes, based on the aforementioned indicators, constructing a comprehensive evaluation model that incorporates node weakness, dynamic response, and fault propagation impact.

[0025]

[0026] In the formula, CE i WI is the comprehensive evaluation value of node i. i DR is a weak point index for nodes. i For dynamic response characteristic values, μ1 and μ2 are the sum of the fault propagation effects of all adjacent nodes on node i, and are the balance coefficients used to adjust the weight of different characteristic indicators in the evaluation.

[0027] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a method for identifying weak nodes in a distribution network based on short-circuit parameters.

[0028] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a method for identifying weak nodes in a distribution network based on short-circuit parameters.

[0029] The beneficial effects of this invention are as follows: This invention not only considers static characteristics but also incorporates the effects of dynamic response and fault propagation, making the evaluation results more comprehensive and reliable. In particular, the entropy weighting method, used in the weight determination stage, avoids the uncertainty of subjective weighting, improving the objectivity of the evaluation results. Each step of the algorithm is supported by a clear mathematical model, ensuring the repeatability and verifiability of the results. In practical applications, the composition of the evaluation indicators and the weight allocation can be adjusted according to specific needs, making the method highly adaptable. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a schematic flowchart of a method for identifying weak nodes in a distribution network based on short-circuit parameters, provided as an embodiment of the present invention. Detailed Implementation

[0032] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0033] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a method for identifying weak nodes in a distribution network based on short-circuit parameters, including:

[0034] S1: Establish a multi-dimensional evaluation system to comprehensively assess the strength characteristics of distribution network nodes.

[0035] S2: Collect electrical parameters, topology parameters, and operating parameters of distribution network nodes, and standardize the collected raw data to eliminate dimensional differences.

[0036] S3: Based on the standardized data, calculate the short-circuit parameter related indicators, voltage stability indicators, and network topology strength indicators of the nodes respectively; use an objective weighting method to determine the weight coefficients of each evaluation indicator and establish a weighted evaluation model.

[0037] S4: Introduce a dynamic response model based on frequency deviation to evaluate the dynamic response characteristics of nodes under disturbance conditions.

[0038] S5: Based on the propagation characteristics of faults in the network, analyze the degree of impact of faults on adjacent nodes and generate a comprehensive evaluation model.

[0039] It should be noted that this embodiment provides an intelligent identification and quantitative assessment method for weak nodes in distribution networks based on multi-dimensional short-circuit parameter analysis and dynamic characteristic fusion. Its core function is to systematically solve the fundamental defects of traditional methods, which rely on a single evaluation dimension and primarily static analysis. By integrating multi-dimensional indicators such as electrical characteristics, topological attributes, and dynamic response, and employing an objective weighting method, this invention achieves accurate and quantitative assessment of node weakness, significantly improving the comprehensiveness and objectivity of the identification results. Simultaneously, this method innovatively introduces dynamic response and fault propagation analysis, enhancing the ability to judge node behavior under complex fault scenarios, and providing advanced and reliable technical support for the safe and stable operation and precise maintenance of distribution networks.

[0040] Example 2 is an embodiment of the present invention. Based on the above embodiment, a method for identifying weak nodes in a distribution network based on short-circuit parameters is provided.

[0041] Furthermore, in this embodiment of the application, step S1 establishes a multi-dimensional evaluation system to comprehensively assess the strength characteristics of distribution network nodes. Specific steps include:

[0042] To comprehensively evaluate the strength characteristics of distribution network nodes, this method establishes a multi-dimensional evaluation index system. This system comprehensively considers the electrical characteristics, topological features, and dynamic response capabilities of nodes, and constructs a node strength index through a weighted combination method:

[0043] SI i =ω1SC i +ω2VS i +ω3TS i

[0044] In the formula, SI i SC is the intensity exponent of node i, dimensionless, reflecting the node's overall disturbance resistance capability; iVS is a short-circuit capacity indicator, measured in MVA, which characterizes a node's ability to withstand faults. i TS is a dimensionless voltage stability index that reflects the stability of node voltages. i ω1, ω2, and ω3 are the topology strength indices, dimensionless, representing the importance of a node in the network structure; ω1, ω2, and ω3 are the corresponding weight coefficients, satisfying ω1 + ω2 + ω3 = 1. The values ​​of the weight coefficients are determined using the analytic hierarchy process (AHP), taking into account the relative importance of each index.

[0045] In this embodiment, a node strength index is constructed by weighted combination to establish a multi-dimensional evaluation system;

[0046] In an optional embodiment, the establishment of a multi-dimensional evaluation system can also be achieved through the direct weighting method based on expert experience. Specifically, fixed weighting coefficients are pre-set for the three indicators of short-circuit capacity, voltage stability, and topology strength, based on industry standards, historical operating experience, and the unified judgment of the domain expert team.

[0047] In another optional embodiment, the establishment of a multi-dimensional evaluation system can also be achieved by the equal weighted average method. Specifically, the evaluation dimensions are set to have equal importance, and the standardized short-circuit capacity index, voltage stability index and topology strength index are arithmetically averaged, and the resulting average value is used as the strength index of the node.

[0048] Furthermore, in this embodiment, step S2 collects the electrical parameters, topology parameters, and operating parameters of the distribution network nodes, and standardizes the collected raw data to eliminate dimensional differences. Specific steps include:

[0049] Standardize the raw data to eliminate the influence of dimensions:

[0050]

[0051] In the formula, X norm X represents the standardized data; X represents the original data; X max X min These represent the maximum and minimum values ​​of the dataset, respectively. Standardization allows for direct comparison of metrics with different dimensions.

[0052] In this embodiment, the original data is standardized using Min-Max standardization;

[0053] In an optional embodiment, the standardization of the raw data can also be achieved through Z-Score standardization. Specifically, the arithmetic mean and standard deviation of the parameters in the raw data are calculated, and each raw data value is subtracted from the mean and then divided by the standard deviation. After this processing, the data is transformed into a distribution with a mean of zero and a standard deviation of one, thereby eliminating dimensions and bringing different indicators to the same order of magnitude.

[0054] In another alternative embodiment, the standardization of the original data can also be achieved through decimal scaling standardization. Specifically, the maximum absolute value of the parameters in the original data is determined, and a corresponding power of 10 is selected as the base based on its order of magnitude. Each original data value is then divided by this base. This method maps the data to the interval [-1, 1], achieving dimensional uniformity.

[0055] Based on standardized data, various evaluation indicators are calculated, including short-circuit capacity, voltage stability, and topology strength, and then normalized.

[0056] Furthermore, in this embodiment, step S3 calculates the short-circuit parameter related indicators, voltage stability indicators, and network topology strength indicators of the nodes based on the standardized data; the weight coefficients of each evaluation indicator are determined using an objective weighting method, and a weighted evaluation model is established. Specific steps include S31-S35:

[0057] S31: Short-circuit capacity is a fundamental indicator for measuring node strength, directly reflecting the node's ability to withstand fault conditions. Considering the relationship between short-circuit capacity and node impedance, the following calculation model is established:

[0058]

[0059] In the formula, V n The rated voltage of the node is expressed in kV, typically 10kV or 35kV; Z ii Let be the self-impedance of node i, in Ω, obtained through the inverse of the node admittance matrix. A smaller self-impedance value indicates a larger short-circuit capacity and stronger fault tolerance of the node. The short-circuit capacity index fully considers the electrical characteristics of the node and is an important basis for evaluating node strength.

[0060] S32: To evaluate the voltage stability of nodes under load fluctuation conditions, an improved voltage stability index is proposed. This index considers the effects of both voltage deviation and load level:

[0061]

[0062] In the formula, ΔV i Node voltage deviation, in kV, represents the difference between the actual voltage and the rated voltage; Vn Rated voltage, unit is kV; S i The actual load of the node is expressed in MVA; S n This represents the system baseline capacity, measured in MVA, typically the capacity of the main transformer in the substation. The value ranges from [0,1], with a higher value indicating better voltage stability at the node. As voltage deviation increases or load levels rise, the voltage stability index decreases accordingly, which aligns with the characteristics of the actual system.

[0063] S33: To comprehensively evaluate the importance of nodes in the network structure, this method is based on complex network theory and incorporates graph theory analysis. A topology strength evaluation model is constructed by calculating the centrality index of nodes.

[0064] TS i =α·BC i +β·CC i

[0065] In the formula, BC i CC is the node betweenness centrality, a dimensionless value that represents the frequency with which a node acts as a transit node in a network path. i α is the node betweenness centrality, a dimensionless value that reflects the average distance between a node and other nodes; α and β are weighting coefficients, satisfying α + β = 1. A higher betweenness centrality indicates a more important role for the node in network information transmission; a higher betweenness centrality indicates a closer connection between the node and other nodes. The combination of these two indicators comprehensively reflects the node's position in the network topology.

[0066] S34: Determine the weights of each indicator using the entropy weight method:

[0067]

[0068] In the formula, w i H represents the weight, which is dimensionless; i Let be the information entropy, reflecting the contribution of this indicator to the evaluation result; n is the total number of indicators. The higher the information entropy, the lower the discrimination of the indicator, and the smaller its corresponding weight.

[0069] In an optional embodiment, the weight coefficients of each evaluation index can also be determined using the Analytic Hierarchy Process (AHP). Specifically, a hierarchical model is constructed, placing evaluation indices such as short-circuit capacity, voltage stability, and topology strength at the same level. Based on professional experience, the relative importance between any two indices is compared pairwise, and a judgment matrix is ​​constructed using the standard scaling method. By calculating the largest eigenvalue and its corresponding eigenvector of this judgment matrix, and normalizing the eigenvector, the subjective weight coefficients of each index are obtained.

[0070] In another optional embodiment, the weight coefficients of each evaluation index can also be determined using the equal weighting method. Specifically, when all evaluation indexes contribute exactly the same to the node strength evaluation, the same weight value is directly assigned to each index, such as short-circuit capacity, voltage stability, and topology strength. The weight of each index is equal to the reciprocal of the total number of indexes, thereby ensuring that the sum of all weights is one.

[0071] S35: A normalized weakness assessment model is established based on the node strength index. This model considers the relative differences in node strength and maps the assessment results to the [0,1] interval through a linear transformation:

[0072]

[0073] In the formula, WI i SI is a weakness index for node i, dimensionless, with a value range of [0,1]. i SI is the node strength index. max SI min These represent the maximum and minimum values ​​of the strength index for all nodes. The closer the weakness index is to 1, the weaker the node; conversely, the closer it is to 0, the stronger the node. This standardization method facilitates horizontal comparisons between different nodes.

[0074] In an optional embodiment, when in the early stages of power grid planning or in the case of rapid diagnosis of large-scale distribution networks, a discretized grading method is used to achieve weakness assessment. The strength indices of all nodes are sorted from largest to smallest, and a predefined weakness level is directly assigned based on the node's position (percentile) in this ordered sequence.

[0075] Furthermore, in this embodiment, step S4 introduces a dynamic response model based on frequency deviation to evaluate the dynamic response characteristics of the node under disturbance conditions. The specific steps include:

[0076] To evaluate the dynamic response characteristics of the node under disturbance conditions, a dynamic response model based on frequency deviation is introduced. This model employs a PD control structure and considers both the instantaneous value and rate of change of the frequency deviation:

[0077]

[0078] In the formula, DR i (t) represents the dynamic response value of node i at time t, in units of puΔf. i (t) represents the frequency deviation in Hz, indicating the difference between the actual frequency and the rated frequency. K represents the rate of change of frequency deviation, in Hz / s. pK is a proportional gain, measured in pu / Hz, representing the response strength of the control system to frequency deviation. d K is the differential coefficient, measured in pu·s / Hz, used to suppress frequency fluctuations. It is adjusted by... p and K d The value of can optimize the dynamic response characteristics of nodes.

[0079] Furthermore, in this embodiment, step S5 analyzes the impact of the fault on neighboring nodes based on the propagation characteristics of the fault in the network and generates a comprehensive evaluation model. Specific steps include S51-S52:

[0080] S51: To analyze the propagation characteristics of faults in a network, a fault propagation model considering electrical distance and impedance relationships is established. This model reflects the degree of impact of the fault on adjacent nodes.

[0081]

[0082] In the formula, FP ij Z represents the dimensionless coefficient of the failure propagation influence of node j on node i. ij Z represents the mutual impedance between nodes i and j, in Ω. ii d represents the self-impedance of node i, in Ω; ij Let be the electrical distance between nodes i and j, in per-unit values; γ be the propagation intensity coefficient, dimensionless, reflecting the initial impact intensity of the fault; and λ be the distance attenuation coefficient, dimensionless, characterizing the rate attenuation of the fault's impact with distance. This model considers the spatial attenuation characteristics of fault propagation, consistent with the physical characteristics of actual systems.

[0083] S52: Based on the aforementioned indicators, a comprehensive evaluation model was constructed that includes node vulnerability, dynamic response, and the impact of fault propagation.

[0084]

[0085] In the formula, CE i WI is the comprehensive evaluation value of node i, dimensionless; i DR is a weak point index for nodes. i This refers to the dynamic response characteristic value; μ1 represents the sum of the fault propagation effects of all adjacent nodes on node i; μ2 and μ1 are balancing coefficients used to adjust the weights of different characteristic indicators in the evaluation. This comprehensive evaluation method can fully reflect the weak characteristics of a node.

[0086] Example 3 is the third embodiment of the present invention, which differs from the previous two embodiments in that:

[0087] This embodiment also provides an electronic device, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the distribution network weak node identification method based on short-circuit parameters proposed in the above embodiment.

[0088] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a method for identifying weak nodes in a distribution network based on short-circuit parameters as proposed in the above embodiments.

[0089] The storage medium proposed in this embodiment belongs to the same inventive concept as the method for identifying weak nodes in a distribution network based on short-circuit parameters proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0090] Based on the above description of the implementation methods, those skilled in the art will clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0091] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0092] Example 4 is the fourth embodiment of the present invention. This embodiment provides a method for identifying weak nodes in a distribution network based on short-circuit parameters, and conducts experimental verification. A 10kV distribution network in a certain area was selected for verification, containing 35 nodes and 42 lines. The experimental data are as follows:

[0093] Table 1: Comparison of recognition results from different methods

[0094] Method Accuracy (%) Recall (%) F1 Score Calculation Time (s) Conventional voltage method 82.5 78.6 0.805 12.3 Power flow sensitivity method 85.7 82.4 0.840 18.5 Short circuit capacity method 88.3 85.9 0.871 15.7 The present method 93.6 91.2 0.924 21.4

[0095] Table 2: Analysis of Weak Node Characteristics

[0096] Indicator type Mean Standard deviation Coefficient of variation Short circuit capacity (MVA) 125.3 18.6 0.148 Voltage stability 0.892 0.075 0.084 Topological strength 0.756 0.112 0.148 Dynamic response 0.834 0.096 0.115

[0097] Table 3: Algorithm Performance Comparison

[0098]

[0099]

[0100] Experimental results show that our proposed method outperforms traditional methods in terms of recognition accuracy, recall, and F1 score. Although the computation time is slightly increased, the significant improvement in recognition accuracy makes this method highly practical. In particular, our method demonstrates stronger adaptability and reliability under complex network structures and multiple fault conditions.

[0101] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A short-circuit parameter based method for identifying weak nodes in a power distribution network, characterized in that: The application relates to a weak node identification method based on short-circuit parameters of a power distribution network. A multi-dimensional evaluation system is established to comprehensively evaluate the strength characteristics of nodes of the power distribution network. Electrical parameters, topological parameters and operating parameters of the nodes of the power distribution network are collected, and the original data collected are standardized to eliminate dimensional differences. Based on the standardized data, short-circuit parameter related indexes, voltage stability indexes and network topological strength indexes of the nodes are calculated. An objective weighting method is used to determine the weight coefficients of the evaluation indexes, and a weighted evaluation model is established. A dynamic response model based on frequency deviation is introduced to evaluate the dynamic response characteristics of the nodes under disturbance conditions. According to the propagation characteristics of faults in the network, the influence degree of the faults on adjacent nodes is analyzed, and a comprehensive evaluation model is generated.

2. The method for identifying weak nodes of a power distribution network based on short-circuit parameters according to claim 1, characterized in that: The multi-dimensional evaluation system comprises short-circuit parameter indexes reflecting the fault resistance of the nodes, operating parameter indexes reflecting voltage stability and topological characteristic indexes reflecting the importance of the network structure, thereby obtaining a multi-dimensional evaluation index system covering electrical characteristics, operating states and network structures.

3. The method for identifying weak nodes of a power distribution network based on short-circuit parameters according to claim 2, characterized in that: The weight coefficients of the evaluation indexes are determined by calculating the information entropy of the index data and distributing the weight coefficients according to the information entropy values, thereby obtaining a weight set in inverse proportion to the index differentiation degree.

4. The method for identifying weak nodes of a power distribution network based on short-circuit parameters according to claim 3, characterized in that: The weighted evaluation model is established by introducing the dynamic response characteristics of the nodes and analyzing and quantifying the influence of fault propagation, thereby obtaining an enhanced comprehensive evaluation index system containing dynamic behavior and fault propagation effects.

5. The method for identifying weak nodes of a power distribution network based on short-circuit parameters according to claim 4, characterized in that: The short-circuit parameter related indexes of the nodes comprise short-circuit capacity, which is a basic index for measuring the strength of the nodes and directly reflects the bearing capacity of the nodes under fault conditions. where V n is the node rated voltage, Z ii is the self-impedance of node i; The smaller the self-impedance value, the greater the short-circuit capacity of the node and the stronger the fault resistance. The voltage stability index comprises: where ΔV i is the node voltage deviation, V n is the rated voltage, S i is the node actual load, S n is the system base capacity; The topological strength index comprises: TS i = a BC i + β CC i In the formula, BC i is the node betweenness centrality, CC i is the node closeness centrality, and α and β are weight coefficients and satisfy α+β=1.

6. The method for identifying weak nodes of a power distribution network based on short-circuit parameters according to claim 5, characterized in that: The dynamic response characteristics of the nodes under disturbance conditions comprise a normalized weakness evaluation model based on the node strength index, and the evaluation results are mapped to the [0, 1] interval through linear transformation: where WI i is the weakness index of node i, SI i is the strength index of node i, SI max , SI min are the maximum and minimum values of all node strength indices, respectively.

7. The method for identifying weak nodes of a power distribution network based on short-circuit parameters according to claim 6, characterized in that: The influence degree of the faults on adjacent nodes is analyzed by evaluating the dynamic response characteristics of the nodes under disturbance conditions, introducing a dynamic response model based on frequency deviation, and using a PD control structure to simultaneously consider the instantaneous value and change rate of the frequency deviation: where DR i (t) is the dynamic response value of node i at time t, Δf i (t) is the frequency deviation, representing the difference between the actual frequency and the rated frequency; K p is the proportional coefficient, controlling the response intensity of the system to the frequency deviation; K d is the differential coefficient, used to suppress frequency fluctuations, and by adjusting the values of K p and K d , the dynamic response characteristics of the node can be optimized.

8. The method for identifying weak nodes of a power distribution network based on short-circuit parameters according to claim 7, characterized in that: The comprehensive evaluation model comprises a comprehensive evaluation model containing node weakness, dynamic response and fault propagation influence, which is constructed on the basis of the indexes, In the formula, CE i is the comprehensive evaluation value of node i, WI i is the node weakness index, DR i is the dynamic response characteristic value, is the sum of the failure propagation influences of all adjacent nodes on node i, and μ1 and μ2 are balance coefficients for adjusting the weights of different characteristic indexes in evaluation. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to realize the steps of the weak node identification method based on short-circuit parameters of a power distribution network according to any one of claims 1-8.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the weak node identification method based on short-circuit parameters of a power distribution network according to any one of claims 1-8.

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