A method, system, device, and medium for identifying sensitive nodes in a distribution network based on sensitivity threshold and topological electrical characteristics.

Abstract

This invention relates to the field of power grid sensitive node identification technology, and discloses a method, system, device, and medium for identifying sensitive nodes in distribution networks based on sensitivity thresholds and topological electrical characteristics. The method includes: constructing a distribution network simulation model, performing power flow calculations, and obtaining a reactive power sensitivity matrix; performing a dual-threshold initial screening on the reactive power sensitivity matrix to obtain a theoretical set of sensitive nodes, and performing a second screening based on topology and electrical characteristics to obtain a final set of sensitive nodes; performing reactive power compensation and control simulation on the final set of sensitive nodes to verify the voltage optimization effect. This invention identifies high-impact nodes from a sensitivity perspective, and then uses degree centrality and electrical distance to exclude terminal and isolated nodes, ensuring that the selected points simultaneously possess strong control capabilities and feasibility; after configuring reactive power compensation at sensitive nodes, it can improve the overall network voltage qualification rate, reduce voltage deviation, and reduce network losses, and effectively suppress overvoltage problems caused by high-penetration distributed power sources.

Description

Technical Field

[0001] This invention relates to the field of power grid sensitive node identification technology, and in particular to a method, system, device and medium for identifying sensitive nodes in distribution networks based on sensitivity thresholds and topological electrical characteristics. Background Technology

[0002] As the final link in the power system, the voltage quality of the distribution network directly affects the reliability of electricity supply for users. Accurate identification of sensitive nodes is a core prerequisite for resolving voltage fluctuations and limit violations in the distribution network. The distribution network, as a crucial link connecting the main grid and users, directly impacts the safety of electrical equipment and the user experience. However, with the large-scale integration of diverse loads such as distributed power sources and electric vehicle charging stations, voltage fluctuations and limit violations in the distribution network have become increasingly prominent, seriously affecting power supply reliability. Accurate identification of sensitive nodes, as key points for voltage regulation in the distribution network, is a core prerequisite for achieving optimized voltage control and an important foundation for improving the economic efficiency and stability of distribution network operation.

[0003] The voltage-power sensitivity matrix, as a core tool for quantifying the response relationship between node voltage and power changes, is also widely used in sensitive node identification research. The sensitivity matrix can quantify the response relationship between node voltage and power changes. However, most current sensitive node identification methods based on the sensitivity matrix focus on the calculation of theoretical sensitivity indicators. In actual engineering applications, due to the lack of sufficient integration with the distribution network topology and electrical operation characteristics, the identification results may deviate from the engineering control requirements, making it difficult to meet the actual needs of precise voltage regulation in the distribution network. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method and system for identifying sensitive nodes in a distribution network based on sensitivity thresholds and topological electrical characteristics, which solves the problems of single screening dimensions, ambiguous threshold standards, complex calculations, and poor engineering feasibility in existing methods for identifying sensitive nodes in a distribution network.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for identifying sensitive nodes in a distribution network based on a sensitivity threshold and topological electrical characteristics, comprising: Construct a power distribution network simulation model, perform power flow calculations, and obtain the reactive power sensitivity matrix; The reactive power sensitivity matrix is ​​initially screened using a dual threshold method to obtain the theoretical set of sensitive nodes. A second screening based on topology and electrical characteristics is performed on the theoretical set of sensitive nodes to obtain the final set of sensitive nodes. Based on the final set of sensitive nodes, a reactive power compensation control simulation was performed to verify the voltage optimization effect.

[0007] As a preferred embodiment of the distribution network sensitive node identification method based on sensitivity threshold and topological electrical characteristics described in this invention, the dual-threshold initial screening includes: The nodes are sorted based on the overall sensitivity index of the network voltage impact of each node, and the nodes ranked in the top preset proportion are selected to form the first candidate set. Nodes whose absolute values ​​of the diagonal elements in the reactive power sensitivity matrix are not less than a set sensitivity value are selected to form a second candidate set; The intersection of the first candidate set and the second candidate set is extracted as the theoretical sensitive node set.

[0008] As a preferred embodiment of the distribution network sensitive node identification method based on sensitivity threshold and topological electrical characteristics described in this invention, the second screening includes: Determine whether the degree centrality of a node is not less than the first threshold, and whether the electrical distance from the node to the main network is not greater than the second threshold.

[0009] The beneficial effects of this preferred technical solution are as follows: by using dual thresholds, a set of theoretically sensitive nodes with high sensitivity and wide influence can be quickly and accurately screened from all network nodes. While ensuring that no key nodes are missed, the number of candidate nodes is greatly reduced, thereby reducing the computational burden of subsequent topology electrical screening and improving identification efficiency.

[0010] As a preferred embodiment of the distribution network sensitive node identification method based on sensitivity threshold and topological electrical characteristics described in this invention, the calculation of the electrical distance from the node to the main network includes: Calculate the sum of the impedances of all branches on the unique topological path from the node to the main network access point; The accumulated impedance value is normalized to a reference impedance to obtain the normalized electrical distance.

[0011] As a preferred embodiment of the distribution network sensitive node identification method based on sensitivity threshold and topological electrical characteristics described in this invention, the method includes: performing reactive power compensation and control simulation based on the final set of sensitive nodes to verify the voltage optimization effect, including: Inject a preset reactive power into the nodes in the final set of sensitive nodes, and perform power flow calculation after regulation; Based on the calculation results, the voltage pass rate and the average voltage deviation index are evaluated to verify the effectiveness of sensitive node identification.

[0012] In a preferred embodiment of the distribution network sensitive node identification method based on sensitivity threshold and topological electrical characteristics described in this invention, the preset ratio is 15%. The set sensitivity value is 1.8 × 10⁻³ pu / Mvar.

[0013] In a preferred embodiment of the distribution network sensitive node identification method based on sensitivity threshold and topological electrical characteristics described in this invention, the first threshold is 2; The degree centrality of a node is the number of branches it connects to.

[0014] Secondly, the present invention provides a distribution network sensitive node identification system based on sensitivity threshold and topological electrical characteristics, comprising: The matrix acquisition module is used to build a power distribution network simulation model, perform power flow calculations, and obtain the reactive power sensitivity matrix. The first screening module is used to perform a double threshold initial screening on the reactive power sensitivity matrix to obtain a theoretical set of sensitive nodes. The second screening module is used to perform a second screening of the theoretical sensitive node set based on topology and electrical characteristics to obtain the final sensitive node set. The verification module is used to perform reactive power compensation control simulation based on the final set of sensitive nodes to verify the voltage optimization effect.

[0015] Thirdly, the present invention provides a computer device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of a method for identifying sensitive nodes in a distribution network based on sensitivity thresholds and topological electrical characteristics.

[0016] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the method for identifying sensitive nodes in a distribution network based on sensitivity thresholds and topological electrical characteristics.

[0017] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention obtains a theoretical set of sensitive nodes by constructing a reactive power sensitivity matrix and performing initial screening using dual thresholds. Then, it combines topology and electrical characteristics for precise secondary screening, ultimately obtaining the optimal set of sensitive nodes. This method first quickly identifies high-impact nodes from a sensitivity perspective, and then uses degree centrality and electrical distance to exclude weak and isolated nodes at the end of the network, ensuring that the selected points possess both strong control capabilities and practical feasibility. After configuring reactive power compensation at sensitive nodes, it can improve the overall network voltage qualification rate, reduce voltage deviation, and decrease network losses. It also effectively suppresses overvoltage problems caused by high-penetration distributed power sources, achieving maximum voltage optimization effects with a small number of devices, thus improving the engineering practical value and economic benefits. Attached Figure Description

[0018] 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.

[0019] Figure 1 This is a schematic diagram of the overall process of a distribution network sensitive node identification method based on sensitivity threshold and topological electrical characteristics according to an embodiment of the present invention.

[0020] Figure 2 This is a schematic diagram of the distribution network topology and final sensitive node labeling in a distribution network sensitive node identification method based on sensitivity threshold and topological electrical characteristics as described in the third embodiment of the present invention. Detailed Implementation

[0021] 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.

[0022] Example 1, referring to Figure 1 As an embodiment of the present invention, a method for identifying sensitive nodes in a distribution network based on sensitivity thresholds and topological electrical characteristics is provided, comprising: S100: Construct a power distribution network simulation model, perform power flow calculations, and obtain the reactive power sensitivity matrix; S200: Perform a double threshold screening on the reactive power sensitivity matrix to obtain the theoretical set of sensitive nodes; S300: Perform a second screening based on topology and electrical characteristics on the theoretical set of sensitive nodes to obtain the final set of sensitive nodes; S400: Based on the final set of sensitive nodes, perform reactive power compensation control simulation to verify the voltage optimization effect.

[0023] Specifically, existing methods often rely solely on the absolute value of sensitivity as the sole criterion, neglecting the topological characteristics of the distribution network. This leads to a large number of isolated, peripheral nodes among the identified theoretically sensitive nodes. If nodes are selected based solely on theoretical sensitivity values, the control effect on these nodes is limited to themselves. This may result in the selection of numerous nodes located at the network's periphery with weak electrical connections. While theoretically sensitive to their own voltage, these peripheral nodes, with few connecting branches, cannot radiate and improve the voltage of surrounding nodes, rendering them of minimal engineering control value and wasting control resources. Even with reactive power compensation and other controls, the effect is difficult to propagate to the entire network, leading to significant resource investment but unsatisfactory improvement results. Current approaches using vague sensitivity ratios for selection lack specific sensitivity calculation indicators, such as column vector cumulative sums, diagonal elements, and threshold combinations. Differences in calculation logic among different personnel result in inconsistent identification results, hindering the development of a standardized identification process. Furthermore, radial distribution networks inherently possess the characteristics of "a clearly defined main grid access point and a unique path from a node to the main grid." Existing methods cannot optimize the calculation logic for this characteristic. To improve accuracy, current methods either introduce complex impedance analysis and multi-path calculations, or rely on hardware measurement data, resulting in high costs; or the identification results vary significantly across different scenarios, lacking standardization, thus increasing computational load and implementation costs, limiting their widespread application in small and medium-sized distribution networks. Therefore, the S100-S400 steps enable accurate identification through dual-threshold screening and secondary screening, and simplify electrical distance calculations by leveraging the unique topological characteristic of radial distribution networks. This low-cost solution offers strong feasibility.

[0024] Example 2, refer to Figure 1 As an embodiment of the present invention, based on the above embodiment, a method for identifying sensitive nodes in a distribution network based on sensitivity thresholds and topological electrical characteristics is provided. The method includes: In this embodiment of the application, step S100 involves constructing a power distribution network simulation model, performing power flow calculations, and obtaining the reactive power sensitivity matrix. In an alternative implementation, S100 can configure the base parameters of the distribution network, topology branch impedances, power supply and load parameters in the simulation software, perform power flow calculations using the Newton-Raphson method, solve for and invert the power flow Jacobian matrix, and extract the reactive power sensitivity matrix. ; For example, based on the above implementation method, the power grid model construction parameter configuration requirements are as follows: the baseline capacity is set to... MVA, reference voltage kV, system frequency 50Hz; main grid access node is set as slack node, and other nodes are PQ nodes; according to the actual situation of the distribution network, distributed photovoltaic (0.5MVA output per node) and charging pile cluster load (0.8MVA load per node) are configured at typical nodes. Sensitivity matrix calculation process: The power flow calculation is completed using the Newton-Raphson method to obtain the power flow Jacobian matrix. ; in, These are the partial derivative subarrays representing active power, voltage phase angle, and amplitude, respectively. These are the partial derivative subarrays for reactive power, voltage phase angle, and amplitude, respectively; after inverting the Jacobian matrix, the reactive power sensitivity subarray is extracted. Matrix elements Indicates to the node When injecting 1Mvar reactive power, the node The change in voltage amplitude (unit: pu / Mvar).

[0025] In another alternative implementation, S100 can also consider obtaining the reactive power sensitivity matrix by combining the forward-backward power flow algorithm with the numerical perturbation method. Specifically, in the simulation model, a distribution network simulation model is built based on quasi-parameters, topology branch impedances, power supply and load parameters, etc., and a complete reference power flow calculation is performed to record the reference voltage amplitude and phase angle of all nodes; each load node is traversed, and a small reactive power perturbation is injected into each node while keeping the reactive power injection of other nodes unchanged; the forward-backward power flow calculation is run again to obtain the voltage of the entire network after the perturbation; the reactive power-voltage sensitivity is calculated, and after traversing all the nodes to be examined, the reactive power sensitivity matrix can be obtained.

[0026] S200: Perform a double threshold screening on the reactive power sensitivity matrix to obtain the theoretical set of sensitive nodes; In this embodiment of the application, the dual-threshold initial screening in step S200 includes the following steps A1-A3: A1: Based on the overall sensitivity index of the network voltage impact of each node, sort the nodes and select the nodes that rank in the top preset proportion to form the first candidate set; In this embodiment of the application, the preset ratio mentioned in step S200 A1 is 15%.

[0027] Specifically, percentage threshold screening: calculate the sensitivity index of the network voltage impact for each node k. ;in, This represents the total number of nodes in the distribution network; the top 15% of nodes are selected after sorting by this indicator in descending order.

[0028] A2: Select nodes in the reactive power sensitivity matrix whose absolute values ​​of the diagonal elements are not less than the set sensitivity value to form a second candidate set; In this embodiment of the application, the set sensitivity value in step S200 A2 is: ≥1.8×10⁻³ pu / Mvar.

[0029] Specifically, numerical threshold filtering: extraction Matrix diagonal elements Nodes with an absolute value of 1.8 × 10⁻³ pu / Mvar are selected, and nodes that have a significant impact on the entire network and whose sensitivity is close to the threshold can be included as appropriate.

[0030] A3: Extract the intersection of the first candidate set and the second candidate set as the theoretical sensitive node set.

[0031] In an optional implementation, the dual threshold judgment in S200 can also consider using an electrical distance-weighted comprehensive sensitivity index combined with an adaptive percentage for initial screening. Specifically, the threshold for each node is first calculated. The comprehensive impact indicators are sorted in descending order according to the indicator values, and the nodes in the first part are selected to form the first candidate set; the original diagonal threshold is retained to obtain the second candidate set; the intersection of the two is taken as the theoretical sensitive node set.

[0032] In another optional implementation, the dual threshold determination in S200 can also be initially screened by combining K-means clustering with local sensitivity anomaly detection. Specifically, the sensitivity matrix... Each column is considered as one 3D eigenvectors, for this K-means clustering is performed on the feature vectors to classify the strong influence class into the first candidate set; then a local self-sensitivity anomaly detection is superimposed to calculate the median M and absolute deviation MAD of all nodes, and a judgment criterion is set based on this, for example: nodes with diagonal threshold > M + 3 × MAD are the second candidate set; the union of the two is taken as the final theoretical sensitive node set.

[0033] S300: Perform a second screening based on topology and electrical characteristics on the theoretical set of sensitive nodes to obtain the final set of sensitive nodes; In this embodiment of the application, the second screening in step S300 includes: Determine whether the degree centrality of a node is not less than the first threshold, and whether the electrical distance from the node to the main network is not greater than the second threshold.

[0034] In this embodiment of the application, the first threshold in step S300 is 2; The degree centrality of a node is the number of branches it connects to.

[0035] Specifically, in S300, for the set of theoretically sensitive nodes, two Boolean logic conditions are verified in sequence, and only nodes that simultaneously satisfy the following conditions are retained: ① Node degree centrality ≥ 2 (first threshold), that is, the number of branches connected to the node is not less than 2; ② Electrical distance from the node to the main network ≤ 0.5pu (second threshold). The electrical distance can be calculated by combining the normalization formula of the national standard with the branch impedance accumulation of the radial topology.

[0036] In one alternative implementation, the S300 medium centrality calculation can be performed by extracting the distribution network topology adjacency matrix using simulation software, calculating the number of node connection branches using the NetworkX library, and filtering nodes with a degree centrality ≥ 2.

[0037] In this embodiment of the application, the calculation of the electrical distance from the node to the main network in step S300 includes: Calculate the sum of the impedances of all branches on the unique topological path from the node to the main network access point; The accumulated impedance value is normalized to a reference impedance to obtain the normalized electrical distance.

[0038] Specifically, based on the above implementation method, the electrical distance calculation can first calculate the reference impedance. Then, the branch impedance from the node to the main network is added together to obtain the equivalent resistance. With reactance According to the formula Calculate and filter nodes with an electrical distance ≤ 0.5 pu.

[0039] In another optional implementation, based on the above implementation, the second threshold for electrical distance in S300 can also be ≤0.6pu, which is slightly more lenient than the original 0.5, and can avoid filtering out a large number of effective nodes of long rural lines to a certain extent.

[0040] In this embodiment of the application, step S400 involves performing reactive power compensation and control simulation based on the final set of sensitive nodes to verify the voltage optimization effect, including the following steps B1-B2: B1: Inject a preset reactive power into the nodes in the final set of sensitive nodes, and perform power flow calculation after regulation; B2: Based on the calculation results, evaluate the voltage qualification rate and the average voltage deviation index to verify the effectiveness of sensitive node identification.

[0041] In one optional implementation, the control parameters in S400 can be set to inject 2Mvar reactive power into the final sensitive node, and the voltage data of each node in the entire network before and after control are recorded. The evaluation index calculation includes the voltage qualification rate (the percentage of nodes with voltage within the range of [0.95, 1.05] pu) and the average voltage deviation (the average difference between the voltage of all nodes in the network and the rated voltage). The comparative verification requires comparing two traditional methods: random point selection control and single sensitivity screening control, to ensure that the superiority verification of the method of this invention is objective and effective.

[0042] In another optional implementation, the regulation verification in S400 can be replaced by a multi-scenario comparative simulation. For example, select a variety of typical operating scenarios, such as heavy-load summer peak and light-load winter night; corresponding to different load quantiles, perform reactive power compensation parallel switching or SVG continuous regulation simulation with the same capacity on multiple sets of nodes; run each group and scenario for 10 to 20 different load level power flow tests, and statistically analyze several indicators such as the improvement of the overall network voltage qualification rate, the percentage decrease in the average voltage deviation, the reduction in network loss, and the total compensation capacity required; and perform visual comparison of the indicators to verify the effectiveness of the method.

[0043] It should be noted that B1-B2, as the preferred verification method, is simpler and easier to implement compared to other verification methods.

[0044] In summary, this invention aims to overcome the shortcomings of existing methods for identifying sensitive nodes in power distribution networks, such as single screening dimensions, vague threshold standards, complex calculations, and poor engineering feasibility. By combining a two-step logic of initial screening with dual thresholds and fine screening based on topological electrical characteristics, it achieves accurate identification of sensitive nodes, eliminates nodes that are not controllable in engineering, and ensures that the identification results are highly matched with the engineering requirements for voltage optimization control in power distribution networks, thereby improving the accuracy and economy of voltage regulation.

[0045] Specifically, by combining sensitivity with a multi-dimensional screening mechanism of topology and electrical characteristics, theoretically sensitive but engineering-uncontrollable peripheral nodes can be effectively eliminated. The identification results combine theoretical rationality and engineering practicality, overcoming the limitations of single-dimensional screening. Step S300 clarifies the dual threshold standard for sensitivity and the judgment rules for topology electrical characteristics, and unifies the identification process and calculation logic, enabling high consistency of identification results under different simulation environments, facilitating engineering promotion and application. The entire scheme fully utilizes the topological characteristics of radial distribution networks to simplify calculations, eliminating the need for hardware measurement equipment. The entire process can be completed solely through simulation software, reducing computational burden and implementation costs, and is suitable for various small and medium-sized distribution networks. The identified sensitive nodes are highly matched with engineering control requirements, achieving significant voltage improvement with minimal investment. Compared to traditional random point selection control, the voltage qualification rate is improved while reducing reactive power compensation capacity waste, thereby enhancing the economy and efficiency of voltage control. It is also not limited by the specific scale and parameters of the distribution network, and is applicable to various radial distribution networks containing distributed power sources and diverse loads, providing a universal solution for distribution network voltage optimization control.

[0046] Example 3, referring to Figure 2 According to Table 1, this embodiment provides an application scheme for a distribution network sensitive node identification method based on sensitivity threshold and topological electrical characteristics to verify the feasibility and effectiveness of the present invention.

[0047] This case study selects a typical radial distribution network (IEEE 9-bus system) and uses Matlab R2024a + Matpower 7.1 to build a simulation model. The core parameters are as follows: Node parameters: base capacity 100MVA, base voltage 13.8kV, system frequency 50Hz; node 1 is the balancing node, nodes 2 and 3 are generator nodes, nodes 4-9 are PQ load nodes; node 6 is connected to distributed photovoltaic, node 8 is connected to the charging pile cluster load, and the load parameters of the remaining nodes are configured according to the system standard. Table 1: Branch Impedance Parameters (Standard Topology Branches in the System):

[0048] (1) Sensitivity matrix calculation: Power flow calculation was performed using Matpower (iteration error 3×10^-7 pu), and a 9×9 reactive power sensitivity submatrix was obtained by solving. .

[0049] In this case, the dual thresholds are set as "sensitivity threshold". pu / Mvar”; Among them, node 6 pu / Mvar (meets the numerical threshold); Node 8 pu / Mvar (included at the discretion of the authorities as they rank in the top 18% of the overall network's impact metrics and are close to the threshold); Node 5 pu / Mvar (not included as the percentage threshold was not met).

[0050] (2) Initial screening results of dual thresholds: Nodes 6, 8 and 3 were obtained by percentage threshold screening; Node 6 was included by numerical threshold screening; combined with the "inclusion at discretion" rule, the final theoretical sensitive node set is nodes 6 and 8.

[0051] (3) Fine screening based on topology and electrical characteristics: Branch association number: Calculated using the topological adjacency matrix of simulation software, node 6 has 2 connected branches and node 8 has 2 connected branches, both of which meet the requirement of ≥2; Electrical distance: The electrical distance of node 6 is calculated to be 0.021 pu and the electrical distance of node 8 is 0.035 pu according to the formula. Both meet the requirement of ≤0.5 pu and are determined to be the final sensitive nodes.

[0052] Example 4 illustrates a schematic scheme for a distribution network sensitive node identification method based on sensitivity threshold and topological electrical characteristics. It should be noted that the technical solution of this distribution network sensitive node identification system based on sensitivity threshold and topological electrical characteristics belongs to the same concept as the technical solution of the distribution network sensitive node identification method based on sensitivity threshold and topological electrical characteristics described above. Details not described in detail in this embodiment of the distribution network sensitive node identification system based on sensitivity threshold and topological electrical characteristics can be found in the description of the distribution network sensitive node identification method based on sensitivity threshold and topological electrical characteristics described above.

[0053] This embodiment also provides a distribution network sensitive node identification system based on sensitivity threshold and topological electrical characteristics, including: The matrix acquisition module is used to build a power distribution network simulation model, perform power flow calculations, and obtain the reactive power sensitivity matrix. The first screening module is used to perform a double threshold initial screening on the reactive power sensitivity matrix to obtain a theoretical set of sensitive nodes. The second screening module is used to perform a second screening of the theoretical sensitive node set based on topology and electrical characteristics to obtain the final sensitive node set. The verification module is used to perform reactive power compensation control simulation based on the final set of sensitive nodes to verify the voltage optimization effect.

[0054] This embodiment also provides a computer device applicable to the identification of sensitive nodes in a distribution network based on sensitivity thresholds and topological electrical characteristics, comprising: 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 implement the method for identifying sensitive nodes in a distribution network based on sensitivity thresholds and topological electrical characteristics as proposed in the above embodiment.

[0055] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a method for identifying sensitive nodes in a distribution network based on sensitivity thresholds and topological electrical characteristics, as proposed in the above embodiments.

[0056] The storage medium proposed in this embodiment belongs to the same inventive concept as the method for identifying sensitive nodes in a distribution network based on sensitivity threshold and topological electrical characteristics 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.

[0057] From 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. 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.

[0058] 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 method for identifying sensitive nodes in a distribution network based on sensitivity thresholds and topological electrical characteristics, characterized in that, include: Construct a power distribution network simulation model, perform power flow calculations, and obtain the reactive power sensitivity matrix; The reactive power sensitivity matrix is ​​initially screened using a dual threshold method to obtain the theoretical set of sensitive nodes. A second screening based on topology and electrical characteristics is performed on the theoretical set of sensitive nodes to obtain the final set of sensitive nodes. Based on the final set of sensitive nodes, a reactive power compensation control simulation was performed to verify the voltage optimization effect.

2. The method for identifying sensitive nodes in a distribution network based on sensitivity threshold and topological electrical characteristics as described in claim 1, characterized in that, The dual-threshold initial screening includes: The nodes are sorted based on the overall sensitivity index of the network voltage impact of each node, and the nodes ranked in the top preset proportion are selected to form the first candidate set. Nodes whose absolute values ​​of the diagonal elements in the reactive power sensitivity matrix are not less than a set sensitivity value are selected to form a second candidate set; The intersection of the first candidate set and the second candidate set is extracted as the theoretical sensitive node set.

3. The method for identifying sensitive nodes in a distribution network based on sensitivity threshold and topological electrical characteristics as described in claim 2, characterized in that, The second screening includes: Determine whether the degree centrality of a node is not less than the first threshold, and whether the electrical distance from the node to the main network is not greater than the second threshold.

4. The method for identifying sensitive nodes in a distribution network based on sensitivity thresholds and topological electrical characteristics as described in claim 3, characterized in that, The calculation of the electrical distance from the node to the main network includes: Calculate the sum of the impedances of all branches on the unique topological path from the node to the main network access point; The accumulated impedance value is normalized to a reference impedance to obtain the normalized electrical distance.

5. The method for identifying sensitive nodes in a distribution network based on sensitivity threshold and topological electrical characteristics as described in claim 4, characterized in that, Based on the final set of sensitive nodes, reactive power compensation and control simulations are performed to verify the voltage optimization effect, including: Inject a preset reactive power into the nodes in the final set of sensitive nodes, and perform power flow calculation after regulation; Based on the calculation results, the voltage pass rate and the average voltage deviation index are evaluated to verify the effectiveness of sensitive node identification.

6. The method for identifying sensitive nodes in a distribution network based on sensitivity threshold and topological electrical characteristics as described in claim 5, characterized in that, The preset ratio is 15%; The set sensitivity value is 1.8 × 10⁻³ pu / Mvar.

7. The method for identifying sensitive nodes in a distribution network based on sensitivity threshold and topological electrical characteristics as described in claim 6, characterized in that, The first threshold is 2; The degree centrality of a node is the number of branches it connects to.

8. A distribution network sensitive node identification system based on sensitivity threshold and topological electrical characteristics, using the method described in any one of claims 1-7, characterized in that, include: The matrix acquisition module is used to build a power distribution network simulation model, perform power flow calculations, and obtain the reactive power sensitivity matrix. The first screening module is used to perform a double threshold initial screening on the reactive power sensitivity matrix to obtain a theoretical set of sensitive nodes. The second screening module is used to perform a second screening of the theoretical sensitive node set based on topology and electrical characteristics to obtain the final sensitive node set. The verification module is used to perform reactive power compensation control simulation based on the final set of sensitive nodes to verify the voltage optimization effect.

9. A computer device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the distribution network sensitive node identification method based on sensitivity threshold and topological electrical characteristics as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores computer-executable instructions, which, when executed by a processor, implement the steps of the distribution network sensitive node identification method based on sensitivity threshold and topological electrical characteristics as described in any one of claims 1 to 7.

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