A method for identifying key nodes in bias current distribution based on complex network identification

Abstract

This invention discloses a method for identifying key nodes in the distribution of bias current based on complex network identification. The method includes: calculating the bias current before and after installing DC blocking devices at each system station using a field-circuit coupling model, and correcting the change in bias current before and after the installation of DC blocking devices based on a bias current balance factor correction strategy; establishing bias current mapping link matrices corresponding to a one-dimensional time-series snapshot complex network model and a two-dimensional time-series snapshot complex network model; establishing a key node model for bias current based on an improved webpage ranking algorithm using two-dimensional time-series snapshots; and solving the PR value ranking of each node under the two-dimensional time-series snapshot complex network model by mapping the one-dimensional time-series snapshot complex network model to complete the identification of key nodes. This invention fully considers the mapping relationship of bias current between nodes in the same dimension and the mapping relationship of bias current between different nodes in the same dimension, thereby optimizing the configuration of DC blocking devices in the power grid from a global perspective.

Description

Technical Field

[0001] This invention relates to the field of high voltage direct current transmission technology, specifically to a method and system for identifying key nodes in the distribution of bias magnetic current based on complex network identification. Background Technology

[0002] In recent years, with the rapid development of DC transmission technology, accidents caused by DC bias resulting from this technology have caused enormous losses in various regions, attracting widespread attention from scholars both domestically and internationally. The causes of DC bias include DC system unipolar blocking and geomagnetic storms. DC bias not only adversely affects the transformer itself, causing increased vibration, heating, and noise, but also further exacerbates reactive power losses and voltage waveform distortion in the power grid, threatening its safe and stable operation.

[0003] Methods such as reverse current injection, neutral point series resistance, neutral point series capacitor, neutral point series RC, line series capacitor, and potential compensation are currently the mainstream methods for suppressing DC bias both domestically and internationally. While these methods are theoretically and practically effective at suppressing bias current, they lack analysis of DC redistribution after adding DC blocking devices to the neutral point, and they also fail to consider the impact of changes in the power system network topology on DC bias. Therefore, these methods can still lead to excessive DC current at the transformer neutral point within the power grid, thus failing to achieve the desired suppression effect. Summary of the Invention

[0004] To address the problem that existing DC bias suppression techniques still result in excessive DC current at the transformer neutral point within the power grid, demonstrating poor suppression effectiveness, this invention provides a method for identifying key nodes in the bias current distribution based on complex network identification. This invention utilizes structural identification based on complex network theory, integrating global planning constraints of the power grid with transformer neutral point bias current constraints to suppress DC bias phenomena in transformers within the power grid.

[0005] This invention is achieved through the following technical solution:

[0006] A method for identifying key nodes in bias current distribution based on complex network identification includes:

[0007] A complex system network model and a field-circuit coupling model are established. The field-circuit coupling model is used to calculate the bias current before and after the DC blocking device is installed at each station of the system. The change in bias current before and after the DC blocking device is installed is corrected based on the bias current balance factor correction strategy.

[0008] Based on the corrected change in bias current before and after the installation of DC blocking device, bias current mapping link matrices corresponding to the one-dimensional time-series snapshot complex network model and the two-dimensional time-series snapshot complex network model are established respectively.

[0009] Considering the mapping relationship between the bias currents of each node and the node bias currents between dimensions, a key node model of bias current based on a two-dimensional time-series snapshot improved webpage ranking algorithm is established. The PR value ranking of each node under the two-dimensional time-series snapshot complex network model is solved by mapping the one-dimensional time-series snapshot complex network model, thus completing the identification of key nodes in the distribution of bias current in the entire system.

[0010] As a preferred embodiment, the bias current balance factor correction strategy of the present invention is as follows: based on the node-allowed maximum bias current improved entropy theory, a dynamic balance coefficient of bias current is introduced to correct the change in bias current before and after the DC blocking device is installed.

[0011] As a preferred embodiment, the correction process for the change in bias current of the present invention is as follows:

[0012]

[0013]

[0014]

[0015] ΔI′ ij =ΔI ij +α k

[0016] In the formula, ΔI ij This represents the change in bias current calculated based on the field-circuit coupling model; ΔI′ ij This represents the correction value for the change in bias current considering the maximum bias current; This represents the bias current value at node k without the DC blocking device installed. This represents the bias current value at node k when the DC blocking device is installed for the mth time; i k Indicates the bias current margin at node k without DC blocking; I max Indicates the maximum bias current allowed through the node; α k α represents the dynamic balance coefficient of the bias current at node k; k The larger the value, the greater the bias current that node k receives.

[0017] As a preferred embodiment, the present invention defines a complex network model for the initial installation of the DC blocking device as a one-dimensional time-series snapshot and a complex network model for each subsequent update and installation of the DC blocking device as a two-dimensional time-series snapshot.

[0018] In a preferred embodiment, the process of establishing the bias current mapping link matrix of the present invention specifically includes:

[0019] The bias current distribution matrix A = [I] is introduced into the system without DC blocking device.ij ], where the diagonal elements are the bias magnetic current flowing through the substation nodes, and the off-diagonal elements are zero;

[0020] Based on the maximum mean difference migration distance to represent the mapping relationship between bias current stations across the entire network, a bias coupling coefficient matrix S = [s] is introduced. ij Establish the bias current mapping link matrix X = [Δx] ij The matrix elements are as follows:

[0021]

[0022] Δx ij =s ij ×ΔI′ ij +I ij i=1,…,nj=1,…,n

[0023] In the formula, S ij This indicates the strength of the bias current mapping relationship between node i and node j; S ij The larger the value, the stronger the mapping relationship of the bias current between node i and node j, ΔI′. ij This represents the correction value for the change in bias current considering the maximum bias current.

[0024] In a preferred embodiment, the key node identification process of the present invention includes:

[0025] Based on the bias current mapping link matrix X∈{X1, X2}, the corresponding bias current mapping transfer matrix G∈{G1, G2} is written, and G=[g ij Based on the relative importance of the h-index, a time-series periodic factor matrix and a biased magnetic weight factor are introduced. A node mapping relationship transfer criticality correction strategy is adopted, and a transfer degree correction transfer factor p is introduced, specifically expressed as follows:

[0026]

[0027]

[0028] U (k+1)T =U (k)T G+T

[0029]

[0030] In the formula, p mdenoted by , represents the transfer degree correction factor when the DC blocking device is added for the mth time; 'out' represents the number of nodes whose absolute value of the bias current decreases when the DC blocking device is added for the mth time; 'in' represents the number of nodes whose absolute value of the bias current increases when the DC blocking device is added for the mth time; 'E' represents an n-dimensional column vector with elements of 1; 'd' represents the damping coefficient, representing the transfer probability between connected nodes; 'E′' represents the reconstructed escape column vector; 'T' is the time-series periodicity factor matrix; 'G' = 'G1' is the transfer matrix established by the one-dimensional time-series snapshot complex network based on the bias current mapping link matrix when X = X1, and 'G' = 'G2' is the transfer matrix established by the two-dimensional time-series snapshot complex network based on the bias current mapping link matrix when X = X2; 'U' represents the PR vector of the model; 'k' is the number of iterations.

[0031] The elements of vector U are sorted in descending order, that is, sorted according to the criticality of each node, to obtain the comprehensive criticality ranking of the bias current nodes of the power grid.

[0032] As a preferred embodiment, the present invention considers the system's connectivity, topology, and operating characteristics to establish a complex system network model and a field-path coupling model.

[0033] Secondly, this invention proposes a key node identification system for bias current distribution based on complex network identification, comprising:

[0034] The calculation and correction module is used to establish a complex system network model and field-circuit coupling model. It uses the field-circuit coupling model to calculate the bias current before and after the DC blocking device is installed at each station of the system, and corrects the change in bias current before and after the DC blocking device is installed based on the bias current balance factor correction strategy.

[0035] The mapping link matrix construction module establishes the bias current mapping link matrices corresponding to the one-dimensional time-series snapshot complex network model and the two-dimensional time-series snapshot complex network model, respectively, based on the corrected change in bias current before and after the installation of the DC blocking device.

[0036] The identification module considers the mapping relationship between the bias current of each node and the bias current of the nodes between dimensions. It establishes a key node model of bias current based on the improved webpage ranking algorithm of two-dimensional time-series snapshots. It solves the ranking of PR values ​​of each node under the two-dimensional time-series snapshot complex network model by mapping the one-dimensional time-series snapshot complex network model, and completes the identification of key nodes of bias current distribution in the entire system.

[0037] Thirdly, 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 the method described in the present invention.

[0038] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the present invention.

[0039] The present invention has the following advantages and beneficial effects:

[0040] 1. This invention establishes a key node identification model for the distribution of bias current in a complex network based on two-dimensional time-series snapshots. This model can fully consider the mapping relationship of bias current between nodes in the same dimension and the mapping relationship of bias current between different nodes in the same dimension, thereby optimizing the configuration of DC blocking devices in the power grid from a global perspective.

[0041] 2. This invention outputs the calculation results of a one-dimensional time-series snapshot complex network model and a two-dimensional time-series snapshot complex network model by using the bias magnetic coupling coefficient matrix based on the maximum mean difference migration distance, which can effectively improve the identification speed of key nodes in the bias magnetic current distribution.

[0042] 3. This invention improves the PageRank algorithm by adopting a PR vector adjustment strategy based on the time-series periodic factor matrix of relative h-index importance and the biased magnetic weight factor. This improves the PR vector allocation value of new web pages and effectively addresses the problem that the PR value of new web pages is too low, which makes them inconspicuous. It effectively ensures the accuracy and rationality of the obtained PR vector ranking of system nodes and has higher effectiveness, making it suitable for analyzing and solving practical problems. Attached Figure Description

[0043] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0044] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention.

[0045] Figure 2 This is a schematic diagram of the computer device structure according to an embodiment of the present invention.

[0046] Figure 3 This is a system principle block diagram according to an embodiment of the present invention.

[0047] Figure 4 This is a schematic diagram of the system topology according to an embodiment of the present invention.

[0048] Figure 5 This is a schematic diagram of the one-dimensional system node bias current mapping relationship according to an embodiment of the present invention.

[0049] Figure 6 This is a schematic diagram of the bias current of a system without DC blocking device according to an embodiment of the present invention.

[0050] Figure 7This is a schematic diagram comparing the bias current of the system without the DC blocking device, with the first installation, and with the second installation, according to an embodiment of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0052] Example 1

[0053] To address the shortcomings of existing DC bias suppression technologies, this invention proposes a method for identifying key nodes in bias current distribution based on complex network identification. This invention considers the impact of current fluctuations before and after DC blocking installation on the system. It introduces a bias current coupling coefficient matrix based on the maximum mean difference migration distance, considers the mapping relationship of bias current between all system nodes (i.e., stations) based on a complex network, establishes a bias current link matrix based on a two-dimensional time-series snapshot, and introduces the PageRank algorithm, a time-series periodic factor, and a bias current weight factor to improve the algorithm's dynamics and make the mapping relationship of bias current between nodes more explicit, thus identifying key nodes in bias current distribution.

[0054] Specifically, such as Figure 1 As shown, the method proposed in this embodiment of the invention specifically includes the following steps:

[0055] Step 1: Consider the system's connectivity, topology, and operating characteristics to establish a complex system network model and a field-path coupling model. Use the field-path coupling model to calculate the bias current before and after installing DC blocking devices at each station in the system. Based on the maximum allowable bias current of the node, improve the entropy theory to introduce a bias current balance factor, and correct the change in bias current before and after installing DC blocking devices based on the bias current balance factor correction strategy.

[0056] Among them, the complex system network model is a directed network model established considering the node connectivity, network topology, and operating characteristics of the DC grounded near-field AC system, used to reflect the mutual influence of the bias currents of each node in the network. The field-circuit coupling model is a circuit-magnetic field coupling model of the complex system network model and the soil equivalent model of the DC grounded near-field AC system, used to calculate the distribution of bias currents in the AC system.

[0057] Step 2: Based on the corrected change in bias current before and after installing the DC blocking device, establish the bias current mapping link matrix corresponding to the one-dimensional time-series snapshot complex network model and the two-dimensional time-series snapshot complex network model, respectively.

[0058] Step 3: Consider the mapping relationship between the bias current of each node and the node bias current between the dimension and degree, establish a key node model of bias current based on the two-dimensional time-series snapshot improved web page ranking algorithm, introduce the time-series period factor and bias weight factor, correct the transition matrix, and solve the PR value ranking of each node under the two-dimensional time-series snapshot complex network model by mapping the one-dimensional time-series snapshot complex network model, and complete the identification of key nodes of the bias current distribution of the entire system.

[0059] As an optional implementation, the bias current balance factor correction strategy in step 1 is specifically as follows: based on the node-allowed maximum bias current improved entropy theory, a dynamic balance coefficient α for the bias current is introduced to correct the change in bias current before and after the installation of the DC blocking device.

[0060]

[0061]

[0062]

[0063] ΔI′ ij =ΔI ij +α k (4)

[0064] In the formula, ΔI ij This represents the change in bias current calculated based on the field-circuit coupling model; ΔI′ ij This represents the correction value for the change in bias current considering the maximum bias current; This represents the bias current value at node k without the DC blocking device installed. This represents the bias current value at node k when the DC blocking device is installed for the mth time; i k Indicates the bias current margin at node k without DC blocking; I max Indicates the maximum bias current allowed through the node; α k α represents the dynamic balance coefficient of the bias current at node k; k The larger the value, the greater the bias current that node k receives.

[0065] As an optional implementation, step 2 is based on a complex system network model and a field-circuit coupling model. It uses the bias current before and after installing the DC blocking device and the bias current mapping link matrix based on the bias current balance factor correction strategy to quantify the bias current mapping relationship between the same node in each dimension and the bias current mapping relationship between different nodes in the same dimension.

[0066] A complex network model is defined as follows: the initial installation of the DC blocking device is a one-dimensional time-series snapshot, and subsequent updates and installations of the DC blocking device are defined as two-dimensional time-series snapshots. The bias current distribution matrix A = [I] is introduced when the DC blocking device is not installed. ij ], where the diagonal elements represent the bias current flowing through substation nodes, and the off-diagonal elements are zero; based on the maximum mean difference migration distance to represent the mapping relationship between bias current stations in the entire network, a bias coupling coefficient matrix S = [s ij Establish the bias current mapping link matrix X = [Δx] ij The matrix elements are as follows:

[0067]

[0068] Δx ij =s ij ×ΔI′ ij +I ij i=1,…,nj=1,…,n (6)

[0069] In the formula, S ij This indicates the strength of the bias current mapping relationship between node i and node j; S ij The larger the value, the stronger the mapping relationship between the bias currents of node i and node j.

[0070] As an optional implementation, considering the mapping relationship between the bias currents of each site and the node bias currents between dimensions, a key node identification model based on bias currents is established using a two-dimensional time-series snapshot-based improved webpage ranking algorithm. Based on the bias current mapping link matrix X∈{X1, X2}, the corresponding bias current mapping transition matrix G∈{G1, G2} is written, and G=[g ij Based on the relative importance of the h-index, a time-series periodic factor matrix and a biased magnetic weight factor are introduced. A node mapping relationship transfer criticality correction strategy is adopted, and a transfer degree correction transfer factor p is introduced, specifically expressed as follows:

[0071]

[0072]

[0073] U (k+1)T =U (k)T G+T (9)

[0074]

[0075] In the formula, p mdenoted by , ...

[0076] This embodiment also proposes a computer device for performing the methods described above in this embodiment.

[0077] Specifically, such as Figure 2 As shown, a computer device includes a processor, internal memory, and a system bus; various device components, including the internal memory and processor, are connected to the system bus. The processor is hardware used to execute computer program instructions through basic arithmetic and logical operations within the computer system. Internal memory is a physical device used for temporary or permanent storage of computational programs or data (e.g., program state information). The system bus can be any of several types of bus architectures, including a memory bus or memory controller, a peripheral bus, and a local bus. The processor and internal memory can communicate via the system bus. Internal memory includes read-only memory (ROM) or flash memory (not shown in the figure), and random access memory (RAM), which typically refers to the main memory loaded with the operating system and computer programs.

[0078] Computer devices typically include an external storage device. The external storage device can be selected from a variety of computer-readable media, which are any usable media accessible by a computer device, including both removable and fixed media. Examples of computer-readable media include, but are not limited to, flash memory (microSD cards), CD-ROMs, digital versatile optical discs (DVDs) or other optical disc storage, magnetic tape cartridges, magnetic tapes, disk storage or other magnetic storage devices, or any other media that can be used to store desired information and is accessible by a computer device.

[0079] Computer devices can logically connect to one or more network terminals in a network environment. Network terminals can be personal computers, servers, routers, smartphones, tablets, or other public network nodes. Computer devices connect to network terminals through network interfaces (LAN interfaces). A Local Area Network (LAN) is a computer network interconnected within a limited area, such as a home, school, computer lab, or office building using network media. WiFi and twisted-pair Ethernet are the two most commonly used technologies for building LANs.

[0080] It should be noted that other computer systems, including more or fewer subsystems than computer equipment, are also applicable to the invention.

[0081] As described in detail above, the computer device applicable to this embodiment can perform the specified operations of the method for identifying key nodes in bias current distribution. The computer device performs these operations through software instructions executed by a processor in a computer-readable medium. These software instructions can be read into memory from a storage device or from another device via a local area network interface. The software instructions stored in memory cause the processor to execute the aforementioned method for processing group membership information. Furthermore, the present invention can also be implemented through hardware circuitry or a combination of hardware circuitry and software instructions. Therefore, implementation of this embodiment is not limited to any specific combination of hardware circuitry and software.

[0082] Example 2

[0083] This invention proposes a key node identification system for bias current distribution based on complex network identification, specifically as follows: Figure 3 As shown, it includes:

[0084] The calculation and correction module considers the system's connectivity, topology, and operating characteristics to establish a complex system network model and field-path coupling model. It uses the field-path coupling model to calculate the bias current before and after the installation of DC blocking devices at each station in the system. Based on the maximum allowable bias current of the node, it introduces a bias current balance factor based on the improved entropy theory. Based on the bias current balance factor correction strategy, it corrects the change in bias current before and after the installation of DC blocking devices.

[0085] The mapping link matrix construction module establishes the bias current mapping link matrices corresponding to the one-dimensional time-series snapshot complex network model and the two-dimensional time-series snapshot complex network model, respectively, based on the corrected change in bias current before and after the installation of the DC blocking device.

[0086] The identification module considers the mapping relationship between the bias current of each node and the bias current of the nodes between dimensions. It establishes a key node model of bias current based on the improved webpage ranking algorithm of multi-dimensional time-series snapshots, introduces time-series period factors and bias weight factors, corrects the transfer matrix, and solves the ranking of PR values ​​of each node under the two-dimensional time-series snapshot complex network model by mapping the one-dimensional time-series snapshot complex network model, thus completing the identification of key nodes in the distribution of bias current of the entire system.

[0087] Example 3

[0088] This invention takes a DC grounding electrode system as an example. The system includes six 500kV substations, eighteen 220kV substations, and two DC converter stations. These substations are numbered sequentially from 1 to 24. The technology proposed in the above embodiment is verified, and the system topology is as follows: Figure 4 As shown, by utilizing the neutral point current magnitudes before and after the first installation of the DC blocking device, the bias current mapping link matrix of the one-dimensional time-series snapshot complex network model of the node system can be obtained, and the one-dimensional system node bias current mapping relationship can be constructed, as follows. Figure 5 As shown. The specific process is as follows.

[0089] Step 1: Consider the system's connectivity, topology, and operating characteristics to establish a complex system network model and a field-circuit coupling model. Use the field-circuit coupling model to calculate the bias current before and after installing DC blocking devices at each station in the system. Based on the maximum allowable bias current of the node, improve the entropy theory to introduce a bias current balance factor. Then, based on the bias current balance factor correction strategy (i.e., the formulas (1)-(4) in the above embodiment 1), correct the change in bias current before and after installing DC blocking devices.

[0090] Step 2: Based on the corrected changes in bias current before and after the installation of the DC blocking device, establish the bias current mapping link matrices corresponding to the one-dimensional time-series snapshot complex network model and the two-dimensional time-series snapshot complex network model, respectively. The system includes a total of n=24 transmission stations. The specific construction process of the bias current mapping link matrices of the time-series snapshot complex network models of each dimension is shown in equations (5)-(6) in the above embodiment 1.

[0091] Step 3: Based on the bias current mapping link matrix X1 of the one-dimensional time-series snapshot complex network model, the bias current mapping transfer matrix G1 of the one-dimensional time-series snapshot complex network model is written using Equation (8), and the vector is solved.

[0092] Step 4, if Then output vector U1, and solve for the two-dimensional matrix X2 based on the bias current calculation results of the one-dimensional time-series snapshot complex network model; otherwise, return to step 3.

[0093] Step 5: Based on the bias current mapping link matrix X2 of the two-dimensional time-series snapshot complex network model, the bias current mapping transfer matrix G2 of the two-dimensional time-series snapshot complex network model is written using Equation (8), and the vector is solved.

[0094] Step 6, if If so, output vector U2; otherwise, return to step 5.

[0095] Step 7: Sort the calculated one-dimensional time-series snapshot complex network model node PR vector (i.e., U1) and two-dimensional time-series snapshot complex network model node PR vector (i.e., U2) according to the criticality of each node to obtain the comprehensive ranking of the criticality of the power grid bias current node, thereby realizing the identification of the critical node of the power grid bias current.

[0096] The sorting results of the two sets of node PR values ​​based on the transmission node number are shown in Table 1.

[0097] Table 1. Comparison of node PR rankings between one-dimensional and two-dimensional temporal snapshot complex network models.

[0098]

[0099]

[0100] As shown in Table 1, the neutral point bias current of the transformers at each node in the system exhibits a consistent mapping relationship. With an increasing number of governance nodes, the first four key sites in the one-dimensional time-series snapshot complex network identification model are identical to the first four key sites in the two-dimensional time-series snapshot complex network model. Figure 6 The calculation results show that there are large DC currents at the neutral points of transformers at stations 6, 9, and 21, indicating severe DC bias. Based on power system operating experience, capacitor-based DC blocking devices are installed at the neutral points of transformers with severe DC bias. Specifically, capacitor-based DC blocking devices are only installed at the neutral points of transformers at stations 6, 9, and 21. At this time, the DC current values ​​at the neutral points of transformers at each node of the system are as follows: Figure 7 The broken line graph shows the results of the first installation of the DC blocking device and the calculation of the complex network model using a one-dimensional time-series snapshot. At this point, it can be seen that the DC bias phenomenon at node 1 is very severe. Therefore, a DC blocking device is subsequently installed at the neutral point of the transformer at node 1. The DC current values ​​at the neutral points of the transformers at each node of the system are as follows: Figure 7 The line graph showing the results of the second DC blocking device installation and the calculation of the complex network model using a two-dimensional time-series snapshot is shown. This precisely verifies the four stations with the highest PR values ​​calculated in Table 1, namely stations 1, 6, 9, and 21. Installing DC blocking devices based on the key node identification results can effectively prevent the DC current at the neutral point of the power grid transformer from exceeding the limit before and after the installation of DC blocking devices.

[0101] The above results demonstrate that the key node identification method proposed in this invention can not only address the issue of bias current based on the mapping relationship of bias current at the same node before and after the installation of DC blocking devices, but also comprehensively analyze the key nodes in the bias current distribution scenario based on the mapping relationship of bias current between different nodes in the system after the installation of DC blocking devices. The ranking results calculated by its one-dimensional time-series snapshot complex network model can quickly locate key points in the current state. Furthermore, by combining the two-dimensional time-series snapshot complex network model with the one-dimensional model, the calculation results can accurately locate key nodes considering global governance. The calculation results are output to an offline database, achieving the goal of managing bias current across the entire network, which is of great significance for maintaining the safety of equipment within the system.

[0102] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for identifying key nodes in bias magnetic current distribution based on complex network identification, characterized in that, include: A complex system network model and a field-circuit coupling model are established. The field-circuit coupling model is used to calculate the bias current before and after the DC blocking device is installed at each station of the system. The change in bias current before and after the DC blocking device is installed is corrected based on the bias current balance factor correction strategy. Based on the corrected change in bias current before and after the installation of DC blocking device, bias current mapping link matrices corresponding to the one-dimensional time-series snapshot complex network model and the two-dimensional time-series snapshot complex network model are established respectively. Considering the mapping relationship between the bias current of each node and the node bias current between the dimension order, a key node model of bias current based on the two-dimensional time-series snapshot improved web page ranking algorithm is established. The PR value ranking of each node under the two-dimensional time-series snapshot complex network model is solved by mapping the one-dimensional time-series snapshot complex network model, and the key node identification of the bias current distribution of the whole system is completed. The specific process for correcting the change in bias current is as follows: ； ； ； ； In the formula, This represents the change in bias current calculated based on the field-circuit coupling model; This represents the correction value for the change in bias current considering the maximum bias current; This indicates the node without a DC blocking device. The value of the bias current; Represents a node The The bias current value of the DC blocking device added later; Represents a node Bias current margin without DC blocking device; Indicates the maximum bias current allowed through the node; Represents a node The dynamic balance coefficient of the bias current; Larger values ​​indicate a larger node The larger the bias current obtained by mapping; The specific process of identifying key nodes includes: Based on bias current mapping link matrix Write out the corresponding bias current mapping transfer matrix. ,and Based on the relative importance of the h-index, a time-series periodic factor matrix and a biased magnetic weight factor are introduced. A node mapping relationship transfer criticality correction strategy is adopted, and a transfer degree correction transfer factor p is introduced, specifically expressed as follows: ； ； ； ； In the formula, This represents the transfer degree correction factor when the DC blocking device is installed for the mth time. This represents the number of nodes where the absolute value of the bias current decreases when the DC blocking device is installed for the mth time. This represents the number of nodes at which the absolute value of the bias current increases when the DC blocking device is installed for the mth time. Represents an n-dimensional column vector with elements all equal to 1; This represents the damping coefficient and the transition probability between connected nodes; Represents the reconstructed escape column vector; For the time-series periodic factor matrix; when hour, For a one-dimensional time-series snapshot complex network, the transition matrix is ​​established based on the bias magnetic current mapping link matrix. hour, The transition matrix is ​​established based on the bias magnetic current mapping link matrix for a two-dimensional time-series snapshot complex network; This represents the PR vector of the model; k is the number of iterations. For vectors Each element is sorted in descending order, that is, sorted according to the criticality of each node, to obtain the comprehensive criticality ranking of the bias current nodes of the power grid.

2. The method for identifying key nodes of bias current distribution based on complex network identification according to claim 1, characterized in that, The specific strategy for correcting the bias current balance factor is as follows: based on the node-allowed maximum bias current improved entropy theory, a dynamic balance coefficient for bias current is introduced to correct the change in bias current before and after the installation of the DC blocking device.

3. The method for identifying key nodes of bias magnetic current distribution based on complex network identification according to claim 1, characterized in that, A complex network model is defined as the initial installation of the DC blocking device as a one-dimensional time-series snapshot, and a complex network model is defined as the subsequent updates and installations of the DC blocking device as two-dimensional time-series snapshots.

4. The method for identifying key nodes of bias current distribution based on complex network identification according to claim 3, characterized in that, The process of establishing the bias current mapping link matrix specifically includes: The bias current distribution matrix of the system without DC blocking device is introduced. In this context, the diagonal elements represent the bias current flowing through the substation nodes, while the off-diagonal elements are zero. The mapping relationship between bias current stations across the entire network is represented by the migration distance based on the maximum mean difference, and a bias coupling coefficient matrix is ​​introduced. Establish the bias current mapping link matrix The matrix elements are as follows: ； ； In the formula, Represents a node and nodes The strength of the bias current mapping relationship between them; Larger values ​​indicate a larger node and nodes The stronger the mapping relationship between the bias currents, the better. This represents the correction value for the change in bias current considering the maximum bias current.

5. A method for identifying key nodes of bias magnetic current distribution based on complex network identification according to any one of claims 1-4, characterized in that, Considering the system's connectivity, topology, and operational characteristics, establish complex system network models and field-path coupling models.

6. A key node identification system for bias magnetic current distribution based on complex network identification, characterized in that, include: The calculation and correction module is used to establish a complex system network model and field-circuit coupling model. It uses the field-circuit coupling model to calculate the bias current before and after the DC blocking device is installed at each station of the system, and corrects the change in bias current before and after the DC blocking device is installed based on the bias current balance factor correction strategy. The mapping link matrix construction module establishes the bias current mapping link matrices corresponding to the one-dimensional time-series snapshot complex network model and the two-dimensional time-series snapshot complex network model, respectively, based on the corrected change in bias current before and after the installation of the DC blocking device. The identification module considers the mapping relationship between the bias currents of each node and the node bias currents between dimensions. It establishes a key node model of bias current based on an improved webpage ranking algorithm using two-dimensional time-series snapshots. The PR value ranking of each node under the two-dimensional time-series snapshot complex network model is solved by mapping a one-dimensional time-series snapshot complex network model, thus completing the identification of key nodes in the bias current distribution of the entire system. The specific process for correcting the change in bias current is as follows: ； ； ； ； In the formula, This represents the change in bias current calculated based on the field-circuit coupling model; This represents the correction value for the change in bias current considering the maximum bias current; This indicates the node without a DC blocking device. The value of the bias current; Represents a node The The bias current value of the DC blocking device added later; Represents a node Bias current margin without DC blocking device; Indicates the maximum bias current allowed through the node; Represents a node The dynamic balance coefficient of the bias current; Larger values ​​indicate a larger node The larger the bias current obtained by mapping; The specific process of identifying key nodes includes: Based on bias current mapping link matrix Write out the corresponding bias current mapping transfer matrix. ,and Based on the relative importance of the h-index, a time-series periodic factor matrix and a biased magnetic weight factor are introduced. A node mapping relationship transfer criticality correction strategy is adopted, and a transfer degree correction transfer factor p is introduced, specifically expressed as follows: ； ； ； ； In the formula, This represents the transfer degree correction factor when the DC blocking device is installed for the mth time. This represents the number of nodes where the absolute value of the bias current decreases when the DC blocking device is installed for the mth time. This represents the number of nodes at which the absolute value of the bias current increases when the DC blocking device is installed for the mth time. Represents an n-dimensional column vector with elements all equal to 1; This represents the damping coefficient and the transition probability between connected nodes; Represents the reconstructed escape column vector; For the time-series periodic factor matrix; when hour, For a one-dimensional time-series snapshot complex network, the transition matrix is ​​established based on the bias magnetic current mapping link matrix. hour, The transition matrix is ​​established based on the bias magnetic current mapping link matrix for a two-dimensional time-series snapshot complex network; This represents the PR vector of the model; k is the number of iterations. For vectors Each element is sorted in descending order, that is, sorted according to the criticality of each node, to obtain the comprehensive criticality ranking of the bias current nodes of the power grid.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-5.

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