A database-based method for evaluating the seismic resilience of an ultra-high voltage converter station

Abstract

The application provides a kind of based on database's ultra-high voltage converter station seismic toughness evaluation method, belongs to converter station technical field, comprising: 1, read JSON file from database and dynamically build converter station before earthquake adjacency matrix A (G);2, determine the seismic vulnerability evaluation criterion of converter station subsystem by finite element method and seismic vulnerability parameter method;3, input measured seismic acceleration time history curve, calculate the failure probability of each subsystem;4, carry out Monte Carlo simulation, determine the working state of each subsystem, generate the post-earthquake adjacency matrix A' (G) of converter station;5, use breadth-first algorithm, calculate the residual function of converter station through post-earthquake adjacency matrix of converter station;6, repeat S4-S5 10000 times, calculate the system function index of converter station.The application truly restores the connection information of each subsystem in converter station, and can accurately calculate the residual function of converter station after earthquake, to achieve the purpose of automatically evaluating the seismic toughness of converter station.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of converter stations, and particularly relates to a method for evaluating the anti-seismic toughness of an ultra-high voltage converter station based on a database. BACKGROUND

[0002] The power system is one of the indispensable infrastructures in modern society, which provides key support for our life, industry and economic development. The power system can transport electric energy from power plants to various power consumption fields such as homes, businesses and industries. In addition, the power system also has important functions such as regulating the balance between supply and demand of electric energy, ensuring the quality of electric energy, and guaranteeing the safety of electric energy. Without the power system, people cannot use basic facilities such as electrical appliances and lighting, and various industries will also be affected. The importance of the power system is self-evident. Therefore, ensuring the safe, reliable and efficient operation of the power system is crucial for the development of modern society and the quality of people's life.

[0003] The converter station is an important part of the power system, which is mainly used to convert alternating current into direct current for long-distance transmission of electric energy. In addition, the converter station can also ensure the stable operation of the power system by controlling voltage and current, and quickly respond to and adjust abnormal conditions of the system. Therefore, the converter station plays a crucial role in the power system.

[0004] The converter station can be divided into three parts: the alternating current area (AC), the alternating current-direct current conversion area (AC-DC) and the direct current area (DC). The alternating current area is responsible for receiving alternating current from the alternating current power supply and providing sufficient alternating current power for the alternating current-direct current conversion area. The alternating current-direct current conversion area is the core area of the converter station, which is responsible for smoothly converting alternating current into direct current. The direct current area is used to process direct current and is responsible for outputting direct current to the transmission line.

[0005] In order to realize the electrical functions of the converter station, the station usually contains seven subsystems: gas insulated transmission line (GIL), alternating current filter group (ACFG), converter transformer loop (CTG), converter valve group (CVG), wall bushing (WB), direct current field loop (DCFL) and direct current filter loop (DCFG). The functions of each subsystem are explained as follows:

[0006] (1) Gas insulated transmission line (GIL): high current carrying capacity, can allow large capacity current transmission.

[0007] (2) Alternating current filter group (ACFG): filters out high-frequency noise and harmonics in alternating current to ensure stable operation of the converter.

[0008] (3) Converter transformer loop (CTG): converts alternating current of high-voltage transmission line into low-voltage direct current.

[0009] (4) Converter Valve Group (CVG): Realizes the mutual conversion between AC and DC, usually uses semiconductor electronic components such as silicon controlled rectifier.

[0010] (5) Wall Bushing (WB): Avoids the direct contact between cable and partition wall, and shields high-frequency noise and interference signal.

[0011] (6) DC Field Loop (DCFL): Controls parameters such as voltage, current and power.

[0012] (7) DC Filter Loop (DCFG): Filters high-frequency noise and harmonic in DC to ensure the smooth work of DC load.

[0013] According to the introduction of the functions of the above subsystems, the power transmission path of the converter station is explained as follows. First, the power is input into the AC area through the three-phase AC field, enters the AC filter (ACFG) after the gas insulated line (GIL) for filtering, and eliminates high-frequency noise, fluctuation or interference signal. After the filtering is completed, the AC power enters the AC-DC conversion area through the post insulator, is reduced in voltage by the converter transformer (CTG), and the conversion from AC to DC is completed by the converter valve group (CVG). After the AC power is converted into DC power, the DC power is transmitted to the DC area (DCFL) through the wall bushing (WB), and finally the AC component in the DC power is removed by the DC filter (DCFL) to output the DC power.

[0014] The subsystems in the AC area (AC) of the converter station are connected in series to form the incoming line end, and the subsystems in the AC-DC conversion area (AC-DC) and the DC area (DC) are connected in series to form the outgoing line end. Generally, one incoming line end can only bear the power of one outgoing line end, and one outgoing line end can only bear the power from at most one incoming line end, and cannot be overloaded. The relationship between the incoming line end and the outgoing line end is not simply in series, and power redistribution can occur between them. When the working number of the incoming line end m IN is not equal to the working number of the outgoing line end m out , the power that can be output by the converter station depends on the minimum value m STATION between the working numbers of the incoming and outgoing line ends. The converter station is usually designed to be bilaterally symmetrical, i.e. the number of incoming line ends is equal to the number of outgoing line ends, both being m.

[0015] After experiencing the effect of an earthquake, the working line of the incoming line end m IN satisfies 0≤m IN ≤m; the working line of the outgoing line end m out satisfies 0≤m out ≤n, then it is known that 0≤m STATION ≤n. The possible value of m STATION is 0, 1, 2, …, m, and the probability of m STATION = 0 is P0.STATION = 1 is P1 …… Then the system function index K is the expectation E(k) of the system residual function k:

[0016]

[0017] The seismic resilience of the converter station is evaluated, i.e. the specific value of the system function index K is obtained. The following briefly describes the existing method for evaluating the seismic resilience of the converter station.

[0018] (1) A whole Simulink simulation model is established based on the layout and connection relationship of each subsystem in the converter station.

[0019] (2) The working condition of each subsystem is obtained by sampling through Monte Carlo simulation combined with the seismic vulnerability curve of the subsystem.

[0020] (3) The system function index K of the power system under a certain PGA is calculated through the Simulink simulation model.

[0021] In the existing method for evaluating the seismic resilience of the converter station, the binary working state (not working, i.e. failure) assumption of the subsystem is usually adopted. When the subsystem fails, the current cannot pass through the subsystem, and the line end where the subsystem is located enters the circuit breaking state. The ground motion acceleration peak value (PGA) is usually taken as the reference index, and it is assumed that the failure probability P of each subsystem obeys the standard normal cumulative distribution with a median value μ and a standard deviation β:

[0022]

[0023] Subsequently, by setting the specific value of PGA, Monte Carlo simulation is repeated 10,000 times. The residual function k and its corresponding probability obtained by each Monte Carlo simulation calculation are counted. Finally, the system function index K is calculated, and the seismic resilience evaluation of the converter station is completed.

[0024] However, the failure probability P of each subsystem in the converter station does not necessarily have to be evaluated using the seismic vulnerability parameter method. A finite element model can also be established and the measured vibration is input, and whether the subsystem fails is judged according to the running result. In addition, the failure probability P of each subsystem can also be analyzed according to statistical data; other reference indexes and analytical functions can also be used for analysis. When the evaluation criterion of the failure probability of the subsystem is changed, the Simulink simulation model needs to be rebuilt.

[0025] In addition, there is a large amount of information in the converter station, and there is a lack of a system for unified management of these information. The specific structural information of the converter station has not been stored, and when trying to restore the connection information of each subsystem in the converter station, the Simulink simulation model needs to be manually interpreted, and cannot be automated.

[0026] Therefore, the existing converter station seismic toughness evaluation process has the following deficiencies:

[0027] (1) There are a large amount of data in the evaluation framework, and there is a lack of a system for unified management of these data.

[0028] (2) Different converter stations can have different structures, and there is no information stored for these structures.

[0029] (3) When trying to replace the seismic vulnerability measurement criteria of the subsystem, the simulation model needs to be rebuilt.

[0030] (4) There is no automatic evaluation process according to the measured vibration.

[0031] Therefore, there is a need for a database-based seismic toughness evaluation method for an ultra-high voltage converter station, which can guide the management of converter station related data and automatically evaluate the seismic toughness of the converter station. SUMMARY

[0032] The purpose of the present application is to provide a database-based seismic toughness evaluation method for an ultra-high voltage converter station, characterized in that it comprises the following steps:

[0033] S1: reading a JSON file from a database and dynamically constructing a pre-earthquake adjacency matrix A(G) of the converter station to restore the connection information of each subsystem in the converter station;

[0034] S2: determining the seismic vulnerability evaluation criteria of the converter station subsystem by the finite element method and the seismic vulnerability parameter method;

[0035] S3: inputting the measured seismic acceleration time history curve, and calculating the failure probability of each subsystem according to the selected seismic vulnerability evaluation criteria of the subsystem;

[0036] S4: performing Monte Carlo simulation to determine the working state of each subsystem, and when there is a subsystem failure, setting the elements of the row and column where the subsystem is located in the pre-earthquake adjacency matrix A(G) to 0, thereby generating a post-earthquake adjacency matrix A'(G) of the converter station;

[0037] S5: using a breadth-first algorithm to calculate the remaining function of the converter station through the post-earthquake adjacency matrix of the converter station;

[0038] S6: repeating S4-S5, counting the values and probabilities of the remaining function of the converter station, calculating the system function index of the converter station, and completing the database-based seismic toughness evaluation of the ultra-high voltage converter station.

[0039] Further, in S1, the JSON file stores the connection information between the converter station subsystems in the form of vertex: vertex out-neighborhood key-value pairs, specifically comprising the following steps:

[0040] S11: read the names of all vertices in the JSON file, and arrange all the names in row order;

[0041] S12: find the corresponding out-neighborhood of the vertex by the vertex name, and traverse the vertices in the out-neighborhood;

[0042] S13: set the current vertex in the i-th row in A(G) and the vertices in the out-neighborhood in the j-th column in A(G), then set the element A ij of the i-th row and the j-th column in A(G) to 1 until all vertices in the out-neighborhood are traversed.

[0043] Further, in S2, the formula of the seismic vulnerability assessment criterion is expressed as:

[0044]

[0045] wherein PGA is the peak ground motion acceleration; μ is the median of seismic capacity; β is the standard deviation of seismic capacity.

[0046] Further, in S2, the seismic vulnerability assessment criterion of the subsystem is dynamically replaced at runtime through the Strategy design pattern. When designing the program, the following steps are specifically taken:

[0047] S21: define the AbstractStrategy abstract class, and define the public interface of all supported algorithms in it;

[0048] S22: define the Context context class for maintaining a reference to an AbstractStrategy object; define an interface for the AbstractStrategy class to access its data;

[0049] S23: implement the seismic vulnerability assessment of the finite element model evaluation subsystem by defining the FEAStrategy class inherited from the AbstractStrategy class; implement the failure probability of the subsystem given by the seismic vulnerability parameter method by defining the VulParamStrategy class inherited from the AbstractStrategy class.

[0050] Further, in S3, the input of the measured seismic motion acceleration time history curve is specifically as follows: the actual occurring seismic motion is imported into the program in the form of a three-dimensional acceleration time history curve through the data of the acceleration sensor, and the failure probability P of each subsystem is calculated by the FEAStrategy class and the VulParamStrategy class through the Context context class sending the data of the three-dimensional acceleration time history curve.

[0051] Further, in S4, a random number r is generated by a U[0, 1] average distribution random number generator, and the random number r is compared with the failure probability P of the subsystem, specifically: when r≤P, the subsystem fails, and no operation is performed on the pre-earthquake adjacency matrix; when r>P, the subsystem fails, and all elements in the row and column where the subsystem is located in the pre-earthquake adjacency matrix A(G) are set to 0, thereby generating the post-earthquake adjacency matrix A'(G).

[0052] Further, in S5, the remaining function k of the converter station is calculated by a breadth-first algorithm, specifically: starting from the starting vertex, the vertices in the out-neighborhood thereof are traversed as the first layer, then the out-neighborhood of all the first layer nodes is traversed as the second layer, then the out-neighborhood of the second layer nodes is traversed as the third layer, and the operation is repeated until the point or vertex ending is reached, thereby forming a node tree, and the number of paths ending at the ending vertex in the node tree is counted as the remaining function k of the converter station.

[0053] Further, in S6, the number of repetitions of S4-S5 is 10000 times.

[0054] Further, in S6, the calculation formula of the system function index K of the converter station is represented as:

[0055]

[0056] Wherein, m is the number of the in-out line ends of the converter station; P i is the probability of the remaining function of the converter station taking the value of .

[0057] Compared with the prior art, the beneficial effects of the present application mainly lie in that the present application realizes the automation of the seismic resilience evaluation of the converter station based on the database, and the method has the characteristics of not depending on the Simulink simulation model and being able to dynamically change the seismic vulnerability evaluation criteria of the subsystem, and simultaneously utilizes the adjacency matrix and the breadth-first algorithm to truly restore the connection information of each subsystem in the converter station and accurately calculate the remaining function k of the converter station after the earthquake, so as to achieve the purpose of automatically evaluating the seismic resilience of the converter station. Moreover, the program design is clear and has strong expandability, which makes it more conducive to be applied to the seismic resilience evaluation of the real converter station. BRIEF DESCRIPTION OF DRAWINGS

[0058] Figure 1 is the flowchart of the seismic resilience evaluation method of the converter station based on the database of the present application.

[0059] Figure 2 is the E-R entity relationship diagram of the converter station database of the present application.

[0060] Figure 3This is a diagram showing the overall structure of the converter station according to the present invention.

[0061] Figure 4 This is a schematic diagram of the JSON file content of the converter station of the present invention.

[0062] Figure 5 This is a schematic diagram of the pre-earthquake adjacency matrix A(G) of the converter station of the present invention (presented in a table).

[0063] Figure 6 This is the UML class diagram of the Strategy design pattern in this invention.

[0064] Figure 7 This is a graphical structure representing a possible initial state of the converter station after an earthquake, as described in this invention.

[0065] Figure 8 This is a schematic diagram of a possible adjacency matrix A'(G) of the converter station after an earthquake, according to the present invention.

[0066] Figure 9 This is an example diagram of the breadth-first search algorithm's tree generation in this invention. Detailed Implementation

[0067] The following will describe in more detail, with reference to the schematic diagram, a database-based seismic toughness assessment method for ultra-high voltage converter stations according to the present invention, which illustrates the preferred embodiments of the present invention. It should be understood that those skilled in the art can modify the present invention described herein while still achieving the advantageous effects of the present invention. Therefore, the following description should be understood as being of general knowledge to those skilled in the art and is not intended to limit the present invention.

[0068] like Figure 1 and 2 As shown, a database-based method for assessing the seismic toughness of an ultra-high voltage converter station includes the following steps:

[0069] Step 1: Read the JSON file from the MySQL database and dynamically construct the pre-earthquake adjacency matrix A(G) of the converter station to restore the connection information of each subsystem in the converter station.

[0070] The JSON file in Step 1 stores connection information between converter station subsystems in a key-value pair format: vertex: vertex out-neighborhood. When dynamically constructing the pre-earthquake adjacency matrix A(G) using the JSON file, A(G) is first initialized as an n×n all-zero matrix, and then the JSON file is read line by line. First, the names of all vertices in the JSON file are read and sorted by row. Then, the corresponding out-neighborhood is found by the vertex name, and the vertices in that out-neighborhood are traversed. Let the current vertex be in the i-th row of A(G), and the vertices in the out-neighborhood be in the j-th column of A(G). Then, the element Ai in the i-th row and j-th column of A(G) is... ijis set to 1 until the out-neighborhood of all vertices is traversed.

[0071] Step 2

[0072] The seismic fragility assessment criteria of converter station subsystems are determined by finite element method and seismic fragility parameter method.

[0073] In Step 2, the seismic fragility assessment criteria of subsystems are dynamically changed during the program running, which is realized by Strategy design pattern. When designing the program, first define AbstractStrategy abstract class, and define the common interface of all supported algorithms in it. Then define Context context class, which maintains a reference to an AbstractStrategy object, and defines an interface that enables an AbstractStrategy class to access its data. Finally, design the specific supported algorithm classes to implement a specific algorithm. The FEAStrategy class inherited from AbstractStrategy class is defined to realize the seismic fragility assessment of subsystems by finite element model. The VulParamStrategy class inherited from AbstractStrategy class is defined to realize the failure probability P of subsystems given by seismic fragility parameter method.

[0074] The formula of seismic fragility assessment criteria is expressed as:

[0075]

[0076] Where PGA is the peak ground motion acceleration; μ is the median of seismic capacity; β is the standard deviation of seismic capacity.

[0077] Step 3

[0078] The input measured ground motion acceleration time history curve, according to the selected seismic fragility assessment criteria of subsystems, the failure probability of each subsystem is calculated.

[0079] The input measured ground motion acceleration time history curve is imported into the program in the form of three-dimensional acceleration time history curve through the data of acceleration sensor. Then the three-dimensional acceleration time history curve data is sent to FEAStrategy class and VulParamStrategy class by Context context class, and the failure probability P of subsystems is calculated by them.

[0080] FEAStrategy class outputs 0 or 1 by finite element calculation, VulParamStrategy class outputs 0 or 1 by The output value is between 0 and 1.

[0081] Step 4

[0082] Monte Carlo simulations generate a random number r using a U[0,1] average distribution random number generator and compare it with the failure probability P of the subsystem. If r ≤ P, the subsystem operates normally, and no operation is performed on the pre-earthquake adjacency matrix A(G); if r > P, the subsystem fails, and all elements in the row and column containing the subsystem in the pre-earthquake adjacency matrix A(G) are set to 0, thus generating the post-earthquake adjacency matrix A'(G).

[0083] Step 5

[0084] The remaining functions of the converter station are calculated using the breadth-first search algorithm based on the post-earthquake adjacency matrix.

[0085] The Breadth-First Search (BFS) algorithm starts with the initial vertex and traverses its out-neighbor vertices as the first level. Then, it traverses the out-neighbors of all nodes in the first level as the second level; then it traverses the out-neighbors of all nodes in the second level as the third level, and so on, repeating this operation until a vertex with an empty out-neighbor set or the final vertex is reached, thus forming a node tree. The number of paths in the tree ending at the final vertex is counted and represented as the remaining function k of the converter station.

[0086] Step 6

[0087] Repeat steps 4 and 5 10,000 times, and count the values ​​and probabilities of the remaining function k of the converter station. Calculate the system function index K of the converter station (i.e., the expected value E(k) of the remaining function k of the converter station).

[0088]

[0089] Where m is the number of incoming and outgoing lines at the converter station; P i The remaining function value of the converter station is set to The probability of.

[0090] The following detailed description of a database-based seismic toughness assessment method for ultra-high voltage converter stations according to the present invention is provided through specific embodiments.

[0091] Example

[0092] To gain a fuller understanding of the features of this invention and its applicability to practical engineering, this invention addresses, for example... Figure 3 The converter station structure shown is subjected to an automated seismic toughness assessment. Vertices sum1 and sum2 were added to simulate the power redistribution behavior of the converter station, while the remaining vertices are actual subsystems existing in the converter station.

[0093] Figure 3 The converter station's overall map structure shown can be used to... Figure 4 The JSON file shown is stored in a structured format.Figure 3 Transform into Figure 4 The principle is to first... Figure 3 All vertices are added to the JSON file line by line as strings. Then, the out-neighborhoods of each vertex are added to the corresponding vertex in the JSON file as an array, forming a key-value pair.

[0094] Depend on Figure 4 Generate as Figure 5 The converter station's pre-earthquake adjacency matrix A(G) is shown. The generation rule is: if there exists an edge from the i-th vertex to the j-th vertex, then the element A in the i-th row and j-th column of the adjacency matrix is... ij Set to 1.

[0095] Through design such Figure 6 The classes shown in the UML class diagram are used to dynamically switch subsystem evaluation criteria during program runtime. Taking the VulParamStrategy class as an example, it calculates the failure probability P of the subsystem by reading the mu and beta attributes of the Context class instance.

[0096] In a Monte Carlo simulation, the post-earthquake state diagram of the converter station is as follows: Figure 7 As shown. In Figure 7 In the middle, some subsystems failed (r>P), therefore from Figure 7 Delete all edges associated with the failed subsystem. Correspondingly, set all elements in the row and column containing the failed subsystem to 0 from the pre-earthquake adjacency matrix A(G) of the converter station, thus generating the post-earthquake adjacency matrix A'(G) of the converter station. Figure 8 As shown. Combined with Figure 8 The post-earthquake adjacency matrix A'(G) shown is used to calculate the remaining power k of the converter station using a breadth-first search algorithm. The remaining power k of the converter station depends on the minimum value between the number of paths from vertex GIL,i = (1,2,3,4) to vertex sum1 and the number of paths from vertex sum1 to vertex sum2.

[0097] Taking the calculation of the number of paths from vertex sum1 to vertex sum2 as an example, the breadth-first search algorithm can generate results like... Figure 9 The tree shown. In this tree, the vertices marked in blue indicate that their out-neighborhoods are empty sets, thus the traversal terminates there, and there is no endpoint sum2. Therefore, the number of all possible paths from sum1 to sum2 is 0, which is consistent with direct observation. Figure 7 This is consistent with the conclusion.

[0098] Afterwards, simply repeat the steps of Monte Carlo simulation → generating the post-earthquake adjacency matrix A'(G) → breadth-first search algorithm to calculate the remaining function k. This will allow us to statistically analyze the values ​​of the remaining function k and their corresponding probabilities, and finally calculate the converter station system function index K, thus completing the seismic toughness assessment of the converter station.

[0099] The above merely describes the preferred embodiments of the present application and does not limit the present application in any way. Any person skilled in the art can make any form of equivalent replacement, modification or change to the technical solutions and technical contents disclosed in the present application without departing from the scope of the technical solutions of the present application, and such still falls within the protection scope of the present application.

Claims

1. A database-based method for assessing the seismic toughness of ultra-high voltage converter stations, characterized in that, Includes the following steps: S1: Read a JSON file from the database and dynamically construct the pre-earthquake adjacency matrix of the converter station. In order to restore the connection information of each subsystem in the converter station; S2: Determine the seismic vulnerability assessment criteria for the converter station subsystem using the finite element method and the seismic vulnerability parameter method; S3: Input the measured ground motion acceleration time history curve, and calculate the failure probability of each subsystem according to the selected subsystem seismic vulnerability assessment criteria; S4: Perform Monte Carlo simulations to determine the operating status of each subsystem. If a subsystem fails, then use the pre-earthquake adjacency matrix... The elements in the row and column containing the subsystem are set to 0, thereby generating the post-earthquake adjacency matrix of the converter station. ; S5: Use the breadth-first search algorithm to calculate the remaining functions of the converter station through the post-earthquake adjacency matrix of the converter station; S6: Repeat S4-S5, statistically analyze the values ​​and probabilities of the remaining functions of the converter station, calculate the system function indicators of the converter station, and complete the seismic toughness assessment of the UHV converter station based on the database. In step S1, the JSON file stores the connection information between converter station subsystems in a vertex: vertex out-neighbor key-value pair format, specifically including the following steps: S11: Read the names of all vertices in the JSON file and sort all the names in line order; S12: Find the corresponding out-neighborhood of a vertex by its name, and traverse the vertices in that out-neighborhood; S13: Set the current vertex to... The first in The vertices in the row and out-neighborhood are in The first in The column will then No. Line 1 Column elements Set it to 1 until all out-neighborhoods of all vertices have been traversed; In S2, the seismic vulnerability assessment criteria of the subsystem are dynamically changed during program runtime using the Strategy design pattern. The specific steps involved in designing the program are as follows: S21: Define the AbstractStrategy abstract class and define the public interface for all supported algorithms in it; S22: Define a Context class to maintain a reference to an AbstractStrategy object; define an interface for the AbstractStrategy class to access its data; S23: The seismic vulnerability of the subsystem is evaluated by the finite element model by defining the FEAStrategy class, which inherits from the AbstractStrategy class; the failure probability of the subsystem is given by the seismic vulnerability parameter method by defining the VulParamStrategy class, which inherits from the AbstractStrategy class. In S4, the Monte Carlo simulation is performed by... A uniformly distributed random number generator generates a random number. , random number With the failure probability of the subsystem In comparison, specifically: when If the subsystem operates normally, it does not perform any operations on the pre-earthquake adjacency matrix; when If this subsystem fails, the pre-earthquake adjacency matrix will be lost. All elements in the row and column containing the subsystem are set to 0, thus generating the post-earthquake adjacency matrix. ; In step S5, the remaining functions of the converter station are calculated using a breadth-first search algorithm. Specifically, starting from the initial vertex, traverse the vertices in its out-neighborhood as the first layer. Then, traverse the out-neighborhoods of all nodes in the first layer as the second layer. Then, traverse the out-neighborhoods of the nodes in the second layer as the third layer. Repeat this operation until a vertex or point with an empty out-neighborhood is reached, thus forming a node tree. Count the number of paths in the node tree that terminate at the ending vertex as the remaining functionality of the converter station. .

2. The database-based seismic toughness assessment method for ultra-high voltage converter stations according to claim 1, characterized in that, In S2, the formula for the seismic vulnerability assessment criterion is expressed as follows: ； in, This represents the peak ground acceleration. This represents the median seismic resistance value. This represents the standard deviation of seismic resistance.

3. The seismic toughness assessment method for UHV converter stations based on a database according to claim 1, characterized in that, In step S3, inputting the measured ground acceleration time history curve specifically involves: importing the actual ground motion data from the acceleration sensor into the program in the form of a triaxial acceleration time history curve, and determining the failure probability of each subsystem. The data of the triaxial acceleration time history curves are sent to the FEAStrategy and VulParamStrategy classes for calculation via the Context class.

4. The seismic toughness assessment method for UHV converter stations based on a database according to claim 1, characterized in that, In S6, S4-S5 are repeated 10,000 times.

5. The seismic toughness assessment method for UHV converter stations based on a database according to claim 1, characterized in that, In S6, the converter station system functional indicators The calculation formula is expressed as: ； in, This refers to the number of incoming and outgoing lines at the converter station. The remaining function value of the converter station is set to The probability of.

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