Electric power system fault fragile line identification system

Abstract

The invention discloses a power system fault fragile line identification system, which belongs to the technical field of cascading fault fragile line identification, and comprises a data acquisition module, a data processing module, an identification module and a positioning module, communication connection is established among the data acquisition module, the data processing module, the identification module and the positioning module, the data acquisition module comprises a database model, an index acquisition unit and a data preprocessing unit, the data processing module comprises a calculation unit and an index data sorting unit, and the calculation unit is used for receiving index data transmitted by the database model and sorting the index data. The identification module comprises a key line identification unit and an attack unit, the attack unit is used for attacking the power transmission line, and the positioning module is used for receiving node data acquired by the data acquisition module. The data acquisition module and the data processing module are designed, so that the real-time performance of fault identification is greatly improved, and the requirement of a modern electric power system for quick response is met.

Description

Technical Field

[0001] The invention belongs to the technical field of cascading fault fragile line identification, and in particular relates to a power system fault fragile line identification system. Background Art

[0002] Fragile lines: There are a few lines in the power grid that play a key role in the development of cascading failures. Severe cascading failures in the power system will lead to the decoupling of the entire power network, causing large-scale power outages and causing huge economic losses to society. With the continuous development of the power system and the gradual formation of the power network, the operation of the power grid is getting closer and closer to its limit level. Therefore, it is very necessary to study and analyze the fragile lines in the cascading failures of the power system.

[0003] There are two main types of existing power system fragile line identification methods, which are based on power system state analysis and complex network analysis respectively. The representative method of power system state analysis is entropy theory, but the state analysis method generally relies on the flow calculation of multiple line disconnections. Even if DC flow calculation is used, the time cost is high and cannot meet the real-time requirements of safety analysis. In addition, the existing key line identification methods rarely consider the node information at both ends of the line and do not consider accidental line failures. Moreover, after the existing power system fault fragile lines are identified, they can only locate high-risk fault paths or fault path locations over a large area, but cannot accurately locate fault nodes, and the fault repair time is long.

[0004] Therefore, there is an urgent need to provide a power system fault vulnerable line identification system to solve the above problems. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the above-mentioned existing power system fragile line identification methods, which are mainly divided into two categories, based on power system state analysis and complex network analysis respectively. The representative method of power system state analysis is entropy theory, but the state analysis method generally relies on the flow calculation of multiple line disconnections. Even if DC flow calculation is adopted, the time cost is relatively high and cannot meet the real-time requirements of safety analysis. In addition, the existing key line identification methods rarely consider the node information at both ends of the line and the accidental failure of the line. Moreover, after the identification is completed, the existing power system fault fragile line can only locate the high-risk fault path or the fault path position on a large scale, but cannot accurately locate the fault node, and the fault repair time is relatively long. Provided is a power system fault fragile line identification system.

[0006] The technical solution adopted to solve the above technical problems is: a power system fault fragile line identification system, including a data acquisition module, a data processing module, an identification module and a positioning module, wherein the data acquisition module, the data processing module, the identification module and the positioning module are all connected by communication, and the system realizes rapid identification and fault location of fragile lines in the power system through the coordinated work of multiple modules;

[0007] The data collection module includes a database model, an indicator collection unit, and a data preprocessing unit. The indicator collection unit is used to collect indicator data and node data, and transmit the collected indicator data to the database model;

[0008] The data processing module includes a calculation unit and an indicator data sorting unit. The calculation unit is used to receive the indicator data transmitted by the database model, and perform real-time calculation and analysis on each of the indicator data to obtain an indicator data set, and wirelessly transmit the indicator data set to the indicator data sorting unit;

[0009] The indicator data sorting unit is used to receive the indicator data set calculated by the calculation unit, and arrange the data combinations in each indicator data set in order to obtain a power line importance sorting set;

[0010] The identification module includes a critical line identification unit and an attack unit, wherein the attack unit is used to attack the transmission line, and attack the line importance ranking set obtained by the indicator data ranking unit in sequence, and verify the importance of the power line in combination with the preset system optimal load loss;

[0011] The positioning module is used to receive the node data collected by the data collection module, and perform calculation and analysis on the node data to locate the position of the faulty node when a power line fault occurs.

[0012] Furthermore, the database model is used to provide a platform for real-time data collection and processing and to provide a real-time protection mechanism, and the development of the database model is based on the Linux system;

[0013] The index data includes voltage stability index data, capacity margin index data, real-time failure rate index data and topology connection index data;

[0014] The data preprocessing unit is used to preprocess the indicator data, including data cleaning, format conversion and standardization.

[0015] Further, the index collection unit uses a data collection and monitoring control component and a phasor measurement component to collect real-time data, the data collection and monitoring control component is used to collect real-time operation data of the power grid, and the phasor measurement component is used to provide high-precision index data and node data;

[0016] The real-time operation data of the power grid includes the impedance of the circuit elements in line i-j to the current, the resistance of line i-j, the active power of line i-j, the quality factor of line i-j, the line impedance of line i-j, the ground admittance of line i-j, the current flowing in line i-j, the line transmission power of line i-j when running, the maximum transmission capacity allowed by line ij, the equivalent shutdown rate of the power grid, the total number of power grid nodes, and the total number of power grid lines;

[0017] The node data includes the ground admittance of node i or node j, the ground admittance of the line end, the voltage vector of node i or node j, the phase angle of node i or node j, and the power of node i or node j.

[0018] Furthermore, the calculation formula of the voltage stability index data in the data processing module is as follows:

[0019] The voltage equation of line ij is:

[0020] U i =U j +I ij (R+JX)

[0021] in,

[0022] Expand the voltage equation into real and imaginary parts separately:

[0023]

[0024] Both equations are about U j The quadratic equation of , if it has a solution, satisfies:

[0025]

[0026] According to the branch end voltage U i The condition for having a solution is to define the voltage stability index L U―ij for:

[0027] L U―ij =max(L U1 ,L U2 )

[0028] in,

[0029]

[0030] Where X is the impedance of the circuit element in line i-j to the current; R is the resistance of line i-j; J is the node-to-ground admittance branch; B is the ground admittance at the end of the line; P is the active power; Q is the quality factor of line i-j; U i and U j are the voltage vectors of nodes i and j respectively; δ is the phase angle difference between nodes i and j, obtained by subtracting the phase angle of node j from the phase angle of node i; R+JX is the line impedance; is the line-to-ground admittance; P i +JQ i and P j +JQ j are the injected power of node i and the outflow power of node j respectively; I ij is the current flowing in the line.

[0031] Furthermore, the calculation formula of the capacity margin index data in the processing module is as follows:

[0032]

[0033] Among them, L S―ij is the capacity margin index data; S ij is the line transmission power of line ij in the current operating state; S ij―max is the maximum transmission capacity allowed by line ij; e is the equivalent outage rate of the power grid.

[0034] Furthermore, the real-time failure rate index data calculation formula in the processing module is as follows:

[0035] The accidental failure rate of line ij under severe weather conditions λ 2―ij :

[0036]

[0037] Among them, λ ij is the statistical average value of the failure rate of line ij, the number of line failures is obtained by the preset sensor in line ij, and the statistical average value of the failure rate is obtained according to the number of line failures; F ij is the probability of route ij being in extreme weather, obtained based on the real-time data in the database model; F′ ij is the ratio of failures occurring under extreme conditions to the total number of failures, obtained based on the data collected in the database model.

[0038] Furthermore, the topological connection index L in the processing module K―ij The calculation formula is as follows:

[0039]

[0040] in,

[0041] Among them, K is the average degree of all nodes in the power grid, which is obtained by obtaining degree data from multiple sensors preset in line i-j and dividing it by the number of sensors; M is the set of all nodes in the power grid, which is calculated based on the total number of power grid nodes; N is the total number of power grid nodes; L is the total number of power grid lines.

[0042] Furthermore, the key line identification unit in the identification module receives voltage stability index data, capacity margin index data, real-time failure rate index data and topology connectivity index data, and identifies and analyzes the key lines based on the index data to obtain the load node set in line i-j and all power loss loads of load node n.

[0043] Furthermore, the optimal load loss of the system in the identification module is:

[0044] The optimal load loss objective function is: min∑ n∈D P loss―n

[0045] Where D is the load node set; P loss―n is the total power loss load of load n.

[0046] Furthermore, the positioning module locates the position of the faulty node when a power line fails, including the following steps:

[0047] S1, list the shortest power supply path from the source node to each target node in the power grid;

[0048] S2. Calculate the stability index data of each target node in the power grid according to the calculation unit in the data processing module, and arrange them in descending order;

[0049] S3. Observe the change in the ranking of other target nodes according to the stability after deleting any target node N, and select the target nodes whose stability is only affected by node N. i Affected nodes N j , where i≠j;

[0050] S4. According to multiple nodes N j N i The strength of the impact is used to infer the fault node where the abnormality occurs, and the fault value of the most upstream node of the abnormal line in the distribution network is evaluated to determine the cause of the fault. If the cause of the fault comes from the main grid transmission line, jump to step S5, otherwise the location of the distribution network is output;

[0051] S5. Use the correlation relationship of the main network nodes to locate the location of the faulty node;

[0052] The shortest distance algorithm execution process from the shortest power supply path n to the node in a is as follows:

[0053] S11, initialization stage, let A = {S}, then A ‘ The shortest distance from node n to a node in a is:

[0054]

[0055] Where D(n) is A ‘ The shortest distance from node n to a node in a, W s，n is the connection weight between nodes s and n;

[0056] S12, in A ‘ Determine the node m with the shortest distance from A ‘ Delete node m from A, set A = {S, m}, and update A ‘ The shortest distance from each node p in to a node in a, that is

[0057] D(p)←min{D(p),D(p)+W s,n};

[0058] S13, repeat step S12 until A ‘ Ends when empty;

[0059] Where D(p) is the node m, let A = {S, m}, update A ‘ ; The source node is s, A is the set of nodes for which the shortest path has been found, U ‘ Indicates the remaining nodes among all nodes in the network whose shortest paths are to be determined.

[0060] The beneficial effects of the present invention are as follows:

[0061] (1) The present invention can perform real-time calculation and analysis on the index data of power lines by designing the acquisition module and the data processing module, and quickly obtain the importance ranking set of power lines based on the calculation results, thereby realizing rapid response and processing of power system faults. Compared with the existing method that relies on multiple disconnection line flow calculation, this greatly improves the real-time performance of fault identification and meets the demand for rapid response of modern power systems;

[0062] (2) The present invention uses the node data collected by the data acquisition module, combined with intelligent algorithms and sensing technology, to enable the system to accurately locate the fault node when a power line fails. This not only improves the accuracy of fault diagnosis, but also shortens the fault repair time, reduces the power outage time and economic losses caused by the fault;

[0063] (3) When identifying vulnerable lines in the power system, the present invention not only considers the index data of the line, but also combines the information of the nodes at both ends of the line and the possibility of accidental failure of the line, so as to obtain a more comprehensive ranking set of power line importance. This method of comprehensively considering multiple factors enables the system to more accurately identify critical lines and improve the safety and stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a flow chart of the power system fault vulnerable line identification system of the present invention;

[0065] Figure 2 is a flow chart of the attack method of the identification module attack unit in the present invention;

[0066] Figure 3 It is a flow chart of the method for locating a faulty node by the locating module in the present invention. DETAILED DESCRIPTION

[0067] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0068] Embodiment 1, as Figure 1 - Figure 3 As shown, a power system fault fragile line identification system includes a data acquisition module 1, a data processing module 2, an identification module 3 and a positioning module 4. Communication connections are established among the data acquisition module 1, the data processing module 2, the identification module 3 and the positioning module 4. The system realizes rapid identification and fault location of fragile lines in the power system through the coordinated work of multiple modules;

[0069] The data acquisition module 1 includes a database model, an index acquisition unit and a data preprocessing unit. The index acquisition unit is used to collect index data and node data. The index acquisition unit uses a data acquisition and monitoring control component and a phasor measurement component to perform real-time data acquisition. The data acquisition and monitoring control component is used to collect real-time operation data of the power grid, and the phasor measurement component is used to provide high-precision index data and node data; the real-time operation data of the power grid includes the impedance of the circuit elements in line i-j to the current, the resistance of line i-j, the active power of line i-j, the quality factor of line i-j, the line impedance of line i-j, the ground admittance of line i-j, the current flowing through line i-j, the line transmission power of line i-j when running, the maximum transmission capacity allowed by line ij, the equivalent shutdown rate of the power grid, the total number of power grid nodes, and the total number of power grid lines; the node data includes the ground admittance of node i or node j, the ground admittance of the end of the line, the voltage vector of node i or node j, the phase angle of node i or node j, and the power of node i or node j;

[0070] By combining the SCADA system and PMU technology, and using high-precision sensors and synchronized phasor measurement technology, real-time data collection of the power system can be achieved, and high-precision voltage and current measurement data can be provided. This not only improves the monitoring and control capabilities of the power system, but also lays the foundation for the intelligent and adaptive operation of the power system; the index data includes voltage stability index data, capacity margin index data, real-time fault rate index data and topological connectivity index data; and the collected index data is transmitted to the database model, which is used to provide a platform for real-time data collection and processing, and provides a real-time protection mechanism. The development of the database model is based on the Linux system; the data preprocessing unit is used to preprocess the index data, including data cleaning, format conversion and standardization.

[0071] The database model is developed based on the Linux system, which makes it have good stability and scalability. The Linux system supports a variety of database management systems, such as MySQL, PostgreSQL and MongoDB, which can efficiently store and manage application data and provide efficient data access and query functions. In addition, the Linux system also supports the use of SQLAlchemy for database operations, which facilitates the development of the database model.

[0072] The index data includes voltage stability index data, capacity margin index data, real-time fault rate index data and topological connectivity index data. Voltage stability is an important index in the power system. It evaluates the voltage stability of the entire power grid by calculating the voltage stability margin of each node in the power grid. The static voltage stability index (SVSI) is an important parameter for measuring the voltage stability performance of the power system, which can be evaluated by calculating the reactive power loss of each node. The capacity margin index reflects the power supply capacity of the system under different load conditions.

[0073] The data preprocessing unit is responsible for cleaning, format conversion and standardization of indicator data. Data preprocessing is an indispensable part of the data analysis process. Its purpose is to make the original data more regular and clear, so as to facilitate subsequent data analysis and modeling. Data preprocessing usually includes data cleaning (processing missing values, outliers and duplicate values), data conversion (standardization, normalization) and feature selection. These steps ensure the quality and consistency of the data, thereby improving the accuracy and reliability of subsequent analysis;

[0074] The real-time monitoring function allows the system to monitor key indicators in real time and visualize them through charts. This real-time monitoring mechanism is crucial to the stable operation of the power system because it can detect potential problems in a timely manner and take corresponding measures. In addition, the system also supports middleware monitoring management and can monitor the information of various mainstream middleware on the market;

[0075] The data processing module 2 includes a calculation unit and an indicator data sorting unit. The calculation unit is used to receive the indicator data transmitted by the database model, and perform real-time calculation and analysis on each indicator data to obtain an indicator data set, and wirelessly transmit the indicator data set to the indicator data sorting unit; the indicator data sorting unit is used to receive the indicator data set calculated by the calculation unit, and arrange the data combinations in each indicator data set in order to obtain a power line importance sorting set;

[0076] The identification module 3 includes a key line identification unit and an attack unit. The attack unit is used to attack the transmission line and attack the line importance ranking set obtained by the indicator data ranking unit in sequence, and verify the importance of the power line in combination with the preset system optimal load loss;

[0077] The positioning module 4 is used to receive the node data collected by the data collection module 1, and perform calculation and analysis on the node data to locate the position of the faulty node when a power line fault occurs.

[0078] Embodiment 2, as Figure 1 - Figure 2 As shown, the calculation formula of the voltage stability index data in the data processing module 2 is as follows:

[0079] The voltage equation of line ij is:

[0080] U i =U j +I ij (R+JX)

[0081] in,

[0082] Expand the voltage equation into real and imaginary parts separately:

[0083]

[0084] Both equations are about U j The quadratic equation of , if it has a solution, satisfies:

[0085]

[0086] According to the branch end voltage U i The condition for having a solution is to define the voltage stability index L U―ij for:

[0087] L U―ij =max(L U1 ,L U2 )

[0088] in,

[0089]

[0090] Where X is the impedance of the circuit element in line i-j to the current; R is the resistance of line i-j; J is the node-to-ground admittance branch; B is the ground admittance at the end of the line; P is the active power; Q is the quality factor of line i-j; U i and U j are the voltage vectors of nodes i and j respectively; δ is the phase angle difference between nodes i and j, obtained by subtracting the phase angle of node j from the phase angle of node i; R+JX is the line impedance; is the line-to-ground admittance; P i +JQ i and P j +JQ j are the injected power of node i and the outflow power of node j respectively; I ij The calculation formula and application method of voltage stability index provide important tools and basis for the voltage stability analysis of power system. Through these indicators, the risk of voltage collapse can be effectively identified and handled to ensure the safe and stable operation of power system.

[0091] The calculation formula of the capacity margin index data in the processing module 2 is as follows:

[0092]

[0093] Among them, L S―ij is the capacity margin index data; S ij is the line transmission power of line ij in the current operating state; S ij―max is the maximum transmission capacity allowed by line ij; e is the equivalent outage rate of the power grid. By calculating the capacity margin index data, the vulnerable lines with high load rates in the identification system are monitored and the transmission power of overloaded lines is reduced, which can effectively reduce the safety risk of system operation. In addition, the capacity margin index can also be used for the calculation and allocation of the capacity benefit margin of the wind power grid-connected system.

[0094] The calculation formula of the real-time failure rate index data in processing module 2 is as follows:

[0095] The accidental failure rate of line ij under severe weather conditions λ 2―ij :

[0096]

[0097] Among them, λ ij is the statistical average value of the failure rate of line ij, the number of line failures is obtained by the preset sensor in line ij, and the statistical average value of the failure rate is obtained according to the number of line failures; F ij is the probability of route ij being in extreme weather, which is obtained based on the real-time data in the database model; F′ ij It is the proportion of failures occurring under extreme conditions to the total number of failures, which is obtained based on the data collected in the database model. The real-time failure rate indicator data is the accidental failure rate, and can be further expanded to the real-time natural failure rate indicator data. The natural failure rate is usually described by the aging failure model and can be characterized by the Weibull distribution function. Its shape and parameters can be obtained by fitting the historical fault statistics of the line through maximum likelihood estimation, least squares method, etc.

[0098] Among them, the topological connection index L in processing module 2 K―ij The calculation formula is as follows:

[0099]

[0100] in,

[0101] in, is the average degree of all nodes in the power grid, obtained by obtaining degree data from multiple sensors preset in line i-j and dividing it by the number of sensors; M is the set of all nodes in the power grid, calculated based on the total number of nodes in the power grid; N is the total number of nodes in the power grid; L is the total number of power grid lines. The degree of each node in the power grid can be calculated, and the topological connection index can be used to evaluate the importance of each line in the power grid, so as to identify key lines, reduce the safety risks caused by overloaded line transmission power, and improve the reliability of power grid operation.

[0102] Among them, the key line identification unit in the identification module 3 receives the voltage stability index data, capacity margin index data, real-time fault rate index data and topological connection index data, and identifies and analyzes the key lines according to the index data to obtain the load node set in line i-j and all the power loss loads of load node n. In this way, the safety and reliability of each line can be comprehensively evaluated, so as to identify the key lines.

[0103] Among them, the optimal load loss of the system in identification module 3 is:

[0104] The optimal load loss objective function is: min∑ n∈D P loss―n

[0105] Where D is the load node set; P loss―n is the total power loss load of load n.

[0106] When a line ij fault occurs in the system, the optimal load loss is calculated according to the above objective function. The specific steps are as follows:

[0107] (1) Calculate the system load loss when each line is disconnected;

[0108] (2) Select the line with the highest load loss to disconnect;

[0109] The above steps are repeated until the system load loss reaches the preset threshold or all lines are evaluated.

[0110] Embodiment 3, as Figure 3 As shown, the positioning module 4 locates the position of the fault node when a power line fails, including the following steps:

[0111] S1, list the shortest power supply path from the source node to each target node in the power grid;

[0112] S2, calculating the stability index data of each target node in the power grid according to the calculation unit in the data processing module 2, and arranging them in descending order;

[0113] S3. Observe the change in the ranking of other target nodes according to the stability after deleting any target node N, and select the target nodes whose stability is only affected by node N. i Affected nodes N j , where i≠j;

[0114] S4. According to multiple nodes N j N i The strength of the impact is used to infer the fault node where the abnormality occurs, and the fault value of the most upstream node of the abnormal line in the distribution network is evaluated to determine the cause of the fault. If the cause of the fault comes from the main grid transmission line, jump to step S5, otherwise the location of the distribution network is output;

[0115] S5. Use the correlation relationship of the main network nodes to locate the location of the faulty node;

[0116] The shortest distance algorithm execution process from the shortest power supply path n to the node in a is as follows:

[0117] S11, initialization stage, let A = {S}, then A ‘ The shortest distance from node n to a node in a is:

[0118]

[0119] Where D(n) is A ‘ The shortest distance from node n to a node in a, W s，n is the connection weight between nodes s and n;

[0120] S12, in A ‘ Determine the node m with the shortest distance from A ‘ Delete node m from A, set A = {S, m}, and update A ‘ The shortest distance from each node p in to a node in a, that is

[0121] D(p)←min{D(p),D(p)+W s,n};

[0122] S13, repeat step S12 until A ‘ Ends when empty;

[0123] Where D(p) is the node m, let A = {S, m}, update A ‘ ; The source node is s, A is the set of nodes for which the shortest path has been found, U ‘ Indicate the remaining nodes of all nodes in the network whose shortest paths are to be determined. By listing the shortest power supply path S1 from the source node to each target node in the power grid, it can be ensured that the shortest path can be found quickly when a fault occurs, thereby improving the accuracy of fault location;

[0124] By calculating the stability index data of each target node in the power grid according to the calculation unit in the data processing module 2 and arranging them in descending order S2, the stability of each node can be evaluated more accurately. Therefore, after deleting any target node, the other target nodes are ranked according to the stability changes, and the nodes whose stability is only affected by the node are selected S3, thereby further improving the accuracy of fault location.

[0125] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.

Claims

1. A power system fault vulnerable line identification system, characterized by: It comprises a data acquisition module (1), a data processing module (2), an identification module (3) and a positioning module (4), wherein communication connections are established among the data acquisition module (1), the data processing module (2), the identification module (3) and the positioning module (4); The data collection module (1) comprises a database model, an index collection unit, and a data preprocessing unit, wherein the index collection unit is used to collect index data and node data, and transmit the collected index data to the database model; The data processing module (2) comprises a calculation unit and an indicator data sorting unit, wherein the calculation unit is used to receive the indicator data transmitted by the database model, and to perform real-time calculation and analysis on each item of the indicator data to obtain an indicator data set, and to wirelessly transmit the indicator data set to the indicator data sorting unit; The indicator data sorting unit is used to receive the indicator data set calculated by the calculation unit, and arrange the data combinations in each indicator data set in order to obtain a power line importance sorting set; The identification module (3) comprises a critical line identification unit and an attack unit, wherein the attack unit is used to attack the power transmission line, and to attack the line importance ranking set obtained by the indicator data ranking unit in sequence, and to verify the importance of the power line in combination with a preset system optimal load loss amount; The positioning module (4) is used to receive the node data collected by the data collection module (1), and to perform calculation and analysis on the node data, so as to locate the position of the faulty node when a fault occurs in the power line.

2. A power system fault vulnerable line identification system according to claim 1, characterized in that: The database model is used to provide a platform for real-time data collection and processing, and to provide a real-time protection mechanism. The development of the database model is based on the Linux system. The index data includes voltage stability index data, capacity margin index data, real-time failure rate index data and topology connection index data; The data preprocessing unit is used to preprocess the indicator data, including data cleaning, format conversion and standardization.

3. A power system fault vulnerable line identification system according to claim 2, characterized in that: The index collection unit uses a data collection and monitoring control component and a phasor measurement component to collect real-time data, wherein the data collection and monitoring control component is used to collect real-time operation data of the power grid, and the phasor measurement component is used to provide high-precision index data and node data; The real-time operation data of the power grid includes the impedance of the circuit elements in line i-j to the current, the resistance of line i-j, the active power of line i-j, the quality factor of line i-j, the line impedance of line i-j, the ground admittance of line i-j, the current flowing in line i-j, the line transmission power of line i-j when running, the maximum transmission capacity allowed by line ij, the equivalent shutdown rate of the power grid, the total number of power grid nodes, and the total number of power grid lines; The node data includes the ground admittance of node i or node j, the ground admittance of the line end, the voltage vector of node i or node j, the phase angle of node i or node j, and the power of node i or node j.

4. A power system fault vulnerable line identification system according to claim 3, characterized in that: The calculation formula of the voltage stability index data in the data processing module (2) is as follows: The voltage equation of line ij is: U i =U j +I ij (R+JX) in, Expand the voltage equation into real and imaginary parts separately: Both equations are about U j The quadratic equation of , if it has a solution, satisfies: According to the branch end voltage U i The condition for having a solution is to define the voltage stability index L U―ij for: L U―ij =max(L U1 ,L U2 ) in, Where X is the impedance of the circuit element in line i-j to the current; R is the resistance of line i-j; J is the node-to-ground admittance branch; B is the ground admittance at the end of the line; P is the active power; Q is the quality factor of line i-j; U i and U j are the voltage vectors of nodes i and j respectively; δ is the phase angle difference between nodes i and j, obtained by subtracting the phase angle of node j from the phase angle of node i; R+JX is the line impedance; is the line-to-ground admittance; P i +JQ i and P j +JQ j are the injected power of node i and the outflow power of node j respectively; I ij is the current flowing in the line.

5. A power system fault vulnerable line identification system according to claim 4, characterized in that: The calculation formula of the capacity margin index data in the processing module (2) is as follows: Among them, L S―ij is the capacity margin index data; S ij is the line transmission power of line ij in the current operating state; S ij―max is the maximum transmission capacity allowed by line ij; e is the equivalent outage rate of the power grid.

6. A power system fault vulnerable line identification system according to claim 5, characterized in that: The calculation formula of the real-time failure rate index data in the processing module (2) is as follows: The accidental failure rate of line ij under severe weather conditions λ 2―ij : Among them, λ ij is the statistical average value of the failure rate of line ij, the number of line failures is obtained by the preset sensor in line ij, and the statistical average value of the failure rate is obtained according to the number of line failures; F ij is the probability of route ij being in extreme weather, obtained based on the real-time data in the database model; F′ ij is the ratio of failures occurring under extreme conditions to the total number of failures, obtained based on the data collected in the database model.

7. A power system fault vulnerable line identification system according to claim 6, characterized in that: The topological connection index L in the processing module (2) K―ij The calculation formula is as follows: in, in, is the average value of the degree of all nodes in the power grid, obtained by obtaining degree data from multiple sensors preset in line i-j and dividing it by the number of sensors; M is the set of all nodes in the power grid, calculated based on the total number of power grid nodes; N is the total number of power grid nodes; L is the total number of power grid lines.

8. A power system fault vulnerable line identification system according to claim 1, characterized in that: The key line identification unit in the identification module (3) receives voltage stability index data, capacity margin index data, real-time fault rate index data and topology connection index data, and identifies and analyzes the key lines based on the index data to obtain the load node set in lines i-j and all power loss loads of load node n.

9. A power system fault vulnerable line identification system according to claim 8, characterized in that: The optimal load loss of the system in the identification module (3) is: The optimal load loss objective function is: min∑ n∈D P loss―n Where D is the load node set; P loss―n is the total power loss load of load n.

10. The power system fault vulnerable line identification system according to claim 1, characterized in that: The positioning module (4) locates the position of the fault node when a power line fails, comprising the following steps: S1, list the shortest power supply path from the source node to each target node in the power grid; S2, calculating the stability index data of each target node in the power grid according to the calculation unit in the data processing module (2), and arranging them in descending order; S3. Observe the change in the ranking of other target nodes according to the stability after deleting any target node N, and select the target nodes whose stability is only affected by node N. i Affected nodes N j , where i≠j; S4. According to multiple nodes N j N i The strength of the impact is used to infer the fault node where the abnormality occurs, and the fault value of the most upstream node of the abnormal line in the distribution network is evaluated to determine the cause of the fault. If the cause of the fault comes from the main grid transmission line, jump to step S5, otherwise the location of the distribution network is output; S5. Use the correlation relationship of the main network nodes to locate the location of the faulty node; The shortest distance algorithm execution process from the shortest power supply path n to the node in a is as follows: S11, initialization stage, let A = {S}, then A ‘ The shortest distance from node n to a node in a is: Where D(n) is A ‘ The shortest distance from node n to a node in a, W s，n is the connection weight between nodes s and n; S12, in A ‘ Determine the node m with the shortest distance from A ‘ Delete node m from A, set A = {S, m}, and update A ‘ The shortest distance from each node p in to a node in a, that is D(p)←min{D(p),D(p)+W s,n }; S13, repeat step S12 until A ‘ Ends when empty; Where D(p) is the node m, let A = {S, m}, update A ‘ ; The source node is s, A is the set of nodes for which the shortest path has been found, U ‘ Indicates the remaining nodes among all nodes in the network whose shortest paths are to be determined.

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