Power grid operation risk intelligent identification method and system, terminal and storage medium

Abstract

The invention belongs to the technical field of power grid management, and particularly relates to a power grid operation risk intelligent identification method and system, a terminal and a storage medium, and the method comprises the steps: obtaining the basic data of a power grid, inputting the basic data into a pre-constructed power grid dynamic model, and comprehensively and accurately mastering the operation state of the power grid. And according to the generated fault occurrence probability value, using a pre-constructed element outage model to analyze outage states of the power grid element, including an independent power grid element outage state and a related power grid element outage state. Consequence value analysis of the power grid fault state is carried out, and potential safety hazards in the power grid can be found and processed in time.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid management, and particularly relates to an intelligent method, system, terminal and storage medium for identifying power grid operation risks. Background Art

[0002] With the continuous development of the power system and the increasing expansion of the power grid scale, the security and stability of power grid operation are facing greater and greater challenges. Traditional power grid risk identification methods mainly rely on manual experience and statistical analysis of historical data. These methods are not only time-consuming and laborious, but also difficult to comprehensively and accurately reflect the actual operation status and potential risks of the power grid. Especially in a complex and changeable power grid environment, various uncertain factors and sudden faults often pose a great threat to power grid operation.

[0003] In the existing power grid operation risk identification technologies, there are some obvious deficiencies. First, the data acquisition and processing capabilities are limited, resulting in an incomplete and inaccurate grasp of the power grid basic data. Second, the calculation method of the fault probability is relatively simple, and the complexity and relevance of various fault probability influencing factors are not fully considered. In addition, for the analysis of the outage state of power grid components and the consequence values of power grid fault states, the existing methods often lack in-depth and systematic research, and it is difficult to effectively evaluate the actual impact of power grid operation risks.

[0004] Especially when facing large-scale power grids and complex fault scenarios, the existing power grid operation risk identification technologies often seem powerless. They cannot timely and accurately identify potential risk points in the power grid, nor can they provide scientific and effective decision-making support for power grid dispatching and operation personnel. Therefore, there is an urgent need for a new method and technical means that can comprehensively and accurately identify power grid operation risks. Summary of the Invention

[0005] In view of the above deficiencies of the prior art, the present invention provides an intelligent method, system, terminal and storage medium for identifying power grid operation risks to solve the above technical problems.

[0006] In a first aspect, the present invention provides an intelligent method for identifying power grid operation risks, including: Obtain power grid basic data, input the obtained power grid basic data into a pre-constructed power grid dynamic model to generate a power grid topology structure; the power grid basic data includes electrical node information, transformer information, transmission line information, switch information and generator information; Set fault probability influencing factors to form a set of contingency faults, and calculate the fault occurrence probability values of each power grid component in the power grid topology structure under each fault probability influencing factor; the fault probability influencing factors include equipment aging, environmental factors and human errors; Analyze the outage state of power grid components based on the generated fault occurrence probability values according to the pre-constructed component outage model; Conduct consequence value analysis of the power grid fault state based on the outage state of power grid components, including the calculation of load shedding risk value, branch overload risk value, and voltage violation risk value; Determine whether the consequence value analysis corresponding to all fault probability influencing factors in the contingency set has been completed; If so, comprehensively calculate and generate the risk value corresponding to the power grid topology structure.

[0007] A further improvement of this technical solution is that the specific method for constructing the power grid dynamic model includes: Obtain historical power grid basic data and perform preprocessing; According to the actual operation situation and requirements of the power grid, select the node-branch model structure and define various model parameters in the node-branch model structure; the model parameters include electrical parameters and operation parameters including transformer turns ratio and switch status; Train the node-branch model structure after the model parameter definition based on the historical power grid basic data to obtain the power grid dynamic model.

[0008] A further improvement of this technical solution is that set fault probability influencing factors to form a contingency set, and calculate the fault occurrence probability values of each power grid component in the power grid topology structure under each fault probability influencing factor based on the contingency set; the fault probability influencing factors include equipment aging, environmental factors, and human errors, and the specific method includes: Establish a corresponding fault mode library according to the type of power grid components, and the fault mode library includes contingency fault types and their corresponding fault probability influencing factors; Define quantization indexes for each fault probability influencing factor, and the quantization indexes include the degree of equipment aging, the threshold of environmental factors, and the frequency of human errors; Combined with the power grid topology structure and the power grid component fault mode library, for each power grid component, generate corresponding fault scenarios according to the set quantization indexes of fault probability influencing factors to form a contingency set; Establish a fault probability calculation model according to the fault probability influencing factors and the fault modes of power grid components, and set the weights of different fault probability influencing factors on the fault probability; Input the obtained power grid basic data into the fault probability calculation model, and calculate the fault occurrence probability values of each power grid component under the corresponding fault probability influencing factors for each fault scenario in the contingency set.

[0009] A further improvement of this technical solution is that the outage state of power grid components includes the outage state of independent power grid components and the outage state of related power grid components, and the outage state of related power grid components includes the outage state of power grid component groups and the outage state of common-cause power grid components.

[0010] A further improvement of this technical solution is that the method steps for calculating the loss-of-load risk value according to the outage state of power grid components include: Based on the outage state of power grid components, perform network island detection using the Jacobian matrix to determine whether there are network islands in the generated power grid topology structure; If not, calculate the load loss according to the power grid topology structure and the obtained basic power grid data; If so, perform in-island loss-of-load analysis on the network island and generate a set of voltage-collapse nodes; Monitor all the automatic backup power supply devices configured for each voltage-collapse node in the set of voltage-collapse nodes and determine whether all the automatic backup power supply devices can be put into operation; If not, calculate the load loss according to the power grid topology structure and the obtained basic power grid data; If so, put the automatic backup power supply devices that can be put into operation into operation, update the power grid topology structure and modify the corresponding Jacobian matrix, and at the same time calculate the load loss according to the updated power grid topology structure and the obtained basic power grid data.

[0011] A further improvement of this technical solution is that the method steps for calculating the branch overload risk value according to the outage state of power grid components include: Obtain the operation data of each power grid node in the power grid topology structure; Conduct sampling analysis according to the outage state of power grid components, simulate the factors causing power grid faults, and determine whether the power grid topology network is disconnected under fault conditions; If so, determine whether the power supply of the power grid is balanced with the power consumption of each branch, and when it is unbalanced, increase or reduce the load, reconstruct the power grid topology structure, and perform power flow calculation on the reconstructed power grid topology structure based on the operation data of the fault point; If not, perform power flow calculation based on the operation data of the fault point to obtain the line power flow of the line where the fault point is located, and determine whether the calculated line power flow is within the preset power flow range; If not satisfied, reduce the load, reconstruct the power grid topology structure, and calculate the overload risk value corresponding to the reconstructed power grid topology structure.

[0012] A further improvement of this technical solution is that the method steps for calculating the voltage over-limit risk value according to the outage state of power grid components include: Obtain the important user indicators, user sensitivity indicators, and power supply contract level indicators of the nodes where the grid components in the outage state are located from the database according to the outage state of the grid components; Input the important user indicators, user sensitivity indicators, and power supply contract level indicators of the nodes where the grid components in the outage state are located, which are obtained, into the pre-constructed voltage over-limit risk value calculation model, and calculate the voltage over-limit risk value corresponding to the grid components in the outage state.

[0013] In a second aspect, the present invention provides an intelligent grid operation risk identification system, including: A basic data acquisition module, configured to acquire grid basic data, input the acquired grid basic data into a pre-constructed grid dynamic model, and generate a grid topology structure; the grid basic data includes electrical node information, transformer information, transmission line information, switch information, and generator information; A fault occurrence probability calculation module, configured to set fault probability influencing factors, form a set of contingency faults, and calculate the fault occurrence probability values of each grid component in the grid topology structure under each fault probability influencing factor based on the set of contingency faults; the fault probability influencing factors include equipment aging, environmental factors, and human errors; A grid component outage state analysis module, configured to analyze the outage state of grid components based on the generated fault occurrence probability values according to a pre-constructed component outage model; A grid fault state consequence value analysis module, configured to perform consequence value analysis of the grid fault state according to the outage state of grid components, including calculation of load loss risk values, branch overload risk values, and voltage over-limit risk values; and determine whether the consequence value analysis corresponding to all fault probability influencing factors in the set of contingency faults is completed; if so, comprehensively calculate and generate the risk value corresponding to the grid topology structure.

[0014] In a third aspect, a terminal is provided, including: A processor and a memory, wherein, The memory is used to store a computer program, The processor is used to call and run the computer program from the memory, so that the terminal executes the method of the above terminal.

[0015] In a fourth aspect, a computer storage medium is provided, and instructions are stored in the computer-readable storage medium, and when it runs on a computer, the computer is made to execute the methods described in the above aspects.

[0016] The beneficial effects of the present invention are as follows: Improve data acquisition and processing capabilities: By acquiring grid basic data and inputting it into a pre-constructed grid dynamic model, the operation state of the grid can be comprehensively and accurately grasped.

[0017] Optimize the fault probability calculation method: set the failure probability influencing factors, including equipment aging, environmental factors and human error, and form a set of expected faults. Based on the expected fault set, calculate the failure probability value of each grid component in the power grid topology structure under each failure probability influencing factor, taking into account the complexity and correlation of various failure probability influencing factors.

[0018] In-depth analysis of grid component outage status and consequence values: Based on the generated fault probability value, the pre-built component outage model is used to analyze the outage status of grid components, including the outage status of independent grid components and the outage status of related grid components. The consequence value analysis of grid fault status is carried out, including the calculation of load loss risk value, branch overload risk value and voltage over-limit risk value, providing scientific and effective decision support for grid dispatching and operation personnel.

[0019] Improve the safety and stability of power grid operation: By comprehensively and accurately identifying the risks of power grid operation, the method and system of the present invention help to timely discover and deal with potential safety hazards in the power grid. It can effectively prevent the occurrence of power grid failures, improve the safety and stability of power grid operation, and ensure the reliability and continuity of power supply.

[0020] In addition, the invention has a reliable design principle, a simple structure and a very broad application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention.

[0023] Figure 2 is a schematic block diagram of a system according to an embodiment of the present invention.

[0024] Figure 3 A schematic diagram of the structure of a terminal provided by an embodiment of the present invention.

[0025] 210 is a basic data acquisition module, 220 is a fault occurrence probability calculation module, 230 is a power grid component outage status analysis module, and 240 is a power grid fault status consequence value analysis module. DETAILED DESCRIPTION

[0026] To enable those skilled in the art to better understand the technical solutions in the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments, and are not intended to limit the present invention.

[0028] The intelligent risk identification method for power grid operation provided by the embodiments of the present invention is executed by a computer device. Correspondingly, the intelligent risk identification system for power grid operation runs in the computer device.

[0029] Figure 1 It is a schematic flowchart of the method according to an embodiment of the present invention. Among them, Figure 1 The execution subject can be an intelligent risk identification system for power grid operation. According to different requirements, the order of the steps in this flowchart can be changed, and some can be omitted.

[0030] As Figure 1 shown, the method includes: Step 110: Obtain power grid basic data, input the obtained power grid basic data into a pre-constructed power grid dynamic model to generate a power grid topological structure; the power grid basic data includes electrical node information, transformer information, transmission line information, switch information, and generator information; Step 120: Set fault probability influencing factors to form a set of contingency faults, and calculate the fault occurrence probability values of each power grid component in the power grid topological structure under each fault probability influencing factor based on the set of contingency faults; the fault probability influencing factors include equipment aging, environmental factors, and human misoperation; Step 130: Analyze the outage state of the power grid components based on the generated fault occurrence probability values according to the pre-constructed component outage model; Step 140: Analyze the consequence values of the power grid fault state according to the outage state of the power grid components, including calculating the load shedding risk value, branch overload risk value, and voltage violation risk value; Step 150: Determine whether the analysis of the consequence values corresponding to all the fault probability influencing factors in the set of contingency faults is completed; if so, go to Step 160; Step 160: Comprehensively calculate and generate the risk value corresponding to the power grid topological structure.

[0031] For the convenience of understanding the present invention, the principle of the intelligent identification method for power grid operation risks of the present invention will be further described below in combination with the process of intelligent identification of power grid operation risks in the embodiments.

[0032] Specifically, the specific method for constructing the power grid dynamic model includes: S111. Obtain the historical power grid basic data and perform preprocessing; S112. According to the actual operation conditions and requirements of the power grid, select the node-branch model structure and define each model parameter in the node-branch model structure; the model parameters include electrical parameters and operation parameters including transformer turns ratio and switch status; S113. Train the node-branch model structure after the model parameters are defined based on the historical power grid basic data to obtain the power grid dynamic model.

[0033] In addition, set the fault probability influencing factors to form a set of contingency faults, and calculate the fault occurrence probability values of each power grid component in the power grid topology structure under each fault probability influencing factor based on the set of contingency faults; the fault probability influencing factors include equipment aging, environmental factors, and human error operations, and the specific method includes: S121. Establish a corresponding fault mode library according to the type of power grid components, and the fault mode library includes the types of contingency faults and their corresponding fault probability influencing factors; S122. Define quantization indexes for each fault probability influencing factor, and the quantization indexes include the degree of equipment aging, the threshold of environmental factors, and the frequency of human error operations; S123. Combine the power grid topology structure and the power grid component fault mode library, and for each power grid component, generate corresponding fault scenarios according to the set quantization indexes of the fault probability influencing factors to form a set of contingency faults; S124. Establish a fault probability calculation model according to the fault probability influencing factors and the fault modes of power grid components, and set the weights of different fault probability influencing factors on the fault probability; S125. Input the obtained power grid basic data into the fault probability calculation model, and calculate the fault occurrence probability values of each power grid component under the corresponding fault probability influencing factors for each fault scenario in the set of contingency faults.

[0034] Furthermore, the outage states of power grid components include the outage state of independent power grid components and the outage state of related power grid components, and the outage state of related power grid components includes the outage state of power grid component groups and the outage state of common-cause power grid components.

[0035] Specifically, in the outage state of independent power grid components, the calculation formula for the power grid outage probability is: ; Among them, is the failure occurrence probability of component i; is the failure occurrence probability of component j; N is the total number of components in the generated power grid topology, and i is always not equal to j.

[0036] When the power grid component group is in the outage state, the calculation formula for the power grid outage probability is: ; Among them, N g is the set of all components within an independent component group.

[0037] For the common-cause power grid component outage state (which refers to the state where multiple components fail simultaneously due to one factor), the calculation formula for the power grid outage probability is: ; Among them, N f is the set of power grid outage components; N m is the set of power grid normally operating components; P c is the probability of the occurrence of common-cause outage; k is the proportion of the number of components affected by the common-cause outage. Specifically, k can be understood as the ratio of the number of components affected by the common-cause outage to the total number of components.

[0038] Specifically, the method steps for calculating the load shedding risk value according to the power grid component outage state include: S1411. Based on the power grid component outage state, perform network island detection based on the Jacobian matrix to determine whether there are network islands in the generated power grid topology; if not, go to S1412; if so, go to S1413; S1412. Calculate the load loss amount according to the power grid topology and the obtained power grid basic data; S1413. Conduct in-island load shedding analysis on the network island and generate a set of voltage-collapse nodes; S1414. Monitor all the backup automatic switching devices configured for each voltage-collapse node in the set of voltage-collapse nodes and determine whether all the backup automatic switching devices can be put into operation; if not, go to S1415; if so, go to S1416; S1415. Calculate the load loss amount according to the power grid topology and the obtained power grid basic data; S1416. Put into operation the backup automatic switching devices that can be put into operation, update the power grid topology and modify the corresponding Jacobian matrix, and at the same time calculate the load loss amount according to the updated power grid topology and the obtained power grid basic data.

[0039] The method steps for calculating the branch overload risk value according to the power grid component outage state include: S1421. Obtain the operation data of each power grid node in the power grid topology structure; S1422. Conduct sampling analysis according to the outage status of power grid components, simulate the factors leading to power grid faults, and determine whether the power grid topology network is disconnected in the fault state; if so, go to S1423; if not, go to S1424; S1423. Judge whether the power supply of the power grid is balanced with the power consumption of each branch. When it is unbalanced, increase or cut the load, reconstruct the power grid topology structure, and conduct power flow calculation on the reconstructed power grid topology structure based on the operation data of the fault point; S1424. Conduct power flow calculation based on the operation data of the fault point to obtain the line power flow of the line where the fault point is located, and judge whether the calculated line power flow is within the preset power flow range; if not satisfied, go to S1425; S1425. Cut the load, reconstruct the power grid topology structure, and calculate the overload risk value corresponding to the reconstructed power grid topology structure.

[0040] The method steps for calculating the voltage violation risk value according to the outage status of power grid components include: S1431. Obtain the important index of the users at the nodes corresponding to the power grid components in the outage state, the user sensitivity index, and the power supply contract level index from the database according to the outage status of the power grid components; S1431. Input the important index of the users at the nodes corresponding to the power grid components in the outage state, the user sensitivity index, and the power supply contract level index obtained into the pre-constructed voltage violation risk value calculation model, and calculate the voltage violation risk value corresponding to the power grid components in the outage state.

[0041] In some embodiments, the power grid operation risk intelligent identification system 200 may include multiple functional modules composed of computer program segments. The computer programs of each program segment in the power grid operation risk intelligent identification system 200 can be stored in the memory of the computer device and executed by at least one processor to execute (see details in Figure 1 description) the functions of power grid operation risk intelligent identification.

[0042] In this embodiment, according to the functions it executes, the power grid operation risk intelligent identification system 200 can be divided into multiple functional modules, such as Figure 2As shown in the figure. The functional modules may include: a basic data acquisition module 210, a fault occurrence probability calculation module 220, a power grid component outage state analysis module 230, and a power grid fault state consequence value analysis module 240. The module referred to in the present invention means a series of computer program segments that can be executed by at least one processor and can complete fixed functions, and are stored in a memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0043] Specifically, the basic data acquisition module is used to acquire power grid basic data, input the acquired power grid basic data into a pre-constructed power grid dynamic model, and generate a power grid topology structure; the power grid basic data includes electrical node information, transformer information, transmission line information, switch information, and generator information; the fault occurrence probability calculation module is used to set fault probability influencing factors, form a set of contingency faults, and calculate the fault occurrence probability value of each power grid component in the power grid topology structure under each fault probability influencing factor based on the set of contingency faults; the fault probability influencing factors include equipment aging, environmental factors, and human error; the power grid component outage state analysis module is used to analyze the outage state of power grid components based on the generated fault occurrence probability value according to a pre-constructed component outage model; the power grid fault state consequence value analysis module is used to perform consequence value analysis of the power grid fault state according to the power grid component outage state, including the calculation of load loss risk value, branch overload risk value, and voltage violation risk value; and determine whether the consequence value analysis corresponding to all fault probability influencing factors in the set of contingency faults is completed; if so, comprehensively calculate and generate the risk value corresponding to the power grid topology structure.

[0044] The present invention also provides a computer storage medium, wherein the computer storage medium can store a program, and when the program is executed, it can include some or all of the steps in the embodiments provided by the present invention. The storage medium can be a magnetic disk, an optical disk, a read-only memory (abbreviation: ROM), or a random access memory (abbreviation: RAM), etc.

[0045] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solutions in the embodiments of the present invention, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product is stored in a storage medium such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc, etc., which can store program codes, and includes several instructions for causing a computer terminal (which may be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0046] For the same or similar parts among the various embodiments in this specification, reference can be made to each other. In particular, for the terminal embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the descriptions in the method embodiments.

[0047] Although the present invention has been described in detail by referring to the accompanying drawings and in combination with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, those of ordinary skill in the art can make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions should all be within the scope of the present invention. / Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention.

Claims

1. An intelligent method for identifying power grid operation risks, characterized in that, Including: Obtain the basic power grid data, input the obtained basic power grid data into a pre-constructed power grid dynamic model, and generate a power grid topology structure; The basic power grid data includes electrical node information, transformer information, transmission line information, switch information, and generator information; Set the fault probability influencing factors to form a set of contingency faults, and calculate the fault occurrence probability values of each power grid component in the power grid topology structure under each fault probability influencing factor based on the set of contingency faults; The fault probability influencing factors include equipment aging, environmental factors, and human errors; Analyze the outage state of power grid components based on the generated fault occurrence probability values according to a pre-constructed component outage model; Analyze the consequence values of the power grid fault state according to the outage state of power grid components, including the calculation of load loss risk values, branch overload risk values, and voltage violation risk values; Judge whether the analysis of the consequence values corresponding to all fault probability influencing factors in the set of contingency faults is completed; If so, comprehensively calculate and generate the risk value corresponding to the power grid topology structure.

2. The intelligent risk identification method for power grid operation according to claim 1, wherein The specific method for constructing the power grid dynamic model includes: Obtain historical basic power grid data and perform preprocessing; According to the actual operation conditions and requirements of the power grid, select a node-branch model structure and define various model parameters in the node-branch model structure; the model parameters include electrical parameters and operation parameters including transformer turns ratio and switch status; Train the node-branch model structure after the model parameter definition based on the historical basic power grid data to obtain a power grid dynamic model.

3. The intelligent risk identification method for power grid operation according to claim 1, wherein Set the fault probability influencing factors to form a set of contingency faults, and calculate the fault occurrence probability values of each power grid component in the power grid topology structure under each fault probability influencing factor based on the set of contingency faults; The fault probability influencing factors include equipment aging, environmental factors, and human errors, and the specific methods include: Establish a corresponding fault mode library according to the type of power grid components. The fault mode library includes contingency fault types and their corresponding fault probability influencing factors; Define quantization indexes for each fault probability influencing factor. The quantization indexes include the degree of equipment aging, the threshold of environmental factors, and the frequency of human errors; Combine the power grid topology structure and the power grid component fault mode library, and for each power grid component, generate corresponding fault scenarios according to the set quantization indexes of the fault probability influencing factors to form a set of contingency faults; Establish a fault probability calculation model according to the fault probability influencing factors and the fault modes of power grid components, and set the weights of different fault probability influencing factors on the fault probability; Input the obtained basic power grid data into the fault probability calculation model, and calculate the fault occurrence probability values of each power grid component under the corresponding fault probability influencing factors for each fault scenario in the set of contingency faults.

4. The intelligent identification method for power grid operation risks according to claim 1, wherein, The outage state of power grid components includes the outage state of independent power grid components and the outage state of related power grid components. The outage state of related power grid components includes the outage state of power grid component groups and the outage state of common-cause power grid components.

5. The intelligent risk identification method for power grid operation according to claim 1, wherein The method steps for calculating the load loss risk value according to the outage state of power grid components include: Based on the outage state of power grid components, perform network island detection based on the Jacobian matrix to judge whether there are network islands in the generated power grid topology structure; If not, calculate the load loss according to the power grid topology structure and the obtained basic power grid data; If so, conduct in-island load loss analysis for the network island and generate a set of voltage-drop nodes; Monitor all the automatic bus transfer devices configured for each voltage-drop node in the set of voltage-drop nodes and determine whether all the automatic bus transfer devices can be put into operation; If not, calculate the load loss according to the power grid topology structure and the obtained basic power grid data; If so, put the automatic bus transfer devices that can be put into operation into operation, update the power grid topology structure and modify the corresponding Jacobian matrix, and at the same time calculate the load loss according to the updated power grid topology structure and the obtained basic power grid data.

6. The intelligent risk identification method for power grid operation according to claim 1, wherein The method steps for calculating the branch overload risk value according to the outage state of power grid components include: Obtain the operation data of each power grid node in the power grid topology structure; Conduct sampling analysis according to the outage state of power grid components, simulate the factors causing power grid faults, and determine whether the power grid topology network is disconnected under the fault state; If so, determine whether the power supply of the power grid is balanced with the power consumption of each branch, and when it is unbalanced, increase or cut the load, reconstruct the power grid topology structure, and conduct power flow calculation on the reconstructed power grid topology structure based on the operation data of the fault point; If not, conduct power flow calculation based on the operation data of the fault point to obtain the line power flow of the line where the fault point is located, and determine whether the calculated line power flow is within the preset power flow range; If not satisfied, cut the load, reconstruct the power grid topology structure, and calculate the overload risk value corresponding to the reconstructed power grid topology structure.

7. The intelligent risk identification method for power grid operation according to claim 1, wherein The method steps for calculating the voltage over-limit risk value according to the outage state of power grid components include: Obtain the user importance index, user sensitivity index, and power supply contract level index corresponding to the grid components in the outage state from the database according to the outage state of power grid components; Input the user importance index, user sensitivity index, and power supply contract level index corresponding to the grid components in the outage state obtained into the pre-constructed voltage over-limit risk value calculation model, and calculate the voltage over-limit risk value corresponding to the grid components in the outage state.

8. An intelligent risk identification system for power grid operation, characterized in that, Include: A basic data acquisition module, which is used to acquire basic power grid data, input the obtained basic power grid data into a pre-constructed power grid dynamic model, and generate a power grid topology structure; The basic power grid data includes electrical node information, transformer information, transmission line information, switch information, and generator information; A fault occurrence probability calculation module, which is used to set the fault probability influencing factors, form a set of contingency faults, and calculate the fault occurrence probability value of each power grid component in the power grid topology structure under each fault probability influencing factor based on the set of contingency faults; The fault probability influencing factors include equipment aging, environmental factors, and human error; A power grid component outage state analysis module, which is used to analyze the outage state of power grid components based on the generated fault occurrence probability value according to a pre-constructed component outage model; A power grid fault status consequence value analysis module is used to analyze the consequence values of power grid fault status according to the outage status of power grid components, including the calculation of load loss risk values, branch overload risk values, and voltage over-limit risk values; and determine whether the consequence value analysis corresponding to all fault probability influencing factors in the contingency set has been completed; if so, comprehensively calculate and generate the risk value corresponding to the power grid topology structure.

9. A terminal, characterized in that, It includes: A processor; A memory for storing the execution instructions of the processor; Wherein, the processor is configured to execute the method according to any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the program is executed by the processor, it implements the method according to any one of claims 1-7.

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