Key cascading failure chain identification method based on SALSA risk degree

Abstract

The invention discloses an SALSA risk degree-based key cascading failure chain identification method, which belongs to the technical field of power system cascading failure screening, and comprises the steps of determining a topological structure and an operation condition of a power system, obtaining a cascading failure layer number and a failure sequence of each layer, and calculating to obtain a branch breaking distribution factor matrix and a system power flow vector. Calculating a Hub vector used for representing the fault probability of each line and an Authority vector used for representing the fault result of each line, carrying out Hadamard product to obtain an SALSA risk degree vector, sorting elements in the SALSA risk degree vector to determine a next potential fault, obtaining a topological structure after the next potential fault, and carrying out iteration on the topological structure and the operation condition; and outputting a high-risk cascading failure chain and sequentially outputting key cascading failure chains according to the serial numbers of the cut-off lines. According to the method, the identification problem of the key cascading failure chain of the electric power system is more effectively solved, and the safety and reliability of the electric power system are improved.

Description

Technical Field

[0001] The invention relates to a method for identifying a critical cascading failure chain based on SALSA risk, and belongs to the technical field of power system cascading failure screening. Background Art

[0002] The main goal of the power system is to ensure continuous and reliable power supply. However, large-scale power system failures pose a real threat to many power facilities. Therefore, it is particularly important to develop power system defense and recovery plans. Identifying high-risk cascading failure chains in power systems is of great significance for in-depth analysis of the propagation law of cascading failures, formulating effective preventive decision-making measures, and improving the overall reliability of the power grid. When solving the problem of identifying critical cascading failure chains in power systems, existing technologies mainly rely on two methods: graph-based analysis methods and model-based simulation methods. The graph-based analysis method constructs a topological structure diagram of the power system and uses the path search algorithm in graph theory to explore potential cascading failure paths. The model-based simulation method establishes a detailed mathematical model of the power system and uses simulation software to simulate the response behavior of the system after the failure occurs, thereby analyzing the propagation process and potential impact of the cascading failure. These methods have achieved certain results in identifying high-risk cascading failure chains and provide strong support for the safe and stable operation of the power system.

[0003] However, the existing technology still has some obvious limitations. Although the analysis method based on graph theory can quickly locate the potential chain failure path, it often ignores the probability and consequences of the failure, resulting in the accuracy of the identification results to be improved. On the other hand, although the simulation method based on the model can simulate the system behavior after the failure, the model construction is complex and the calculation is large, making it difficult to achieve rapid response in practical applications. In addition, most of the existing methods lack comprehensive consideration of multiple fault scenarios, making it difficult to comprehensively assess the risk level of the power system. Therefore, the existing technology still needs to be further improved and optimized in identifying high-risk chain failure chains in power systems. Although the existing technology has made certain progress in identifying key chain failure chains in power systems, it still has some obvious defects. First, although the analysis method based on graph theory can quickly find potential chain failure paths, it often ignores the probability and consequences of the failure, resulting in inaccurate identification results. Secondly, although the simulation method based on the model can simulate the system behavior after the failure, the model construction is complex and the calculation is large, making it difficult to achieve rapid response in practical applications. In addition, most of the existing methods lack comprehensive consideration of different fault scenarios, making it difficult to comprehensively assess the risk level of the power system. Therefore, the existing technology still has certain limitations in identifying high-risk cascading failure chains in power systems and needs to be further improved and optimized. Summary of the invention

[0004] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a method for identifying critical chain fault chains based on SALSA risk. By integrating the SALSA algorithm and power flow analysis, the SALSA risk index is used to iteratively screen high-risk faults, and finally the critical chain fault chains that meet the preset depth and breadth parameters are output to more effectively solve the identification problem of critical chain fault chains in power systems and improve the safety and reliability of power systems.

[0005] In order to solve the above technical problems, the present invention is implemented by adopting the following technical solutions.

[0006] The present invention provides a method for identifying a critical chain of failures based on SALSA risk, which is characterized by comprising:

[0007] The topological structure is obtained according to the number of lines and nodes in the power system and the parameters of the power system are initialized to obtain the operating conditions of the power system;

[0008] Repeat the following steps until the output critical chain of faults meets the preset fault depth and width parameters:

[0009] According to the topological structure and operating conditions, the number of cascading failure layers and the failure sequence of each layer are obtained;

[0010] According to the number of cascading fault layers and the fault sequence of each layer, the branch disconnection distribution factor matrix and system power flow vector are calculated;

[0011] According to the branch disconnection distribution factor matrix and the system power flow vector, the Hub vector used to characterize the probability of each line failure and the Authority vector used to characterize the consequence of each line failure are calculated and Hadamard product is performed to obtain the SALSA risk vector.

[0012] According to the risk calculation result of the SALSA risk vector, the elements in the SALSA risk vector are sorted from large to small, the risk of the potential fault is determined according to the sorting result, the potential fault with the highest risk is taken as the next potential fault, and the topological structure and operating conditions after the next potential fault are obtained for iteration;

[0013] After the iteration is completed, the high-risk chain fault chains are output and the critical chain fault chains are output in sequence according to the disconnected line numbers.

[0014] Furthermore, the branch breaking distribution factor matrix is ​​expressed as:

[0015] ;

[0016] In the formula, Indicates The branch disconnection distribution factor matrix after layer fault, Indicates After layer failure Order branch susceptance matrix, Indicates After layer failure Order node-branch association matrix The transposed matrix of Indicates After layer failure Order branch susceptance matrix The inverse matrix of express The unit matrix, Indicates After layer failure order power transfer distribution factor, ” represents the Hadamard product, Indicates the number of lines. Indicates the number of nodes.

[0017] Furthermore, according to the number of cascading fault layers and the fault sequence of each layer, the branch disconnection distribution factor matrix and the system power flow vector are calculated, including:

[0018] According to the branch disconnection distribution factor matrix, the influence of branch disconnection on the active power flow change of other branches of the power system is quantified, and the system power flow vector after the potential fault occurs is calculated, which is expressed as:

[0019] ;

[0020] In the formula, Indicates The system power flow vector after layer failure, Indicates The first distribution factor matrix of branch disconnection after layer fault Column vector, used to characterize the loss index caused by the breaking fault of line L, express Vidi System power flow vector after layer failure No. The row element is used to characterize the probability index of the line L breaking fault.

[0021] Furthermore, according to the branch disconnection distribution factor matrix and the system power flow vector, the Hub vector used to characterize the probability of each line failure and the Authority vector used to characterize the consequence of each line failure are calculated and the Hadamard product is performed to obtain the SALSA risk vector, including:

[0022] Based on the similarity between web link propagation and cascading failure evolution, the Hub vector and Authority vector are defined using the system power flow vector and its sensitivity.

[0023] After a fault occurs, the Hub vector and the Authority vector are initialized using the active power vector corresponding to the fault occurrence layer, and the initialized Hub vector update matrix and the Authority vector update matrix are obtained;

[0024] Based on the initialized Hub vector update matrix and Authority vector update matrix, the Hub vector update matrix and Authority vector update matrix after the fault occurs are constructed according to the row normalization matrix and column normalization matrix of the branch disconnection distribution factor matrix of the fault occurrence layer;

[0025] The initialized Hub vector and Authority vector are updated using the Hub vector update matrix and the Authority vector update matrix. The Hub vector and Authority vector are continuously updated according to the preset iteration until the convergence condition is met to obtain the updated Hub vector and Authority vector.

[0026] Perform Hadamard product on the updated Hub vector and Authority vector to obtain the SALSA risk vector.

[0027] Furthermore, after a fault occurs, the Hub vector and the Authority vector are initialized using the active power vector corresponding to the fault occurrence layer, and the initialized Hub vector update matrix and the Authority vector update matrix are obtained, which are expressed as:

[0028] ;

[0029] In the formula, Indicates Initialized Hub vector update matrix after layer failure, Indicates The initialized authority vector update matrix after layer failure, Indicates System power flow vector after layer failure.

[0030] Furthermore, the initialized Hub vector and Authority vector are updated using the Hub vector update matrix and the Authority vector update matrix. The Hub vector and Authority vector are continuously updated according to the preset iteration until the convergence condition is met, and the updated Hub vector and Authority vector are obtained, which are respectively expressed as:

[0031] ;

[0032] In the formula, Indicates Updated Hub vector after layer failure Hub vector of order, Indicates Updated Hub vector after layer failure The Authority vector updates the matrix, Indicates The column normalized matrix of the branch disconnection distribution factor matrix after layer fault, Indicates Row normalization matrix of branch disconnection distribution factor matrix after layer fault The transpose of .

[0033] Furthermore, the Hub vector update matrix and the Authority vector update matrix are expressed as:

[0034] ;

[0035] In the formula, Indicates Update after layer failure Iteration The Hub vector updates the matrix after that. Indicates Update after layer failure Iteration The Authority vector updates the matrix after that. Indicates Update after layer failure Iteration The Hub vector updates the matrix after that. Indicates Update after layer failure Iteration The Authority vector updates the matrix after . Further, the convergence condition is expressed as:

[0036] ;

[0037] In the formula, Indicates Update after layer failure Iteration The Hub vector updates the matrix after that. Indicates Update after layer failure Iteration The Authority vector updates the matrix after that. Indicates Update after layer failure Iteration The Hub vector updates the matrix after that. Indicates Update after layer failure Iteration The Authority vector updates the matrix after that. represents the infinite norm; and They represent the convergence criteria of the Hub vector and the Authority vector respectively.

[0038] Furthermore, the Hadamard product is performed on the updated Hub vector and the Authority vector to obtain the SALSA risk vector, which is expressed as:

[0039] ;

[0040] In the formula, express dimensional SALSA risk vector, " represents the Hadamard product operator, which is used to obtain the product of the elements in the same position of two matrices of the same dimension; | | represents the absolute value operator, which is used to obtain the absolute value of each element in the matrix. After the layer failure, the SALSA risk vector is expressed as , , and Indicates SALSA risk vector, Hub vector, and Authority vector after layer failure.

[0041] Furthermore, according to the risk calculation result of the SALSA risk vector, the elements in the SALSA risk vector are sorted from large to small, the risk of the potential fault is determined according to the sorting result, the potential fault with the highest risk is taken as the next potential fault, and the topological structure and operating condition after the next potential fault are obtained and iterated, wherein, in each iteration, the potential fault with the highest risk, i.e., the first potential fault, is taken as the next potential fault. Layer fault is the first choice to disconnect the line until the number of faults screened out Satisfy the conditions , where Indicates Layer failure number sequence.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] 1. The present invention calculates the probability of each line failure, i.e., the Hub vector, and the consequence of each line failure, i.e., the Authority vector, by comprehensively considering the topological structure, operating conditions, and branch disconnection distribution factors and system power flow changes of the power system during fault propagation. The SALSA risk vector is obtained through the Hadamard product of the Hub vector and the Authority vector, which is used to comprehensively reflect the risk level of each line in a chain failure. At the same time, by iteratively screening the potential faults with the highest risk, the high-risk critical chain failure chain can be accurately located, which not only improves the accuracy of power grid security assessment, but also provides a scientific basis for formulating targeted preventive control measures, which is helpful to improve the overall safety level of the power grid;

[0044] 2. Compared with the traditional model-based simulation method, the present invention gradually constructs a critical chain fault chain that meets the preset fault depth and breadth parameters through an iterative method, and in each iteration, the potential fault with the highest risk is selected as the next potential fault according to the sorting result of the SALSA risk vector, and the topological structure and operating conditions of the power system are updated. The iterative screening method not only avoids a large number of repeated simulation calculations, but also significantly improves the calculation efficiency and optimizes the utilization of computing resources. At the same time, the output critical chain fault chain is arranged in sequence according to the disconnection line number, which is easier to understand and analyze, further improving the practicality and operability of the present invention;

[0045] 3. The present invention quantifies the impact of branch disconnection on the active power flow changes of other branches of the power system by calculating the branch disconnection distribution factor matrix to construct the system power flow vector, and uses the system power flow vector and its sensitivity to define the Hub vector and the Authority vector. By iteratively updating the Hub vector and the Authority vector until the convergence conditions are met, the SALSA risk vector that can comprehensively reflect the failure probability and consequences of each line is obtained. This method, based on the deep integration of the physical characteristics of the power system and the fault propagation mechanism, not only considers the probability of fault occurrence, but also considers the impact of the fault on the system power flow and the severity of the consequences of the fault, thereby achieving high-precision risk assessment of the chain failure chain of the power system.

[0046] 4. In addition, the present invention arranges the output key chain fault chains in sequence according to the disconnection line numbers, which is easy to understand and analyze, and further improves the practicality and operability of the method. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 The figure is a schematic diagram of the overall process of a method for identifying a critical chain of failures based on SALSA risk provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0049] The term "and / or" is only a description of the association relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " generally indicates that the related objects are in an "or" relationship.

[0050] Example 1

[0051] like Figure 1 As shown, this embodiment introduces a method for identifying a critical chain failure chain based on SALSA risk, including:

[0052] Step 1: Obtain the topology structure according to the number of lines and nodes in the power system and initialize the parameters of the power system to obtain the operating conditions of the power system.

[0053] The present invention constructs the topology of the power system according to the number of lines and nodes in the power system, and performs parameter initialization on the power system, including setting the voltage, current, power and other parameters of each node to obtain initialization parameters to simulate the actual operating conditions of the power system. By establishing a basic model of the power system and the obtained initialization parameters, the current state of the power system can be reflected, providing initial conditions for the analysis of cascading failures.

[0054] Step 2: Repeat the following steps until the output critical chain of faults meets the preset fault depth and width parameters:

[0055] According to the topological structure and operating conditions, the number of cascading failure layers and the failure sequence of each layer are obtained.

[0056] The present invention simulates the chain reaction process of the power system when a fault occurs according to the current topological structure and operating conditions. By analyzing the propagation path and level of the chain fault, the number of chain fault layers and the fault sequence of each layer can be obtained, which reveals the propagation mechanism of the chain fault and clarifies the sequence of faults at each layer, which is helpful to analyze the propagation process of the fault.

[0057] According to the number of cascading fault layers and the fault sequence of each layer, the branch disconnection distribution factor matrix and system power flow vector are calculated.

[0058] The branch disconnection distribution factor matrix reflects the impact of branch disconnection on the flow changes of other branches, and the system flow vector describes the flow distribution of each branch in the power system.

[0059] The present invention provides data support for subsequent calculations by quantifying the impact of branch circuit breaking on system power flow, and also reveals the distribution law of power flow in the power system, which is helpful for analyzing the impact of faults on the system.

[0060] According to the branch disconnection distribution factor matrix and the system power flow vector, the Hub vector used to characterize the failure probability of each line and the Authority vector used to characterize the consequences of each line failure are calculated and the Hadamard product is performed to obtain the SALSA risk vector.

[0061] The present invention uses the Hub vector to characterize the failure probability of each line, and the Authority vector to characterize the failure consequence of each line, reflecting the importance of the line in the cascading failure and the impact of the line failure on the system. The SALSA risk vector is obtained by the Hadamard product of the Hub vector and the Authority vector, reflecting the risk level of each line in the cascading failure. Furthermore, the present invention realizes the quantitative evaluation of the line failure probability and the failure consequence, and obtains the SALSA risk vector that can fully reflect the risk level of each line in the cascading failure.

[0062] According to the risk calculation result of the SALSA risk vector, the elements in the SALSA risk vector are sorted from large to small, and the risk of the potential fault is determined according to the sorting result. The potential fault with the highest risk is taken as the next potential fault, and the topological structure and operating conditions after the next potential fault are obtained for iteration.

[0063] The present invention also sorts the elements in the vector according to the risk calculation result of the SALSA risk vector, and determines the potential fault with the highest risk according to the sorting result, and iterates it as the next potential fault, thereby realizing the sorting and screening of potential fault risks, determining the next potential fault, and providing direction for the iterative process.

[0064] In addition, in each iteration, the present invention updates the topology and operating conditions of the power system according to the determined next potential fault, and also reflects the state change of the power system after the fault occurs, providing updated initial conditions for the next iteration.

[0065] Step 3: After the iteration is completed, the high-risk cascading fault chains are output and the critical cascading fault chains are output in sequence according to the disconnected line numbers.

[0066] The present invention outputs a high-risk cascading fault chain according to the fault sequence recorded during the iteration process after the iteration, providing detailed information on the high-risk cascading fault chain, which is helpful for formulating targeted preventive control measures. The present invention also arranges the output key cascading fault chains in sequence according to the disconnection line numbers, which improves the readability and practicality of the results.

[0067] In summary, the entire process of the present invention simulates the cascading fault propagation process of the power system, calculates the branch disconnection distribution factor matrix and the system power flow vector, and then quantitatively evaluates the risk level of each line in the cascading fault, and determines the high-risk cascading fault chain through iterative screening. This not only improves the accuracy of power grid security assessment, but also provides a scientific basis for formulating targeted preventive control measures.

[0068] Example 2

[0069] Based on the same inventive concept as Example 1, this example introduces a method for identifying a critical chain failure chain based on SALSA risk, which specifically includes the following steps:

[0070] Step 1: Obtain the topology structure according to the number of lines and nodes in the power system and initialize the parameters of the power system to obtain the operating conditions of the power system.

[0071] Step 2: Repeat the following steps until the output critical chain of faults meets the preset fault depth and width parameters:

[0072] Step 2.1: According to the topological structure and operating conditions, the number of cascading failure layers and the failure sequence of each layer are obtained.

[0073] In this embodiment, the total number of cascading failure layers is determined based on the topological structure and operating conditions. And the fault sequence of each layer ;Remember the cascading failure layer , the fault sequence number in the cascading fault layer , initialize the system parameters and determine the number of lines in the system , number of nodes .

[0074] Step 2.2: According to the number of cascading fault layers and the fault sequence of each layer, the branch disconnection distribution factor matrix and the system power flow vector are calculated.

[0075] In this embodiment, the branch disconnection distribution factor matrix is ​​expressed as:

[0076] ;

[0077] In the formula, Indicates The branch disconnection distribution factor matrix after layer fault, Indicates After layer failure Order branch susceptance matrix, Indicates After layer failure Order node-branch association matrix The transposed matrix of Indicates After layer failure Order branch susceptance matrix The inverse matrix of express The unit matrix, Indicates After layer failure order power transfer distribution factor, ” represents the Hadamard product, Indicates the number of lines. Indicates the number of nodes.

[0078] Step 2.3: According to the branch disconnection distribution factor matrix and the system power flow vector, the Hub vector used to characterize the failure probability of each line and the Authority vector used to characterize the consequences of each line failure are calculated and Hadamard product is performed to obtain the SALSA risk vector.

[0079] In this embodiment, according to the branch disconnection distribution factor matrix, the influence of branch disconnection on the active power flow change of other branches of the power system is quantified, and the system power flow vector after the potential fault occurs is calculated, which is expressed as:

[0080] ;

[0081] In the formula, Indicates The system power flow vector after layer failure, Indicates The first distribution factor matrix of branch disconnection after layer fault Column vector, used to characterize the loss index caused by the breaking fault of line L, express Vidi System power flow vector after layer failure No. The row element is used to characterize the probability index of the line L breaking fault.

[0082] In this embodiment, according to the branch disconnection distribution factor matrix and the system power flow vector, the Hub vector used to characterize the probability of each line failure and the Authority vector used to characterize the consequence of each line failure are calculated and the Hadamard product is performed to obtain the SALSA risk vector, including:

[0083] Based on the similarity between web link propagation and cascading failure evolution, the Hub vector and Authority vector are defined using the system power flow vector and its sensitivity.

[0084] After a fault occurs, the active power vector corresponding to the fault layer is used to initialize the Hub vector and the Authority vector, and the initialized Hub vector update matrix and the Authority vector update matrix are obtained, which are expressed as:

[0085] ;

[0086] In the formula, Indicates Initialized Hub vector update matrix after layer failure, Indicates The initialized authority vector update matrix after layer failure, Indicates System power flow vector after layer failure.

[0087] Based on the initialized Hub vector update matrix and Authority vector update matrix, the Hub vector update matrix and Authority vector update matrix after the fault occurs are constructed according to the row normalization matrix and column normalization matrix of the branch disconnection distribution factor matrix of the fault occurrence layer;

[0088] The initialized Hub vector and Authority vector are updated using the Hub vector update matrix and the Authority vector update matrix. The Hub vector and Authority vector are continuously updated according to the preset iteration until the convergence condition is met. The updated Hub vector and Authority vector are obtained, which are expressed as:

[0089] ;

[0090] In the formula, Indicates Updated Hub vector after layer failure Hub vector of order, Indicates Updated Hub vector after layer failure The Authority vector updates the matrix, Indicates The column normalized matrix of the branch disconnection distribution factor matrix after layer fault, Indicates Row normalization matrix of branch disconnection distribution factor matrix after layer fault The transpose of .

[0091] In this embodiment, the Hub vector update matrix and the Authority vector update matrix are expressed as:

[0092] ;

[0093] In the formula, Indicates Update after layer failure Iteration The Hub vector updates the matrix after that. Indicates Update after layer failure Iteration The Authority vector updates the matrix after that. Indicates Update after layer failure Iteration The Hub vector updates the matrix after that. Indicates Update after layer failure Iteration The Authority vector updates the matrix after times. In this embodiment, the convergence condition is expressed as:

[0094] ;

[0095] In the formula, Indicates Update after layer failure Iteration The Hub vector updates the matrix after that. Indicates Update after layer failure Iteration The Authority vector updates the matrix after that. Indicates Update after layer failure Iteration The Hub vector updates the matrix after that. Indicates Update after layer failure Iteration The Authority vector updates the matrix after that. represents the infinite norm; and They represent the convergence criteria of the Hub vector and the Authority vector respectively.

[0096] Perform Hadamard product on the updated Hub vector and Authority vector to obtain the SALSA risk vector, which is expressed as:

[0097] ;

[0098] In the formula, express dimensional SALSA risk vector, " represents the Hadamard product operator, which is used to obtain the product of the elements in the same position of two matrices of the same dimension; | | represents the absolute value operator, which is used to obtain the absolute value of each element in the matrix. After the layer failure, the SALSA risk vector is expressed as , , and Indicates SALSA risk vector, Hub vector, and Authority vector after layer failure.

[0099] Step 2.4: According to the risk calculation result of the SALSA risk vector, sort the elements in the SALSA risk vector from large to small, determine the risk of the potential fault according to the sorting result, take the potential fault with the highest risk as the next potential fault, and obtain the topological structure and operating conditions after the next potential fault for iteration.

[0100] In an embodiment, according to the risk calculation result of the SALSA risk vector, the elements in the SALSA risk vector are sorted from large to small, the risk of the potential fault is determined according to the sorting result, the potential fault with the highest risk is taken as the next potential fault, and the topological structure and operating condition after the next potential fault are obtained and iterated, wherein, in each iteration, the potential fault with the highest risk, i.e., the first potential fault, is taken as the next potential fault. Layer fault is the first choice to disconnect the line until the number of faults screened out Satisfy the conditions , where Indicates Layer failure number sequence.

[0101] Step 3: After the iteration is completed, the high-risk cascading fault chains are output and the critical cascading fault chains are output in sequence according to the disconnected line numbers.

[0102] In summary, the present invention calculates the probability of failure of each line, i.e., the Hub vector, and the consequence of failure of each line, i.e., the Authority vector, by comprehensively considering the topological structure, operating conditions, and branch disconnection distribution factors and system power flow changes of the power system during fault propagation. The SALSA risk vector is obtained through the Hadamard product of the Hub vector and the Authority vector, which is used to comprehensively reflect the risk level of each line in a chain failure. At the same time, by iteratively screening the potential faults with the highest risk, the high-risk critical chain failure chain can be accurately located, which not only improves the accuracy of power grid security assessment, but also provides a scientific basis for formulating targeted preventive control measures, which is helpful to improve the overall safety level of the power grid.

[0103] Compared with the traditional model-based simulation method, the present invention gradually constructs a critical chain of faults that meets the preset fault depth and breadth parameters through an iterative manner. In each iteration, the potential fault with the highest risk is selected as the next potential fault according to the sorting result of the SALSA risk vector, and the topological structure and operating conditions of the power system are updated. The iterative screening method not only avoids a large number of repeated simulation calculations, but also significantly improves the calculation efficiency and optimizes the utilization of computing resources. At the same time, the output critical chain of faults is arranged in sequence according to the disconnected line number, which is easier to understand and analyze, further improving the practicality and operability of the present invention.

[0104] The present invention quantifies the impact of branch disconnection on the active power flow changes of other branches of the power system by calculating the branch disconnection distribution factor matrix to construct the system power flow vector, and uses the system power flow vector and its sensitivity to define the Hub vector and the Authority vector. By iteratively updating the Hub vector and the Authority vector until the convergence conditions are met, the SALSA risk vector that can comprehensively reflect the failure probability and consequences of each line is obtained. This method, based on the deep integration of the physical characteristics of the power system and the fault propagation mechanism, not only considers the probability of fault occurrence, but also considers the impact of the fault on the system power flow and the severity of the consequences of the fault, thereby realizing high-precision risk assessment of the chain failure chain of the power system.

[0105] In addition, the present invention arranges the output key chain fault chains in sequence according to the disconnection line numbers, which is easy to understand and analyze, and further improves the practicality and operability of the method.

[0106] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0107] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0108] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0109] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0110] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. A method for identifying critical chain failures based on SALSA risk, characterized in that: include: The topological structure is obtained according to the number of lines and nodes in the power system and the parameters of the power system are initialized to obtain the operating conditions of the power system; Repeat the following steps until the output critical chain of faults meets the preset fault depth and width parameters: According to the topological structure and operating conditions, the number of cascading failure layers and the failure sequence of each layer are obtained; According to the number of cascading fault layers and the fault sequence of each layer, the branch disconnection distribution factor matrix and system power flow vector are calculated; According to the branch disconnection distribution factor matrix and the system power flow vector, the Hub vector used to characterize the failure probability of each line and the Authority vector used to characterize the consequences of each line failure are calculated and Hadamard products are performed to obtain the SALSA risk vector. According to the risk calculation result of the SALSA risk vector, the elements in the SALSA risk vector are sorted from large to small, the risk of the potential fault is determined according to the sorting result, the potential fault with the highest risk is taken as the next potential fault, and the topological structure and operating conditions after the next potential fault are obtained for iteration; After the iteration is completed, the high-risk chain fault chains are output and the critical chain fault chains are output in sequence according to the disconnected line numbers.

2. The method for identifying critical chain failures based on SALSA risk according to claim 1, characterized in that: The branch circuit breaking distribution factor matrix is ​​expressed as: ； In the formula, Indicates The branch disconnection distribution factor matrix after layer fault, Indicates After layer failure Order branch susceptance matrix, Indicates After layer failure Order node-branch association matrix The transposed matrix of Indicates After layer failure Order branch susceptance matrix The inverse matrix of express The unit matrix, Indicates After layer failure Order power transfer distribution factor, " ” represents the Hadamard product, Indicates the number of lines. Indicates the number of nodes.

3. The method for identifying critical chain failures based on SALSA risk according to claim 2, characterized in that: According to the number of cascading fault layers and the fault sequence of each layer, the branch disconnection distribution factor matrix and system power flow vector are calculated, including: According to the branch disconnection distribution factor matrix, the influence of branch disconnection on the active power flow change of other branches of the power system is quantified, and the system power flow vector after the potential fault occurs is calculated, which is expressed as: ； In the formula, Indicates The system power flow vector after layer failure, Indicates The first distribution factor matrix of branch disconnection after layer fault Column vector, used to characterize the loss index caused by the breaking fault of line L, express Vidi System power flow vector after layer failure No. The row element is used to characterize the probability index of the line L breaking fault.

4. The method for identifying critical chain failures based on SALSA risk according to claim 1, characterized in that: According to the branch disconnection distribution factor matrix and the system power flow vector, the Hub vector used to characterize the probability of each line failure and the Authority vector used to characterize the consequences of each line failure are calculated and Hadamard products are performed to obtain the SALSA risk vector, including: Based on the similarity between web link propagation and cascading failure evolution, the Hub vector and Authority vector are defined using the system power flow vector and its sensitivity. After a fault occurs, the Hub vector and the Authority vector are initialized using the active power vector corresponding to the fault occurrence layer, and the initialized Hub vector update matrix and the Authority vector update matrix are obtained; Based on the initialized Hub vector update matrix and Authority vector update matrix, and according to the row normalization matrix and column normalization matrix of the branch disconnection distribution factor matrix of the fault occurrence layer, the Hub vector update matrix and Authority vector update matrix after the fault occurs are constructed; The initialized Hub vector and Authority vector are updated using the Hub vector update matrix and the Authority vector update matrix. The Hub vector and Authority vector are continuously updated according to the preset iteration until the convergence condition is met to obtain the updated Hub vector and Authority vector. Perform Hadamard product on the updated Hub vector and Authority vector to obtain the SALSA risk vector.

5. The method for identifying critical chain failures based on SALSA risk according to claim 4, characterized in that: After a fault occurs, the active power vector corresponding to the fault layer is used to initialize the Hub vector and the Authority vector, and the initialized Hub vector update matrix and the Authority vector update matrix are obtained, which are expressed as: ； In the formula, Indicates Initialized Hub vector update matrix after layer failure, Indicates The initialized authority vector update matrix after layer failure, Indicates System power flow vector after layer failure.

6. The method for identifying critical chain failures based on SALSA risk according to claim 5, characterized in that: The initialized Hub vector and Authority vector are updated using the Hub vector update matrix and the Authority vector update matrix. The Hub vector and Authority vector are continuously updated according to the preset iteration until the convergence condition is met. The updated Hub vector and Authority vector are obtained, which are expressed as: ； In the formula, Indicates Updated Hub vector after layer failure Hub vector of order, Indicates Updated Hub vector after layer failure The Authority vector updates the matrix, Indicates The column normalized matrix of the branch disconnection distribution factor matrix after layer fault, Indicates Row normalization matrix of branch disconnection distribution factor matrix after layer fault The transpose of .

7. The method for identifying critical chain failures based on SALSA risk according to claim 6, characterized in that: The Hub vector update matrix and the Authority vector update matrix are expressed as: ； In the formula, Indicates Update after layer failure Iteration The Hub vector updates the matrix after that. Indicates Update after layer failure Iteration The Authority vector updates the matrix after that. Indicates Update after layer failure Iteration The Hub vector updates the matrix after that. Indicates Update after layer failure Iteration The Authority vector updates the matrix after this time.

8. The method for identifying critical chain failures based on SALSA risk according to claim 7, characterized in that: The convergence condition is expressed as: ； In the formula, Indicates Update after layer failure Iteration The Hub vector updates the matrix after that. Indicates Update after layer failure Iteration The Authority vector updates the matrix after that. Indicates Update after layer failure Iteration The Hub vector updates the matrix after that. Indicates Update after layer failure Iteration The Authority vector updates the matrix after that. represents the infinite norm; and They represent the convergence criteria of the Hub vector and the Authority vector respectively.

9. The method for identifying critical chain failures based on SALSA risk according to claim 1, characterized in that: Perform Hadamard product on the updated Hub vector and Authority vector to obtain the SALSA risk vector, which is expressed as: ； In the formula, express dimensional SALSA risk vector, " " represents the Hadamard product operator, which is used to obtain the product of the elements in the same position of two matrices of the same dimension; | | represents the absolute value operator, which is used to obtain the absolute value of each element in the matrix. After the layer failure, the SALSA risk vector is expressed as , , and Indicates SALSA risk vector, Hub vector, and Authority vector after layer failure.

10. The method for identifying critical chain failures based on SALSA risk according to claim 1, characterized in that: According to the risk calculation result of the SALSA risk vector, the elements in the SALSA risk vector are sorted from large to small, and the risk of the potential fault is determined according to the sorting result. The potential fault with the highest risk is taken as the next potential fault and the topological structure and operating conditions after the next potential fault are obtained for iteration. In each iteration, the potential fault with the highest risk, i.e., the first potential fault, is taken as the next potential fault. Layer fault is the first choice to disconnect the line until the number of faults screened out Satisfy the conditions , where Indicates Layer failure number sequence.

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