A method for analyzing transmission power limit and card fault of power system transmission section

Abstract

The application discloses a power system transmission section transmission power limit and fault analysis method. A system topology undirected graph capable of representing the topology structure of a power system is constructed, a transmission section parameter model of a standardized operation mode is established, and the sending end and the receiving end of the transmission section are determined. A power system transmission section sending end unit automatic search model based on a topology analysis method is further constructed, and fault cards for transient stability analysis of different elements and fault types are generated in batches. A transmission section transmission power analysis model is constructed based on GRPO, and a transmission section transient stability transmission limit calculation model is constructed by adopting a repeated power flow strategy to determine the transient stability transmission limit of the target transmission section. A transmission section fault identification model considering transient stability constraints is constructed to identify the key fault restricting the transmission capacity of the target transmission section.

Description

Technical Field

[0001] This invention relates to the field of power system dispatching and operation control technology, specifically to a method for analyzing transmission power limits and bottleneck faults at power system transmission sections. Background Technology

[0002] Power grid transmission sections are the weakest links between different regions of the system. By searching for transmission sections and identifying critical sections, dimensionality-reduced monitoring of the power system can be achieved, which helps ensure the safe and stable operation of the power grid. Calculating the transmission limits of power system transmission sections and monitoring and identifying bottleneck faults affecting the transmission capacity of system sections can improve the system's early warning and emergency response capabilities, which is particularly important for ensuring the rapid self-healing and safe operation of the power grid.

[0003] In calculating transmission limits for power transmission sections, traditional methods primarily rely on repetitive power flow calculations combined with transient stability time-domain simulations. This approach involves manually or semi-automatically adjusting power at both the sending and receiving ends, verifying numerous anticipated faults one by one. The workflow is complex, computationally resource-intensive, and time-consuming, making it difficult to meet the demands of modern large-scale power grids for online analysis and rapid decision-making. Furthermore, existing methods often rely on dispatchers' experience-based pre-sets or simple proportional allocation methods when determining the unit combination corresponding to the limit power, lacking systematic optimization. This makes it difficult to guarantee the economy and robustness of the resulting unit combination, potentially leading to conservatively calculated transmission limits that fail to fully exploit the power grid's transmission potential.

[0004] In terms of fault identification at transmission checkpoints, existing technologies lack an automated analysis model that systematically correlates transient stability constraints, transmission section power limits, and the dominant faults leading to instability. Dispatchers struggle to quickly identify the key faults truly restricting transmission capacity from massive amounts of fault simulation results, thus failing to provide the most direct and efficient decision support for grid reinforcement and security control strategy formulation.

[0005] By mining massive data resources and constructing intelligent analysis and decision-making models, we can overcome the difficulties and computational complexity of traditional modeling, address the power flow diversification issues caused by the integration of high-proportion renewable energy systems, and build power transmission section bottleneck fault and transmission power limit models based on reinforcement learning models to provide timely support for the safety and stability of power systems. Summary of the Invention

[0006] The technical problem this invention aims to solve is that existing technologies lack an automated analysis model that systematically correlates transient stability constraints, transmission section power limits, and dominant faults leading to instability. Dispatchers struggle to quickly identify the key faults truly restricting transmission capacity from massive amounts of fault simulation results, thus failing to provide the most direct and efficient decision support for grid reinforcement and security control strategy formulation. The purpose is to provide a method for analyzing transmission section power limits and bottleneck faults in power systems, solving the technical problem of not being able to provide the most direct and efficient decision support for grid reinforcement and security control strategy formulation.

[0007] This invention is achieved through the following technical solution:

[0008] In a first aspect, the present invention provides a method for analyzing the transmission power limit and bottleneck faults of power transmission sections in a power system, comprising:

[0009] Based on the topological connections of the power system and the component information of the target transmission section, the sending-end region of the target transmission section and the set of adjustable generator sets within that region are identified.

[0010] Based on the preset fault setting rules, a fault set for transient stability analysis is generated;

[0011] Based on the adjustable generator set, a group relative strategy optimization algorithm is used to solve the preset generator set combination optimization model to obtain various generator set combination methods and their corresponding cross-sectional transmission power; wherein, the generator set combination optimization model includes at least an optimization objective; wherein, the optimization objective is configured to minimize the deviation between the actual transmission power of the target transmission section and the preset target power, as well as the generator set operating cost;

[0012] Starting with the unit combination mode and the transmission power of the cross section, the transmission power of the target transmission section is iteratively increased using a repetitive power flow strategy. After each iteration, a fault set is used to perform transient stability simulation verification on the new operating mode until at least one fault causes system instability. The power of the previous iteration step is then determined as the transient stability transmission limit of the target transmission section.

[0013] Based on the transient stability transmission limit, the severity of faults that cause system instability is analyzed, and a list of bottleneck faults that restrict the transmission capacity of the target power transmission section is identified and output.

[0014] Furthermore, the step of identifying the sending-end region of the target transmission section and the set of adjustable generator units within that region based on the topological connections of the power system and the component information of the target transmission section includes:

[0015] Construct an undirected graph representing the topology of the power system; in this undirected graph, power plants, substations and load nodes are abstracted as topology nodes, and transmission lines are abstracted as topology tie lines.

[0016] Based on the undirected graph and the line information included in the target transmission section, a graph theory connectivity analysis algorithm is used to compare the connected components of the undirected graph before and after the disconnection in order to identify the newly added connected regions due to the disconnection.

[0017] Within the newly added connected area, all generator nodes are searched and identified to form the adjustable generator set set.

[0018] Furthermore, the step of generating a fault set for transient stability analysis based on preset fault setting rules includes:

[0019] Based on preset fault types and parameter settings, fault cards for transient stability analysis corresponding to different power system components are generated in batches to form the fault set;

[0020] The preset fault types include: N-1 single faults and N-2 complex faults set for at least one component among transmission lines, transformers and busbars;

[0021] The preset parameter settings include: the time of fault occurrence, the fault clearing delay, and the safety and stability control switching parameters.

[0022] Further, the step of solving the preset unit combination optimization model based on the adjustable generator set using a group relative strategy optimization algorithm to obtain multiple unit combination methods and their corresponding cross-sectional transmission power includes:

[0023] The adjustable generator set is divided into multiple generator set groups based on at least one of the following criteria: unit type, cost characteristics, or geographical region.

[0024] Within each unit group, based on the current load status of the power system, the power status of the transmission section, and the operating status of the unit, start-up and shutdown decisions for the units within the group are generated.

[0025] Merge the start-up and shutdown decisions of all unit groups, calculate the relative advantages between groups, and update the policy network of each unit group.

[0026] After fixing the start-up and shutdown status of the generating units, under the constraints of node voltage stability and transient power angle stability, the optimization objective is solved by quadratic programming to obtain the optimal output of each generating unit.

[0027] Verify whether the solution meets the power flow constraints and system safety and stability constraints. If not, introduce a penalty term to modify the optimization objective and iterate again until all constraints are met.

[0028] Furthermore, the transient power angle stability constraint is configured such that, within a preset fault verification period, the absolute value of the power angle difference between any two generators in the power system does not exceed the preset maximum power angle difference.

[0029] Further, the step of iteratively increasing the transmission power of the target transmission section using a repetitive power flow strategy, starting from the unit combination mode and the transmission power of the section, and performing transient stability simulation verification on the new operating mode using a fault set after each iteration, until at least one fault causes system instability, and determining the power of the previous iteration step as the transient stability transmission limit of the target transmission section, includes:

[0030] The power increase step is configured as follows: starting from the current unit combination mode and its corresponding cross-sectional transmission power, increase the total output of the units in the sending-end area according to the preset power increase step size, and adjust the load in the receiving-end area accordingly to form a new system operation mode.

[0031] The steps for re-acquiring unit combination and transient stability simulation are configured as follows: for the new system operation mode, re-acquiring the unit combination mode that satisfies the power flow constraints, and using the fault set to perform transient stability simulation on the acquired unit combination mode;

[0032] The iterative steps are configured as follows: iteratively execute the steps of increasing power, re-acquiring unit combination and transient stability simulation, until at least one of the verified unit combination methods causes system instability due to fault concentration at a certain cross-sectional power level.

[0033] The determination step is configured to: determine the highest cross-sectional power level before triggering system instability as the transient stability transmission limit of the target transmission section.

[0034] Furthermore, the step of performing severity analysis on faults causing system instability based on the transient stability transmission limit, identifying and outputting a list of bottleneck faults restricting the transmission capacity of the target transmission section, includes:

[0035] Under the transient stability transmission limit, acquire all faults that cause system instability;

[0036] Based on the reduction in transmission capacity caused by each fault, the probability of fault occurrence, and the severity of instability consequences, a severity index for each fault is calculated.

[0037] All faults leading to instability are ranked according to the severity index;

[0038] Based on the ranking results, one or more faults with the highest severity are identified and output as a list of bottleneck faults that restrict the transmission capacity of the target power transmission section.

[0039] Secondly, the present invention provides a power transmission section transmission power limit and bottleneck fault analysis device for power systems, comprising:

[0040] The identification module is used to identify the sending-end region of the target transmission section and the set of adjustable generator sets within that region based on the topological connection relationship of the power system and the component information of the target transmission section.

[0041] The generation module is used to generate a fault set for transient stability analysis based on preset fault setting rules.

[0042] The solution module is used to solve a preset unit combination optimization model based on the adjustable generator set using a group relative strategy optimization algorithm, to obtain various unit combination methods and their corresponding cross-sectional transmission power; wherein, the unit combination optimization model includes at least an optimization objective; wherein, the optimization objective is configured to minimize the deviation between the actual transmission power of the target transmission section and the preset target power, as well as the unit operating cost;

[0043] The iterative module is used to iteratively increase the transmission power of the target transmission section using a repetitive power flow strategy, starting from the unit combination mode and the transmission power of the section. After each iteration, the fault set is used to perform transient stability simulation verification on the new operating mode until at least one fault causes the system to become unstable. The power of the previous iteration step is then determined as the transient stability transmission limit of the target transmission section.

[0044] The output module is used to perform severity analysis on faults that cause system instability based on the transient stability transmission limit, identify and output a list of bottleneck faults that restrict the transmission capacity of the target power transmission section.

[0045] Thirdly, the present invention provides an electronic device, comprising: a memory, and one or more processors communicatively connected to the memory; the memory stores instructions executable by the one or more processors, the instructions being executed by the one or more processors to cause the one or more processors to implement the method described above.

[0046] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0047] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0048] This embodiment addresses the technical problems of traditional methods, which rely on manual labor, involve complex calculations, and struggle to accurately identify key faults. Specifically, it first automatically and accurately identifies the set of adjustable generator units at the sending end of the target transmission section through topology connection analysis, providing a precise input basis for subsequent optimization analysis. Next, it generates standardized fault sets for transient stability analysis in batches based on preset rules, providing a complete test scenario for full-process stability verification. On this basis, a group-relative strategy optimization algorithm is used to solve the unit combination optimization model aimed at minimizing section power deviation and unit operating costs. Intelligent optimization automatically generates multiple unit combination schemes that satisfy power flow constraints, overcoming the limitations of traditional manual preset unit output. Subsequently, starting with the obtained unit combination and section power, an iterative optimization strategy is used to gradually improve the section power while simultaneously performing transient stability simulation verification, achieving accurate and automated determination of the transient stability transmission limit of the transmission section. Ultimately, under extreme transmission power, the severity of instability faults is quantitatively analyzed based on the reduction magnitude, probability of occurrence, and severity of consequences. The system automatically identifies and outputs a list of the most critical bottleneck faults, enabling dispatchers to quickly focus on safety bottlenecks from a massive number of faults, and providing direct and efficient decision support for power grid reinforcement and security control strategy formulation. Attached Figure Description

[0049] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0050] Figure 1 This is an overall framework diagram of the intelligent analysis method for power system transmission section bottleneck faults and transmission power limits provided in the embodiments of this specification;

[0051] Figure 2 This is a flowchart illustrating the steps for implementing the automatic search model of the sending-end generating unit of the power system transmission section provided in the embodiments of this specification.

[0052] Figure 3 This document provides a flowchart for analyzing the transmission power of a power transmission section based on the GRPO algorithm, as shown in the embodiments of this specification.

[0053] Figure 4 The critical transmission sections selected for the standard IEEE 39-bus system provided in Test Example 1 of this manual;

[0054] Figure 5The limit transmission power diagrams for different cross sections under the typical operating conditions provided in Test Example 1 of this manual;

[0055] Figure 6 The limit transmission power diagram of section S1 under different operating modes is provided in Test Example 1 of this manual;

[0056] Figure 7 The critical section S1 limit transmission power diagram considering the N-1 principle is provided for test example 1 in this specification;

[0057] Figure 8 This is a schematic diagram of the voltage disturbance curve of the KS section of the S power grid under the power transmission mode during the summer off-peak conditions provided in Test Example 2 of this manual. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0059] This embodiment provides a method for analyzing transmission power limits and bottleneck faults at power transmission sections in a power system. The execution entity of the method can be a server of the power system, which can be configured in the computer room of the power dispatch center. The execution entity of the method can be an industrial control computer or a cloud computing platform.

[0060] The method may include:

[0061] Step S12: Based on the topological connection relationship of the power system and the component information of the target transmission section, identify the sending-end area of ​​the target transmission section and the set of adjustable generator sets in that area.

[0062] In this embodiment, the topological connection relationship of the power system can be represented as the electrical connection logic and physical layout association between various electrical components (power plants, substations, transmission lines, transformers, etc.) in the power system, which is used to characterize the connection nodes, connection methods and hierarchical relationships of each component.

[0063] The component information of the target transmission section can be represented as the basic parameters and attribute data of all power components constituting the target transmission section, which may include: component type, unique identifier number, connection node number, electrical parameters (such as rated voltage, rated capacity), and operating status parameters, etc. The sending-end region can be represented as the power system region within the target transmission section that undertakes the function of power output, where generator sets transmit power to the receiving-end region through the transmission section. The adjustable generator set set can be represented as the set of all generator sets within the sending-end region that can flexibly adjust their output and start / stop status according to system operating requirements, and that meet the constraints of safe and stable system operation.

[0064] In one possible and specific implementation, the sending-end region of the target transmission section and the set of adjustable generator units within that region can be identified in the following way:

[0065] First, the topological connectivity data of the power system is acquired. This data can come from power system dispatch automation systems, geographic information systems, or equipment parameter databases, and may include: location information of power plants, substations, and load nodes; start and end connection information of transmission lines and transformers; and the physical connection methods of each component (e.g., overhead line connections, cable connections, transformer coupling connections, etc.). Then, an undirected graph representing the entire power system topology is constructed. Next, in this undirected graph, based on the line information contained in the target transmission section, the line is temporarily removed from the graph (i.e., considered disconnected). Then, a connectivity analysis algorithm in graph theory (e.g., breadth-first search or depth-first search) is used to perform topological analysis on the system after the line disconnection, identifying the newly generated connected regions due to the disconnection. By comparing the sets of connected components of the system before and after the disconnection, it can be determined which node regions are separated from the main grid due to the disconnection. Finally, all generator nodes located in the sending-end region (which may be the power outflow region) are selected to form the adjustable generator set set. The units in this set are considered resources that can participate in subsequent optimization and adjustment to improve the transmission capacity of the section.

[0066] Step S14: Generate a fault set for transient stability analysis according to the preset fault setting rules.

[0067] In this embodiment, the preset fault setting rules can be standardized configuration guidelines for fault types and fault parameters, predefined for transient stability simulation calculations. Specifically, they can include fault type setting rules and fault parameter setting rules. Specifically, the fault type can represent a power component fault scenario to be simulated according to the power grid safety and stability calculation procedure. It can include N-1 single faults, that is, faults where a single component, such as transmission lines, transformers, and busbars, is set to disconnect without fault or be cleared after a short circuit for each critical component. Secondly, it can also include N-2 complex faults, such as the simultaneous disconnection of two parallel lines on the same pole or the simultaneous faulting of multiple related busbars within the same substation.

[0068] In this embodiment, the fault set for transient stability analysis can be represented as a set of all anticipated faults of key power components that are generated in batches according to preset fault setting rules, including multiple fault types. Each fault can exist in the form of a standardized fault card, which is convenient for subsequent transient stability simulation software to call.

[0069] Step S16: Based on the adjustable generator set, the preset generator set combination optimization model is solved using a group relative strategy optimization algorithm to obtain multiple generator set combination methods and their corresponding cross-sectional transmission power; wherein, the generator set combination optimization model includes at least an optimization objective; wherein, the optimization objective is configured to minimize the deviation between the actual transmission power of the target transmission section and the preset target power, as well as the generator set operating cost.

[0070] In this embodiment, the Group Relative Strategy Optimization Algorithm (GRPO) can be an intelligent optimization algorithm based on grouping strategy and relative advantage evaluation. It achieves the optimal solution of the global unit combination scheme by grouping adjustable generator sets, with each group independently generating an operating strategy and performing inter-group interactive optimization.

[0071] In this embodiment, the preset unit combination optimization model can be represented as a pre-established mathematical model for solving unit combination schemes. Specifically, it can include: input parameters, optimization objectives, and constraints, used to obtain the optimal unit operation combination under the premise of satisfying system safety and stability constraints. More specifically, the optimization objective can be a dual objective, namely, minimizing the deviation between the actual transmission power of the target transmission section and the preset target power, and minimizing the unit operating cost.

[0072] In this embodiment, the preset target power can be expressed as the transmission power value that the target transmission section should achieve, pre-set based on factors such as power system operation plan, load demand forecast, and grid security constraints. The unit operating cost can be expressed as the sum of various expenses incurred by the generator unit during operation, including fuel costs, start-up and shutdown costs, maintenance costs, and loss costs, etc. The unit combination method can be expressed as the combination of start-up and shutdown states and output allocation scheme of each unit in the adjustable generator set. The section transmission power can be expressed as the actual active power value transmitted by the target transmission section under a specific unit combination method, equal to the difference between the total output of the units operating in the sending-end area and the total load demand in the receiving-end area.

[0073] In one possible and specific implementation, firstly, the adjustable generator set can be divided into multiple groupings based on factors such as unit type, cost characteristics, or geographical region. Then, each group can independently generate start-up and shutdown decisions for its units based on the current system state (including load, cross-sectional power, and unit operating status). Next, the decisions of all groups are merged, and the advantage of each group's decision relative to the overall objective (i.e., relative advantage between groups) is calculated, and the strategy network of each group is updated accordingly. After the start-up and shutdown states of the units are determined, these states are fixed, and the problem is transformed into a power allocation subproblem. Finally, a mathematical programming method (e.g., quadratic programming) is used to solve this subproblem, determining the optimal power output of each unit while satisfying constraints such as upper and lower limits of unit output, thereby obtaining the cross-sectional transmission power.

[0074] In a possible and specific implementation plan, a dual-objective design can be adopted. The first objective is to minimize the deviation between the actual transmitted power of the target transmission section and the preset target power, making the transmitted power of the section as close to the preset target as possible. The deviation can be calculated in the form of absolute deviation or squared deviation. The second objective is to minimize the operating cost of the units, so as to minimize the total cost of all operating units. The two objectives can be balanced by weighting coefficients, which can be adjusted according to the system operation priority: during peak load periods, the weight of minimizing the section power deviation can be increased to prioritize ensuring power transmission matching; during cost-sensitive periods, the weight of minimizing operating costs can be increased to prioritize controlling economic losses.

[0075] In one possible and specific implementation, the constraints may include: node voltage stability constraints, transient power angle stability constraints, unit operation constraints, power flow constraints, and system safety and stability constraints.

[0076] Step S18: Starting from the unit combination mode and the transmission power of the section, the transmission power of the target transmission section is iteratively increased using a repetitive power flow strategy. After each iteration, the fault set is used to perform transient stability simulation verification on the new operating mode until at least one fault causes system instability. The power of the previous iteration step is then determined as the transient stability transmission limit of the target transmission section.

[0077] In this embodiment, the initial point can be represented by any feasible unit combination obtained in step S16 and its corresponding cross-sectional transmission power. This initial point satisfies the current safety and stability constraints and optimization objectives of the system, serving as the starting benchmark for iterative improvement. The repeated power flow strategy can be represented as a strategy that continuously increases the cross-sectional transmission power by gradually adjusting system operating parameters, repeatedly performing power flow calculations and stability checks until a stability limit is found.

[0078] In this embodiment, the iterative increase can be achieved by gradually increasing the cross-sectional transmission power according to a preset step size or ratio, forming a new system operating mode after each increase, and continuing until the system becomes unstable. Transient stability simulation verification can be performed using preset transient stability simulation software to simulate the occurrence process of faults in a fault concentration, record the system's dynamic response, and determine whether the system maintains stability. System instability can refer to a state where the system cannot maintain normal operation after a fault, such as power angle instability, voltage collapse, or frequency collapse.

[0079] In this embodiment, the transient stability transmission limit can be expressed as the maximum active power that the target transmission section can safely transmit under the premise of satisfying transient stability constraints, and can be the power limit for stable system operation.

[0080] In one possible and specific implementation, the initial point for iteration can first be determined. Any one of the various feasible unit combinations obtained in step S16 can be selected as the initial point. The selection method can be random selection, selection of the optimal cost combination, or selection of the optimal power matching combination, etc. To improve iteration efficiency, combinations with cross-sectional transmission power close to the preset target power can be preferentially selected as the initial point to reduce the number of iterations.

[0081] Secondly, set the power growth step size. The step size is the increase in the cross-sectional transmission power in each iteration. It can be set to a fixed value or a dynamic value. For example, it can be adjusted according to the deviation between the current cross-sectional power and the preset target power (use a large step size when the deviation is large and a small step size when the deviation is small), or it can be set according to a fixed proportion of the current power (e.g., 5%, 10%).

[0082] Then, an iterative upgrade operation is performed. Based on the initial unit combination and cross-sectional transmission power, the total output of the adjustable generator units in the sending-end area is increased according to a preset step size. Specific output upgrade methods can include proportional allocation (each operating unit is upgraded synchronously according to its current output proportion), allocation based on regulation performance (more fast-regulating units are upgraded), and allocation based on cost priority (more low-cost units are upgraded), etc. While increasing output, the load in the receiving-end area is adjusted simultaneously: the total receiving-end load can be increased proportionally to absorb the new power, or the load distribution can be adjusted to make power transmission more balanced, thereby achieving a balance between system power supply and demand and forming a new system operating mode.

[0083] Once the new operating mode is established, a matching unit combination is re-obtained. The unit combination adapted to the new operating mode can be obtained by re-invoking the group-relative strategy optimization algorithm to solve the model.

[0084] Next, transient stability simulation verification is performed. The fault set generated in step S14 can be used, and transient stability simulation software can be employed to perform a full fault set simulation of the new operating mode and the matched unit combination. The occurrence, development, and clearing process of each fault is simulated one by one, and the system dynamic response data is recorded. Criteria for judging system stability may include: unit power angle difference maintained within preset limits, node voltage within a safe range, system frequency stable near rated values, and component power flow not exceeding thermal stability limits. If the system is stable after all fault simulations, it indicates that the cross-sectional power can still be increased, and the iteration can return to the output increase step. If at least one fault causes system instability, the iteration terminates.

[0085] Finally, the transient stability transmission limit is determined. The transmission power of the section during the last stable operation before the termination of the iteration can be determined as the transient stability transmission limit of the target transmission section.

[0086] Step S110: Based on the transient stability transmission limit, perform severity analysis on the faults that cause system instability, identify and output a list of bottleneck faults that restrict the transmission capacity of the target power transmission section.

[0087] In this embodiment, the severity analysis can be represented as a quantitative assessment of the impact of the fault from multiple dimensions. The checkpoint fault list can be a list of critical faults sorted by severity, output after the analysis.

[0088] This embodiment addresses the technical problems of traditional methods, which rely on manual labor, involve complex calculations, and struggle to accurately identify key faults. Specifically, it first automatically and accurately identifies the set of adjustable generator units at the sending end of the target transmission section through topology connection analysis, providing a precise input basis for subsequent optimization analysis. Next, it generates standardized fault sets for transient stability analysis in batches based on preset rules, providing a complete test scenario for full-process stability verification. On this basis, a group-relative strategy optimization algorithm is used to solve the unit combination optimization model aimed at minimizing section power deviation and unit operating costs. Intelligent optimization automatically generates multiple unit combination schemes that satisfy power flow constraints, overcoming the limitations of traditional manual preset unit output. Subsequently, starting with the obtained unit combination and section power, an iterative optimization strategy is used to gradually improve the section power while simultaneously performing transient stability simulation verification, achieving accurate and automated determination of the transient stability transmission limit of the transmission section. Ultimately, under extreme transmission power, the severity of instability faults is quantitatively analyzed based on the reduction magnitude, probability of occurrence, and severity of consequences. The system automatically identifies and outputs a list of the most critical bottleneck faults, enabling dispatchers to quickly focus on safety bottlenecks from a massive number of faults, and providing direct and efficient decision support for power grid reinforcement and security control strategy formulation.

[0089] In some implementations, the step of identifying the sending-end region of the target transmission section and the set of adjustable generator units within that region based on the topological connections of the power system and the component information of the target transmission section includes:

[0090] Step S122: Construct an undirected graph representing the topology of the power system; wherein, in this undirected graph, power plants, substations and load nodes are abstracted as topology nodes, and transmission lines are abstracted as topology tie lines.

[0091] In this implementation, firstly, basic topology data of the power system can be acquired (which may originate from a power system dispatch automation system or an equipment parameter management database). Then, node abstraction is performed. Power plants, substations, and load nodes can be mapped to topology nodes in an undirected graph. Next, tie-line abstraction is performed. Each transmission line is mapped to a topology tie between two corresponding topology nodes in the undirected graph. Finally, the undirected graph is constructed and stored. The connection relationships between the abstracted topology nodes and topology tie lines can be digitally stored using an adjacency matrix or adjacency list data structure. The adjacency matrix can be presented as a two-dimensional array, where a value of 1 indicates a topology tie between topology nodes in the corresponding row and column, and a value of 0 indicates no connection. The adjacency list can store the adjacent nodes and corresponding topology tie information of each topology node in a linked list format.

[0092] Step S124: Based on the undirected graph and the line information included in the target transmission section, the connected components of the undirected graph before and after the disconnection are compared using a graph theory connectivity analysis algorithm to identify the newly added connected regions due to the disconnection.

[0093] In this embodiment, the graph connectivity analysis algorithm can be defined as an algorithm based on graph theory used to determine the connectivity between nodes in an undirected graph and to divide it into connected components. Specifically, a breadth-first search (BFS) algorithm can be used. The connected components can be represented as a set of interconnected topological nodes in an undirected graph, where there is at least one path connecting any two topological nodes within the set, and topological nodes outside the set cannot be connected to nodes within the set via this path.

[0094] In one possible and specific implementation, firstly, the line information of the target transmission section can be obtained and associated with topology tie lines. The line information of the target transmission section can be obtained from the power system section parameter management system, and specifically may include: section name, a list of transmission lines constituting the section (line name, unique number, starting and ending substations and busbars), and the corresponding topology tie line identifier number in the undirected graph. Through the starting and ending connection relationships of the lines, each transmission line included in the section can be associated one-to-one with the topology tie lines in the undirected graph constructed in step S122. Secondly, the connectivity check of the undirected graph before disconnection is performed. A breadth-first search algorithm can be used to traverse the complete undirected graph to determine the connected components before disconnection. Then, the virtual disconnection operation of the target section line is performed. Based on the set of topology tie lines associated in the above steps, the topology tie line can be temporarily removed from the undirected graph (i.e., the connection relationship between nodes is broken), forming the undirected graph after disconnection. Next, the connectivity check of the undirected graph after disconnection is performed. A breadth-first search algorithm, identical to that before the disconnection, can be used to traverse the undirected graph after the disconnection, repeating the above steps to identify all connected components after the disconnection and recording the topological node list and identifier number of each connected component. Finally, the newly added regions are identified by comparing the connected components. This can be done by comparing the connected components before and after the disconnection one by one, using set operations to determine the differences. Specifically, if a connected component after the disconnection has no completely matching node set in any of the connected components before the disconnection, then that connected component is a newly added connected region due to the disconnection.

[0095] Step S126: In the newly added connected region, search and determine all generator nodes to form the adjustable generator set set.

[0096] In this embodiment, based on preset screening criteria for adjustable generator sets, all generator nodes can be searched and determined within the newly added connected area to form the adjustable generator set set. Specifically, the preset screening criteria for adjustable generator sets can be: adjustable output, available status, and compliance with constraints. More specifically, adjustable output can mean that the generator set's output can be flexibly adjusted between minimum and maximum output, or that it has the ability to switch between start-stop states (i.e., it can start or stop operation according to dispatch instructions). Available status can mean that it is currently in operation or standby. Compliance with constraints can mean that the unit's adjustment performance can meet the system's transient stability constraints, and that output adjustment will not cause safety hazards to itself or the system.

[0097] In one possible and specific implementation, firstly, generator nodes within the newly added connected area can be filtered. Based on the topology node identifier numbers of the sending-end area determined in step S124, information on all generator sets associated with these nodes can be extracted from the corresponding database (e.g., a power system generator parameter database). Then, the generator set information is filtered based on preset adjustable generator set filtering criteria to obtain a set of adjustable generator sets.

[0098] In some implementations, the step of generating a fault set for transient stability analysis according to preset fault setting rules includes:

[0099] Step S142: Based on preset fault types and parameter settings, generate fault cards for transient stability analysis corresponding to different power system components in batches to form the fault set. The preset fault types include: N-1 single faults and N-2 complex faults set for at least one component among transmission lines, transformers, and busbars. The preset parameter settings include: fault occurrence time, fault clearing delay, and safety and stability control tripping parameters.

[0100] In this embodiment, N-1 single fault can be any independent component in the power system (e.g., a line, a transformer) that fails and is tripped by the protection device. N-2 complex fault can be two related components that fail simultaneously or successively. Specifically, N-1 single fault can be a transmission line fault, for example, a DC transmission line that fails and is tripped by the protection device. N-1 single fault can also be a transformer fault or a bus fault, etc. N-2 complex fault can be a double-circuit line fault on the same tower, for example, two transmission lines erected on the same tower that trip simultaneously due to external damage, etc. N-2 complex fault can also be an associated bus fault, etc.

[0101] In this embodiment, the safety and stability control switching parameters may include switching delay parameters, switching method parameters, and switching capacity parameters, etc.

[0102] In one possible and specific implementation, firstly, basic data (e.g., a list of power system components, component operating parameters, and protection device configuration parameters) can be obtained. Specifically, this basic data can be obtained from a power system dispatch automation system or from an equipment parameter management database. Then, based on this basic data and preset fault types and parameter settings, a corresponding fault card is generated for each target component. Specifically, for each transmission line, transformer, and bus component in the target component list, a corresponding N-1 fault card can be created. For a double-circuit transmission line on the same tower, in addition to generating an N-1 fault card for a single circuit, an additional N-2 fault card can be generated for simultaneous faults on both circuits.

[0103] In one possible and specific implementation, each fault card may include the following information: a unique fault card number, the name and number of the faulty component, the type of the faulty component (transmission line / transformer / bus), the type of fault (N-1 single fault / N-2 complex fault), the specific form of the fault (three-phase short circuit / two-phase short circuit, etc.), the time of the fault occurrence, the fault clearing delay (faulty side / non-faulty side), the safety and stability control tripping parameters (tripping delay, tripping node, tripping capacity), and a description of the protection action logic, etc.

[0104] In some implementations, the step of solving a preset unit combination optimization model based on the adjustable generator set using a group-relative strategy optimization algorithm to obtain various unit combination methods and their corresponding cross-sectional transmission power includes:

[0105] Step S162: Divide the adjustable generator set into multiple generator set groups according to at least one of the following criteria: generator set type, cost characteristics, or geographical region.

[0106] In this embodiment, the adjustable generator set can be divided into multiple groupings using a single criterion or a combination of multiple criteria. The generator set types can specifically include: thermal power units, hydropower units, new energy generator sets, gas turbine units, and waste heat generator sets, etc. The cost characteristics can specifically include: high-cost units, low-cost units, start-up and shutdown cost-sensitive units, and output cost-sensitive units, etc. The geographical areas can specifically include: units within the same power plant, units within the same county / city, units within the same power supply zone, and hydropower units within the same river basin, etc.

[0107] Step S164: Within each unit group, based on the current load status of the power system, the power status of the transmission section, and the operating status of the unit, generate start-up and shutdown decisions for the units within the group.

[0108] In this embodiment, the current load status of the power system can be the real-time operating characteristics such as the total load demand of the power system, load distribution, and load growth / decline trends after the generator units are divided into groups and start / stop decisions are generated. The power status of the transmission section can be the current actual transmission power value of the target transmission section, the deviation of this power from the preset target power, and the power change trend.

[0109] In a possible and specific implementation plan, firstly, multi-dimensional state information can be acquired. Specifically, the load state information of the power system can be collected in real time from the dispatch automation system, the power state information of the transmission section can be obtained from the section power monitoring system, and the unit operation state information can be extracted from the unit monitoring system and the equipment management database. Then, the operation of generating unit start-up and shutdown decisions within the group is executed. Specifically, each unit group can independently maintain its corresponding strategy network. This strategy network can be a decision model trained based on a group-relative strategy optimization algorithm, used to output reasonable start-up and shutdown decisions based on the input multi-dimensional state information. Specifically, the decision needs to meet system load demand (ensuring that the total output potential of the started units can cover the current and short-term load gap), match the transmission section power target (minimizing the section power deviation after the start-up and shutdown decision), comply with unit operation constraints (not exceeding the unit start-up and shutdown limit, cumulative operating time limit, etc.), and take into account operating costs (prioritizing the start-up of low-cost units and reducing unnecessary start-ups of high-cost units). The collected load status, power status of transmission sections, and unit operating status data can be input into the strategy network of each group. The strategy network analyzes the matching degree between load demand and unit output potential, the correlation between section power deviation and unit start-up and shutdown, unit operating constraints and start-up and shutdown feasibility, and outputs the start-up and shutdown decisions for each unit in the group.

[0110] Step S166: Merge the start-up and shutdown decisions of all unit groups, calculate the relative advantages between groups, and update the policy network of each unit group.

[0111] In this embodiment, the local decisions of all groups can be combined into a global grouping scheme. The contribution of each group's decision to the global scheme can be evaluated. Specifically, the improvement of the global objective (e.g., cross-sectional power deviation and total cost) can be calculated using a value function or an advantage function if the decisions of one group are adopted while the decisions of other groups are fixed. Based on the calculated relative advantage, the parameters (e.g., weights and biases) of each group's policy network can be adjusted using a policy gradient method, so that the policy network is more inclined to produce decisions that bring higher relative advantage in the future.

[0112] In a possible and specific implementation plan, the evaluation dimensions of relative advantage can correspond to the global optimization objective, and may include two dimensions: one is the contribution dimension of cross-sectional power deviation (the degree to which the decision scheme reduces the deviation between the actual cross-sectional power and the preset target power), and the other is the contribution dimension of operating cost (the degree to which the decision scheme reduces the total operating cost of the unit). Weight coefficients can be assigned to the two evaluation dimensions, and these coefficients can be adjusted according to the system's operational priority. Then, the single-dimensional contribution value of each group of decisions can be calculated. Specifically, for the cross-sectional power deviation contribution dimension, the change in cross-sectional power deviation after the implementation of the decision scheme (compared to the baseline decision scheme) can be calculated; the larger the change (the smaller the deviation), the higher the contribution value. For the operating cost contribution dimension, the change in the total operating cost of the unit after the implementation of the decision scheme (compared to the baseline decision scheme) can be calculated; the larger the change (the lower the cost), the higher the contribution value. Finally, a weighted summation method can be used to calculate the comprehensive relative advantage value of each group.

[0113] Step S168: After fixing the start-up and shutdown status of the units, under the constraints of node voltage stability and transient power angle stability, the optimization objective is solved by quadratic programming to obtain the optimal output of each unit.

[0114] In this embodiment, the fixed unit start-stop state can be represented as fixing the global start-stop decision scheme determined in step S166 as a constraint condition, and not changing the start-stop state of the unit during subsequent output calculation; only the specific output size is calculated for the operating units. The node voltage stability constraint can be a constraint requirement that the voltage amplitude of each node in the power system must be maintained within a preset safety range. The transient power angle stability constraint can be a constraint requirement that, after a power system fault, the absolute value of the power angle difference between any two operating units does not exceed a preset maximum value. The optimal output can be represented as the specific output value of each operating unit when the optimization objective (minimizing cross-sectional power deviation and minimizing unit operating cost) is optimal, provided that all constraints are met.

[0115] In one possible and specific implementation, the objective function of the quadratic programming method can be the dual optimization objective defined in step S161 (minimizing cross-sectional power deviation + minimizing unit operating cost). The input data, objective function, and constraints can be imported into a quadratic programming solver (or specialized optimization software). The solver performs calculations according to preset convergence criteria (e.g., iteration error less than 0.001 or the number of iterations reaching a preset upper limit). During the solution process, the tool automatically adjusts the output distribution of each operating unit to optimize the dual optimization objective while satisfying all constraints. After the solution is completed, the optimal output values ​​of each operating unit can be output, forming a list of optimal unit outputs.

[0116] Step S1610: Verify whether the solution results satisfy the power flow constraints and system safety and stability constraints. If not, introduce a penalty term to correct the optimization objective and iterate again until all constraints are satisfied and multiple unit combinations and corresponding cross-sectional transmission power are obtained.

[0117] In this embodiment, the power flow constraint can be expressed as the constraint requirement that the power flow (active power and reactive power) transmission values ​​of each transmission line, transformer, and other component in the power system do not exceed their safe operation limits. The system safety and stability constraint can be various constraints that ensure the overall safe and stable operation of the power system (e.g., static stability constraints, frequency stability constraints, and voltage collapse prevention constraints, etc.). The penalty term is an additional term introduced to correct the optimization objective when the solution result does not meet the constraints. Its value is positively correlated with the degree of constraint violation, and its purpose is to prompt subsequent iterations to avoid the constraint violation region.

[0118] In this embodiment, if the conditions are not met, the process can return to step S162 and re-execute operations such as grouping, decision generation, strategy updating, and output solving based on the revised optimization objective, until a feasible solution that satisfies all constraints is obtained.

[0119] In one possible and specific implementation, power flow constraint verification can be performed in the following manner:

[0120] The optimal output solution can be substituted into the power system power flow calculation model to calculate the actual power flow values ​​(active power and reactive power) of each transmission line and transformer. The actual power flow values ​​are then compared with the corresponding power flow safety limits to determine whether they exceed the limits. If the actual power flow values ​​of all components are within the limits, the power flow constraints are satisfied; otherwise, they are not satisfied.

[0121] In some implementations, the transient power angle stability constraint is configured such that, within a preset fault verification period, the absolute value of the power angle difference between any two generators in the power system does not exceed a preset maximum power angle difference.

[0122] In this embodiment, the preset fault verification period can be defined as a pre-defined time interval used to monitor changes in generator power angle during the transient process following a power system fault. Specifically, this period can cover the stages of fault occurrence, development, and gradual system recovery to stability. The preset maximum power angle difference can be a pre-determined critical value for ensuring system transient stability; it is a quantitative standard for judging whether transient power angle stability constraints are met. Specifically, the maximum power angle difference can be 360 ​​degrees, meaning that during the dynamic process following a fault, the power angle difference between any two generators is not allowed to exceed one cycle. Once exceeded, it is considered that the two generators have lost synchronization, and the system is transiently unstable.

[0123] In some implementations, the step of iteratively increasing the transmission power of the target transmission section using a repetitive power flow strategy, starting from the unit combination mode and the transmission power of the section, and performing transient stability simulation verification on the new operating mode using a fault set after each iteration, until at least one fault causes system instability, and determining the power of the previous iteration step as the transient stability transmission limit of the target transmission section, includes:

[0124] The power enhancement step is configured as follows: starting from the current unit combination mode and its corresponding cross-sectional transmission power, the total output of the units in the sending-end area is increased according to the preset power growth step size, and the load in the receiving-end area is adjusted accordingly to form a new system operation mode.

[0125] In this embodiment, the current unit combination is the unit start-up and shutdown state and output allocation scheme obtained in the previous iteration, which satisfies all constraints. The preset power increase step size is pre-set, representing the increase in the inter-plane transmission power in each iteration.

[0126] In one possible and specific implementation, the power increase step size can be a fixed step size or a dynamic step size. The total output of the units in the sending-end region can be increased based on a proportional allocation method. For example, each operating unit can be increased synchronously according to its current output as a proportion of the total output to ensure a balanced distribution of unit output. Alternatively, the total output of the units in the sending-end region can be increased based on regulation performance allocation. For example, the output of fast-regulating units (gas turbines, pumped storage units) can be increased first, followed by adjustments to slow-regulating units (coal-fired power units).

[0127] In one possible and specific implementation plan, the load in the receiving-end area can be adjusted accordingly as follows: The total load can be increased synchronously according to the current load proportion of each load node in the receiving-end area to ensure a balanced load distribution and directly absorb the new power output from the sending end. Alternatively, the load in the load center area or key load nodes can be increased first to optimize power flow distribution, reduce power flow congestion at the transmission section, and thus indirectly increase the transmittable power of the section.

[0128] The steps for re-acquiring unit combination and transient stability simulation are configured as follows: for the new system operation mode, re-acquiring the unit combination mode that satisfies the power flow constraints, and using the fault set to perform transient stability simulation on the acquired unit combination mode.

[0129] In this embodiment, the process of re-acquiring a unit combination mode that satisfies power flow constraints can involve re-invoking the group relative strategy optimization algorithm to solve the unit combination optimization model, considering the load state and cross-sectional power state changes under the new operating mode, and generating a completely new unit start-up and shutdown decision and output allocation scheme. Alternatively, it can be based on the original unit combination mode, adjusting only the output allocation while keeping the start-up and shutdown states unchanged, and re-solving the optimal output using a quadratic programming method to shorten the computation time.

[0130] In this embodiment, the transient stability simulation can be expressed as using transient stability simulation software (PSASP) to simulate the occurrence, development and clearance process of faults in the fault set, record the dynamic response of the system (power angle, voltage, frequency, etc.), and determine whether the system remains stable under the new operating mode.

[0131] The iterative steps are configured as follows: iteratively execute the steps of increasing power, re-acquiring unit combination and transient stability simulation, until at least one of the verified unit combination methods causes system instability due to fault concentration at a certain cross-sectional power level.

[0132] In this embodiment, the power level of a certain cross-section can be a new cross-section transmission power value obtained in a certain round of the iteration process. The statement that at least one type of system instability occurs due to faults in the fault set can be expressed as follows: at this cross-section power level, not all unit combinations can pass the full fault set stability check; rather, if at least one combination triggers system instability under a certain fault scenario, it indicates that the cross-section power is approaching its stability limit. This system instability can be power angle instability, voltage instability, or frequency instability, etc.

[0133] The determination step is configured to: determine the highest cross-sectional power level before triggering system instability as the transient stability transmission limit of the target transmission section.

[0134] In some implementations, the step of performing severity analysis on faults causing system instability based on the transient stability transmission limit, identifying and outputting a list of bottleneck faults restricting the transmission capacity of the target transmission section, includes:

[0135] Step S1102: Under the transient stable transmission limit, acquire all faults that cause system instability.

[0136] In this embodiment, the fault that causes system instability can be either N-1 single faults or N-2 complex faults in the fault set generated in step S14.

[0137] In one possible and specific implementation, transient stability simulation records and iteration logs can be traversed to filter out all fault records marked as causing instability. For each fault record, key information is extracted, such as fault identifier, fault parameters, instability characteristics, and associated operating modes. Then, the extracted information is standardized, organized, and verified according to a preset format to form a structured instability fault information table.

[0138] Step S1104: Based on the reduction in transmission capacity caused by each fault, the probability of fault occurrence, and the severity of instability consequences, calculate the severity index of each fault.

[0139] In this embodiment, the reduction in transmission capacity can be expressed as the difference between the target transmission section's reduction from the transient stable transmission limit to the maximum safe power required for the system to restore stable operation after a fault occurs. The severity of the instability consequences can be the magnitude of the losses to the safe operation of the power system, user power supply, and socio-economic development caused by the fault leading to system instability.

[0140] In this implementation, the probability of a fault occurrence can be determined based on historical data statistics. For example, the historical occurrence frequency of similar faults can be extracted from annual power system fault statistics reports and equipment fault databases, and the average annual occurrence probability can be calculated by combining the equipment's operating years and maintenance cycle. Alternatively, the probability of a fault occurrence can be determined based on equipment reliability parameters or expert evaluation methods.

[0141] In this embodiment, the severity of the instability consequences can be determined based on the range of power supply impact and economic losses.

[0142] In this embodiment, the severity index of each fault can be calculated using the following formula: Severity Index = Transmission Capacity Reduction Amount × Weight 1 + Fault Occurrence Probability × Weight 2 + Severity of Instability Consequences × Weight 3. The sum of Weight 1, Weight 2, and Weight 3 is 1.

[0143] In a possible and specific implementation plan, firstly, the reduction in transmission capacity for each fault can be quantitatively calculated. For each instability fault, the corresponding fault scenario can be retrieved from the transient stability simulation system, and the maximum transmission power of the system under stable operation can be determined by following the process of gradually reducing the cross-sectional power → simulating and verifying the stable state. Then, combined with the determined transient stability transmission limit, the transmission capacity reduction value for each fault can be calculated. Secondly, the probability of occurrence of each fault can be determined. The corresponding probability value method can be selected according to the fault type. For N-1 faults (single ordinary transmission line faults), the probability can be calculated using historical data statistical methods. For N-2 complex faults, the probability can be determined using expert evaluation methods or equipment reliability parameter correction methods. Then, based on the quantitative scoring criteria for instability consequences, the severity of the instability consequences of each fault can be quantitatively assessed. Finally, based on the above calculation results, a severity index is calculated.

[0144] Step S1106: Sort all the faults that cause instability according to the severity index.

[0145] In this embodiment, a descending order can be used, that is, the higher the severity index value of the fault, the higher it is ranked, indicating that its restrictive effect on the cross-sectional transmission capacity is stronger.

[0146] Step S1108: Based on the ranking results, identify and output one or more faults with the highest severity as a list of bottleneck faults that restrict the transmission capacity of the target power transmission section.

[0147] In this embodiment, identifying and outputting one or more faults with the highest severity can specifically be done by: selecting the top N faults as checkpoint faults, or setting a severity index threshold (e.g., 80 points) and selecting all faults with index values ​​higher than the threshold as checkpoint faults. Alternatively, the top M% of faults can be selected as checkpoint faults.

[0148] In one possible and specific implementation, the checkpoint fault list may further include: a unique fault identifier (ID), the name and number of the faulty component, the fault type (N-1 / N-2), the specific form of the fault, the severity index and ranking, the reduction in transmission capacity, the probability of fault occurrence, a description of the instability consequences, and the limiting characteristics.

[0149] To intuitively analyze the transmission limits and bottleneck faults restricting the transmission capacity of transmission sections under different operating modes, this implementation plan designs a reasonable framework for an intelligent analysis method of bottleneck faults and transmission power limits of power system transmission sections that takes into account transient stability constraints. First, a system topology undirected graph representing the power system topology is constructed, and a parameter model of the transmission section under standardized operating modes is established, clearly defining the sending and receiving ends of the transmission section. Second, an automatic search model for sending-end units of power system transmission sections based on topology analysis methods is constructed, and fault cards for transient stability analysis of different components and fault types are generated in batches. Third, a transmission power analysis model of the transmission section is constructed based on GRPO, and a transient stability transmission limit calculation model of the transmission section is constructed using a repetitive power flow strategy to determine the transient stability transmission limit of the target transmission section. Finally, a bottleneck fault identification model of the transmission section that takes into account transient stability constraints is constructed to identify key faults restricting the transmission capacity of the target transmission section.

[0150] In a specific implementation plan, an intelligent analysis method for power system transmission section bottleneck faults and transmission power limits considering transient stability constraints is provided, which specifically includes the following steps:

[0151] Step 1: Transform the power grid topology into a directed graph. Simplify the power plants, substations, and load nodes in the initial power system into topology graph nodes, simplify the transmission lines into topology graph tie lines, and merge multiple transmission lines.

[0152] The power network topology is digitally stored using an adjacency matrix or adjacency list data structure based on graph theory. The merging of multiple transmission lines on the same tower refers to converting multiple transmission lines with similar electrical parameters and related operating modes within the same corridor into a single tie line at the topological level, thereby simplifying the computational complexity of subsequent analysis.

[0153] Step 2: Establish a parameter model for the transmission section under standardized operation mode. The parameter model includes a component parameter table and a constraint parameter table, and clarifies the sending end and receiving end of the transmission section.

[0154] The component parameter table includes information on the transmission lines included in the transmission section, such as line name, number, and connection relationship; the limit parameter table, also known as the control limit parameter table, is used to store the boundary constraints for safe operation of the system, including power flow active transmission limits, node voltage operation limits, and system safety margin indicators.

[0155] Step 3: Based on the power system topology diagram constructed in Step 1, and considering the target section element information included in Step 2, construct an automatic search model for power system transmission section sending-end generating units based on topology analysis methods, as follows:

[0156] First, input the AC line connections, transformer connections, generator connections, etc., from the PSASP 51 file corresponding to the target system. Second, query the component information table of the target transmission section, inputting the lines included in the section and their end node numbers from the PSASP 51 file. Then, perform a system connectivity check: use a breadth-first search (BFS) algorithm to determine if the target system is a complete topology graph. If not, determine the number of partitioned subgraphs. The calculation process of the BFS algorithm can be described as follows:

[0157] This approach utilizes connected component analysis from graph theory. First, Breadth-First Search (BFS) is used to traverse all its neighbors, starting from any unvisited node in the graph. BFS is a graph traversal algorithm that starts from a vertex v0. After visiting v0, BFS sequentially visits its directly connected but unvisited neighbors w1, w2, ..., adding them to a queue in order. Next, BFS visits each node in the queue sequentially, first visiting w1's unvisited neighbors, then w2's, and so on, until all nodes have been traversed. BFS uses a queue to manage the nodes to be visited, ensuring that nodes are processed in the order they are visited, while avoiding duplicate visits and guaranteeing that each node is visited only once.

[0158] The following steps are used to check the connectivity of the system using BFS:

[0159] 1. Select an unvisited node from the graph as the starting point for traversal. Mark this node as "visited" and add it as the first-level node to the set of nodes to be traversed.

[0160] 2. Traverse neighboring nodes, enter the loop, and process all nodes in the current layer. Traverse all nodes in the current layer and check the directly connected neighboring nodes. If a neighboring node has not been visited, mark it as "visited" and add it to the set of nodes to be processed in the next round;

[0161] 3. Once all nodes in the current layer have been processed, the next node is set as the new current layer, and the traversal continues. This process "spreads" layer by layer until no new nodes can be visited.

[0162] 4. Once no more nodes can be visited, it means that all nodes that can be traversed from the starting node have been found. They constitute a connected component;

[0163] 5. Find unvisited nodes from all nodes in the graph and use them as the starting point for the next BFS. Repeat steps 1 to 4 until all nodes have been visited.

[0164] 6. Each BFS iteration generates a connected component (a set of nodes). These connected components are then returned sequentially. The system performs connectivity checks and outputs the contents of each connected component.

[0165] Disconnect the transmission lines contained in the target section, and use graph theory algorithms to perform connectivity detection on the current topology of the system. Compare and analyze the set of connected components identified after the line break with the connected components of the system before the line break. Due to the breakage of the cut sets, nodes that were originally in the same connected region may be partitioned into different subgraphs, thus appearing as newly generated connected components in the connectivity detection results. Extract these newly added connected regions due to the line break using set comparison methods. Re-search the number of partitioned subgraphs in the target system and number the newly appearing partitioned subgraphs.

[0166] Based on the sub-map number, search for all node information within the region, and search for generator nodes contained in the generator file in the PSASP 51 file. Store the generator information in an Excel file to realize the search for generator sets at the sending end of the power transmission section of the power system.

[0167] Step 4: Based on the fault setting and protection principles stipulated by the power grid, construct a transient stability fault setting model to generate fault cards for transient stability analysis for different components and fault types in batches. The setting principles are as follows: 1. According to the power grid safety and stability calculation regulations, preset fault sets of N-1 single faults and N-2 complex faults for key components such as transmission lines, transformers, and busbars. 2. Fault and protection settings: The network fault time is generally set to 1 second. The fault clearing delay meets the following requirements: 0.09s for the fault side of 500kV lines; 0.1s for the non-fault side of 500kV lines; 0.1s for the fault side of 220kV lines; 0.12s for the non-fault side of 220kV lines. 3. For lines with high-voltage reactors, the high-voltage reactors need to be cleared when the line is cleared by protection. 4. Safety control generator tripping settings: The safety control generator tripping delay is 0.25s after the fault, and the tripping setting is performed using the "node disturbance" method. 5. It is necessary to check whether it is a double-circuit line on the same tower (there is a corresponding table in the target power grid annual calculation report). If it is a double-circuit line on the same tower, an N-2 fault needs to be set for the line. The specific setting principle for the fault of each line is as follows (refer to 1).

[0168] Step 5: Construct a transmission section power analysis model based on the GRPO algorithm. By setting the target transmission power of the transmission section, obtain various unit combinations that satisfy power flow constraints, and calculate the actual transmission power of the target transmission section under all obtained unit combinations. The flowchart of the transmission section power analysis process based on the GRPO algorithm is shown below. Figure 3 As shown, the specific steps are as follows:

[0169] Step 5.1: Establish the unit combination optimization model framework. Input power system parameters, including: unit set G={1,2, …, Ng} and operating parameters (minimum output) of each unit. , maximum output The following parameters are considered: unit start-up and shutdown cost Ci, ramp rate Ri, transmission channel set L={1, 2, …,Nl}, and the transmission power target value Ptarget and thermal stability safety threshold for each channel. System load demand Dt and unit standby requirement Rt (t=1, 2, …, T, where T represents the upper limit of operating time);

[0170] Step 5.2: Define the objective function of the optimization model. This is based on the actual transmission power of the transmission channel. With target value Minimizing the deviation is the optimization objective; the objective function is constructed as follows:

[0171] (1)

[0172] In formula (1), t is the operating time, T is the upper limit of the operating time, i.e., the total number of time periods analyzed, and L is the set of transmission channels. Let t be the actual transmission power of the l-th transmission channel; λ represents the target transmission power of the transmission channel (the preset expected transmission power); K is the calculation norm of the deviation, k=1 or 2 (corresponding to L1 or L2 norm); G is the set of generating units, where i is a specific generating unit; λ is the weighting coefficient. Let t be the operating cost of unit i at time t, the value of which is determined by the start-up and shutdown status of the unit and its actual output. This represents the start / stop status of unit i at time t (0 = out of service, 1 = running). This represents the actual output of unit i at time t.

[0173] Step 5.3: Apply the GRPO algorithm to solve the optimization model.

[0174] 1. Grouping strategy: Divide the units into K groups G1, G2, …, GK according to their characteristics (grouping criteria include unit type, cost characteristics, or geographical region).

[0175] 2. Group Policy Interaction: Each group maintains a policy network πk, which is iteratively updated through the following steps:

[0176] a. Based on the current state st (including load, channel power, and unit status), generate group action ak (unit start-up and shutdown decision within the group) for each group.

[0177] b. Combine all group actions a={a1, a2, …, aK} and calculate the relative advantage between groups Ak(st, a);

[0178] c. Update the group policy gradient, see the following formula for details:

[0179] (2)

[0180] In formula (2), For gradient operators, The objective function of the unit combination optimization model is... The policy network parameters for the k-th machine group are... For expectation operators; The policy network for the kth machine group (each group maintains one policy network); Group actions (i.e., start-up and shutdown decisions of units within the group) are generated for the kth unit group. Let t be the current state at time t; Group the k-th unit in the "current state" Select Action The relative advantage between groups at a given time is used to evaluate the contribution of the action to the objective function. It can be understood that the direction of parameter updates in the policy network is determined by both the gradient of the action selection probability and the relative advantage of the action. If the action... relative advantages If the action is larger (the action is better), the policy network will be more inclined to choose this type of action subsequently. If the action... relative advantages If the action is small (indicating poor performance), the policy network will subsequently reduce the probability of selecting this type of action. Ultimately, this iterative optimization of each policy network group will make the overall unit combination scheme more closely match the objective function.

[0181] 3. Output allocation sub-problem: After fixing the start-up and shutdown status of the units, use quadratic programming to solve for the optimal output of each unit (in actual systems, the unit output mode often only considers three types: fully open, half open, and off) to satisfy the objective function and constraints.

[0182] Step 5.4: Optimize model constraint settings. Considering that the system reactive power is usually balanced locally, while frequency stability depends on global balance, the constraint settings of this invention do not consider voltage and frequency stability constraints under large disturbances. The main considerations are node voltage stability constraints and transient power angle stability constraints.

[0183] Using equation (5) to constrain the upper and lower limits of node voltage safety, the node voltage stability constraint is as follows:

[0184] (3)

[0185] In the formula, vi represents the voltage magnitude at node i. and This represents the upper and lower limits of the voltage amplitude at node i.

[0186] The transient work angle stability constraint is manifested as follows:

[0187] (4)

[0188] In formula (4), Let be the power angle of unit a at time t. To preset the maximum power angle difference, this invention uses 360 degrees; For all generators; Let t be any two generators; t0 is the preset fault start time, and tmax is the maximum running time.

[0189] Step 5.5: Safety verification of the optimized scheme. This involves verifying whether the static safety constraints of the transmission channel and the transient stability constraints of the system are met. If the constraints are not met, a penalty term is introduced to modify the objective function, and steps 5.3 and 5.4 are executed again.

[0190] Step 5.6: Output optimization scheme. Generate multiple unit start-up and shutdown plans and unit combination schemes to make the power transmission channel transmit power close to the set target value.

[0191] Step 6: Construct a transient stability transmission limit calculation model for the transmission section based on the repetitive power flow strategy. By iteratively increasing the power of the transmission section and performing transient stability verification, determine the transient stability transmission limit of the target transmission section.

[0192] For the transmission section, a transient stability fault sample set is set using PSASP simulation. Based on the repetitive power flow strategy, a calculation model for the transient stability transmission limit of the transmission section is constructed. The specific calculation process is as follows:

[0193] Step 6.1: Based on the unit combination and cross-sectional power obtained in Step 5, set a power increase step size, gradually increase the total output of the units in the sending-end area according to this step size, and simultaneously adjust the load in the receiving-end area proportionally to absorb the increased power, forming a new system operation mode;

[0194] Step 6.2: For the new operating mode obtained by increasing the cross-sectional power, re-execute step 5 to obtain the matching unit combination mode, and call the fault card generated in step 4 to use the transient stability discrimination module of PSASP to perform transient stability simulation verification of the entire fault set.

[0195] Step 6.3: The iterative process continues until at least one of the pre-set faults in all the unit combination schemes under the target section power level fails to meet the transient stability requirements. At this point, the calculation is terminated, and the previous power level is determined as the transient stability transmission limit of the transmission section.

[0196] Step 7: Based on the transient stability transmission limit determined in Step 6, construct a transmission section bottleneck fault identification model that takes into account transient stability constraints, and identify the key faults that restrict the transmission capacity of the target transmission section.

[0197] The specific steps for constructing a transmission section bottleneck fault identification model that takes into account transient stability constraints are as follows:

[0198] Step 7.1: Under the limit transmission power determined in Step 6, sort and classify all faults that cause system instability according to the severity of the reduction in transmission capacity caused by the fault, the probability of occurrence, and the severity of the consequences.

[0199] Step 7.2: By comparing the key state variables of the system before and after the fault, identify the transient stability bottleneck faults that limit the transmission capacity of the transmission section, and generate a bottleneck fault list containing faulty components and fault types.

[0200] Test Example 1

[0201] To verify the effectiveness and adaptability of the proposed analysis framework for critical section faults and transmission power limits in power systems, a specific implementation plan uses the standard IEEE 39-bus system as a test case. Based on the IEEE 39-bus system, four transmission sections were selected as test objects. The line numbers of the transmission sections are shown in Table 1. This verifies the comprehensive performance of the proposed critical section limit calculation and fault identification method. The selected critical transmission sections in the standard IEEE 39-bus system are listed below. Figure 4 .

[0202] Table 1. Information on selected critical transmission sections for the IEEE 39-bus system

[0203]

[0204] Results analysis of test case 1

[0205] This example calculates the cross-sectional quotas of the IEEE 39-node system under typical operating conditions. The specific load distribution is shown in Table 2 to verify the feasibility of using an optimization algorithm to calculate the quotas for different cross-sections.

[0206] Table 2 Load distribution under typical operating modes

[0207]

[0208] The transmission limit calculation results for the selected critical sections are shown in Table 3. The transmission limit results for different transmission sections are shown in Table 4. Figure 5As shown, since section S1 contains more lines and is a critical section, the limit result of S1 is greater than that of S2. This reflects the difference in transmission capacity of different sections in the power grid. Critical sections usually carry a larger load and more lines. By focusing on the management and optimization of the limits of critical sections, the transmission capacity of the entire power grid can be effectively improved.

[0209] Table 3. Limiting transmission power of different cross sections under typical operating conditions

[0210]

[0211] Sections S2 and S3 contain the same number of lines, but under typical operating conditions, the load at node 16 is greater than that at node 15, resulting in a higher limit for S3 than for S2. This indicates that the load distribution of each node must be comprehensively considered when evaluating section limits to achieve the optimal limit configuration. Furthermore, section S4, which contains only one line, has the lowest calculated limit value, at only 480MW. The number of lines in a section also affects the limit; sections with fewer lines typically have lower limits. However, these sections can still play an important role under reasonable monitoring and management, such as serving as backup lines or emergency recovery channels, thereby improving the reliability and resilience of the power grid. Analysis of the above calculation results shows that the method proposed in this invention can ensure the feasibility and rationality of calculating section limits.

[0212] In actual power grid operation, critical sections typically bear significant loads and lines. Their load distribution may change under different operating modes, leading to variations in the limit values ​​for those sections and lines. Based on the above embodiments, the load distribution of the IEEE 39-node system is modified. The total load in typical operating mode W1 is 6254.23 MW. Operating mode W2 involves randomly selecting node 24 and changing its load, resulting in a total load of 6154.23 MW. While changing the load of more nodes compared to W1, the total load remains unchanged at 6254.23 MW. Critical section S1 is more representative; therefore, this section uses S1 as an example to analyze the impact of load changes on the limit calculation results by operating the system under different load conditions. The calculation results are shown in Table 4.

[0213] Compared to W1, the total load decreases under W2, and the limits for all transmission lines change. The limits for lines 1-39, 3-4, and 25-26 are all reduced, and the limit for section S1 also decreases accordingly. When the total grid load decreases, the load on each line also decreases, and its transmission power decreases accordingly, allowing for more flexible control. W3 operation mode, based on W1, significantly changes the load at each node while maintaining a constant total grid load. Changes in node load alter the power flow of all transmission lines, and the section limits change accordingly. For details on the transmission limits for section S1 under the different operation modes calculated above, please refer to [link to relevant documentation]. Figure 6 .

[0214] Table 4. Limits for Section S1 under Different Operating Modes

[0215]

[0216] For the same cross-section, the cross-section limits and the limits for branches included in the cross-section will change under different operating conditions. For cross-sections with multiple operating modes, a dynamic adjustment management strategy can be adopted according to the actual situation. The cross-section limits can be calculated using optimization algorithms and adjusted in a timely manner to improve the operating efficiency and safety stability of the power system.

[0217] The transmission limits for critical transmission sections are influenced by a variety of factors, which can be mainly divided into constraints on the equipment itself and system steady-state transmission power constraints. The latter includes grid security and stability constraints, grid voltage constraints, etc. In actual grid operation, N-1 constraints are one of the most common forms of limiting the limits for critical transmission sections.

[0218] The calculation of transmission section limits considering the N-1 principle can be summarized as follows: Under normal operating conditions, if any line in the section is disconnected, the power flow of the remaining branches will not be overloaded. Calculate the power limit of the section under static safety and stability constraints. The specific steps are as follows: 1) Select critical sections: Determine the critical sections that need to be analyzed using N-1, such as S1 in this example; 2) N-1 line disconnection: Disconnect each line in the selected section one by one and record the limit value at this time; 3) Impact analysis: For each disconnection situation, analyze the impact on other lines and analyze whether overload will occur. When performing N-1 analysis on a real large power grid, it is not necessary to disconnect all lines in the network one by one. It is only necessary to analyze the impact of branch disconnection in the section on other lines. This algorithm can effectively deal with sudden situations and ensure power supply reliability. This embodiment still uses the critical section S1 as an example for N-1 analysis. The transmission limit of the critical section S1 considering the N-1 principle can be found in [reference needed]. Figure 7 The specific calculation results are shown in Table 5:

[0219] Table 5. Critical section limits considering the N-1 principle

[0220]

[0221] Considering the N-1 principle for the three operating modes of the critical section S1, the section limits obtained under different load conditions are different. Taking the typical operating mode W1 as an example, the linear optimization method calculates 820.4MW under normal conditions; considering the N-1 principle, the transmission section limit is 670.02MW. It is easy to see that the section limit value obtained by considering the N-1 principle is relatively conservative. In addition, the linear optimization method reaches the power limit when line 1-39 of the section is disconnected. This indicates that line 1-39 is a critical line. In actual operation, precise control is required to improve the transmission limit of the section and avoid cascading tripping accidents caused by the disconnection of the critical line.

[0222] Based on the transmission section bottleneck fault identification model proposed in this invention, which takes into account transient stability constraints, the standard IEEE 39-node system was used to analyze bottleneck faults that restrict the transmission capacity of the four selected transmission sections. The specific results can be found in Table 6.

[0223] Table 6. Fault identification results of key transmission sections selected in the IEEE 39-bus system

[0224]

[0225] Test Case 2

[0226] Test Example 2, to verify the effectiveness and adaptability of the proposed method in actual power grid applications, selected a power grid in a southwestern province (hereinafter referred to as the S power grid) as the test target. The S power grid considered transformers and generator units above 500kV. Three transmission channels within the S province (YA section, LM section, and KS section) were selected as test targets. Transient stability transmission limits were calculated and bottleneck faults were identified under 12 full-connection / planned maintenance operation modes (including 8 full-connection operation modes for summer high / flat transmission / flat reception / small load and winter high / flat transmission / flat reception / small load, as well as 4 planned maintenance operation modes performing N-1 fault analysis under a certain adverse operation mode), totaling 36 sets of data samples.

[0227] Based on the PSASP simulation software, a set of expected faults is set for each key section "bottleneck". The fault type is three-phase short circuit. The power angle and voltage timing characteristic curves of the "bottleneck" are generated. The training results obtained by combining the fault curves generated by historical data are used to perform section transient stability judgment on the initial section.

[0228] Results analysis of test case 2

[0229] Taking sections YA, LM, and KS of the S power grid as examples, topology analysis is used to search for generating units within the sections. By adjusting the start-up and shutdown of these units, the power output of the sections is gradually increased. Ten operating modes are generated in each iteration. The N-1 and N-2 transient stability simulation results for sections YA, LM, and KS under these twelve different operating modes are calculated. Finally, the transient stability transmission limits for sections YA, LM, and KS under different fault constraints are obtained. The transient stability transmission limits for the three sections calculated based on the model proposed in this invention are compared with the transmission limits obtained using the optimal power flow method. The computation time of the proposed method and the traditional method are compared, as detailed in Table 7.

[0230] Table 7 Calculation results of transmission line costs for critical transmission sections considering transient stability

[0231]

[0232] As shown in Table 7, for key sections in actual power systems, the method proposed in this implementation scheme has a very high similarity to the traditional optimal power flow method in calculating the transmission limit of the transmission section, and greatly shortens the calculation time, further proving the rationality and efficiency of the proposed method.

[0233] Furthermore, the proposed bottleneck fault identification model analyzes three provincial sections under 12 operating modes of the S power grid. The specific bottleneck fault identification results for key transmission sections considering transient stability are shown in Table 8.

[0234] Table 8. Fault identification results at key transmission sections considering transient stability

[0235]

[0236] The transient stability checkpoint fault identification results were verified through simulation. Transient stability calculations and analyses were performed on each section using PSASP simulation software. Taking the actual checkpoint fault verification results affecting the transmission capacity of the section under the Xiaping power transmission method as an example, the transient stability verification result curve of section KS is shown below. Figure 8 As shown.

[0237] By continuously adjusting the power flow, transient stability fault cards KS N-1 and KS N-2 were set at the KS section for transient stability safety verification. Under the KS N-2 fault, transient voltage instability occurred at the KS section, indicating that KS N-2 is a bottleneck fault affecting the transmission capacity of the Kangshu section under this method. The same verification was performed on other transmission sections.

[0238] The identification results were analyzed and evaluated in accordance with the "Regulations for Stable Operation of S Power Grid" issued in that year. The key section bottleneck faults with differences in identification results were summarized. The specific results can be found in Table 9.

[0239] Table 9. Identification Results of Key Transmission Section Cross-Sections with Different Identification Differences

[0240]

[0241] As shown in Table 9, the identification accuracy of the checkpoint identification results obtained based on the proposed model reached 34 / 36=94.44% compared with the actual checkpoint faults under the target operating mode. Among them, the results with differences in identification were all operating modes with low load levels under transient stability constraints. After analysis and judgment, the main reason is that the start-up mode of the operating mode with low load level is significantly different from that of the high load mode, resulting in different start-up sequences, which in turn leads to certain differences in the checkpoint faults that limit the power transmission capacity of the section.

[0242] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for analyzing transmission power limits and bottleneck faults at power system transmission sections, characterized in that, include: Based on the topological connections of the power system and the component information of the target transmission section, the sending-end region of the target transmission section and the set of adjustable generator sets within that region are identified. Based on the preset fault setting rules, a fault set for transient stability analysis is generated; Based on the adjustable generator set, a group relative strategy optimization algorithm is used to solve the preset generator set combination optimization model to obtain various generator set combination methods and their corresponding cross-sectional transmission power; wherein, the generator set combination optimization model includes at least an optimization objective; wherein, the optimization objective is configured to minimize the deviation between the actual transmission power of the target transmission section and the preset target power, as well as the generator set operating cost; Starting with the unit combination mode and the transmission power of the cross section, the transmission power of the target transmission section is iteratively increased using a repetitive power flow strategy. After each iteration, a fault set is used to perform transient stability simulation verification on the new operating mode until at least one fault causes system instability. The power of the previous iteration step is then determined as the transient stability transmission limit of the target transmission section. Based on the transient stability transmission limit, the severity of faults that cause system instability is analyzed, and a list of bottleneck faults that restrict the transmission capacity of the target power transmission section is identified and output.

2. The method according to claim 1, characterized in that, The step of identifying the sending-end region of the target transmission section and the set of adjustable generator units within that region based on the topological connections of the power system and the component information of the target transmission section includes: Construct an undirected graph representing the topology of the power system; in this undirected graph, power plants, substations and load nodes are abstracted as topology nodes, and transmission lines are abstracted as topology tie lines. Based on the undirected graph and the line information included in the target transmission section, a graph theory connectivity analysis algorithm is used to compare the connected components of the undirected graph before and after the disconnection in order to identify the newly added connected regions due to the disconnection. Within the newly added connected area, all generator nodes are searched and identified to form the adjustable generator set set.

3. The method according to claim 1, characterized in that, The step of generating a fault set for transient stability analysis according to preset fault setting rules includes: Based on preset fault types and parameter settings, fault cards for transient stability analysis corresponding to different power system components are generated in batches to form the fault set; The preset fault types include: N-1 single faults and N-2 complex faults set for at least one component among transmission lines, transformers and busbars; The preset parameter settings include: the time of fault occurrence, the fault clearing delay, and the safety and stability control switching parameters.

4. The method according to claim 3, characterized in that, The step of solving the preset unit combination optimization model based on the adjustable generator set using a group-relative strategy optimization algorithm to obtain various unit combination methods and their corresponding cross-sectional transmission power includes: The adjustable generator set is divided into multiple generator set groups based on at least one of the following criteria: unit type, cost characteristics, or geographical region. Within each unit group, based on the current load status of the power system, the power status of the transmission section, and the operating status of the unit, start-up and shutdown decisions for the units within the group are generated. Merge the start-up and shutdown decisions of all unit groups, calculate the relative advantages between groups, and update the policy network of each unit group. After fixing the start-up and shutdown status of the generating units, under the constraints of node voltage stability and transient power angle stability, the optimization objective is solved by quadratic programming to obtain the optimal output of each generating unit. Verify whether the solution meets the power flow constraints and system safety and stability constraints. If not, introduce a penalty term to modify the optimization objective and iterate again until all constraints are met.

5. The method according to claim 4, characterized in that, The transient power angle stability constraint is configured such that, within a preset fault verification period, the absolute value of the power angle difference between any two generators in the power system does not exceed the preset maximum power angle difference.

6. The method according to claim 4, characterized in that, The step of iteratively increasing the transmission power of the target transmission section using a repetitive power flow strategy, starting from the unit combination mode and the transmission power of the section, and performing transient stability simulation verification on the new operating mode using a fault set after each iteration, until at least one fault causes system instability, and determining the power of the previous iteration step as the transient stability transmission limit of the target transmission section, includes: The power increase step is configured as follows: starting from the current unit combination mode and its corresponding cross-sectional transmission power, increase the total output of the units in the sending-end area according to the preset power increase step size, and adjust the load in the receiving-end area accordingly to form a new system operation mode; The steps for re-acquiring unit combination and transient stability simulation are configured as follows: for the new system operation mode, re-acquiring the unit combination mode that satisfies the power flow constraints, and using the fault set to perform transient stability simulation on the acquired unit combination mode; The iterative steps are configured as follows: iteratively execute the steps of increasing power, re-acquiring unit combination and transient stability simulation, until at least one of the verified unit combination methods causes system instability due to fault concentration at a certain cross-sectional power level. The determination step is configured to: determine the highest cross-sectional power level before triggering system instability as the transient stability transmission limit of the target transmission section.

7. The method according to claim 6, characterized in that, The step of performing severity analysis on faults causing system instability based on the transient stability transmission limit, identifying and outputting a list of bottleneck faults restricting the transmission capacity of the target transmission section, includes: Under the transient stability transmission limit, acquire all faults that cause system instability; Based on the reduction in transmission capacity caused by each fault, the probability of fault occurrence, and the severity of instability consequences, a severity index for each fault is calculated. All faults leading to instability are ranked according to the severity index; Based on the ranking results, one or more faults with the highest severity are identified and output as a list of bottleneck faults that restrict the transmission capacity of the target power transmission section.

8. A device for analyzing the transmission power limit and bottleneck faults of a power system transmission section, characterized in that, include: The identification module is used to identify the sending-end region of the target transmission section and the set of adjustable generator sets within that region based on the topological connection relationship of the power system and the component information of the target transmission section. The generation module is used to generate a fault set for transient stability analysis based on preset fault setting rules. The solution module is used to solve a preset unit combination optimization model based on the adjustable generator set using a group relative strategy optimization algorithm, to obtain various unit combination methods and their corresponding cross-sectional transmission power; wherein, the unit combination optimization model includes at least an optimization objective; wherein, the optimization objective is configured to minimize the deviation between the actual transmission power of the target transmission section and the preset target power, as well as the unit operating cost; The iterative module is used to iteratively increase the transmission power of the target transmission section using a repetitive power flow strategy, starting from the unit combination mode and the transmission power of the section. After each iteration, the fault set is used to perform transient stability simulation verification on the new operating mode until at least one fault causes the system to become unstable. The power of the previous iteration step is then determined as the transient stability transmission limit of the target transmission section. The output module is used to perform severity analysis on faults that cause system instability based on the transient stability transmission limit, identify and output a list of bottleneck faults that restrict the transmission capacity of the target power transmission section.

9. An electronic device, characterized in that, include: A memory, and one or more processors communicatively connected to the memory; The memory stores instructions that can be executed by the one or more processors to cause the one or more processors to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 7.

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