Method for evaluating joint fault deduction and collaborative treatment capability of multi-level power grid of provincial and regional power grid

Abstract

The method can construct a provincial, regional and distribution network model, accurately deduce cross-level fault propagation caused by information island and lack of coordination mechanism, and quantitatively evaluate the coordination disposal capacity.

Description

Technical Field

[0001] This invention belongs to the field of multi-level dispatch and coordinated control technology, and in particular relates to a method for assessing the joint fault simulation and coordinated handling capabilities of multi-level power grids in provinces and regions. Background Technology

[0002] With the rapid development of new power systems, the large-scale integration of new energy sources, the enhanced interaction between power sources, grids, loads and storage, and the increasingly complex power grid structure, the coupling relationship between provincial main grids, municipal transmission and distribution networks and distribution networks is becoming increasingly close.

[0003] Traditional power grid fault analysis and handling models are typically conducted in a fragmented manner according to control levels. Provincial, municipal, and distribution control centers each rely on independent data models and decision-making systems, lacking a unified understanding of topology and operational status. This leads to problems such as information silos, delayed responses, and conflicting actions when dealing with cross-level cascading faults. For example, patent document CN115549293A proposes a method for multi-person online collaborative handling of provincial power grid faults. Especially in scenarios such as extreme weather or sudden equipment failures, faults can easily propagate rapidly between the provincial, municipal, and distribution levels through electrical connections and control logic, triggering the risk of large-scale power outages.

[0004] Existing technologies, such as the patent document with publication number CN114509642A, match real-time fault information with pre-stored provincial-local collaborative response plans. If the conditions for collaborative response are met, real-time response information for executing the provincial-local collaborative response plan is obtained through online analysis applications. The provincial dispatch center determines the fault response scope based on the real-time response information and simultaneously uploads the fault response scope to the regional dispatch center. The provincial and regional dispatch centers then execute the provincial-local collaborative response plan within the fault response scope. This method can only monitor the online fault diagnosis results of provincial and regional dispatch centers and match them with provincial-local collaborative response plans. However, it is difficult to accurately simulate the joint evolution process of faults in multi-level power grids, such as provincial and regional distribution networks. It also lacks a systematic quantitative evaluation method for the collaborative response capabilities of multi-level control entities, and cannot effectively support the collaborative optimization and resilience improvement of the dispatch system.

[0005] Therefore, there is an urgent need for a method to assess the joint fault simulation and collaborative handling capabilities of multi-level power grids in provinces and regions. Summary of the Invention

[0006] The purpose of this invention is to provide a method for joint fault simulation and collaborative handling capability assessment of multi-level power grids at the provincial, local, and distribution levels, in order to solve the problems mentioned in the background art, such as the difficulty in accurately simulating cross-level fault propagation and the inability to quantitatively assess collaborative handling capability due to model fragmentation, information silos, and lack of collaborative mechanisms in the three-level power grids at the provincial, local, and distribution levels.

[0007] To achieve the above objectives, the present invention aims to provide a method for assessing the joint fault simulation and collaborative handling capabilities of multi-level power grids in a province, comprising the following steps: S1. Construct a unified power grid model based on the power grid topology and operation data of the provincial, regional, and distribution levels; S2. Based on the unified power grid model, generate fault scenarios and deduce the chain propagation path of faults among the provincial, local and distribution power grids, identify the key nodes and key actions of the fault chain, and obtain the fault chain. S3. Based on the chain propagation path, key nodes, key actions and failure chain, establish a multi-level control and collaborative handling process model and generate a collaborative handling process. S4. Input the fault scenario and collaborative handling process into the simulation engine to simulate the information interaction and handling actions of multi-level control during the fault evolution process; S5. Construct a multi-dimensional capability assessment index system based on the data output by the simulation engine, and quantify the collaborative handling at the provincial, regional, and distribution levels. S6. Determine the level of joint disposal capability at the provincial, municipal, and distribution levels based on the quantitative scoring results.

[0008] More preferably, in S1, a unified power grid model is constructed based on the three-level power grid topology and operation data of the provincial control center, municipal control center, and distribution control domain, specifically including: S1.1 After inputting the power grid operation data source for the provincial, regional, and distribution levels respectively, collect the operation data, preprocess the operation data, and construct standardized data; S1.2 Based on the hierarchical structure of the provincial, regional, and distribution networks, cross-level associations are performed on the main network, distribution network, and substation topology. Mapping relationships are established between key nodes, including cross-level tie lines, substation main transformers, and feeder outgoing lines, to generate the overall topology skeleton. S1.3. Normalize and bind the standardized data according to the set equipment identifier library and equipment classification system to establish equipment-level state vectors and operating parameter sets; S1.4 Based on the topology skeleton and the set of operating parameters, construct a unified power grid model, including node models, line models, equipment models and their attributes, and generate electrical equivalent relationships, timing relationships and constraints for fault inference.

[0009] More preferably, in S1.4, the specific implementation method for constructing a unified power grid model and generating electrical equivalence relationships, timing relationships, and constraints for fault prediction includes: Based on the topology skeleton and operating parameter set, the cross-level electrical connection structure is reconstructed by re-unifying equipment identification, integrating the relationships between provincial, regional, and distribution level nodes and connections, and mapping equipment and line attributes and real-time operating data to the corresponding nodes and connections; and a set of operating state equations is constructed by combining the power flow equations and control constraints of the main grid and distribution network to generate a unified power grid model. The unified power grid model includes node power balance equations, line power flow calculations, and equipment state and constraint equations. Based on the topology of the unified power grid model, impedance, admittance, and power supply models are established for nodes and lines using the equivalent circuit method; the operating logic, protection actions, and dispatching instructions of each device and line are extracted, and the state change relationships arranged in time sequence are constructed to record the fault triggering and response sequence; a set of constraints is formulated by combining the rated capacity of the equipment, power flow constraints, protection action conditions, and cross-level control authority.

[0010] More preferably, S2, based on a unified power grid model, generates fault scenarios and deduces the cascading propagation path of faults among the provincial, regional, and distribution power grids, identifying key nodes and key actions in the fault chain, specifically including: S2.1. Obtain equipment types, line attributes, and historical fault records based on the unified power grid model, identify the fault types to be analyzed, and generate a preliminary fault scenario set according to time, location, and load conditions; S2.2 Construct triggering conditions for each fault scenario, including equipment status, power flow exceeding threshold, voltage drop amplitude, and switch malfunction probability, and map the triggering conditions to the operating parameters of the corresponding nodes or edges in the unified power grid model; S2.3. Based on the electrical topology, establish fault cascading propagation rules among the provincial, local, and distribution power grids, including cross-level influence relationships, electrical cascading constraints, and action triggering conditions. S2.4 Based on the unified power grid model and fault cascading propagation rules, the power flow iteration algorithm is used to calculate the propagation path of the initial fault among the provincial, regional, and distribution power grids, and the fault-triggered nodes, affected equipment, and cascading effects are recorded. S2.5 During the fault propagation process, assess the impact of each node on the fault spread, identify the key nodes that cause large-scale power outages or system instability, and include the key nodes in the set of key nodes. S2.6 For each critical node, analyze the control actions triggered during the fault evolution process to form a sequence of critical actions; S2.7 Integrate the propagation path, key nodes, and key actions of each fault scenario according to time sequence and hierarchical relationship to generate a complete fault chain.

[0011] More preferably, in S2.4, the propagation path of the initial fault among the provincial, regional, and distribution power grids is calculated using a power flow iteration algorithm, including the following steps: S2.41. Initialize the node voltages, line power flows, and equipment states of the unified power grid model to the operating states before the fault occurred, and mark the initial fault node or equipment as the fault-triggered state. S2.42. Based on the fault triggering conditions defined in step S2.2, modify the node / equipment parameters corresponding to the initial fault to an abnormal state, and transmit the abnormal state to the relevant connection nodes of the unified power grid model. S2.43. Using abnormal states as input, perform power flow iterative calculations; S2.44. Based on the fault cascading propagation rules established in step S2.3, determine whether the power flow iteration results trigger a fault in the next level node. S2.45. Mark the triggered node as a fault state and record the propagation path of the fault in the time series; use the new abnormal state as the input for the next iteration. S2.46. Repeat the power flow iteration and fault propagation judgment until the preset maximum time step is reached; S2.47. Based on time series data of fault evolution, a complete fault propagation path is formed across provincial, regional, and distribution three-level power grids.

[0012] More preferably, in S2.43, the power flow iterative calculation is performed using the abnormal state as input, including the following specific steps: Obtain the actual active and reactive loads of all load nodes in the unified power grid model, and weight the node loads according to the load disturbance coefficient to obtain the weighted adjusted active and reactive loads; Update the adjusted active and reactive loads to the node attributes of the unified power grid model; The node load after load disturbance adjustment and the mapped fault and abnormal states are used as the input for power flow iteration; The Newton-Raphson method is used to perform iterative calculations to solve for the voltage magnitude and phase angle of each node and the power flow of each line step by step.

[0013] More preferably, in S3, a multi-level control and collaborative handling process model is established based on the chain propagation path, key nodes, key actions, and fault chains to generate a collaborative handling process, specifically including: S3.1 Use fault chain, critical node and critical action information as input data; S3.2 Constructing a multi-level control hierarchy and scope; S3.3 Establish a mapping relationship between the key actions in the fault chain and the corresponding key nodes, and construct the triggering conditions, response delay and execution order for each action to form a set of node-action pairs; S3.4. Based on the power grid topology, cascading propagation paths, and control authority, construct cross-level action triggering and information interaction rules; S3.5. Generate a collaborative handling process model based on the mapping relationship between key actions and corresponding key nodes and multi-level collaborative logic; S3.6 Transform the collaborative handling process model into a collaborative handling process.

[0014] More preferably, in S3.5, a collaborative handling process model is generated based on the mapping relationship between key actions and corresponding key nodes and multi-level collaborative logic. The specific implementation steps include: Extract each key node and its corresponding key action sequence, including action type, triggering condition, execution order, delay time and duration, and associate the key action sequence with the attributes of the key node; The nodes and actions at the provincial, regional, and distribution levels are grouped according to multi-level collaborative logic, clarifying the scope of responsibilities, operable equipment, and permissions of each level, and marking cross-level action dependencies. Based on the fault propagation path and cross-level control rules, the action sequence is mapped to the information interaction event to form a set of action and information interaction nodes; By integrating node action sequences and information interaction events according to time order and hierarchical dependencies, a collaborative processing flow model is constructed.

[0015] More preferably, the multi-level collaborative logic is a set of rules for the division of responsibilities, action dependencies, and information interaction followed by the provincial control, municipal control, and distribution control domains during the fault handling process; Among them, the action dependency relationship clearly defines the constraint of the superior control instructions on the subordinate control actions; Based on the location and impact range of key nodes in the fault chain, determine the collaborating entities; set the information transmission sequence between each level during the collaboration process, including fault perception reporting, handling instruction issuance, execution feedback loop, and status synchronization update.

[0016] More preferably, the cross-level control rules describe the triggering conditions, coordination methods, and execution priorities of actions between different control levels during fault propagation, and are constructed using a condition-action format, specifically including: The higher-level controller issues control commands to the lower-level controller. Before execution, the lower-level controller needs to verify the compatibility between the command and the local device status. If there is a conflict, it will be reported for coordination. Clearly define the operating permissions for equipment at each level; control of equipment spanning voltage levels requires confirmation from the superior authority. When multiple control levels issue action commands to the same device simultaneously, the commands are executed in the order of priority of superior, priority of urgency, and priority of time.

[0017] More preferably, in S3.6, the collaborative handling process model is transformed into a collaborative handling process, and the specific implementation steps include: The nodes, actions, triggering conditions, execution order, and information interaction paths in the collaborative handling process model are analyzed, and each key action is mapped to its corresponding node and triggering condition. Based on the multi-level control logic, the action sequence is grouped into three levels: province, region, and distribution, and the operational authority and scope of responsibility of each level under fault conditions are clearly defined. Sort the action sequences and information interaction events according to time order and hierarchical dependency; Embed the priority, execution delay, duration, and cross-level information confirmation mechanism of actions into the process; The integrated process is standardized and formatted to generate collaborative handling process documents or data structures, resulting in a fault handling process.

[0018] More preferably, the cross-level information confirmation mechanism specifically includes instruction issuance confirmation, execution feedback confirmation, status synchronization confirmation, and abnormal retry and alarm stages: The instruction issuance confirmation process refers to the requirement that when a higher-level controller sends a disposal instruction to a lower-level controller, the lower-level controller must return a confirmation message within a preset time window. If no confirmation is received within the time limit, the higher-level controller will automatically resend the instruction or activate a backup communication channel. The execution feedback confirmation means that after the lower-level controller completes the instruction action, it needs to feed back the execution result to the upper-level controller; the upper-level controller executes step S1 based on the feedback result to update the unified power grid model; Status synchronization confirmation refers to the process where, when the status of a device at a certain level changes, the device proactively pushes a status update to the relevant upper and lower level controllers, and the receiving party needs to return a synchronization confirmation. Abnormal retry and alarm means that if information exchange fails more than a set number of times consecutively, the communication link is marked as abnormal and an alarm is triggered; before communication is restored, the relevant level continues to execute the handling process using local preset policies.

[0019] More preferably, in S4, the fault scenario and collaborative handling process are input into the simulation engine to simulate the information interaction and handling actions of multi-level control during the fault evolution process, including the following steps: S4.1. Take the fault scenario and collaborative handling process as input; S4.2 Initialize the node voltages, line power flows, and equipment states of the unified power grid model to the operating states before the fault occurred; mark the abnormal states of the initial fault nodes; S4.3. Based on the power flow calculation results, update the node voltage, line power flow and trigger status, and determine whether any new nodes have entered an abnormal state. S4.4. Based on the collaborative handling process model and triggering conditions, identify the actions that need to be performed at each level; S4.5 Execute the control actions in the order of the action queue, update the node state, update the power flow state according to the action execution results, and form the input for the next iteration; S4.6 Repeat steps S4.3-S4.5 for fault propagation and collaborative handling until the preset maximum number of rounds is reached.

[0020] Further preferably, S5, based on the data output by the simulation engine, constructs a multi-dimensional capability assessment index system and quantifies the coordinated handling at the provincial, regional, and distribution levels. Specific steps include: S5.1 Extracting time series data based on the data output by the inference engine; S5.2 Based on multi-level control and coordination business scenarios, the capability assessment is divided into the following dimensions: fault response timeliness and cross-level coordination efficiency; among which, multi-level control and coordination business scenarios include provincial-local joint handling scenarios, local-distribution joint handling scenarios, provincial-local-distribution three-level joint handling scenarios, and distribution network autonomous handling scenarios; S5.3 Based on action timestamps, state change sequences, and operational quantification data, calculation formulas are constructed for the basic indicators of action response delay under the fault response timeliness dimension and the basic indicators of instruction issuance and feedback time difference under the cross-level collaboration efficiency dimension, and the basic indicators are normalized. S5.4 According to the dimensional structure, the standardized scores of each basic indicator are weighted and summed to obtain the independent scores of the fault response timeliness and cross-level collaboration efficiency dimensions. S5.5. The scores for fault response timeliness and cross-level collaboration efficiency are weighted and aggregated in a secondary manner to obtain a quantitative score.

[0021] More preferably, in S6, the level of joint response capability at the provincial, municipal, and distribution levels is determined based on the quantitative scoring results. Specific steps include: The quantitative scoring results are mapped to a preset multi-level capability level system. By comparing with the level threshold range, the level of handling capability is determined, and a joint handling capability level report containing comprehensive level and dimension score is output.

[0022] This invention also proposes a provincial and regional multi-level power grid joint fault simulation and collaborative handling capability assessment system, including a power grid model construction module, a fault identification module, a handling process generation module, a handling simulation module, a handling assessment module, and a handling capability level determination module: The power grid model building module constructs a unified power grid model based on the power grid topology and operation data at the provincial, regional, and distribution levels. The fault identification module generates fault scenarios based on a unified power grid model and deduces the chain propagation path of faults among the provincial, local, and distribution power grids. It identifies the key nodes and key actions of the fault chain and obtains the fault chain. The handling process generation module establishes a multi-level control collaborative handling process model based on the chain propagation path, key nodes, key actions and failure chains, and generates a collaborative handling process. The fault simulation module inputs fault scenarios and collaborative handling processes into the simulation engine to simulate information interaction and handling actions of multi-level control during the fault evolution process; The disposal assessment module constructs a multi-dimensional capability assessment index system based on the data output by the simulation engine, and quantifies the collaborative disposal at the provincial, local, and distribution levels. The disposal capacity level assessment module determines the level of joint disposal capacity at the provincial, local, and distribution levels based on quantitative scoring results.

[0023] The present invention also proposes a terminal, including a processor and a storage medium: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps according to the method described above.

[0024] The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0025] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention constructs a unified power grid model that integrates the topology of the main grid, distribution network, and substations, along with equipment attributes and real-time operational data. By combining electrical equivalence relations, power flow equations, and protection constraints, this method can realistically recreate the dynamic process of fault propagation from the provincial backbone network to prefecture-level cities and distribution levels. Particularly in identifying key nodes and actions, it introduces fault propagation weighting and time series analysis to accurately pinpoint the core links that cause large-scale power outages or system instability, providing a scientific basis for developing targeted prevention and control strategies.

[0026] 2. This invention couples fault scenarios with collaborative handling processes into a simulation engine to simulate the actual response behavior of multi-level control entities in information interaction, command issuance, and action execution. It also constructs normalized evaluation indicators based on dimensions such as action delay and command feedback time difference. Through a weighted comprehensive scoring and grading mechanism, it not only quantitatively reflects the performance of three-level control in terms of timeliness and collaborative efficiency but also automatically generates capability level reports, providing data-driven decision support for scheduling procedure revision, automated system upgrades, and emergency drill design. Attached Figure Description

[0027] Figure 1 This is a flowchart of the method for assessing the joint fault simulation and collaborative handling capabilities of multi-level power grids in the province, as described in this invention. Figure 2 This is a flowchart of the method according to Embodiment 1 of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0029] like Figure 1 As shown, this invention proposes a method for assessing the joint fault simulation and collaborative handling capabilities of multi-level power grids in provinces and regions. The specific steps include: S1. Construct a unified power grid model based on the power grid topology and operation data of the provincial, regional, and distribution levels; In S1, a unified power grid model is constructed based on the power grid topology and operational data of the provincial control center, municipal control centers, and distribution control domains. Specifically, this includes: S1.1 After inputting the power grid operation data source for the provincial, regional, and distribution levels respectively, collect the operation data, preprocess the operation data, and construct standardized data; S1.2 Based on the hierarchical structure of the provincial, regional, and distribution networks, cross-level associations are performed on the main network, distribution network, and substation topology. Mapping relationships are established between key nodes, including cross-level tie lines, substation main transformers, and feeder outgoing lines, to generate the overall topology skeleton. S1.3. Normalize and bind the standardized data according to the set equipment identifier library and equipment classification system to establish equipment-level state vectors and operating parameter sets; S1.4 Based on the topology skeleton and the set of operating parameters, construct a unified power grid model, including node models, line models, equipment models and their attributes, and generate electrical equivalent relationships, timing relationships and constraints for fault inference.

[0030] In S1.4, the specific implementation methods for constructing a unified power grid model and generating electrical equivalent relationships, temporal relationships, and constraints for fault prediction include: Based on the topology skeleton and operating parameter set, the cross-level electrical connection structure is reconstructed by re-unifying equipment identification, integrating the relationships between provincial, regional, and distribution level nodes and connections, and mapping equipment and line attributes and real-time operating data to the corresponding nodes and connections; and a set of operating state equations is constructed by combining the power flow equations and control constraints of the main grid and distribution network to generate a unified power grid model. The unified power grid model includes node power balance equations, line power flow calculations, and equipment state and constraint equations. Based on the topology of the unified power grid model, impedance, admittance, and power supply models are established for nodes and lines using the equivalent circuit method; the operating logic, protection actions, and dispatching instructions of each device and line are extracted, and the state change relationships arranged in time sequence are constructed to record the fault triggering and response sequence; a set of constraints is formulated by combining the rated capacity of the equipment, power flow constraints, protection action conditions, and cross-level control authority.

[0031] S2. Based on the unified power grid model, generate fault scenarios and deduce the chain propagation path of faults among the provincial, local and distribution power grids, identify the key nodes and key actions of the fault chain, and obtain the fault chain. S2. Based on a unified power grid model, generate fault scenarios and deduce the cascading propagation path of faults among provincial, regional, and distribution power grids, identifying key nodes and key actions in the fault chain, specifically including: S2.1. Obtain equipment types, line attributes, and historical fault records based on the unified power grid model, identify the fault types to be analyzed, and generate a preliminary fault scenario set according to time, location, and load conditions; S2.2 Construct triggering conditions for each fault scenario, including equipment status, power flow exceeding threshold, voltage drop amplitude, and switch malfunction probability, and map the triggering conditions to the operating parameters of the corresponding nodes or edges in the unified power grid model; S2.3. Based on the electrical topology, establish fault cascading propagation rules among the provincial, local, and distribution power grids, including cross-level influence relationships, electrical cascading constraints, and action triggering conditions. S2.4 Based on the unified power grid model and fault cascading propagation rules, the power flow iteration algorithm is used to calculate the propagation path of the initial fault among the provincial, regional, and distribution power grids, and the fault-triggered nodes, affected equipment, and cascading effects are recorded. In S2.4, the propagation path of the initial fault among the provincial, regional, and distribution power grids is calculated using a power flow iterative algorithm, including the following steps: S2.41. Initialize the node voltages, line power flows, and equipment states of the unified power grid model to the operating states before the fault occurred, and mark the initial fault node or equipment as the fault-triggered state. S2.42. Based on the fault triggering conditions defined in step S2.2, modify the node / equipment parameters corresponding to the initial fault to an abnormal state, and transmit the abnormal state to the relevant connection nodes of the unified power grid model. S2.43. Using abnormal states as input, perform power flow iterative calculations; In S2.43, the power flow iterative calculation is performed using abnormal states as input, including the following specific steps: Obtain the actual active and reactive loads of all load nodes in the unified power grid model, and weight the node loads according to the load disturbance coefficient to obtain the weighted adjusted active and reactive loads; Update the adjusted active and reactive loads to the node attributes of the unified power grid model; The node load after load disturbance adjustment and the mapped fault and abnormal states are used as the input for power flow iteration; The Newton-Raphson method is used to perform iterative calculations to solve for the voltage magnitude and phase angle of each node and the power flow of each line step by step.

[0032] S2.44. Based on the fault cascading propagation rules established in step S2.3, determine whether the power flow iteration results trigger a fault in the next level node. S2.45. Mark the triggered node as a fault state and record the propagation path of the fault in the time series; use the new abnormal state as the input for the next iteration. S2.46. Repeat the power flow iteration and fault propagation judgment until the preset maximum time step is reached; S2.47. Based on time series data of fault evolution, a complete fault propagation path is formed across provincial, regional, and distribution three-level power grids.

[0033] S2.5 During the fault propagation process, assess the impact of each node on the fault spread, identify the key nodes that cause large-scale power outages or system instability, and include the key nodes in the set of key nodes. S2.6 For each critical node, analyze the control actions triggered during the fault evolution process to form a sequence of critical actions; S2.7 Integrate the propagation path, key nodes, and key actions of each fault scenario according to time sequence and hierarchical relationship to generate a complete fault chain.

[0034] S3. Based on the chain propagation path, key nodes, key actions and failure chain, establish a multi-level control and collaborative handling process model and generate a collaborative handling process. In S3, a multi-level control and collaborative handling process model is established based on the chain propagation path, key nodes, key actions, and failure chains to generate a collaborative handling process, specifically including: S3.1 Use fault chain, critical node and critical action information as input data; S3.2 Constructing a multi-level control hierarchy and scope; S3.3 Establish a mapping relationship between the key actions in the fault chain and the corresponding key nodes, and construct the triggering conditions, response delay and execution order for each action to form a set of node-action pairs; S3.4. Based on the power grid topology, cascading propagation paths, and control authority, construct cross-level action triggering and information interaction rules; S3.5. Generate a collaborative handling process model based on the mapping relationship between key actions and corresponding key nodes and multi-level collaborative logic; The multi-level collaborative logic is a set of rules for the division of responsibilities, action dependencies, and information interaction that are followed by provincial-level control, municipal-level control, and distribution control domains during the fault handling process. Among them, the action dependency relationship clearly defines the constraint of the superior control instructions on the subordinate control actions; Based on the location and impact range of key nodes in the fault chain, determine the collaborating entities; set the information transmission sequence between each level during the collaboration process, including fault perception reporting, handling instruction issuance, execution feedback loop, and status synchronization update.

[0035] In S3.5, a collaborative handling process model is generated based on the mapping relationship between key actions and corresponding key nodes and multi-level collaborative logic. The specific implementation steps include: Extract each key node and its corresponding key action sequence, including action type, triggering condition, execution order, delay time and duration, and associate the key action sequence with the attributes of the key node; The nodes and actions at the provincial, regional, and distribution levels are grouped according to multi-level collaborative logic, clarifying the scope of responsibilities, operable equipment, and permissions of each level, and marking cross-level action dependencies. Based on the fault propagation path and cross-level control rules, the action sequence is mapped to the information interaction event to form a set of action and information interaction nodes; By integrating node action sequences and information interaction events according to time order and hierarchical dependencies, a collaborative processing flow model is constructed.

[0036] The cross-level control rules describe the triggering conditions, coordination methods, and execution priorities of actions between different control levels during fault propagation. They are constructed using a condition-action format and specifically include: The higher-level controller issues control commands to the lower-level controller. Before execution, the lower-level controller needs to verify the compatibility between the command and the local device status. If there is a conflict, it will be reported for coordination. Clearly define the operating permissions for equipment at each level; control of equipment spanning voltage levels requires confirmation from the superior authority. When multiple control levels issue action commands to the same device simultaneously, the commands are executed in the order of priority of superior, priority of urgency, and priority of time.

[0037] S3.6 Transform the collaborative handling process model into a collaborative handling process.

[0038] In S3.6, the collaborative handling process model is transformed into a collaborative handling process. The specific implementation steps include: The nodes, actions, triggering conditions, execution order, and information interaction paths in the collaborative handling process model are analyzed, and each key action is mapped to its corresponding node and triggering condition. Based on the multi-level control logic, the action sequence is grouped into three levels: province, region, and distribution, and the operational authority and scope of responsibility of each level under fault conditions are clearly defined. Sort the action sequences and information interaction events according to time order and hierarchical dependency; Embed the priority, execution delay, duration, and cross-level information confirmation mechanism of actions into the process; The integrated process is standardized and formatted to generate collaborative handling process documents or data structures, resulting in a fault handling process.

[0039] The cross-level information confirmation mechanism specifically includes instruction issuance confirmation, execution feedback confirmation, status synchronization confirmation, and abnormal retry and alarm procedures: The instruction issuance confirmation process refers to the requirement that when a higher-level controller sends a disposal instruction to a lower-level controller, the lower-level controller must return a confirmation message within a preset time window. If no confirmation is received within the time limit, the higher-level controller will automatically resend the instruction or activate a backup communication channel. The execution feedback confirmation means that after the lower-level controller completes the instruction action, it needs to feed back the execution result to the upper-level controller; the upper-level controller executes step S1 based on the feedback result to update the unified power grid model; Status synchronization confirmation refers to the process where, when the status of a device at a certain level changes, the device proactively pushes a status update to the relevant upper and lower level controllers, and the receiving party needs to return a synchronization confirmation. Abnormal retry and alarm means that if information exchange fails more than a set number of times consecutively, the communication link is marked as abnormal and an alarm is triggered; before communication is restored, the relevant level continues to execute the handling process using local preset policies.

[0040] S4. Input the fault scenario and collaborative handling process into the simulation engine to simulate the information interaction and handling actions of multi-level control during the fault evolution process; In S4, the fault scenario and collaborative handling process are input into the simulation engine to simulate the information interaction and handling actions of multi-level control during the fault evolution process, including the following steps: S4.1. Take the fault scenario and collaborative handling process as input; S4.2 Initialize the node voltages, line power flows, and equipment states of the unified power grid model to the operating states before the fault occurred; mark the abnormal states of the initial fault nodes; S4.3. Based on the power flow calculation results, update the node voltage, line power flow and trigger status, and determine whether any new nodes have entered an abnormal state. S4.4. Based on the collaborative handling process model and triggering conditions, identify the actions that need to be performed at each level; S4.5 Execute the control actions in the order of the action queue, update the node state, update the power flow state according to the action execution results, and form the input for the next iteration; S4.6 Repeat steps S4.3-S4.5 for fault propagation and collaborative handling until the preset maximum number of rounds is reached.

[0041] S5. Construct a multi-dimensional capability assessment index system based on the data output by the simulation engine, and quantify the collaborative handling at the provincial, regional, and distribution levels. S5. Based on the data output by the simulation engine, construct a multi-dimensional capability assessment index system and quantify the coordinated handling at the provincial, regional, and distribution levels. Specific steps include: S5.1 Extracting time series data based on the data output by the inference engine; S5.2 Based on multi-level control and coordination business scenarios, the capability assessment is divided into the following dimensions: fault response timeliness and cross-level coordination efficiency; among which, multi-level control and coordination business scenarios include provincial-local joint handling scenarios, local-distribution joint handling scenarios, provincial-local-distribution three-level joint handling scenarios, and distribution network autonomous handling scenarios; S5.3 Based on action timestamps, state change sequences, and operational quantification data, calculation formulas are constructed for the basic indicators of action response delay under the fault response timeliness dimension and the basic indicators of instruction issuance and feedback time difference under the cross-level collaboration efficiency dimension, and the basic indicators are normalized. S5.4 According to the dimensional structure, the standardized scores of each basic indicator are weighted and summed to obtain the independent scores of the fault response timeliness and cross-level collaboration efficiency dimensions. S5.5. The scores for fault response timeliness and cross-level collaboration efficiency are weighted and aggregated in a secondary manner to obtain a quantitative score.

[0042] S6. Determine the level of joint disposal capability at the provincial, municipal, and distribution levels based on the quantitative scoring results.

[0043] In S6, the level of joint response capability at the provincial, municipal, and distribution levels is determined based on the quantitative scoring results. Specific steps include: The quantitative scoring results are mapped to a preset multi-level capability level system. By comparing with the level threshold range, the level of handling capability is determined, and a joint handling capability level report containing comprehensive level and dimension score is output.

[0044] This invention also proposes a provincial and regional multi-level power grid joint fault simulation and collaborative handling capability assessment system, including a power grid model construction module, a fault identification module, a handling process generation module, a handling simulation module, a handling assessment module, and a handling capability level determination module: The power grid model building module constructs a unified power grid model based on the power grid topology and operation data at the provincial, regional, and distribution levels. The fault identification module generates fault scenarios based on a unified power grid model and deduces the chain propagation path of faults among the provincial, local, and distribution power grids. It identifies the key nodes and key actions of the fault chain and obtains the fault chain. The handling process generation module establishes a multi-level control collaborative handling process model based on the chain propagation path, key nodes, key actions and failure chains, and generates a collaborative handling process. The fault simulation module inputs fault scenarios and collaborative handling processes into the simulation engine to simulate information interaction and handling actions of multi-level control during the fault evolution process; The disposal assessment module constructs a multi-dimensional capability assessment index system based on the data output by the simulation engine, and quantifies the collaborative disposal at the provincial, local, and distribution levels. The disposal capacity level assessment module determines the level of joint disposal capacity at the provincial, local, and distribution levels based on quantitative scoring results.

[0045] The present invention also proposes a terminal, including a processor and a storage medium: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps according to the method described above.

[0046] The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0047] Example 1: Please see Figure 2 As shown in the figure, this embodiment provides a method for assessing the joint fault simulation and collaborative handling capabilities of multi-level power grids in a province, including the following steps: S1. Construct a unified power grid model based on the three-level power grid topology and operation data of the provincial control center, municipal control center and distribution control domain; In this embodiment, a unified power grid model is constructed based on the three-level power grid topology and operational data of the provincial control center, municipal control center, and distribution control domain. This includes the following steps: S1.1 Access the power grid operation data sources from the provincial control center, municipal control center and distribution control domain respectively, collect operation data, including at least topology, equipment files, real-time measurement data, control protocols and historical dispatch records, and preprocess the operation data, including format parsing, consistency verification, missing value filling, coding standard alignment, and unit unification, and standardize the naming differences, sampling period differences and data accuracy differences between different levels to provide standardized data for the construction of a unified model; For ease of description, in this invention, the "provincial control center, municipal control center and distribution control domain three levels" are uniformly referred to as "provincial, municipal and distribution three levels"; S1.2 Based on the hierarchical structure of the provincial, regional and distribution three-level network, cross-level associations are carried out on the main network, distribution network and station topology, and mapping relationships are established for key nodes such as cross-level tie lines, substation main transformers and feeder outgoing lines to form an overall topology skeleton that can reflect the characteristics of multi-level electrical connections. Specifically, the nodes and lines of the provincial, regional, and distribution networks are first standardized according to a unified equipment identifier. Then, corresponding relationships are established for key nodes across levels (such as cross-level tie lines, substation main transformers, feeder outgoing lines, etc.). The networks at each level are linked through electrical connections between nodes, feeder relationships, and switch status. At the same time, node attributes, line attributes, and connection constraints are integrated and verified to ultimately form an overall topology skeleton that can simultaneously characterize the electrical connection characteristics of multiple levels and reflect the topology relationship between the main network, distribution network, and substation. S1.3. Normalize and bind standardized data (voltage, current, power flow, load, switch status, etc.) according to a unified equipment identifier library and equipment classification system, and establish equipment-level state vectors and operating parameter sets so that the model can depict the operating status of multi-level power grids at the same time. Specifically, the process involves: first, mapping various equipment (such as transformers, circuit breakers, feeders, load nodes, etc.) in the three-tiered power grid according to a unified equipment identification and classification system; then, binding the real-time operating data (voltage, current, power flow, switch status, load, etc.) of each equipment to the corresponding equipment node, constructing a state vector represented in time series form; and integrating the rated parameters, control permissions, and historical operating characteristics of the equipment to form a complete set of operating parameters, enabling the dynamic operating status and attribute information of each equipment to be directly called by the unified model, supporting subsequent fault simulation and collaborative handling analysis. S1.4 Based on the fused topology skeleton and operating parameter set, a unified power grid model with cross-level structural features is constructed, including node model, line model, equipment model and their attributes, and electrical equivalent relationships, timing relationships and constraints are generated for fault inference. Specifically, the process involves: First, based on the topology of the unified power grid model, impedance, admittance, and power source models are established for nodes and lines using the equivalent circuit method, forming equivalent relationships that reflect actual electrical characteristics. Simultaneously, the operating logic, protection actions, and dispatch instructions of each device and line are extracted to construct state change relationships arranged in a time series, recording possible fault triggering and response sequences. Finally, a set of constraints is formulated by combining equipment rated capacity, power flow constraints, protection action conditions, and cross-level control authority to ensure that the power grid operating state conforms to physical, electrical, and control limitations during fault simulation, thus achieving simulateable fault evolution analysis. The construction of a unified power grid model involves the following specific steps: Based on the fused topology skeleton and operating parameter set, the cross-level electrical connection structure is reconstructed by re-unifying equipment identifiers, integrating the relationships between provincial, regional, and distribution level nodes and connections, mapping equipment and line attributes and real-time operating data to the corresponding nodes and connections, and constructing a unified set of operating state equations by combining the power flow equations and control constraints of the main grid and distribution network, thereby generating a unified power grid model that can be directly called by the simulation engine and has both multi-level topology characteristics and complete electrical attributes. The unified power grid model includes node power balance equations (described to reflect the balance of active and reactive power at each node, reflecting the relationship between node injected power, load, and line power flow), line power flow calculations (described the flow of voltage, current, and power between nodes, as well as line load conditions), and equipment status and constraint equations (representing the operating status of various electrical equipment (such as switches, circuit breakers, and transformers), as well as capacity constraints, protection action conditions, and control permissions, specifically including: equipment status description: the operating status of each piece of equipment is represented by state variables, such as the on / off state of switches, the operating position of transformers, the connection status of load nodes, etc., reflecting the working status of equipment in real time; capacity and electrical constraints: constraining equipment to operate within safe limits, including the rated power limits of lines and equipment, upper and lower limits of voltage amplitude, conductor thermal limits, transformer load rates, etc., ensuring that equipment will not be damaged due to overload or overvoltage; protection action conditions: defining the triggering conditions of equipment under abnormal or fault conditions, such as overcurrent, undervoltage, overload, frequency abnormality, etc., triggering corresponding protection actions (tripping, reclosing, load shedding) when the conditions are met). Furthermore, the node power balance equation is as follows: ; ; The power flow calculation for the line is as follows: ; In the formula, For nodes Injected active power (power injected by generators or upstream power grid). For nodes Injected reactive power, For nodes The active load (power consumption). For nodes reactive load, , For nodes and nodes Voltage amplitude (scalar, unit V). For nodes With nodes The voltage phase angle difference (in radians). For nodes With nodes The real part of the admittance (conductance) of the line between them. For nodes With nodes The imaginary part of the admittance (susceptance) of the line between them. For nodes The set of all connected nodes.

[0048] S2. Generate fault scenarios based on a unified power grid model and deduce the chain propagation path of faults among three-level power grids, and identify key nodes and key actions in the fault chain. In this embodiment, a fault scenario is generated based on a unified power grid model, and the cascading propagation path of the fault between three levels of power grid is deduced. The key nodes and key actions of the fault chain are identified, including the following steps: S2.1. Obtain equipment type, line attribute and historical fault records based on the unified power grid model, identify the fault type to be analyzed (such as line tripping, transformer failure, load surge, equipment failure, etc.), and generate a preliminary fault scenario set according to time, location and load conditions. S2.2 Construct triggering conditions for each fault scenario, including equipment status, power flow exceeding threshold, voltage drop amplitude, and probability of switch malfunction, and map the triggering conditions to the operating parameters of the corresponding nodes or edges in the unified power grid model to achieve coupling between the fault scenario and the power grid model. S2.3 Establish fault cascading propagation rules between three-level power grids based on electrical topology, including: provincial, municipal, and distribution cross-level impact relationships (e.g., provincial main grid faults causing municipal grid feeder tripping); electrical cascading constraints (e.g., power flow backflow, overload-triggered protection); and action triggering conditions (e.g., protection actions, dispatching commands, automatic reclosing). S2.4 Based on the unified power grid model and fault cascading propagation rules, the power flow iteration algorithm is used to calculate the propagation path of the initial fault among the provincial, regional, and distribution power grids, and the fault-triggered nodes, affected equipment, and cascading effects are recorded. The calculation of the propagation path of the initial fault among the provincial, regional, and distribution power grids using a power flow iteration algorithm includes the following steps: S2.41. Initialize the node voltages, line power flows, and equipment states of the unified power grid model to the operating states before the fault occurred, and mark the initial fault node (or equipment) as the fault-triggered state. S2.42. Based on the fault triggering conditions defined in step S2.2, modify the node / equipment parameters corresponding to the initial fault to an abnormal state (such as line disconnection, voltage drop, load abnormality), and transmit the abnormal state to the relevant connection nodes of the unified power grid model. S2.43. Using abnormal states as input, perform power flow iterative calculations; Furthermore, this invention addresses the problem that existing fault simulation methods often employ overly static load models, making it difficult to reflect the impact of load fluctuations or sudden changes on fault evolution during actual operation. Traditional simulations typically use fixed load values ​​or simple constant power models, neglecting the dynamic responses of the load during fault processes (such as motor restarts, temperature-controlled load recovery, and changes in user behavior) and their feedback effects on power flow distribution, voltage stability, and protection actions, leading to discrepancies between the simulation results and actual system behavior. This invention introduces a load disturbance coefficient to dynamically adjust the active / reactive loads at each node, enabling a more realistic simulation of the uncertainties and disturbance characteristics of the load side during fault simulation, thereby improving the accuracy of power flow calculations and the realism of fault propagation path simulation. This mechanism not only endows the unified power grid model with steady-state representation capabilities but also enhances its adaptability to dynamic operating scenarios, significantly outperforming existing static or quasi-static load modeling methods. Using abnormal states as input, power flow iterative calculations are performed, involving the following specific steps: Obtain the actual active load of all load nodes in the unified power grid model. With reactive load And based on the load disturbance coefficient Adjusting node loads to simulate operational fluctuations or sudden increases / decreases in load, generating adjusted active load Pi' and reactive load. : , The adjusted active power load With reactive load The node attributes in the unified power grid model are updated to provide input for power flow iterative calculations; the node loads after load disturbance adjustment and the mapped fault and abnormal states are used as inputs for power flow iteration; the Newton-Raphson method is used to perform iterative calculations, progressively solving for the voltage magnitude and phase angle of each node. and the trend of each line ; S2.44. Based on the fault cascading propagation rules established in step S2.3, determine whether the power flow iteration results trigger faults or protection actions at the next level node. For example: when a line experiences overcurrent, the circuit breaker trips; when the main transformer is overloaded or undervoltage, the upstream dispatching action is triggered; when a load branch loses power, the distribution network cascading effect is triggered. S2.45. Mark the triggered node / line as a fault state and record the propagation path of the fault in the time series, including the fault node, affected nodes and triggering actions; use the new abnormal state as the input for the next iteration. S2.46. Repeat the power flow iteration and fault propagation judgment until the preset maximum time step is reached. ; S2.47. Organize the time series data of fault evolution to form a complete fault propagation path across provincial, regional, and distribution three-level power grids; S2.5 During the fault propagation process, assess the impact of each node on the fault spread, identify key nodes that cause large-scale power outages or system instability (such as important substations, main lines, and distribution network hubs), and include them in the set of key nodes. Specifically, the process involves: recording the state changes, triggering actions, and affected equipment of each node during the fault evolution process in a time series, and calculating the cascading impact of node faults on neighboring and downstream nodes, including the scope of impact (number of affected nodes), load interruption, line overcurrent, or voltage anomalies; secondly, assigning fault propagation weights to each node based on these indicators, such as node importance index or fault propagation contribution, reflecting the node's potential impact on fault spread and system stability; and finally, by comparing the weight values ​​or impact levels of nodes, identifying the nodes with the greatest impact on large-scale power outages, main line interruptions, or system voltage / frequency stability, and including these nodes in the critical node set. S2.6 For each critical node, analyze the control actions triggered during the fault evolution process (such as switching actions, automatic reclosing, load shedding, dispatching command issuance, etc.) to form a critical action sequence; Specifically, for each critical node, its state change time series during fault propagation is extracted, including information such as voltage dips, power flow changes, and load anomalies. Then, based on the equipment type of the node and the power grid protection and control logic, control actions that may be triggered under different abnormal states are identified, such as circuit breaker tripping, automatic reclosing, load shedding, dispatching instructions, or other automatic control operations. Next, each action is mapped to its triggering conditions, execution time, execution delay, and execution order, and arranged according to the time sequence of fault evolution to form a complete sequence of critical actions. At the same time, the critical actions are associated with the corresponding nodes and affected equipment to ensure that the action sequence can reflect the actual response of the nodes during the fault process and their impact on the system. Finally, the action sequences of all critical nodes are integrated to form a set of critical actions that can be used for collaborative handling process modeling and simulation. S2.7 Integrate the propagation path, key nodes, and key actions of each fault scenario according to time sequence and hierarchical relationship to generate a complete fault chain; Specifically, the fault propagation paths, key nodes, and corresponding key actions generated in steps S2.4-S2.6 are sorted according to the fault evolution time to ensure a clear temporal relationship between the action sequence, node state changes, and fault triggering events. Then, each key node is mapped to its triggered action to form a set of node-action pairs, while the triggering conditions, execution delays, and cross-level control relationships of the actions are marked. Next, nodes and actions at different levels (provincial, municipal, and distribution levels) are identified according to the control level to establish cross-level dependencies and information interaction relationships, ensuring that the fault chain can reflect the action sequence and response logic of multi-level control. Subsequently, the fault triggering events, node state changes, and action execution results on the time series are integrated to form a complete fault chain data structure that includes fault scenarios, node topology locations, action sequences, triggering conditions, and hierarchical relationships.

[0049] S3. Establish a multi-level control and collaborative handling process model based on the chain propagation path, and generate a collaborative handling process; In this embodiment, a multi-level control and coordinated response process model is established based on the chain propagation path to generate a coordinated response process, including the following steps: S3.1. Use fault chain, key node and key action information as input data. Each key node includes attributes such as node type, geographical location, voltage level and equipment status. Each key action includes action type and triggering conditions such as protection action, switch operation and dispatching instruction. S3.2 Construct a multi-level control hierarchy and scope (clarify the responsibilities, controllable equipment, and operating authority of provincial control, municipal control, and distribution control domains; establish cross-level relationships, such as provincial control can issue action commands to the main lines of the municipal grid, and municipal control can perform load shedding on the distribution network branches). S3.3 Establish a mapping relationship between the key actions in the fault chain and the corresponding key nodes, and construct the triggering conditions, response delay and execution order for each action to form a set of node-action pairs, providing execution logic for the collaborative handling process; S3.4 Based on the power grid topology, cascading propagation paths, and control authority, construct cross-level action triggering and information interaction rules, including: coordination constraints of higher-level control commands on lower-level actions; priority ranking of actions at the same level or across domains; and rapid response strategies when faults propagate. Among these, the rules are formalized into logical expressions or state machines that can be used for process modeling. S3.5. Based on the node-action mapping relationship and multi-level collaborative logic, generate a collaborative handling process model to represent the action sequence and information interaction path of each level of regulation during the fault evolution process. First, extract each key node and its corresponding key action sequence, including action type, triggering condition, execution order, delay time and duration, and associate them with the node's attributes (such as geographical location, voltage level, and device type). Then, the nodes and actions at the provincial, municipal, and distribution levels are grouped according to multi-level collaborative logic, clarifying the scope of responsibilities, operable equipment, and permissions of each level, and marking the cross-level action dependencies. The multi-level collaborative logic refers to the set of rules governing the division of responsibilities, action dependencies, and information exchange among provincial-level control, municipal-level control, and distribution control domains during fault handling. Specifically, it includes: Hierarchical responsibility definition: Provincial-level control is responsible for macro-level decision-making on the backbone network and cross-regional power balance; municipal-level control is responsible for the operation and control of the transmission network and important substations under its jurisdiction; distribution control domain is responsible for the management and coordination of distribution network feeder automation, distributed power sources and load-side resources.

[0050] Action dependency relationship: Clearly define the binding force of higher-level control instructions on lower-level control actions, such as the power adjustment instructions issued by provincial-level control taking precedence over the local control strategies of prefecture-level cities; at the same time, support the autonomous actions of lower-level control in emergency situations, but require subsequent reporting and confirmation.

[0051] Collaborative triggering mechanism: Based on the location and impact range of key nodes in the fault chain, the collaborative entities are dynamically determined. For example, when a fault simultaneously affects both provincial trunk lines and municipal load centers, a joint response at the provincial and municipal levels is triggered; if the fault is confined to the distribution network, it is handled autonomously by the distribution control domain.

[0052] Information interaction sequence: Defines the information transmission sequence between each level in the collaboration process, including fault perception and reporting, issuance of handling instructions, execution feedback loop, status synchronization and updating, etc., forming a closed-loop logic of "perception - decision-making - execution - feedback"; Next, based on the fault propagation path and cross-level control rules, the action sequence is mapped to information interaction events (such as command issuance, status feedback, and confirmation response) to form a set of action and information interaction nodes; The cross-level control rules are used to describe the triggering conditions, coordination methods, and execution priorities of actions between different control levels during fault propagation, specifically including: Command transmission rules: The upper-level controller can issue control commands to the lower-level controller (such as load shedding, generator output adjustment, switch opening and closing, etc.). Before execution, the lower-level controller needs to verify the compatibility between the command and the local equipment status. If there is a conflict, it will be reported for coordination.

[0053] Authority Boundary Rules: Clearly define the operational authority of each level over equipment. For example, provincial-level control centers have direct control over equipment with voltage levels of 500kV and above; municipal-level control centers have control over equipment with voltage levels of 220kV and below; and distribution control zones have control over distribution network equipment with voltage levels of 10kV and below. Control of equipment across voltage levels requires authorization from a higher level or joint confirmation.

[0054] Conflict resolution rules: When multiple control levels simultaneously issue action commands to the same device, arbitration is conducted in the order of "superior priority, urgency priority, and time priority." Specifically: superior commands take precedence over subordinate commands; in scenarios of rapid fault propagation, protection action commands take precedence over scheduling commands; if commands have the same priority, the command that arrives first takes effect, and subsequent commands need to be re-coordinated.

[0055] Formal expression of the rules: The above rules are modeled in the form of "condition-action" pairs, for example: IF (provincial-level control issues load shedding order AND municipal-level control does not report conflict) THEN Execute the provincial-level order; ELSE Initiate conflict arbitration process; Subsequently, the node action sequence and information interaction events are integrated according to time order and hierarchical dependency to construct a collaborative handling process model (the collaborative handling process model consists of nodes, actions, triggering conditions, execution order and information interaction path, where nodes represent key power grid equipment or monitoring points, actions represent control operations performed by nodes during fault evolution (such as switching operations, load shedding, dispatching instructions, etc.), triggering conditions define the power grid state or event on which the action execution depends, the execution order reflects the sequential relationship of actions in time and hierarchy, and the information interaction path represents the logical flow of instruction issuance, status feedback and confirmation response between each level. The combination of these elements forms a complete process structure that can reflect the multi-level control collaborative response and fault handling logic), ensuring that the action triggering, execution order and information flow logic can reflect the actual control response; S3.6 Transform the collaborative handling process model into a collaborative handling process, including: the actions that each control level should perform under fault conditions; the triggering conditions, priorities, delays, and durations of the actions; and cross-level information exchange and confirmation mechanisms. Specifically, the process involves: analyzing the nodes, actions, triggering conditions, execution order, and information interaction paths in the collaborative response process model, clearly mapping each key action to its corresponding node and triggering condition; then, based on multi-level control logic, grouping the action sequence by level (provincial, municipal, and distribution level), clarifying the operational authority and responsibility scope of each level under fault conditions; next, sorting the action sequence and information interaction events according to time sequence and hierarchical dependency to form an executable operation process, ensuring that action triggering, execution, and information feedback conform to the collaborative logic; subsequently, embedding the priority, execution delay, duration, and cross-level information confirmation mechanism of actions into the process, enabling the collaborative response process to reflect actual control strategies and support real-time simulation; finally, standardizing and formatting the integrated process to generate a collaborative response process document or data structure, which can be directly used as input to the simulation engine to simulate the collaborative action response and information interaction of multi-level power grids during fault evolution, realizing an executable, analyzable, and evaluable fault response process.

[0056] The cross-level information confirmation mechanism is used to ensure the reliability, temporal consistency, and traceability of information transmission during multi-level control and collaborative handling processes, specifically including: Command issuance confirmation: When the superior control sends a disposal command to the subordinate, the subordinate must return "received" confirmation within a preset time window (e.g., 1 second); if no confirmation is received within the time limit, the superior control will automatically resend the command or activate the backup communication channel.

[0057] Execution feedback confirmation: After the lower-level controller completes the command action, it needs to feed back the execution result (success / failure, execution time, equipment status change) to the upper-level controller to form a closed loop; the upper-level controller updates the unified power grid model status based on the feedback result.

[0058] Status synchronization confirmation: When the status of a critical device at a certain level changes (such as switch tripping or protection action), the status update is actively pushed to the relevant upper and lower level controllers. The receiving party needs to return a synchronization confirmation to ensure the consistency of the status of the multi-level model.

[0059] Abnormal retry and alarm: If information exchange fails more than a set number of times (e.g., 3 times), the system marks the communication link as abnormal and triggers an alarm to prompt maintenance personnel to intervene; before communication is restored, the relevant levels continue to execute the handling process using local preset policies.

[0060] S4. Input the fault scenario and collaborative handling process into the simulation engine to simulate the information interaction and handling actions of multi-level control during the fault evolution process. In this embodiment, the fault scenario and collaborative handling process are input into the simulation engine to simulate the information interaction and handling actions of multi-level control during the fault evolution process, including the following steps: S4.1. Take the fault scenario and collaborative handling process as input; S4.2 Initialize the node voltage, line power flow, and equipment status of the unified power grid model to the operating state before the fault occurred; mark the abnormal state of the initial fault node or equipment, and load parameters such as load disturbance coefficient and protection action threshold; initialize the action queue and information interaction cache of each control level; S4.3. Based on the power flow iteration calculation results in step S2.4, update the node voltage, line power flow, and trigger status, and determine whether any new nodes / equipment have entered an abnormal state. Specifically, update the voltage amplitude and phase angle of each node, the power flow of each line, and the operating status of equipment obtained from the power flow iteration calculation to the unified power grid model to form the latest operating status snapshot. Then, for each node and line, check whether it has entered an abnormal state according to the preset abnormal judgment conditions (such as voltage over / under voltage thresholds, current / power flow exceeding rated values, load abnormalities, protection action trigger probability, etc.). Next, those that meet the conditions will be... Nodes or devices with abnormal conditions are marked as abnormal states, and the trigger time and cause are recorded. At the same time, their impact is propagated to related nodes or downstream devices, forming a possible chain effect. Subsequently, for newly entered abnormal states, the control actions that may be triggered (such as circuit breaker tripping, load shedding, dispatching command issuance, automatic reclosing, etc.) are extracted and the action queue is updated. Finally, the updated node status, line power flow and action information are integrated as input for the next iteration, and the fault propagation and collaborative handling judgment are executed cyclically until the preset maximum number of iterations is reached or the fault propagation is stable. S4.4. Based on the collaborative handling process model and triggering conditions, identify the actions (switching operations, load shedding, dispatching instructions, etc.) that need to be executed at each level. Specifically, extract the information of each key node and its associated actions in the collaborative handling process model, including action type, triggering conditions, execution delay, and duration. Then, based on the real-time status of the current node and equipment (such as voltage, current, load, and protection action triggering status), determine which actions have met the triggering conditions. Next, according to the multi-level control logic, classify the actions that meet the conditions by level (provincial, municipal, and distribution level), and clarify the operating authority and executable actions of each level under the current fault state. Subsequently, for actions that may conflict or depend on each other at the same level or across levels, filter and sort them according to priority, triggering order, and cross-level coordination rules to form a list of executable actions within and across levels. S4.5 Execute control actions in the order of the action queue, update node / line status, update power flow status based on action execution results, and form the input for the next iteration; S4.6 Repeat steps S4.3-S4.5 for fault propagation and collaborative handling until the preset maximum number of rounds r is reached.

[0061] S5. Construct a multi-dimensional capability assessment index system based on the data output by the simulation engine, and quantify the performance of the three-level collaborative response. In this embodiment, a multi-dimensional capability evaluation index system is constructed based on the data output by the simulation engine, and the performance of the three-level collaborative response is quantitatively scored, including the following steps: S5.1 Extract time series data based on the data output by the simulation engine, including at least: node voltage changes, line power flow evolution, equipment status switching, and control command issuance and feedback time; S5.2 Based on multi-level control and collaborative business scenarios, capability assessment is divided into the following dimensions: fault response timeliness and cross-level collaborative efficiency; The aforementioned multi-level control and coordination business scenarios refer to typical collaborative handling models categorized according to fault type, impact range, and control response method. These models are used to guide the construction and weighting of capability assessment indicators and specifically include the following scenario types: Provincial-municipal joint response scenario: The fault occurs in the provincial backbone network, but significantly impacts the power supply capacity of the municipal power grid (e.g., a fault in the main transformer N-1 leads to load constraints in the municipality). In this scenario, the provincial control center is responsible for the overall power balance and recovery strategy, while the municipal control center is responsible for executing load control and voltage regulation. The evaluation focuses on command transmission delay and execution consistency.

[0062] Joint handling scenario between local and distribution networks: The fault occurs in the area where the municipal transmission and distribution networks intersect (such as a substation busbar fault or a feeder outgoing line fault), requiring the municipal control and distribution control domains to work together to isolate the fault and transfer the load. The assessment focuses on cross-level fault location coordination and recovery time.

[0063] Provincial-local-distribution three-level joint response scenario: The fault spans multiple voltage levels, such as an ultra-high voltage line fault causing power oscillations in the provincial power grid, which in turn affects the municipal main grid and distribution network. In this scenario, the three-level control needs to work together to complete oscillation suppression, load control, and distributed resource regulation. The key assessment points are the integrity of information exchange, consistency of actions, and overall recovery efficiency.

[0064] Autonomous fault handling scenario in distribution networks: Faults are confined within the distribution network (such as feeder short circuits), and fault isolation and recovery are completed independently by the distribution control domain without the need for intervention from higher-level control. The evaluation focuses on the accuracy and timeliness of automated actions.

[0065] S5.3 Based on action timestamps, state change sequences and operational quantification data, calculation formulas are constructed for basic indicators under each dimension, and the basic indicators are normalized, that is, indicators with different dimensions and directions are uniformly mapped to the [0,1] interval, and negative indicators (such as delay and recovery time) are reverse-converted to ensure that different indicators can be weighted and superimposed. The calculation formula for the basic indicators under each dimension is as follows: The basic indicator for fault response timeliness is: action response delay. Actions on node i: In the formula, This refers to the action trigger time; This refers to the actual execution time of the action; Action response delay Perform normalization to generate normalized action response delay. : ; In the formula, This represents the minimum action response delay across the entire failure scenario or evaluation sample. This represents the maximum action response delay across the entire failure scenario or evaluation sample. The basic indicator for cross-level collaboration efficiency is the time difference between instruction issuance and feedback. The time it takes for a cross-level instruction to be issued from the superior to the subordinate for confirmation: In the formula, For level l to level Feedback time for issued instructions hierarchical The start time of issuing the instruction; Time difference between instruction issuance and feedback Normalization is performed to generate a normalized time difference between instruction issuance and feedback. : ; In the formula, To minimize latency across all cross-level instructions, To achieve maximum latency across all cross-level instructions; S5.4 According to the dimensional structure, the standardized scores of each basic indicator are weighted and summed to obtain the independent scores of the fault response timeliness and cross-level collaboration efficiency dimensions. Among them, the independent score of the fault response time dimension for: ; Independent scores for cross-level collaborative efficiency for: ; In the formula, For node indexing, For nodes Weighting of fault response timeliness dimension Cross-level instructions The weight, For cross-level action numbers, indicating the level from which the action originates. All actions up to level l; S5.5. The scores for fault response timeliness and cross-level coordination efficiency are weighted and aggregated in a secondary manner to obtain the final quantitative score representing the overall coordinated handling capability of the multi-level power grid in this fault scenario. In the formula, This represents the weight of the fault response timeliness dimension, reflecting its importance in the overall score. Its value typically ranges from 0 to 1. This represents the weight of the cross-level collaboration efficiency dimension, reflecting the importance of cross-level collaboration efficiency in the overall score, with a value ranging from 0 to 1.

[0066] S6. Determine the level of the three-level joint response capability based on the quantitative scoring results; In this embodiment, the three-level joint handling capability level is determined based on the quantitative scoring results, involving the following steps: Based on the quantitative scoring results, the performance of the provincial, local, and distribution levels in joint fault handling is mapped to a preset multi-level capability level system. By comparing with the level threshold range (such as A, B, C, and D levels), the handling capability level is determined, and a joint handling capability level report containing the comprehensive level and dimension score is output to support subsequent operation optimization and collaborative mechanism improvement. Among them, based on the comprehensive score Based on the numerical range, a multi-level capability rating system is constructed, dividing joint response capabilities into four levels, for example: Level A (Excellent): Possesses high-timeliness, high-quality, and high-consistency collaborative response capabilities; Level B (Good): Possesses relatively stable cross-level response capabilities, with a small amount of acceptable deviation; Level C (Average): Insufficient collaborative efficiency and action quality, which has a certain impact on the effectiveness of fault handling; Level D (Poor): Weak cross-level collaborative capabilities, with obvious time delays, errors, and conflicts; Based on the standards, experience data, or statistical models of the operating unit, the comprehensive score is divided into corresponding level ranges, for example: Level A: ≥85; Grade B: 70≤ <85; Grade C: 55≤ <70; Grade D: <55; Comprehensive score Mapping to the corresponding level range yields the overall joint handling capability level; the system automatically compares the scoring results with the level threshold range and outputs the corresponding level label.

[0067] Example 2: This invention also proposes a provincial and regional multi-level power grid joint fault simulation and collaborative handling capability assessment system, including a power grid model construction module, a fault identification module, a handling process generation module, a handling simulation module, a handling assessment module, and a handling capability level determination module: The power grid model building module constructs a unified power grid model based on the power grid topology and operation data at the provincial, regional, and distribution levels. The fault identification module generates fault scenarios based on a unified power grid model and deduces the chain propagation path of faults among the provincial, local, and distribution power grids. It identifies the key nodes and key actions of the fault chain and obtains the fault chain. The handling process generation module establishes a multi-level control collaborative handling process model based on the chain propagation path, key nodes, key actions and failure chains, and generates a collaborative handling process. The fault simulation module inputs fault scenarios and collaborative handling processes into the simulation engine to simulate information interaction and handling actions of multi-level control during the fault evolution process; The disposal assessment module constructs a multi-dimensional capability assessment index system based on the data output by the simulation engine, and quantifies the collaborative disposal at the provincial, local, and distribution levels. The disposal capacity level assessment module determines the level of joint disposal capacity at the provincial, local, and distribution levels based on quantitative scoring results.

[0068] Example 3: The present invention also proposes a terminal, including a processor and a storage medium: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps according to the method described above.

[0069] Example 4: The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0070] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0071] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0072] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0073] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for assessing the joint fault simulation and collaborative handling capabilities of provincial and local power grids at multiple levels, characterized in that... include: S1. Construct a unified power grid model based on the power grid topology and operation data of the provincial, regional, and distribution levels; S2. Based on the unified power grid model, generate fault scenarios and deduce the chain propagation path of faults among the provincial, local and distribution power grids, identify the key nodes and key actions of the fault chain, and obtain the fault chain. S3. Based on the chain propagation path, key nodes, key actions and failure chain, establish a multi-level control and collaborative handling process model and generate a collaborative handling process. S4. Input the fault scenario and collaborative handling process into the simulation engine to simulate the information interaction and handling actions of multi-level control during the fault evolution process; S5. Construct a multi-dimensional capability assessment index system based on the data output by the simulation engine, and quantify the collaborative handling at the provincial, regional, and distribution levels. S6. Determine the level of joint disposal capability at the provincial, municipal, and distribution levels based on the quantitative scoring results.

2. The method for assessing the joint fault simulation and collaborative handling capabilities of provincial and local power grids according to claim 1, characterized in that: In S1, a unified power grid model is constructed based on the three-level power grid topology and operational data of the provincial control center, municipal control centers, and distribution control domains. Specifically, this includes: S1.1 After inputting the power grid operation data source for the provincial, regional, and distribution levels respectively, collect the operation data, preprocess the operation data, and construct standardized data; S1.2 Based on the hierarchical structure of the provincial, regional, and distribution networks, cross-level associations are performed on the main network, distribution network, and substation topology. Mapping relationships are established between key nodes, including cross-level tie lines, substation main transformers, and feeder outgoing lines, to generate the overall topology skeleton. S1.

3. Normalize and bind the standardized data according to the set equipment identifier library and equipment classification system to establish equipment-level state vectors and operating parameter sets; S1.4 Based on the topology skeleton and the set of operating parameters, construct a unified power grid model, including node models, line models, equipment models and their attributes, and generate electrical equivalent relationships, timing relationships and constraints for fault inference.

3. The method for assessing the joint fault simulation and collaborative handling capabilities of provincial and local power grids according to claim 2, characterized in that: In S1.4, the specific implementation methods for constructing a unified power grid model and generating electrical equivalent relationships, temporal relationships, and constraints for fault prediction include: Based on the topology skeleton and operating parameter set, the cross-level electrical connection structure is reconstructed by re-unifying equipment identification, integrating the relationships between provincial, regional, and distribution level nodes and connections, and mapping equipment and line attributes and real-time operating data to the corresponding nodes and connections; and a set of operating state equations is constructed by combining the power flow equations and control constraints of the main grid and distribution network to generate a unified power grid model. The unified power grid model includes node power balance equations, line power flow calculations, and equipment state and constraint equations. Based on the topology of the unified power grid model, impedance, admittance, and power source models are established for nodes and lines using the equivalent circuit method; the operating logic, protection actions, and dispatching instructions of each device and line are extracted, and the state change relationships arranged in time sequence are constructed to record the fault triggering and response sequence; a set of constraints is formulated by combining the rated capacity of the equipment, power flow constraints, protection action conditions, and cross-level control authority.

4. The method for assessing the joint fault simulation and collaborative handling capabilities of provincial and local power grids according to claim 1, characterized in that: S2. Based on a unified power grid model, generate fault scenarios and deduce the cascading propagation path of faults among provincial, regional, and distribution power grids, identifying key nodes and key actions in the fault chain, specifically including: S2.

1. Obtain equipment types, line attributes, and historical fault records based on the unified power grid model, identify the fault types to be analyzed, and generate a preliminary fault scenario set according to time, location, and load conditions; S2.2 Construct triggering conditions for each fault scenario, including equipment status, power flow exceeding threshold, voltage drop amplitude, and switch malfunction probability, and map the triggering conditions to the operating parameters of the corresponding nodes or edges in the unified power grid model; S2.

3. Based on the electrical topology, establish fault cascading propagation rules among the provincial, local, and distribution power grids, including cross-level influence relationships, electrical cascading constraints, and action triggering conditions. S2.4 Based on the unified power grid model and fault cascading propagation rules, the power flow iteration algorithm is used to calculate the propagation path of the initial fault among the provincial, regional, and distribution power grids, and the fault-triggered nodes, affected equipment, and cascading effects are recorded. S2.5 During the fault propagation process, assess the impact of each node on the fault spread, identify the key nodes that cause large-scale power outages or system instability, and include the key nodes in the set of key nodes. S2.6 For each critical node, analyze the control actions triggered during the fault evolution process to form a sequence of critical actions; S2.7 Integrate the propagation path, key nodes, and key actions of each fault scenario according to time sequence and hierarchical relationship to generate a complete fault chain.

5. The method for assessing the joint fault simulation and collaborative handling capabilities of provincial and local power grids according to claim 4, characterized in that: In S2.4, the propagation path of the initial fault among the provincial, regional, and distribution power grids is calculated using a power flow iterative algorithm, including the following steps: S2.

41. Initialize the node voltages, line power flows, and equipment states of the unified power grid model to the operating states before the fault occurred, and mark the initial fault node or equipment as the fault-triggered state. S2.

42. Based on the fault triggering conditions defined in step S2.2, modify the node / equipment parameters corresponding to the initial fault to an abnormal state, and transmit the abnormal state to the relevant connection nodes of the unified power grid model. S2.

43. Using abnormal states as input, perform power flow iterative calculations; S2.

44. Based on the fault cascading propagation rules established in step S2.3, determine whether the power flow iteration results trigger a fault in the next level node. S2.

45. Mark the triggered node as a fault state and record the propagation path of the fault in the time series; use the new abnormal state as the input for the next iteration. S2.

46. Repeat the power flow iteration and fault propagation judgment until the preset maximum time step is reached; S2.

47. Based on time series data of fault evolution, a complete fault propagation path is formed for the three-level power grid spanning provinces, regions, and distribution.

6. The method for assessing the joint fault simulation and collaborative handling capabilities of provincial and local power grids according to claim 5, characterized in that: In S2.43, the power flow iterative calculation is performed using abnormal states as input, including the following specific steps: Obtain the actual active and reactive loads of all load nodes in the unified power grid model, and weight the node loads according to the load disturbance coefficient to obtain the weighted adjusted active and reactive loads; Update the adjusted active and reactive loads to the node attributes of the unified power grid model; The node load after load disturbance adjustment and the mapped fault and abnormal states are used as the input for power flow iteration; The Newton-Raphson method is used to perform iterative calculations to solve for the voltage magnitude and phase angle of each node and the power flow of each line step by step.

7. The method for assessing the joint fault simulation and collaborative handling capabilities of provincial and local power grids according to claim 1, characterized in that: In S3, a multi-level control and collaborative handling process model is established based on the chain propagation path, key nodes, key actions, and failure chains to generate a collaborative handling process, specifically including: S3.1 Use fault chain, critical node and critical action information as input data; S3.2 Constructing a multi-level control hierarchy and scope; S3.3 Establish a mapping relationship between the key actions in the fault chain and the corresponding key nodes, and construct the triggering conditions, response delay and execution order for each action to form a set of node-action pairs; S3.

4. Based on the power grid topology, cascading propagation paths, and control authority, construct cross-level action triggering and information interaction rules; S3.

5. Generate a collaborative handling process model based on the mapping relationship between key actions and corresponding key nodes and multi-level collaborative logic; S3.6 Transform the collaborative handling process model into a collaborative handling process.

8. The method for assessing the joint fault simulation and collaborative handling capabilities of provincial and local power grids according to claim 7, characterized in that: In S3.5, a collaborative handling process model is generated based on the mapping relationship between key actions and corresponding key nodes and multi-level collaborative logic. The specific implementation steps include: Extract each key node and its corresponding key action sequence, including action type, triggering condition, execution order, delay time and duration, and associate the key action sequence with the attributes of the key node; The nodes and actions at the provincial, regional, and distribution levels are grouped according to multi-level collaborative logic, clarifying the scope of responsibilities, operable equipment, and permissions of each level, and marking cross-level action dependencies. Based on the fault propagation path and cross-level control rules, the action sequence is mapped to the information interaction event to form a set of action and information interaction nodes; By integrating node action sequences and information interaction events according to time order and hierarchical dependencies, a collaborative processing flow model is constructed.

9. The method for assessing the joint fault simulation and collaborative handling capabilities of provincial and local power grids according to claim 8, characterized in that: The multi-level collaborative logic is a set of rules for the division of responsibilities, action dependencies, and information interaction that are followed by provincial-level control, municipal-level control, and distribution control domains during the fault handling process. Among them, the action dependency relationship clearly defines the constraint of the superior control instructions on the subordinate control actions; Based on the location and impact range of key nodes in the fault chain, determine the collaborating entities; set the information transmission sequence between each level during the collaboration process, including fault perception reporting, handling instruction issuance, execution feedback loop, and status synchronization update.

10. The method for assessing the joint fault simulation and collaborative handling capabilities of provincial and local power grids according to claim 7, characterized in that: The cross-level control rules describe the triggering conditions, coordination methods, and execution priorities of actions between different control levels during fault propagation. They are constructed using a condition-action format and specifically include: The higher-level controller issues control commands to the lower-level controller. Before execution, the lower-level controller needs to verify the compatibility between the command and the local device status. If there is a conflict, it will be reported for coordination. Clearly define the operating permissions for equipment at each level; control of equipment spanning voltage levels requires confirmation from the superior authority. When multiple control levels issue action commands to the same device simultaneously, the commands are executed in the order of priority of superior, priority of urgency, and priority of time.

11. The method for assessing the joint fault simulation and collaborative handling capabilities of provincial and local power grids according to claim 7, characterized in that: In S3.6, the collaborative handling process model is transformed into a collaborative handling process. The specific implementation steps include: The nodes, actions, triggering conditions, execution order, and information interaction paths in the collaborative handling process model are analyzed, and each key action is mapped to its corresponding node and triggering condition. Based on the multi-level control logic, the action sequence is grouped into three levels: province, region, and distribution, and the operational authority and scope of responsibility of each level under fault conditions are clearly defined. Sort the action sequences and information interaction events according to time order and hierarchical dependency; Embed the priority, execution delay, duration, and cross-level information confirmation mechanism of actions into the process; The integrated process is standardized and formatted to generate collaborative handling process documents or data structures, resulting in a fault handling process.

12. The method for assessing the joint fault simulation and collaborative handling capabilities of provincial and local power grids according to claim 11, characterized in that: The cross-level information confirmation mechanism specifically includes instruction issuance confirmation, execution feedback confirmation, status synchronization confirmation, and abnormal retry and alarm procedures: The instruction issuance confirmation process refers to the requirement that when a higher-level controller sends a disposal instruction to a lower-level controller, the lower-level controller must return a confirmation message within a preset time window. If no confirmation is received within the time limit, the higher-level controller will automatically resend the instruction or activate a backup communication channel. Execution feedback confirmation means that after a lower-level controller completes an instruction action, it must send the execution result back to the higher-level controller. Based on the feedback results, the higher-level control system executes step S1 to update the unified power grid model; Status synchronization confirmation refers to the process where, when the status of a device at a certain level changes, the device proactively pushes a status update to the relevant upper and lower level controllers, and the receiving party needs to return a synchronization confirmation. Abnormal retry and alarm means that if information exchange fails more than a set number of times consecutively, the communication link is marked as abnormal and an alarm is triggered; before communication is restored, the relevant level continues to execute the handling process using local preset policies.

13. The method for assessing the joint fault simulation and collaborative handling capabilities of provincial and local power grids according to claim 1 or 4, characterized in that: In S4, the fault scenario and collaborative handling process are input into the simulation engine to simulate the information interaction and handling actions of multi-level control during the fault evolution process, including the following steps: S4.

1. Take the fault scenario and collaborative handling process as input; S4.2 Initialize the node voltages, line power flows, and equipment states of the unified power grid model to the operating states before the fault occurred; mark the abnormal states of the initial fault nodes; S4.

3. Based on the power flow calculation results, update the node voltage, line power flow and trigger status, and determine whether any new nodes have entered an abnormal state. S4.

4. Based on the collaborative handling process model and triggering conditions, identify the actions that need to be performed at each level; S4.5 Execute the control actions in the order of the action queue, update the node state, update the power flow state according to the action execution results, and form the input for the next iteration; S4.6 Repeat steps S4.3-S4.5 for fault propagation and collaborative handling until the preset maximum number of rounds is reached.

14. The method for assessing the joint fault simulation and collaborative handling capabilities of provincial and local power grids according to claim 1, characterized in that: S5. Based on the data output by the simulation engine, construct a multi-dimensional capability assessment index system and quantify the coordinated handling at the provincial, regional, and distribution levels. Specific steps include: S5.1 Extracting time series data based on the data output by the inference engine; S5.2 Based on multi-level control and coordination business scenarios, the capability assessment is divided into the following dimensions: fault response timeliness and cross-level coordination efficiency; among which, multi-level control and coordination business scenarios include provincial-local joint handling scenarios, local-distribution joint handling scenarios, provincial-local-distribution three-level joint handling scenarios, and distribution network autonomous handling scenarios; S5.3 Based on action timestamps, state change sequences, and operational quantification data, calculation formulas are constructed for the basic indicators of action response delay under the fault response timeliness dimension and the basic indicators of instruction issuance and feedback time difference under the cross-level collaboration efficiency dimension, and the basic indicators are normalized. S5.4 According to the dimensional structure, the standardized scores of each basic indicator are weighted and summed to obtain the independent scores of the fault response timeliness and cross-level collaboration efficiency dimensions. S5.

5. The scores for fault response timeliness and cross-level collaboration efficiency are weighted and aggregated in a secondary manner to obtain a quantitative score.

15. The method for assessing the joint fault simulation and collaborative handling capabilities of provincial and local power grids according to claim 1, characterized in that: In S6, the level of joint response capability at the provincial, municipal, and distribution levels is determined based on the quantitative scoring results. Specific steps include: The quantitative scoring results are mapped to a preset multi-level capability level system. By comparing with the level threshold range, the level of handling capability is determined, and a joint handling capability level report containing comprehensive level and dimension score is output.

16. A system for assessing the joint fault simulation and collaborative handling capabilities of a multi-level power grid in a province using the method described in any one of claims 1-15, comprising a power grid model construction module, a fault identification module, a handling process generation module, a handling simulation module, a handling assessment module, and a handling capability level determination module, characterized in that: The power grid model building module constructs a unified power grid model based on the power grid topology and operation data at the provincial, regional, and distribution levels. The fault identification module generates fault scenarios based on a unified power grid model and deduces the chain propagation path of faults among the provincial, local, and distribution power grids. It identifies the key nodes and key actions of the fault chain and obtains the fault chain. The handling process generation module establishes a multi-level control collaborative handling process model based on the chain propagation path, key nodes, key actions and failure chains, and generates a collaborative handling process. The fault simulation module inputs fault scenarios and collaborative handling processes into the simulation engine to simulate information interaction and handling actions of multi-level control during the fault evolution process; The disposal assessment module constructs a multi-dimensional capability assessment index system based on the data output by the simulation engine, and quantifies the collaborative disposal at the provincial, local, and distribution levels. The disposal capacity level assessment module determines the level of joint disposal capacity at the provincial, local, and distribution levels based on quantitative scoring results.

17. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-15.

18. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-15.

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