Power distribution topology dynamic reconstruction risk assessment method, system, device and medium

Abstract

The invention discloses a power distribution topology dynamic reconstruction risk assessment method, system and device and a medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring real-time state data of an intelligent component to construct an initial topological structure, and identifying a changed target node by sending a detection signal; and then carrying out dynamic reconstruction on the topological structure in the three logic layers. And performing electrical characteristic verification on the reconstructed topological structure. And after the electrical characteristic verification is passed, determining a connection relationship of upstream and downstream power distribution cabinets and a component association link based on the target node, and calculating a risk level in combination with historical operation parameters. And formulating a temporary coping strategy according to the risk level, and generating a risk assessment report containing the topological influence range, the risk level and the coping strategy. Real-time monitoring and risk early warning of topological change of the power distribution system are realized in the whole process. By implementing the technical scheme provided by the invention, the risk assessment accuracy of the dynamic reconstruction of the power distribution topology can be improved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a risk assessment method, system, device, and medium for dynamic reconfiguration of power distribution topology. Background Technology

[0002] With the continuous advancement of smart grid construction, power distribution systems are gradually developing towards intelligence and automation. Various intelligent components are widely used in power distribution systems, and the system topology is also dynamically changing with the needs of power load distribution, equipment maintenance, and other requirements.

[0003] Currently, power distribution systems employ a condition monitoring-based risk assessment method, which analyzes the system's operating status by collecting operational data from smart components and conducts risk assessments when topology changes are detected.

[0004] However, in practical applications, due to the increasingly complex structure of power distribution systems, existing assessment methods often treat changes in the power distribution system as independent events, failing to fully consider the relationships between different components in the power distribution network system. The assessment results lack hierarchical analysis, resulting in insufficient accuracy in risk assessment of dynamic reconfiguration of the power distribution topology, which affects the safe and stable operation of the power distribution system. Summary of the Invention

[0005] This application provides a risk assessment method, system, device, and medium for dynamic reconfiguration of power distribution topology, which can improve the accuracy of risk assessment for dynamic reconfiguration of power distribution topology.

[0006] Firstly, this application provides a risk assessment method for dynamic reconfiguration of power distribution topology, including: Acquire real-time status data of intelligent components in the power distribution system, and construct a first topology of the power distribution system based on the real-time status data; Send detection signals to each topology node in the first topology structure. If no feedback signal is received from any topology node for a preset number of consecutive times, the topology node is identified as the target node where the topology change has occurred. Based on the topology change type of the target node, the first topology is dynamically reconstructed in multiple logical layers to obtain a second topology. The logical layers include a micro layer, a meso layer, and a macro layer. Perform electrical characteristic verification on any closed loop in the second topology; When the electrical characteristics verification is passed, based on the association relationship in the second topology, the distribution cabinet connection relationship is determined from the target node in the upstream direction, the component association link is determined in the downstream direction, the topological influence range of the distribution cabinet connection relationship and the component association link is determined, and the risk level is calculated in combination with the historical operating parameters of the intelligent components within the topological influence range; Generate corresponding temporary response strategies based on the risk level; Based on the topological impact range, the risk level, and the temporary response strategy, a risk assessment report for the power distribution system is generated.

[0007] By adopting the above technical solution, a first topology is constructed by acquiring real-time status data of intelligent components in the power distribution system. The target node where the topology change occurs is determined by detecting signals. Then, the first topology is dynamically reconstructed at multiple logical layers (micro, meso, and macro) to obtain a second topology. The electrical characteristics of the second topology are verified. After the verification is passed, the topological impact range of the distribution cabinet connection relationship and component association link is determined based on the correlation in the second topology. The risk level is calculated by combining the historical operating parameters of the intelligent components within this range, and then a temporary response strategy and risk assessment report are generated. This achieves multi-level analysis of the topology changes in the power distribution system, fully considers the correlation between different components, improves the accuracy of risk assessment for dynamic reconstruction of the power distribution topology, and helps to ensure the safe and stable operation of the power distribution system.

[0008] Optionally, static and dynamic data stored in the built-in topology protocol chip of each of the aforementioned smart components are acquired. The static data includes a unique identifier of the component, rated current, and interface type. The dynamic data includes online status, real-time operating current, and temperature. A target smart component in a powered-on state is identified based on the online status, and neighboring smart components of the target smart component are acquired based on the unique identifier of the component. For each neighboring smart component, if the matching degree between the rated current and the interface type is greater than a preset threshold, the neighboring smart component is identified as a target neighboring smart component. A series-parallel connection relationship is established between the target neighboring smart components using the weighted values ​​of the real-time operating current and the temperature as edge weights, generating a first topology.

[0009] Optionally, a logic layer system is constructed, consisting of a micro-layer with intelligent components as nodes, a meso-layer with circuits within the distribution cabinet as units, and a macro-layer with the power distribution system as a whole. Within this logic layer system, when the intelligent components corresponding to the target node change, the connection relationships between the intelligent components and upstream input and downstream output nodes are adjusted at the micro-layer, while keeping the meso-layer and macro-layer unchanged. When any circuit within the distribution cabinet corresponding to the target node fails, the series or parallel logic of the intelligent components in the faulty circuit is deleted at the meso-layer, and the external connection status of the distribution cabinet in the macro-layer is updated synchronously. When the feeder between distribution cabinets corresponding to the target node fails, the feeder connection logic is reconstructed at the macro-layer and synchronized downwards to the meso-layer, and the circuit status of the distribution cabinet is marked as offline.

[0010] Optionally, any closed loop and nodes in the second topology are extracted; for each closed loop, the current flowing through each line segment is collected, and the voltage drop vector of each line segment is calculated based on the current flowing through each line segment, and the sum of the voltage drop vectors is verified to be zero; for each node, the current vectors of each branch flowing into and out of the node are collected, and the sum of the current vectors of each branch is verified to be zero; if the sum of the voltage drop vectors and the sum of the current vectors of each branch are zero, the electrical characteristic verification is deemed to have passed.

[0011] Optionally, the identification of the inlet circuit breaker of the distribution cabinet where the target node is located is obtained, and the connection relationship of the distribution cabinets is obtained by tracing upwards from the inlet circuit breaker identification to the main distribution cabinet; the downstream intelligent components connected to the target node are determined based on the output port identification of the target node, and the load branches of the downstream intelligent components are recursively obtained to generate the component association links; according to the distribution cabinet connection relationship and the component association links, the upstream distribution cabinets and downstream intelligent components that affect the topology change of the target node are marked; the coverage area of ​​the upstream distribution cabinets and the downstream intelligent components that affect the topology influence range is determined.

[0012] Optionally, the output port identifiers of the target node are traversed to obtain the downstream intelligent components corresponding to the output port identifiers; the load type and load capacity of the downstream intelligent components are extracted, and a corresponding component cascading table is established; based on the component cascading table, the downstream intelligent components are recursively traversed until the terminal load device is reached; the downstream intelligent components recursively traversed are sorted according to the power supply order to obtain multiple load branches, and each load branch is mapped to a preset tree structure to obtain the component association link.

[0013] Optionally, historical operating parameters of each smart component within the topology's influence range are collected over a preset time period. These historical operating parameters include overload counts, load type, rated power, and real-time current. The importance weight of each smart component within the topology's influence range is determined based on the load type. The ratio of real-time current to rated current for each smart component is calculated to obtain the current load rate. A weighted calculation is performed based on the importance weight, current load rate, and overload count of each smart component to obtain a risk level score for the corresponding smart component. For each smart component, a corresponding risk level is assigned based on the numerical range of the risk level score.

[0014] A second aspect of this application provides a risk assessment system for dynamic reconfiguration of power distribution topology, the system comprising: The data acquisition module is used to acquire real-time status data of intelligent components in the power distribution system, and construct a first topology of the power distribution system based on the real-time status data. The dynamic reconstruction module is used to send detection signals to each topology node in the first topology structure. When no feedback signal is received from any topology node for a preset number of consecutive times, the topology node is identified as the target node where a topology change has occurred. According to the topology change type of the target node, the first topology structure is dynamically reconstructed in multiple logical layers to obtain a second topology structure. The logical layers include a micro layer, a meso layer, and a macro layer. The risk level determination module is used to calculate the total port current of the smart components and the total voltage drop of the lines within the closed loop in the second topology, and to verify whether the vector sum of the total port current and the total voltage drop satisfies the condition that the vector sum is zero. When the condition is satisfied, based on the association relationship in the second topology, the module determines the distribution cabinet connection relationship upstream from the target node and the component association link downstream, determines the topological influence range of the distribution cabinet connection relationship and the component association link, and calculates the risk level by combining the historical operating parameters of the smart components within the topological influence range. The risk assessment module is used to generate corresponding temporary response strategies based on the risk level; and to generate a risk assessment report for the power distribution system based on the topology impact range, the risk level, and the temporary response strategies.

[0015] A third aspect of this application provides an electronic device including a memory, a processor, and a program stored in the memory and executable on the processor, the program being loaded and executed by the processor to implement a risk assessment method for dynamic reconfiguration of a power distribution topology.

[0016] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement a risk assessment method for dynamic reconfiguration of power distribution topology.

[0017] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages: By adopting the above technical solution, a first topology is constructed by acquiring real-time status data of intelligent components in the power distribution system. The target node where the topology change occurs is determined by detecting signals. Then, the first topology is dynamically reconstructed at multiple logical layers (micro, meso, and macro) to obtain a second topology. The electrical characteristics of the second topology are verified. After the verification is passed, the topological impact range of the distribution cabinet connection relationship and component association link is determined based on the correlation in the second topology. The risk level is calculated by combining the historical operating parameters of the intelligent components within this range, and then a temporary response strategy and risk assessment report are generated. This achieves multi-level analysis of the topology changes in the power distribution system, fully considers the correlation between different components, improves the accuracy of risk assessment for dynamic reconstruction of the power distribution topology, and helps to ensure the safe and stable operation of the power distribution system. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a risk assessment method for dynamic reconfiguration of power distribution topology provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a risk assessment system for dynamic reconfiguration of power distribution topology provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0019] Explanation of reference numerals in the attached drawings: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed Implementation

[0020] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0021] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0022] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0023] This application provides a risk assessment method for dynamic reconfiguration of power distribution topology. In one embodiment, please refer to... Figure 1 , Figure 1 This is a flowchart illustrating the risk assessment method for dynamic reconfiguration of distribution topology provided in this application. This method can be implemented using a computer program, which can be integrated into an application or run as a standalone utility application. The method can also be implemented using a microcontroller and can run on a risk assessment system for dynamic reconfiguration of distribution topology based on the von Neumann architecture. Specifically, the method may include the following steps: Step 101: Obtain real-time status data of intelligent components in the power distribution system, and construct the first topology of the power distribution system based on the real-time status data.

[0024] Among them, intelligent components refer to power distribution equipment with data acquisition, communication and control functions, such as intelligent circuit breakers, intelligent switches, and smart meters; real-time status data refers to the current working status information of intelligent components, including operating parameters such as switch status, current value, voltage value, and temperature; the first topology represents the network structure model of the physical connection relationship and logical association relationship between various intelligent components in the power distribution system.

[0025] Specifically, during the operation of a power distribution system, it is necessary to monitor the system's operating status and topology in real time. This step involves continuously collecting real-time operating data from various intelligent components through the data acquisition module of the power distribution automation system. After preprocessing and filtering, the collected data, combined with the static attribute information of the intelligent components (such as equipment type and rated parameters), constructs a topology model reflecting the actual operating status of the current power distribution system. This model not only includes the physical connections between devices but also information such as the on / off status of each circuit and load distribution, providing fundamental data support for subsequent topology reconfiguration and risk assessment.

[0026] In some embodiments, real-time status data acquisition and topology construction can be achieved in multiple ways: Optionally, firstly, the distribution automation master station system sends data acquisition commands to each intelligent component. After receiving the commands, the intelligent components upload their current operating status data to the master station system. Then, the acquired data undergoes timestamp verification and data quality analysis to remove abnormal data. Finally, based on the device topology description file template, qualified real-time data is filled into the corresponding data items to generate a complete topology model. Optionally, the operating data is automatically collected by the status monitoring module built into the intelligent components and periodically reported to the data acquisition center via a communication network. The data acquisition center classifies, organizes, and uniformly formats the received data. Finally, a graphical modeling tool is used to convert the processed data into a standard topology description file. It is understood that other data acquisition methods and topology construction methods can also be used to achieve this function, and no limitation is made here.

[0027] Based on the above embodiments, as an optional embodiment, step 101: constructing the first topology of the power distribution system based on real-time status data, this step may further include the following steps: Step 201: Obtain the static and dynamic data stored in the built-in topology protocol chip of each smart component. The static data includes the component's unique identifier, rated current, and interface type. The dynamic data includes online status, real-time operating current, and temperature. Identify the target smart component that is powered on based on the online status, and obtain the adjacent smart components of the target smart component based on the component's unique identifier.

[0028] Among them, the built-in topology protocol chip is a dedicated chip integrated inside the smart component for storing device information and performing communication functions; static data refers to the inherent basic attribute information of the device, including the identification code used to uniquely identify the device, the rated operating current value of the device, and the physical and electrical characteristics of the device interface; dynamic data refers to the real-time operating status information of the device, including whether the device is in a powered-on state, the actual current value flowing through the device, and the operating temperature of the device.

[0029] Specifically, this step establishes a communication connection with the topology protocol chip built into the smart component and reads the device information stored in the chip. First, static data is read to obtain the component's unique identifier (e.g., "SW001", "CB002", etc.), rated current value (e.g., "400A"), and interface type (e.g., "three-phase four-wire"). Simultaneously, dynamic data is read, including the device's online status flag (1 indicates online, 0 indicates offline), real-time current value, and temperature value. The system filters out smart components currently in a powered-on state based on the online status flag and marks these components as target smart components. For each target smart component, the system queries the device topology database using its unique identifier to obtain information about the upstream and downstream devices physically connected to that component. For example, for a smart circuit breaker, the system will obtain information about its upstream busbar connection devices and downstream load devices. This information provides the basic data support for subsequently constructing a complete power distribution system topology. In its implementation, the system first establishes a data exchange channel with the topology protocol chip via standard communication protocols such as Modbus or IEC61850, and then reads various types of information sequentially according to a predefined data structure. For the acquired dynamic data, the system verifies the data validity to ensure its accuracy. By analyzing the physical connections between devices, the system establishes an adjacency table for intelligent components, recording the information of directly connected devices for each device.

[0030] Step 202: For adjacent smart components, when the matching degree between the rated current and the interface type is greater than a preset threshold, the adjacent smart component is identified as the target adjacent smart component.

[0031] Among them, the matching degree represents the degree of compatibility of electrical parameters between adjacent smart components; the preset threshold is the standard value for judging the matching degree between components; the target adjacent smart components refer to devices that have been confirmed to be able to be safely connected after the matching degree verification.

[0032] Specifically, this step performs an electrical parameter matching analysis on each pair of adjacent smart components. First, the ratio of the rated currents of the two components is calculated. For example, if the upstream component has a rated current of 400A and the downstream component has a rated current of 300A, the ratio is 0.75. Then, the compatibility of the interface types is checked, including parameters such as the number of phases, voltage level, and wiring method. The matching status of each parameter is quantified into a value between 0 and 1. The current ratio and the interface parameter matching degree are weighted at a ratio of 6:4 to obtain the overall matching degree. When the overall matching degree exceeds a preset threshold (e.g., 0.85), it is confirmed that the pair of adjacent components can be safely connected, and they are marked as target adjacent smart components. Through this screening, unsuitable device combinations are eliminated, ensuring that the connection relationships in the topology meet electrical safety requirements.

[0033] Step 203: Using the weighted values ​​of real-time operating current and temperature as edge weights, establish the series and parallel connection relationships between adjacent target smart components to generate the first topology.

[0034] Among them, edge weight represents the importance index of the connecting lines in the topology; series and parallel connection relationship refers to the electrical connection method between intelligent components; the first topology is a network model that reflects the actual connection status of the current power distribution system.

[0035] Specifically, this step constructs a topology model of the power distribution system. First, the real-time operating current and temperature of each target adjacent smart component are normalized. The current value is divided by the rated current to obtain the load factor, and the temperature value is converted to a percentage of the device's rated operating temperature. Weights of 0.7 and 0.3 are multiplied by the normalized current and temperature values ​​respectively, and summed to obtain the edge weight values ​​for that connection relationship. Series and parallel relationships are identified based on the physical connection method of the devices. For example, multiple circuit breakers connected to the same busbar are in parallel, while circuit breakers connected in series to form a power supply branch are in series. All target adjacent smart components are connected according to their actual connection methods, and the calculated edge weight values ​​are marked on each connection relationship, ultimately generating a complete first topology. This topology is represented graphically, where nodes represent smart components, lines represent connections between devices, and the weight values ​​on the lines reflect the load level and operating status of that connection.

[0036] Step 102: Send detection signals to each topology node in the first topology structure. If no feedback signal is received from any topology node for a preset number of consecutive times, determine any topology node as the target node where the topology change has occurred.

[0037] In this context, a topology node represents the connection point of a smart component in the power distribution system topology; a detection signal refers to a data packet used to verify the communication status of a node; a feedback signal represents the response data returned by the node after receiving the detection signal; a preset number of times refers to the threshold number of consecutive detection failures set by the system; and a target node refers to the specific node location where a topology change occurs.

[0038] Specifically, after constructing the first topology, the system needs to monitor changes in the topology in real time. This step involves periodically sending detection signals to all nodes in the topology to monitor topology changes. The system sends a detection data packet containing a node identifier, timestamp, and checksum to each topology node at predefined time intervals (e.g., 100ms). Normally functioning nodes, upon receiving the detection signal, will return a feedback signal with status information within a specified time (e.g., 50ms). When a node fails to return a valid feedback signal multiple times consecutively (e.g., a preset number of 3 times), the system determines that the node has undergone a topology change, marks it as a target node, and uses this information for subsequent topology reconstruction and risk assessment.

[0039] In some embodiments, topology node changes can be detected in several ways: Optionally, a TCP / IP-based communication link is first established to send heartbeat packets to each node; then, a timer is used to monitor the response time of each node; finally, the node status is determined based on the number of response timeouts, and a node change is confirmed when the number of timeouts reaches a preset value. Optionally, a master-slave polling mechanism is adopted, with the distribution master station acting as the master device periodically polling the status of each slave device node; then, the response result of each node is recorded in a status table; finally, the continuous non-response records in the status table are analyzed to identify the target node that has changed. It is understood that other communication protocols and detection mechanisms can also be used to monitor topology node changes, and this is not limited here.

[0040] Step 103: Based on the topology change type of the target node, dynamically reconstruct the first topology structure in multiple logical layers to obtain the second topology structure. The logical layers include the micro layer, the meso layer, and the macro layer.

[0041] Among them, the topology change type indicates the specific form of change of the target node, including node failure, node replacement or node addition, etc.; the micro layer refers to the logical level with a single intelligent component as the basic unit; the meso layer refers to the logical level with the circuit in the distribution cabinet as the unit; the macro layer refers to the logical level with the entire power distribution system as the object; the second topology structure represents the reconstructed power distribution system network model.

[0042] Specifically, when a topology change is detected at a target node, the system needs to adjust the power distribution network structure. This step first identifies the type of topology change at the target node, such as node failure due to equipment malfunction, parameter changes caused by equipment replacement, or node expansion due to the addition of new equipment. Then, based on the type of change, corresponding structural reconstruction is performed at different logical levels. At the micro level, the connection relationships and operating parameters of individual intelligent components are adjusted; at the meso level, the loop structure and load distribution within the distribution cabinet are reconstructed; at the macro level, the power supply path and load distribution of the entire power distribution system are optimized. Through this multi-level dynamic reconstruction process, the system generates a second topology structure that reflects the current actual operating state. This structure contains complete equipment connection relationships, loop configurations, and system-level topology information.

[0043] In some embodiments, dynamic topology reconfiguration can be achieved in several ways: Optionally, firstly, the changing characteristics of the target nodes are analyzed and the affected logical levels are determined; then, at the micro level, the connection relationships between devices are updated by modifying the component connection tables; next, at the meso level, the load distribution of the loops is recalculated and the protection settings are adjusted; finally, at the macro level, the power supply scheme of the system is optimized and the global topology model is updated. Optionally, a multi-level topology reconfiguration model is constructed based on graph theory algorithms; then, an independent reconfiguration rule set is established for each logical layer; finally, the reconfiguration operations of each layer are executed sequentially in a bottom-up manner, and a data synchronization mechanism is established between layers. It is understood that other algorithms and strategies can also be used to achieve multi-level topology reconfiguration, which are not limited here.

[0044] Based on the above embodiments, as an optional embodiment, step 103: dynamically reconstructing the first topology structure in multiple logical layers according to the topology change type of the target node, this step may further include the following steps: Step 301: Construct a logical layer system consisting of a micro layer with intelligent components as nodes, a meso layer with circuits within the power distribution cabinet as units, and a macro layer with the power distribution system as a whole.

[0045] Among them, the logic layer architecture represents a hierarchical structural model of the power distribution system divided according to different granularities; the micro-layer node refers to the smallest topological unit representing a single intelligent component; the circuit in the distribution cabinet refers to the power supply line composed of multiple intelligent components; the overall power distribution system includes the complete network structure of all distribution cabinets and lines.

[0046] Specifically, this step constructs a three-layer logical structure model of the power distribution system. At the micro level, each intelligent component (such as circuit breakers, switches, and meters) is treated as an independent node, recording its operating parameters, connection ports, and communication addresses. At the meso level, using the distribution cabinet as the boundary, the intelligent components within the cabinet are combined into power supply circuit units, recording the circuit's topology, protection configuration, and load distribution. At the macro level, each distribution cabinet is treated as a node, with feeder connections as edges, constructing the network structure diagram of the entire power distribution system, recording the system-level power supply topology, load partitioning, and interconnections. This hierarchical modeling approach achieves a multi-scale description of the power distribution system from components to the system as a whole, providing a structured processing framework for subsequent topology reconfiguration.

[0047] Step 302: In the logic layer system, when the intelligent components corresponding to the target node change, the connection relationship between the intelligent components and the upstream input nodes and downstream output nodes is adjusted at the micro level, while keeping the meso and macro levels unchanged.

[0048] The upstream input node refers to the power supply-side equipment that supplies power to the target intelligent component; the downstream output node refers to the load-side equipment that supplies power to the target intelligent component; the connection relationship includes the physical connection method and electrical parameter configuration.

[0049] Specifically, this step handles topology changes at the smart component level. When a fault, replacement, or parameter change is detected in a smart component, the upstream and downstream connected devices of that component are first located at the micro level. For upstream devices, the connection status and output parameters of their output ports are updated; for downstream devices, the connection configuration and protection settings of their input ports are adjusted. For example, when a 400A circuit breaker is replaced with a new 630A device, its connection parameters with the upstream bus and downstream load need to be modified. These adjustments are limited to the device connection relationships at the micro level and do not affect the overall structure of the distribution cabinet circuit (meso level) or the system-level power supply topology (macro level). Through this localized adjustment method, precise responses to changes in individual devices are achieved while maintaining the stable operation of other parts of the system.

[0050] Step 303: When any circuit in the distribution cabinet corresponding to the target node fails, delete the series or parallel logic of the intelligent components in the faulty circuit at the meso-level, and simultaneously update the external connection status of the distribution cabinet in the macro-level.

[0051] Among them, circuit fault indicates abnormal conditions such as open circuit, short circuit or overload in the power supply line inside the distribution cabinet; series logic refers to the power supply path formed by connecting the first and last intelligent components; parallel logic refers to the power supply mode of multiple intelligent components connected in parallel; external connection status refers to the electrical connection relationship between the distribution cabinet and other equipment.

[0052] Specifically, this step handles circuit-level faults within the distribution cabinet. When a fault occurs in a circuit within the distribution cabinet, the system first identifies all intelligent components contained in the faulty circuit. At the meso-level, the series or parallel connections between these components are deleted, and the faulty circuit is disconnected from the distribution cabinet busbar. For example, when a short-circuit fault occurs in a three-phase circuit, all circuit breakers and switches within that circuit need to be disconnected, and the busbar load distribution needs to be updated. Simultaneously, the fault information is transmitted upwards to the macro-level, modifying the distribution cabinet's external connection parameters, including available capacity, load rate, and protection range. This cross-layer linkage ensures timely synchronization of fault information across different levels, preventing localized faults from escalating to the entire system.

[0053] Step 304: When the feeder between the distribution cabinets corresponding to the target node fails, the feeder connection logic is reconstructed at the macro level and synchronized down to the meso level, and the circuit status of the distribution cabinet is marked as offline.

[0054] Among them, the feeder between distribution cabinets refers to the power supply line connecting different distribution cabinets; the feeder connection logic represents the power supply relationship and communication link between distribution cabinets; the circuit status refers to the working status of each power supply circuit in the distribution cabinet.

[0055] Specifically, this step handles feeder faults between distribution cabinets. When a feeder connecting two distribution cabinets fails, the affected power supply path is first identified at the macro level. The system-level power supply scheme is then redesigned, including adjusting feeder connections, reallocating loads, and updating protection configurations. These changes are propagated down to the meso level via inter-layer data synchronization mechanisms, updating the internal circuit configurations of the relevant distribution cabinets. For distribution cabinets that have lost power, all their circuits are marked as offline, and an emergency power supply scheme is triggered. For example, when a main feeder fails, the system will activate a backup feeder or ring network power supply scheme and adjust the operating parameters of each distribution cabinet accordingly. Through this top-down collaborative processing, rapid response and orderly recovery from system-level faults are achieved.

[0056] Step 104: Perform electrical characteristic verification on any closed loop in the second topology.

[0057] In this context, a closed loop refers to the part of the circuit that forms a complete current path in the power distribution system, including the power source, load, and connecting wires; electrical characteristic verification refers to the process of verifying the voltage and current characteristics in the circuit; a line segment refers to the part of the conductor between two nodes in a closed loop; a voltage drop vector represents the magnitude and direction of the voltage drop on the line; a branch current vector represents the magnitude and direction of the current flowing through the line; and a pass verification indicates that the circuit meets the basic requirements of Kirchhoff's laws.

[0058] Specifically, this step is performed after the topology reconstruction to obtain the second topology structure, and is used to verify whether the reconstructed topology structure meets the electrical characteristic requirements. First, all closed loops and their node information are extracted from the second topology structure. Then, each closed loop is verified in two dimensions: first, the voltage drop vector is calculated based on the current values ​​of each line segment, and the sum of the voltage drop vectors in the loop is verified to be zero, ensuring that Kirchhoff's voltage law is satisfied; second, for each node in the loop, all branch current vectors flowing into and out of that node are collected, and their sum is verified to be zero, ensuring that Kirchhoff's current law is satisfied. Only when both of these verifications pass can the electrical characteristic verification of the closed loop be considered successful.

[0059] In some embodiments, the electrical characteristics verification of closed loops can be achieved in several ways: Optionally, a depth-first search algorithm can be used to identify closed loops. For each line segment, a smart meter is used to collect real-time current values. The current value is multiplied by the line impedance to obtain the voltage drop. The direction of the voltage drop vector is determined by considering the current direction. All voltage drop vectors are accumulated to verify whether their sum is zero. At the same time, a current balance equation is established at each node, with the inflow current set to positive and the outflow current set to negative, to verify whether the equation holds true. Optionally, a network connectivity graph can be constructed based on graph theory methods. A minimum loop search algorithm is used to find all closed loops. The active and reactive power of each branch is collected using a power analyzer. The voltage drop and current values ​​in complex form are calculated to verify whether the node current conservation and loop voltage balance laws are satisfied. It is understood that other electrical characteristic verification methods can also be used to verify closed loops, which are not limited here.

[0060] Based on the above embodiments, as an optional embodiment, step 104, which involves verifying the electrical characteristics of any closed loop in the second topology, may further include the following steps: Step 401: Extract any closed loop and the nodes in the closed loop from the second topology.

[0061] Here, a closed loop represents a circuit part that forms a complete path in a power distribution system; a node represents a connection point in a power distribution system, including component terminals, line crossings, etc.; and extraction operation refers to the process of identifying and obtaining specific circuit elements from the topology.

[0062] Specifically, the extraction process of closed loops and nodes in the second topology includes the following: First, a depth-first search algorithm is used to traverse the connection relationships of the second topology. Starting from the starting node, the search proceeds forward along the connection relationships until it returns to the starting node, thus identifying a complete closed loop. During the search, all nodes and connecting lines traversed are recorded, and this information is saved to the closed loop data structure. For example, a simple distribution circuit may contain circuit breaker node A, busbar node B, load switch node C, etc., and a closed loop ABCA can be obtained through the search. Simultaneously, the information of each node in the loop is extracted and saved individually, including node type, location coordinates, connection relationships, and other attributes. In this way, the complete closed loop topology and node distribution can be obtained.

[0063] Step 402: For a closed loop, collect the current flowing through each line segment, calculate the voltage drop vector of each line segment based on the current flowing through each line segment, and verify whether the sum of the voltage drop vectors is zero.

[0064] Among them, the current flowing through the line represents the actual current value passing through the line; the voltage drop vector represents the magnitude and direction of the voltage drop on the line; and the line segment represents the conductor portion between two adjacent nodes in a closed loop.

[0065] Specifically, the verification process for the electrical characteristics of each line segment in the closed loop includes the following steps: First, an intelligent current detection device is installed in each line segment to collect real-time current values. High-precision current transformers are used for measurement, with a sampling frequency of no less than 1kHz to ensure data accuracy. Then, the voltage drop vector is calculated based on the acquired current values. In the calculation, the resistance of the line segment is multiplied by the current flowing through it to obtain the voltage drop magnitude, and the direction of the voltage drop vector is determined according to the current flow direction. Taking line segment AB as an example, if its resistance is 0.1Ω and the current flowing through it is 100A, then the voltage drop is 10V; if the current flows from A to B, then the voltage drop direction is from A to B. After performing similar calculations for all line segments in the closed loop, all voltage drop vectors are vector-added. According to Kirchhoff's voltage law, the sum of the voltage drop vectors in the closed loop should be zero. By verifying this characteristic, it can be determined whether the reconstructed topology meets the basic electrical characteristic requirements.

[0066] Step 403: For each node, collect the current vectors of each branch flowing into and out of the node, and verify whether the sum of the current vectors of each branch is zero.

[0067] Among them, the branch current vector represents the magnitude and direction of the current flowing through each connected branch of the node; the inflow current represents the current entering the node; the outflow current represents the current leaving the node; and the sum of the current vectors represents the algebraic sum of all currents at the node.

[0068] Specifically, the verification process for the current characteristics of a node is as follows: First, current sensors are installed on each connecting branch of the node to collect real-time current data. High-precision Hall effect current sensors are used, with a sampling rate set above 1kHz to ensure the timeliness and accuracy of the collected data. For each node, the directionality of the current is clearly defined, with the current flowing into the node being positive and the current flowing out of the node being negative. For example, if a distribution cabinet connection point has three branches: branch 1 has an inflow current of 50A, branch 2 has an inflow current of 30A, and branch 3 has an outflow current of 80A, these can be represented as +50A, +30A, and -80A, respectively. Then, the currents of all branches are vector-superimposed, and the algebraic sum of all inflow and outflow currents is added together. According to Kirchhoff's Current Law, the algebraic sum of the currents at the node should be zero, reflecting the physical essence of charge conservation. This method verifies the current balance state at the node.

[0069] Step 404: If the sum of the voltage drop vectors and the sum of the branch current vectors are zero, the electrical characteristic verification is considered passed.

[0070] Among them, passing the electrical characteristic verification indicates that the topology satisfies the basic laws of circuits; the total voltage drop vector sum represents the vector sum of all voltage drops in the closed loop; and the total branch current vector sum represents the vector sum of all currents at the node.

[0071] Specifically, the process for determining whether the electrical characteristic verification passes is as follows: First, check the voltage characteristics of the closed loop by performing precise vector addition on the previously calculated voltage drop vectors for each line segment. The calculation considers both the magnitude and direction of the voltage drop, using complex numbers to represent the voltage drop vectors, and summing the real and imaginary parts of all voltage drops separately. For example, if three line segments have voltage drops of 5∠30°V, 8∠150°V, and 10∠270°V respectively, the sum of these complex numbers must be calculated to ensure it is zero. Simultaneously, check the current characteristics of each node by summing all current vectors flowing into and out of the node, again using complex numbers. Only when the sum of the voltage drop vectors in the closed loop is zero and the sum of the branch current vectors at each node is zero can the topology be confirmed to meet the electrical characteristic requirements, and the verification is considered passed. This dual verification ensures that the reconstructed topology satisfies both voltage balance and current conservation.

[0072] Step 105: When the electrical characteristics verification is passed, based on the correlation in the second topology, determine the distribution cabinet connection relationship from the target node upstream and the component correlation link from the downstream, determine the topology influence range of the distribution cabinet connection relationship and the component correlation link, and calculate the risk level by combining the historical operating parameters of the intelligent components within the topology influence range.

[0073] Among them, the relationship refers to the electrical and logical connection between equipment in the power distribution system; the cabinet connection refers to the power supply path and feeder connection between cabinets; the component association link refers to the series and parallel connection sequence between intelligent components; the topology impact range refers to the set of all equipment and lines affected by the change of the target node; the historical operating parameters refer to the current, temperature, status and other data recorded by intelligent components during past operation; and the risk level is used to indicate the degree of impact of topology changes on the safety of system operation.

[0074] Specifically, once the second topology meets the electrical characteristic verification, i.e., Kirchhoff's laws verification, the system needs to assess the impact range and risk level of the topology change. This step first starts from the target node, tracing upstream along the power supply direction to the power source side to determine the connection path of the affected distribution cabinets; simultaneously, it extends downstream to the load side to identify the associated smart component links. Within the determined impact range, historical operating data for each device is extracted, including load rate, temperature change trends, and number of failures. Risk indicators, such as equipment overload probability and fault propagation likelihood, are calculated based on these parameters. Through comprehensive analysis of the impact range and historical data, the system operational risks brought about by the topology change are quantitatively assessed.

[0075] In some embodiments, the assessment of topological impact range and risk level can be achieved in several ways: Optionally, a breadth-first search algorithm is first used to expand the search for affected devices from the target node upstream and downstream; then, an impact propagation model is established based on the electrical coupling relationship between devices; next, historical operating data of devices within the impact range is extracted and standardized; finally, a weighted scoring method is used to calculate the comprehensive risk level. Optionally, a risk assessment model based on a graph neural network is constructed; then, historical device data is converted into feature vectors in time series and input into the model; next, the impact relationship between devices is calculated through node embedding; finally, a risk level threshold is determined based on the model output. It is understood that other data analysis and machine learning methods can also be used to assess topological impact and risk, and this is not limited here.

[0076] Based on the above embodiments, as an optional embodiment, in step 105: determining the distribution cabinet connection relationship along the upstream direction and the component association link along the downstream direction based on the association relationship in the second topology, and determining the topological influence range of the distribution cabinet connection relationship and the component association link, this step may further include the following steps: Step 501: Obtain the inlet circuit breaker identifier of the distribution cabinet where the target node is located, and trace upwards level by level based on the inlet circuit breaker identifier until the main distribution cabinet to obtain the distribution cabinet connection relationship.

[0077] Among them, the entrance circuit breaker identifier represents the unique identification code of the main incoming circuit breaker of the distribution cabinet; the main distribution cabinet refers to the highest-level power distribution equipment in the power supply system; the distribution cabinet connection relationship refers to the power supply path and hierarchical relationship between distribution cabinets.

[0078] Specifically, it is necessary to determine the complete power supply path from the target node's distribution cabinet to the main distribution cabinet. The system first locates the inlet circuit breaker of the distribution cabinet where the target node is located and obtains the circuit breaker's identification information. Then, based on the power distribution system's topology database, it queries the upstream distribution cabinet of that inlet circuit breaker. This process is repeated, tracing upwards level by level to the inlet circuit breaker of each distribution cabinet until the main distribution cabinet is found. During this tracing process, the connection method between each level of distribution cabinet is recorded, including feeder number, circuit breaker capacity, and protection configuration information, ultimately forming a complete power supply link from the main distribution cabinet to the target distribution cabinet.

[0079] In some embodiments, the tracing of distribution cabinet connection relationships can be achieved in several ways: Optionally, firstly, a distribution cabinet index based on a hash table is established, using the entry circuit breaker identifier as the key; then, the corresponding parent distribution cabinet information is queried using the circuit breaker identifier; next, the queried distribution cabinet information is pushed onto a stack structure; finally, the complete connection path is obtained by traversing from the bottom to the top of the stack. Optionally, a hierarchical data structure of the power distribution system is constructed; then, the position of the target distribution cabinet in the data structure is located based on the entry circuit breaker identifier; next, the parent node is searched upwards recursively; finally, the search path is converted into a sequence of connection relationships. It is understood that other data structures and query algorithms can also be used to achieve the tracing of distribution cabinet connection relationships, which is not limited here.

[0080] Step 502: Based on the output port identifier of the target node, determine the downstream smart components connected to the target node, recursively obtain the load branches of the downstream smart components, and generate the component association links.

[0081] Among them, the output port identifier represents the unique code of the external power supply interface of the intelligent component; the downstream intelligent component refers to the device directly powered by the target node; the load branch refers to the power supply line from the intelligent component to the final power-consuming equipment; and the component association link represents the connection order and hierarchical relationship between intelligent components.

[0082] Specifically, a complete power supply path needs to be established from the target node to the final load. The system first reads the output port information of the target node and searches for directly connected downstream devices in the topology database based on the port identifier. For each downstream device, it continues to query the devices connected to its output port, recursively extending downwards until the terminal load is reached. During the search process, the type, capacity, and protection parameters of each smart component, as well as the connection methods and electrical parameters between devices, are recorded, ultimately generating a hierarchical link structure containing all related devices.

[0083] In some embodiments, the generation of component association links can be achieved in several ways: Optionally, firstly, a device connection graph based on an adjacency list is constructed; then, a depth-first search is performed starting from the target node; next, the technical parameters and connection information of each searched device are recorded; finally, the search results are organized into a tree-structured association link. Optionally, a relational database of device ports is established; then, downstream connected devices are recursively searched using SQL queries; next, the query results are converted into a hierarchical JSON data structure; finally, a complete association link is generated based on the data structure. It is understood that other graph algorithms and data processing methods can also be used to generate component association links, which are not limited here.

[0084] Based on the above embodiments, as an optional embodiment, in step 502: determining the downstream intelligent components connected to the target node based on the output port identifier of the target node, recursively obtaining the load branches of the downstream intelligent components, and generating component association links, this step may further include the following steps: Step 512: Traverse the output port identifiers of the target node, obtain the downstream smart components corresponding to the output port identifiers; extract the load type and load capacity of the downstream smart components, and establish the corresponding component cascade table.

[0085] Among them, the output port identifier indicates the interface number of the intelligent component to supply power to the downstream; the downstream intelligent component refers to the device that directly receives power from the target node; the load type indicates the functional category of the electrical equipment; the load capacity refers to the rated power or current value of the device; and the component cascade table is a data structure that records the power supply relationship between devices.

[0086] Specifically, this step constructs a connection relationship table from the target node to direct downstream devices. The system sequentially reads all output port identifiers of the target node and queries the topology database for information on the downstream devices connected to each port. For each downstream smart component, its technical parameters are extracted, including equipment type (such as circuit breaker, distribution box, transformer, etc.), rated current, rated power, and other load characteristic data. This information is organized into a standardized data structure to establish a component cascade table. This table contains fields such as port identifier, device identifier, and load parameters, used to record the direct connection relationships and technical characteristics between devices.

[0087] Step 522: Based on the component cascade table, perform recursive traversal of downstream smart components until the terminal load device is reached; sort the downstream smart components recursively traversed according to the power supply order to obtain multiple load branches, and map each load branch to a preset tree structure to obtain the component association link.

[0088] Among them, recursive traversal refers to the process of repeatedly searching for devices downwards; terminal load device refers to the power-consuming device at the end of the power supply link; load branch refers to the complete power supply path from the target node to the terminal load; tree structure refers to a data organization form with hierarchical relationships; component association link represents the power supply sequence and hierarchical relationship between devices.

[0089] Specifically, this step constructs the complete power supply link structure recursively. Based on a component cascade table, the system starts with each directly downstream device and repeatedly executes the downstream device search process. For each found device, it continues to query the next-level device connected to its output port until a terminal load that no longer supplies power is found. All found devices are sorted according to their power supply order, forming a complete power supply path from the source to the load. Then, a tree-like data structure is created, with the target node as the root node. Devices at each level establish parent-child relationships according to their power supply order, ultimately generating a complete component association link.

[0090] Step 503: Based on the connection relationship of the distribution cabinet and the associated links of the components, mark the upstream impact of the target node topology change on the distribution cabinet and the downstream impact on the smart components.

[0091] Among them, upstream affected distribution cabinets refer to power distribution equipment affected by topology changes on the power supply path from the target node to the main distribution cabinet; downstream affected smart components refer to equipment affected by topology changes on the power supply path from the target node to the terminal load; topology changes refer to changes in the connection relationship, operating status, or technical parameters of the target node.

[0092] Specifically, the affected devices of the topology change need to be determined based on the connection relationships obtained in the previous steps. The system first analyzes the type of topology change of the target node, including changes in connection relationships, adjustments to operating parameters, or state switching. In the upstream direction, the system traverses the connection relationships of distribution cabinets to identify those distribution cabinets that have electrical or protection coordination relationships with the target node, and marks these distribution cabinets as upstream affected devices. In the downstream direction, the system traverses the component association links to identify smart components whose load distribution or protection configuration has been changed due to the change in the target node, and marks these devices as downstream affected devices. Through this bidirectional analysis, the complete scope of the topology change's impact is fully identified.

[0093] In some embodiments, the marking of affected devices can be achieved in several ways: Optionally, firstly, an influence attenuation model based on electrical distance is established; then, the electrical distance from each device to the target node is calculated; next, the degree of influence is determined based on the distance value and the coupling relationship between devices; finally, devices whose influence exceeds a threshold are marked as affected devices. Optionally, an inter-device correlation matrix is ​​constructed; then, the influence transmission coefficient between devices is calculated through matrix operations; next, the diffusion range of the influence is determined based on the influence coefficient; finally, devices within the diffusion range are marked as affected devices. It is understood that other mathematical models and analysis methods can also be used to mark affected devices, and this is not limited here.

[0094] Step 504: Determine the coverage area of ​​the upstream affecting the distribution cabinet and the downstream affecting the smart components as the topology influence range.

[0095] The coverage area refers to the set of equipment consisting of upstream power distribution cabinets and downstream smart components; the topology influence range represents the spatial range of all equipment and lines affected by the topology change of the target node.

[0096] Specifically, the marked affected devices need to be integrated into a complete impact range. The system first collects all upstream distribution cabinets marked as affected, determining their physical distribution and electrical connection boundaries. Simultaneously, it organizes all downstream smart components marked as affected, determining their installation locations and power supply ranges. The distribution ranges of the upstream distribution cabinets and the power supply ranges of the downstream smart components are then merged to obtain a complete spatial range encompassing all devices and lines affected by the target node's topology change.

[0097] In some embodiments, the scope of influence can be determined in several ways: Optionally, firstly, a device distribution map based on a geographic information system is constructed; then, the affected devices are marked on the distribution map; next, the spatial boundaries of these devices are calculated using a convex hull algorithm; finally, the area within the boundary is determined as the scope of influence. Optionally, a hierarchical regional division model of the devices is established; then, the affected devices are mapped to corresponding regional units; next, the regional units containing the affected devices are merged; finally, the complete boundary of the scope of influence is generated. It is understood that other spatial analysis and regional division methods can also be used to determine the scope of influence, and this is not limited here.

[0098] Based on the above embodiments, as an optional embodiment, step 105, which calculates the risk level by combining the historical operating parameters of intelligent components within the topological influence range, may further include the following steps: Step 505: Collect historical operating parameters of each smart component within the topology's influence range over a preset time period. The historical operating parameters include the number of overloads, load type, rated power, and real-time current. Determine the importance weight of each smart component within the topology's influence range based on the load type.

[0099] Among them, historical operating parameters represent various operating data recorded by intelligent components during past operation; overload count refers to the cumulative number of times the equipment has exceeded its rated load; load type represents the functional classification of electrical equipment, such as fire protection, emergency lighting, important production equipment, etc.; rated power refers to the nominal power value of the equipment during normal operation; real-time current represents the load current of the equipment currently in operation; importance weight represents the relative importance of the equipment in the system, usually represented by a value between 0 and 1.

[0100] Specifically, this step is executed after the topology's influence range is determined, and it is used to collect equipment operating data and determine weights. The system first sets a data collection time window, such as the last 30 or 90 days, and extracts the operating records of all intelligent components within the influence range from the database during that period. For each device, the number of overload operations is counted, the device's load type information is recorded, rated power parameters are extracted, and the current real-time current value is collected. Then, according to a predefined load type importance classification standard, weight coefficients are assigned to different types of loads. For example, fire-fighting equipment has a weight of 1.0, emergency lighting 0.9, important production equipment 0.8, and general lighting 0.6, etc. In this way, a quantitative index system reflecting the importance of equipment is established.

[0101] Step 506: Calculate the ratio of the real-time current to the rated current of each smart component to obtain the current load rate.

[0102] Among them, real-time current represents the current value of the intelligent component at the current operating moment, in amperes (A); rated current refers to the maximum current value that the equipment is allowed to continuously pass under normal operating conditions, in amperes (A); current load factor represents the percentage of actual operating current to rated current, expressed in decimal form; intelligent components refer to power distribution equipment with electrical parameter measurement and communication functions.

[0103] Specifically, this step is executed after acquiring the equipment's operating parameters and is used to calculate the equipment's load level. The system first reads the current value of each smart component from the real-time database, in amperes. Simultaneously, it extracts the rated current parameters of each device from the equipment ledger database. For each smart component, its real-time current value is divided by its rated current value, yielding a ratio between 0 and 1, i.e., the current load factor. For example, if a circuit breaker has a rated current of 100A and a current real-time current of 75A, its current load factor is 0.75. This load factor reflects the actual load level of the equipment and is an important indicator for evaluating the equipment's operating status.

[0104] Step 507: Perform a weighted calculation based on the importance weight, current load rate, and overload count of each smart component to obtain the risk level score of the corresponding smart component; for each smart component, classify the corresponding risk level according to the numerical range of the risk level score.

[0105] Among them, importance weight represents the relative importance of the equipment in the system, with a value range of 0 to 1; current load rate represents the ratio of real-time current to rated current; overload count refers to the cumulative number of times the equipment operates beyond its rated load; risk level score refers to the risk quantification index obtained through weighted calculation; risk level represents the classification result of the equipment's safe operating status, usually divided into three levels: low risk, medium risk, and high risk.

[0106] Specifically, this step involves performing a risk assessment calculation after obtaining various equipment parameters. The system uses a weighted summation method to calculate the risk level score, with the formula: Risk Level Score = W1 × Importance Weight + W2 × Current Load Rate + W3 × (Number of Overloads / Baseline Value), where W1, W2, and W3 are the weight coefficients of each indicator, and the baseline value is a standardization factor. For example, if a circuit breaker has an importance weight of 0.9, a current load rate of 0.75, 5 overloads, a baseline value of 10, and weight coefficients of 0.4, 0.3, and 0.3 respectively, then its risk level score is: 0.4 × 0.9 + 0.3 × 0.75 + 0.3 × (5 / 10) = 0.36 + 0.225 + 0.15 = 0.735. Then, the risk level is divided according to a preset score range: a score less than 0.6 is low risk, 0.6 to 0.8 is medium risk, and a score greater than 0.8 is high risk.

[0107] In some embodiments, risk level assessment can be achieved in several ways: Optionally, firstly, an assessment model based on fuzzy mathematics is constructed; then, various indicators are fuzzily quantified; next, the risk membership degree is calculated through fuzzy comprehensive evaluation; finally, the risk level is determined according to the principle of maximum membership degree. Optionally, a risk assessment system based on the analytic hierarchy process (AHP) is established; then, the indicator weights are determined through expert scoring; next, the standardized scores of each indicator are calculated; finally, a weighted summation is used to obtain the comprehensive risk value. It is understood that other mathematical models and assessment methods can also be used to assess risk levels, and this is not limited here.

[0108] Step 106: Generate corresponding temporary response strategies based on the risk level; generate a risk assessment report for the power distribution system based on the topology impact range, risk level, and temporary response strategies.

[0109] Among them, the temporary response strategy refers to the short-term prevention and control measures formulated in response to the current risk situation; the risk level refers to the quantitative assessment result of the system's operational security; the topology impact range refers to the set of equipment and lines affected by the changes in the system structure; and the risk assessment report is a comprehensive analysis document of the system's security status, including risk description, impact range, and countermeasures.

[0110] Specifically, after calculating the risk level, corresponding prevention and control measures need to be formulated and a complete assessment report needs to be generated. This step first selects matching prevention and control measures from a pre-set response strategy library based on the calculated risk level, including specific measures such as adjusting equipment monitoring frequency, modifying protection settings, and optimizing operating modes. Then, a comprehensive analysis of the equipment status within the topology's impact range, the quantitative indicators of the risk level, and the formulated response strategies is conducted to generate a structured risk assessment report. The report details the risk source identification results, impact range analysis, risk level determination criteria, response measures, and implementation recommendations, providing decision support for operations and maintenance personnel.

[0111] In some embodiments, risk assessment reports can be generated in several ways: Optionally, firstly, a strategy matching rule base based on risk level is constructed; then, a set of corresponding response strategies is extracted according to the current risk level; next, the applicability of the strategies is assessed in conjunction with the topological impact range; finally, an assessment report document is generated according to a standardized template format. Optionally, an expert knowledge base system for risk assessment is established; then, the risk level and impact range are used as input parameters; next, targeted response suggestions are generated through knowledge reasoning; finally, the assessment process and results are integrated into a complete report. It is understood that other decision support and document generation methods can also be used to produce risk assessment reports, and this is not limited here.

[0112] Reference Figure 2This application provides a risk assessment system for dynamic reconfiguration of power distribution topology. The system includes: a data acquisition module, a dynamic reconfiguration module, a risk level determination module, and a risk assessment module, wherein: The data acquisition module is used to acquire real-time status data of intelligent components in the power distribution system and construct the first topology of the power distribution system based on the real-time status data. The dynamic reconstruction module is used to send detection signals to each topology node in the first topology structure. When no feedback signal is received from any topology node for a preset number of consecutive times, the topology node is identified as the target node where the topology change has occurred. According to the type of topology change of the target node, the first topology structure is dynamically reconstructed in multiple logical layers to obtain the second topology structure. The logical layers include the micro layer, the meso layer, and the macro layer. The risk level determination module is used to verify the electrical characteristics of any closed loop in the second topology. When the electrical characteristics verification is passed, based on the correlation in the second topology, the module determines the distribution cabinet connection relationship from the target node upstream and the component correlation link from the downstream direction, determines the topological influence range of the distribution cabinet connection relationship and the component correlation link, and calculates the risk level by combining the historical operating parameters of the intelligent components within the topological influence range. The risk assessment module is used to generate corresponding temporary response strategies based on the risk level; and to generate a risk assessment report for the power distribution system based on the topological impact range, risk level, and temporary response strategies.

[0113] Based on the above embodiments, the data acquisition module is also used to acquire static and dynamic data stored in the built-in topology protocol chip of each smart component. The static data includes the component's unique identifier, rated current, and interface type, while the dynamic data includes online status, real-time operating current, and temperature. The module identifies the target smart component in a powered-on state based on its online status and acquires its neighboring smart components based on their unique identifier. For neighboring smart components, when the matching degree between rated current and interface type is greater than a preset threshold, the neighboring smart component is identified as the target neighboring smart component. Using the weighted values ​​of real-time operating current and temperature as edge weights, a series-parallel connection relationship is established between the target neighboring smart components to generate a first topology structure.

[0114] Based on the above embodiments, the dynamic reconfiguration module is also used to construct a logic layer system with intelligent components as nodes, a meso-level layer with circuits within the distribution cabinet as units, and a macro-level layer with the power distribution system as a whole. In the logic layer system, when the intelligent components corresponding to the target node change, the connection relationship between the intelligent components and the upstream input nodes and downstream output nodes is adjusted at the micro-level, while keeping the meso-level and macro-level unchanged. When any circuit within the distribution cabinet corresponding to the target node fails, the series or parallel logic of the intelligent components in the faulty circuit is deleted at the meso-level, and the external connection status of the distribution cabinet in the macro-level is updated synchronously. When the feeder between distribution cabinets corresponding to the target node fails, the feeder connection logic is reconstructed at the macro-level and synchronized down to the meso-level, and the circuit status of the distribution cabinet is marked as offline.

[0115] Based on the above embodiments, the risk level determination module is also used to extract any closed loop and nodes in the second topology; for the closed loop, the current flowing through each line segment is collected, and the voltage drop vector of each line segment is calculated based on the current flowing through each line segment, and the sum of each voltage drop vector is verified to be zero; for the node, the current vectors of each branch flowing into and out of the node are collected, and the sum of each branch current vector is verified to be zero; if the sum of each voltage drop vector and the sum of each branch current vector are zero, the electrical characteristic verification is determined to be passed.

[0116] Based on the above embodiments, the risk level determination module is also used to obtain the inlet circuit breaker identifier of the distribution cabinet where the target node is located, and trace upwards step by step to the main distribution cabinet based on the inlet circuit breaker identifier to obtain the distribution cabinet connection relationship; based on the output port identifier of the target node, determine the downstream intelligent components connected to the target node, recursively obtain the load branches of the downstream intelligent components, and generate component association links; according to the distribution cabinet connection relationship and component association links, mark the upstream distribution cabinets and downstream intelligent components affected by the topology change of the target node; and determine the coverage area of ​​the upstream distribution cabinets and downstream intelligent components affected by the topology change as the topology influence range.

[0117] Based on the above embodiments, the risk level determination module is also used to traverse the output port identifiers of the target node, obtain the downstream intelligent components corresponding to the output port identifiers; extract the load type and load capacity of the downstream intelligent components, and establish the corresponding component cascading table; based on the component cascading table, perform recursive traversal on the downstream intelligent components until the terminal load device is reached; sort the downstream intelligent components recursively traversed according to the power supply order to obtain multiple load branches, and map each load branch to a preset tree structure to obtain the component association link.

[0118] Based on the above embodiments, the risk level determination module is also used to collect historical operating parameters of each smart component within the topology influence range within a preset time period. The historical operating parameters include overload count, load type, rated power, and real-time current. The module determines the importance weight of each smart component within the topology influence range according to the load type. It calculates the ratio of the real-time current to the rated current of each smart component to obtain the current load rate. Based on the importance weight, current load rate, and overload count of each smart component, a weighted calculation is performed to obtain the risk level score of the corresponding smart component. For each smart component, the module classifies the corresponding risk level according to the numerical range of the risk level score.

[0119] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0120] This application also discloses an electronic device. (See reference...) Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0121] The communication bus 302 is used to enable communication between these components.

[0122] The user interface 303 may include a display interface and a camera interface. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0123] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0124] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface graphics, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0125] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. (Refer to...) Figure 3 The memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a risk assessment method of dynamic reconfiguration of power distribution topology.

[0126] exist Figure 3In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and acquire user input data; while the processor 301 can be used to call an application program stored in the memory 305 for a risk assessment method of dynamic reconfiguration of power distribution topology. When executed by one or more processors 301, the electronic device 300 performs one or more methods as described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0127] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0128] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0129] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0130] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0131] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0132] The above are merely exemplary embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and practical disclosure.

[0133] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only.

Claims

1. A risk assessment method for dynamic reconfiguration of power distribution topology, characterized in that, include: Acquire real-time status data of intelligent components in the power distribution system, and construct a first topology of the power distribution system based on the real-time status data; Send detection signals to each topology node in the first topology structure. If no feedback signal is received from any topology node for a preset number of consecutive times, the topology node is identified as the target node where the topology change has occurred. Based on the topology change type of the target node, the first topology is dynamically reconstructed in multiple logical layers to obtain a second topology. The logical layers include a micro layer, a meso layer, and a macro layer. Perform electrical characteristic verification on any closed loop in the second topology; When the electrical characteristics verification is passed, based on the association relationship in the second topology, the distribution cabinet connection relationship is determined from the target node in the upstream direction, the component association link is determined in the downstream direction, the topological influence range of the distribution cabinet connection relationship and the component association link is determined, and the risk level is calculated in combination with the historical operating parameters of the intelligent components within the topological influence range; Generate corresponding temporary response strategies based on the risk level; Based on the topological impact range, the risk level, and the temporary response strategy, a risk assessment report for the power distribution system is generated.

2. The risk assessment method for dynamic reconfiguration of distribution topology according to claim 1, characterized in that, The construction of the first topology of the power distribution system based on the real-time status data includes: Obtain static and dynamic data stored in the built-in topology protocol chip of each of the aforementioned smart components. The static data includes the component's unique identifier, rated current, and interface type. The dynamic data includes online status, real-time operating current, and temperature. The target intelligent component in the power-on state is identified based on the online status, and the adjacent intelligent components of the target intelligent component are obtained based on the unique identifier of the component. For the adjacent smart components, when the matching degree between the rated current and the interface type is greater than a preset threshold, the adjacent smart components are identified as target adjacent smart components. Using the weighted values ​​of the real-time operating current and the temperature as edge weights, a series-parallel connection relationship is established between the target adjacent smart components to generate a first topology.

3. The risk assessment method for dynamic reconfiguration of distribution topology according to claim 1, characterized in that, The step of dynamically reconstructing the first topology structure across multiple logical layers based on the topology change type of the target node includes: A logical layer system is constructed, consisting of a micro-layer with intelligent components as nodes, a meso-layer with circuits within the power distribution cabinet as units, and a macro-layer with the power distribution system as a whole. In the logic layer system, when the intelligent component corresponding to the target node changes, the connection relationship between the intelligent component and the upstream input node and the downstream output node is adjusted at the micro level, while the meso level and macro level remain unchanged. When any circuit in the power distribution cabinet corresponding to the target node fails, the series or parallel logic of the intelligent components in the faulty circuit is deleted in the meso-level, and the external connection status of the power distribution cabinet in the macro-level is updated synchronously. When the feeder between the distribution cabinets corresponding to the target node fails, the feeder connection logic is reconstructed at the macro level and synchronized down to the meso level, and the circuit status of the distribution cabinet is marked as offline.

4. The risk assessment method for dynamic reconfiguration of power distribution topology according to claim 1, characterized in that, The electrical characteristic verification of any closed loop in the second topology includes: Extract any closed loop and the nodes in the second topology; For the closed loop, the current flowing through each line segment is collected, and based on the current flowing through each line segment, the voltage drop vector of each line segment is calculated, and it is verified whether the sum of the voltage drop vectors is zero. For the node, collect the current vectors of each branch flowing into and out of the node, and verify whether the sum of the current vectors of each branch is zero; If the sum of the voltage drop vectors and the sum of the branch current vectors are zero, the electrical characteristic verification is deemed to have passed.

5. The risk assessment method for dynamic reconfiguration of distribution topology according to claim 1, characterized in that, The process of determining the distribution cabinet connection relationship upstream and the component association link downstream from the target node, and determining the topological influence range of the distribution cabinet connection relationship and the component association link, includes: Obtain the inlet circuit breaker identifier of the distribution cabinet where the target node is located, and trace upwards level by level based on the inlet circuit breaker identifier until the main distribution cabinet to obtain the distribution cabinet connection relationship; Based on the output port identifier of the target node, determine the downstream smart components connected to the target node, recursively obtain the load branches of the downstream smart components, and generate the component association links. Based on the connection relationship of the power distribution cabinet and the associated links of the components, mark the upstream impact of the target node topology change on the power distribution cabinet and the downstream impact on the smart components. The coverage area of ​​the upstream power distribution cabinet and the downstream smart components is defined as the topological influence range.

6. The risk assessment method for dynamic reconfiguration of distribution topology according to claim 5, characterized in that, The process of determining downstream intelligent components connected to the target node based on the output port identifier of the target node, recursively obtaining the load branches of the downstream intelligent components, and generating component association links includes: Traverse the output port identifiers of the target node to obtain the downstream smart components corresponding to the output port identifiers; Extract the load type and load capacity of the downstream smart components and establish the corresponding component cascade table; Based on the component cascade table, a recursive traversal is performed on the downstream smart components until the terminal load device is reached. The downstream intelligent components recursively traversed are sorted according to the power supply order to obtain multiple load branches, and each load branch is mapped to a preset tree structure to obtain the component association link.

7. The risk assessment method for dynamic reconfiguration of distribution topology according to claim 1, characterized in that, The calculation of the risk level based on the historical operating parameters of intelligent components within the influence range of the topology includes: Collect historical operating parameters of each smart component within the topology's influence range over a preset time period. These historical operating parameters include overload count, load type, rated power, and real-time current. The importance weight of each smart component within the topology's influence range is determined based on the load type. Calculate the ratio of the real-time current to the rated current of each intelligent component to obtain the current load rate; The risk level score of the corresponding smart component is obtained by weighting the importance weight, current load rate and overload number of each smart component. For each of the aforementioned intelligent components, a corresponding risk level is determined based on the numerical range of the risk level score.

8. A risk assessment system for dynamic reconfiguration of power distribution topology, characterized in that, The system includes: The data acquisition module is used to acquire real-time status data of intelligent components in the power distribution system, and construct a first topology of the power distribution system based on the real-time status data. The dynamic reconstruction module is used to send detection signals to each topology node in the first topology structure. When no feedback signal is received from any topology node for a preset number of consecutive times, the topology node is identified as the target node where a topology change has occurred. According to the topology change type of the target node, the first topology structure is dynamically reconstructed in multiple logical layers to obtain a second topology structure. The logical layers include a micro layer, a meso layer, and a macro layer. The risk level determination module is used to perform electrical characteristic verification on any closed loop in the second topology. When the electrical characteristic verification passes, based on the association relationship in the second topology, the module determines the distribution cabinet connection relationship upstream from the target node and the component association link downstream, determines the topological influence range of the distribution cabinet connection relationship and the component association link, and calculates the risk level by combining the historical operating parameters of the intelligent components within the topological influence range. The risk assessment module is used to generate corresponding temporary response strategies based on the risk level; and to generate a risk assessment report for the power distribution system based on the topology impact range, the risk level, and the temporary response strategies.

9. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to enable the electronic device to perform the risk assessment method for dynamic reconfiguration of power distribution topology as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the risk assessment method for dynamic reconfiguration of power distribution topology as described in any one of claims 1-7.

Images